Digital Commons Network^™

User-Centered Design For Public Financial Services: A Federal Student Loan Management App Case Study, Carmen River Christopher

Theses

Researchers have studied several facets of federal student loans, such as the impacts it has on borrowers’ finances, mentality, and life goals, plus the history of the federal student system and potential changes. There has been little research conducted on how borrowers make decisions regarding repayment, including choosing plans, navigating the repayment system, and understanding the long term implications of their choices. Since this research largely does not exist, there are few tools and resources to help guide borrowers as they manage the large, complex, and dynamic landscape that is navigating the federal loan system and repaying their debt. I …

Historic Churches Of Sheboygan, Erin Brehmer

Theses

This project focuses on the histories and architecture of six nineteenth- and early twentieth-century churches in Sheboygan, Wisconsin, and developing a museum program about them. The aim of the project is to add to the art historical knowledge of Midwest architecture by researching the Greek Revival architecture used by First Baptist Church of Sheboygan; the Gothic Revival styles used by Grace Episcopal Church, St. Luke United Methodist Church, Hope Reformed Church, and St. Clement Catholic Church; and the Byzantine ornamentation used by St. Spyridon Greek Orthodox Church. Furthermore, this project uses the historical findings to create a museum public program …

Leveraging Generative Artificial Intelligence To Launch An Independent Game Studio, Douglas Evans

Theses

This project report examines how Generative Artificial Intelligence (Generative AI) can be integrated into the early formation and operations of an independent/indie game studio. Using a qualitative, practice-based single-case study approach, the project focused on Boar’s Head Studio, LLC. A human-in-the-loop approach was maintained with Generative AI used to support company formation, ideation, planning, documentation, analysis, coding assistance, marketing preparation, and community-building preparation

The report concludes that Generative AI can serve as a meaningful system integrated into the studio pipeline when used with structured, repeatable, human-in-the-loop processes. The project contributes a practical, case-based framework that may be useful to other …

Artificial Intelligence In Video Production, Jonathan Ringor

Theses

This project examines the role of artificial intelligence in video advertisement production and explores whether AI can help bridge the gap between everyday users and professional advertising. The project combined both creative production and audience research. To do this, three original AI-generated advertisements were created in distinct commercial formats: a pharmaceutical advertisement, a petcentered narrative commercial, and a lifestyle montage advertisement. After the advertisements were completed, audience response was measured through a survey of U.S.-based participants. Participants evaluated each advertisement on realism, emotional engagement, trustworthiness, polish, and authenticity. The results showed that the advertisements were not perceived equally. The pet …

The Road To Effective Leadership: The Role Of Practical Training In Shaping Leadership Skills Of Students For Success In Educational Leadership, Fatima Ali Saeed Salem Alnuaimi

Theses

Educational leadership programs play an important role in preparing future leaders to deal with the challenges of educational settings. However, relying only on theoretical knowledge may not be enough to develop the skills and mindset required for an effective leadership role. In addition, this study aimed to explore the role of practical training and experiential learning in shaping leadership skills and leadership identity among students enrolled in educational leadership programs. This study utilized a mixed-methods design. First, quantitative data were collected via a questionnaire distributed to 24 participants, including master’s and PhD students and educators in leadership roles in schools. …

Political Legacies Of Malcolm X, Sumair Ahluwalia

Theses

This study explores the ways in which Malcolm X’s legacy has been co-opted by various institutions and actors, including, but not limited to, politicians, historians, the general public, the federal government, and other commentators. As a result of this co-optation, Malcolm’s revolutionary ideals have been diluted or sanitized in the years since his assassination in 1965. Different approaches have been utilized to reframe Malcolm’s legacy, which include associating him with American liberalism, framing his arguments at the end of his life as markedly less radical, and external interference with popular works about Malcolm, including The Autobiography of Malcolm X. Additionally, …

Evaluation Of Otolith Microchemistry For Identifying Walleye Stocked As Fry Or Fingerlings, Joshua Fluur

Theses

Walleye (Sander vitreus) is a popular sport fish whose populations are often supported by maintenance or supplemental stocking, with most fish stocked as fry, fingerlings, or advanced fingerlings. Evaluation of stocking efficacy is important to inform decisions regarding fish stocking rates and sizes and allocating hatchery fish to locations where maintenance or supplemental stocking is most needed. Several types of artificial tags or marks have been proven useful in the identification of stocked fish, but they often have various drawbacks. Natural chemical markers in otoliths offer several advantages compared to conventional tags or marks and can be used to identify …

A Reconfigurable Architecture For Dense And Sparse Matrix Multiplication In Processing-In-Memory, Timothy Avila

Theses

Traditional Von-Neumann computing architectures suffer from a fundamental limitation known as the memory wall, where performance is constrained by the cost of data movement between memory and processing units rather than by computational capability. This limitation is particularly pronounced in data-intensive workloads such as machine learning (ML) and hyperdimensional computing (HDC), where matrix-vector operations dominate execution and are often memory-bound. Processing-in-Memory (PIM) architectures have emerged as a promising approach to mitigate this bottleneck by enabling computation to occur within or near memory, thereby reducing data movement overhead. However, due to these processors' specialized nature and the limited integration of general-purpose …

Traumatic Stress Recruits An Excitatory Orbitofrontal-Amygdala Pathway To Drive Maladaptive Aggression, Mikaela Aholt

Theses

Traumatic stress is a reliable predictor of heightened aggression, yet the mechanisms linking stress exposure to aggression remain poorly understood. The Nordman lab previously found that traumatic stress activates the posterior ventral segment of the medial amygdala (MeApv) to drive persistent increases in aggressive behavior. However, why traumatic stress would engage an aggression-promoting pathway is unclear. To address this, the inputs to the MeApv were mapped and a population of excitatory, but not inhibitory, neurons in layer 5 of the medial orbitofrontal cortex (mOFC) were identified. The OFC is classically viewed as an inhibitory brake on amygdala-driven impulses, yet emerging …

Deep Reinforcement Learning-Based Equitable Post Disaster Resource Allocation Incorporating Social Vulnerability, Trevor John Mogaka

Theses

Prior Deep Reinforcement Learning (DRL) approaches frame post‑disaster recovery as a sequential decision-making problem over infrastructure elements, using graph-based system models and resilience-oriented rewards. This thesis builds on an existing DRL framework by introducing a modified CDC Social Vulnerability Index (mCDC-SVI), developed using Max-min scaling, to quantify social vulnerability.A synthetic interdependent infrastructure testbed is divided into four socio‑economic regions using federal poverty and SNAP eligibility guidelines. The DRL simulation and Deep Q‑Network architecture are retrained under a multi-objective reward function that is efficiency-equity weighted (α:β). Agents are evaluated across unconstrained and constrained budget scenarios to examine how varying α and …

Development Of Chemical Reaction Airbag Safety System For Multi-Rotor Uav To Mitigate Free-Fall Collision Impact, Brady Kent Villiger

Theses

With Unmanned Aerial Vehicles (UAVs) becoming more advanced, the demand of companies that plan to use them for commercial use has only increased. Amazon plans to use UAVs for shipping purposes, Esri uses UAVs for surveying purposes, and many government organizations use UAVs for search and rescue, infrastructure inspection, and power line inspection. With all these uses, UAV safety becomes a major concern. If a UAV were to fail mid-flight there would be an issue if it collided with a person or an expensive piece of equipment after a free-fall. In this paper, the proposed safety system would be a …

Prediction Of The Nga-West2 Average Vertical Peak Ground Acceleration Using Genetic Expression Programming, Dipankar Karki

Theses

Ground Motion Predicting Equation (GMPEs) has been proposed with the use of Genetic Expression Programming (GEP) tool using 10,080 ground motion records obtained from the NGA West2 database. For this research, moment magnitude, dip angle, rake angle, Joyner Boore Distance, depth to top of fault distance, closest distance to ruptured fault area and the shear wave velocity in top 30 m of the site have been selected as the predictor set. It was found that the results from the GEP model compared well with the existing GMPEs and validates the use of GEPs for creating GMPEs along with other regressions …

Predicting Flight Fares With Machine Learning: Enhancing Aviation Industry Pricing Forecasting, Reem Almulla

Theses

This research investigates the increasing challenge of accurate flight fare prediction for travel agencies that are functioning and working in the post-pandemic aviation market. As the prices fluctuate while demand is unstable and competition pressure increases, therefore it influences decision-making and profitability. In most cases, traditional ticket pricing methods often can be ineffective when capturing complex and non-linear relationships within factors that influence the ticket fare dynamics. As a result, highlighting the urge of more adaptive pricing and data-driven predictive machine learning models. In response to this challenge, the research examines the effectiveness of machine learning techniques for enhancing flight …

Genui: Customizing And Improving User Interfaces In Real-Time With Generative Ai, Ursula Parker

Theses

MOTIVATION: Users must conform to the interfaces they are given, which are designed for a generalized user base rather than individual workflows or preferences. Existing UI customizations are often limited to fixed options predetermined by developers, restricting meaningful user control. Advances in generative AI introduce the possibility of modifying the underlying code of a production system that defines interfaces, enabling more flexible and targeted adaptations. However, the feasibility, scalability, and usability of such user-driven customization remain open questions. GOAL: The goal of this work is to help people customize their user interfaces, by investigating how they can linguistically specify the …

Yes, I Think So?, Madeeha Ahmad

Theses

Yes, I Think So? is a body of work that explores memory as something unstable and alterable by emotion, time, and lived experience. Influenced by my experience with epilepsy, the show meditates on how dreams, seizures, and waking life can blur together, fracturing a sense of consistency and continuity. Rather than thinking of memory as a definitive record of the past, I encounter it as a process of reconstruction. Wood serves as both a material and a metaphor. It records time visibly with its growth yet remains unpredictable in its resistance to complete control. Indigo dye moves the work farther …

Energy And Cost In Cold Climates: Comparing Passive And Active Solar Design Upgrades For Homes And Their Ability To Reduce Energy Bills In Western New York, Gillespie Merod

Theses

This study examines the comparative effectiveness of passive solar design strategies and active solar systems for reducing residential energy expenditures while maintaining thermal comfort in Climate Zone 6A/B, with a focus on Western New York. A baseline single-family residence was created in Autodesk Revit and analyzed with Autodesk Insight (EnergyPlus) to assess energy performance across multiple upgrade scenarios. These scenarios encompassed targeted envelope enhancements, passive solar design strategies, deep energy retrofits, and photovoltaic (PV) system integrations. Results indicate passive measures, especially air sealing and insulation, can cut energy use by up to 76% with deep retrofits, though upfront costs and …

Dark Current And Persistence In Hgcdte-On-Si Infrared Detectors, Lazar Buntic

Theses

This thesis investigates the performance of Raytheon’s mercury-cadmium-telluride-on-silicon (MCT-on-Si) infrared detector arrays, with particular emphasis on the impact of the heteroepitaxial silicon growth process on dark current and persistence characteristics. Infrared detectors have been the cornerstone of astronomical discovery for the past four decades and remain essential for current and future flagship observatories, including the James Webb Space Telescope and the Nancy Grace Roman Space Telescope. Traditionally, the highest-performance HgCdTe (MCT) arrays are grown on lattice-matched cadmium-zinc-telluride (CZT) substrates, which are costly and limited in size. To enable low-cost, large-format arrays, Raytheon developed a process for growth of MCT on …

Antidistillation Sampling For Classification Models, Khawaja Abaid Ullah

Theses

Knowledge distillation enables adversaries to replicate the functionality of proprietary machine learning models by querying their APIs and training surrogate student models on the returned soft-label distributions. Antidistillation Sampling (ADS), recently proposed for large language models, perturbs the output distribution of a teacher model at inference time to degrade the quality of the resulting distilled model while preserving utility for legitimate users. We adapt ADS to the supervised classification setting and identify a structural obstacle to its direct transfer: the high-confidence, near-one-hot output distributions characteristic of well-trained classifiers leave insufficient probability mass on non-target classes for the additive penalty to …

Atmospheric Noise Analysis In Observations From The Tomographic Ionized-Carbon Mapping Experiment, Audrey Dunn

Theses

The Tomographic Ionized-carbon Mapping Experiment (TIME) instrument is a ground-based millimeter-wavelength grating spectrometer that illuminates a cryogenically cooled array of 1920 transition-edge sensor (TES) bolometers. The goal of TIME is to generate line intensity maps of singly ionized carbon ([CII]) during the Epoch of Reionization, when hydrogen became reionized by stellar radiation and the first galaxies were forming. This measurement requires a detailed understanding of the noise, most notably the 1/f noise from the time-varying atmosphere. In my thesis work, I performed an analysis of the power spectral density (PSD) and a traditional principal component analysis (PCA) on TIME data …

Like A Rolling Lychee, George Meng

Theses

I grew up in Shanghai, China—a megacity with over 25 million people. I was privileged enough to avoid the grueling fate of taking the National College Entrance Examination (Gaokao). At 16, I moved to Sewanee, a small college town in rural Appalachia with a population of only 3,000. Overshadowed by that curiosity was the collision of two worlds and my sense of belonging and identity. I wasn’t fully aware of how I watched and copied others’ behaviors and mannerisms to blend in as an adolescent. Being a minority in the middle of nowhere Tennessee meant I was conditioned into fitting …

Reconfigurable Dataflow For Efficient Matrix-Matrix Multiplication For Dnn Acceleration, Daniel Laudico

Theses

General matrix-matrix multiplication (GeMM) is a principal computational bottleneck in modern deep learning workloads where performance can be heavily impeded by frequent memory accesses. This thesis introduces a multi-dataflow aware processing element (PE) designed to perform the multiply-accumulate (MAC) operations fundamental to GeMMs while utilizing a data flow that best fits the data provided. A salient feature of this architecture is its programmable flexibility, which enables the implementation and execution of different data flow strategies, including Output Stationary, Weight Stationary, Input Stationary, and Row Stationary. The flexible design supports both dense and sparse GeMM and General matrix-vector multiplication (GeMV). To …

Energy Loss Hotspot Mapping In Power Distribution Networks, Abdulla Alhammadi

Theses

The electricity systems used in power distribution in the United States also consume a lot of electricity to warm up until it gets to homes, business or industries. These are technical losses which are caused by the physical resistance of the aging electrical equipment like transformers, conductors and distribution lines. With the aging of infrastructure, the resistance rises, and the amount of wasted energy increases. Although this is a large problem, most utility companies continue to use rough age estimations or customer complaints instead of using more direct and objective data-driven techniques to determine the concentration of the worst losses. …

Kosovo Pisa Monitoring Via System Signals Benchmarking 2015/2018/2022, Peer Clusters, Early-Warning Screening, And Mixed-Methods Triangulation In Grade 10 Mathematics, Leon Cana

Theses

This thesis develops a practical monitoring approach for Kosovo’s PISA outcomes by linking country-level benchmarking of PISA 2015, 2018, and 2022 with measurable system signals and a focused school-level triangulation. Quantitatively, it benchmarks Kosovo against Western Balkans Six (WB6) and selected European peers through mathematics mean scores, below-Level-2 shares, and top-performing shares. It then uses k-means clustering to define empirical peer groups and tests an early-warning screening exercise in which 2015/2018 information is used to classify held-out 2022 high-risk profiles. Qualitatively, it triangulates the statistical signal patterns with policy document analysis and a low-burden Grade 10 mathematics micro-study in Prishtina …

Mathematical Modeling Of Ocular Surface Deformation Due To Contact Lens Wear That Accounts For Intraocular Pressure, Riley K. Supple

Theses

Myopia is one of the most common ocular disorders, and is expected to affect approximately five billion people worldwide by 2050. One treatment currently available for myopia is soft contact lenses. In general, about one in ten Americans wear contact lenses, however one in three will stop wearing them due to discomfort. The goal of this dissertation is to develop a mathematical model that predicts the mechanical interactions between the contact lens and the ocular surface. Assuming the ocular tissue is a linear elastic material, we first develop a mathematical model to predict ocular deformation that is anatomically accurate in …

Optimizing Product Placement Using Purchase Pattern Analysis, Mohra Shamaa

Theses

Retail product placement has traditionally relied on static shelf layouts and techniques such as market basket analysis, often guided by managerial intuition and category based organization. However, such approaches frequently overlook the complexity of consumer purchasing behavior as reflected in the transactional data, and this study investigates how data driven analysis of purchase relationships can inform more effective product placement strategies in physical retail environments. Unlike the sequential models, this study focuses on co occurrence relationships due to dataset constraints, prioritizing interpretability for retail applications. Using transaction level data from the Instacart dataset, this research applies association rule mining through …

Inverse Laplacian Solution For Spherical Harmonic Decomposition Of Black Hole Initial Data, Nikolaus Vernon Kent

Theses

In the construction of initial data, by way of describing one or more black holes for Numerical Relativity, solving the Hamiltonian Constraint remains a fundamental challenge, as the presence of coordinate singularities in the so-called "puncture" formalism often complicates traditional grid-based numerical methods. This thesis addresses this challenge by developing a semi-analytical framework based on an iterative spherical harmonic modal expansion. We begin by decomposing the source term of the Hamiltonian Constraint, given in terms of the extrinsic curvature, into a basis of spherical harmonics. To navigate the non-linearity of the governing equation, we implement an iterative scheme derived from …

Ai In Action: Redefining Loan Default Prediction For The Digital Lending Era, Joe Vinson Ukken

Theses

Credit risk assessment remains a very important part of financial institutions, particularly within the rapidly evolving digital lending environment. The research explores the effectiveness of five machine learning models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine—in predicting loan default using both financial indicators and categorical borrower attributes. The study is motivated by the growing availability of structured borrower data and the need to evaluate whether advanced machine learning algorithms can be implemented over conventional credit scoring methods. A publicly available L&T Vehicle Loan Default Prediction dataset comprising 233,154 borrower records and 41 structured attributes, the CRISP-DM …

Subject. Object. Thing., Cristin Hand

Theses

Subject. Object. Thing. is part of an accumulating record of self-construction and self-reflection. The work considers the complications of visibility through self-portraiture, how the desire to be seen exists alongside the impulse to withhold, obscure, and substitute. Working across photography, performance, collage, and sculptural constructions made for the camera, I fragment, disassemble, and reconfigure my body as photographic subject and material. This exploration is structured around a series of oppositions; concealment/exposure, anonymity/specificity, subject/object, naked/nude, and destruction/reconstruction. I do not attempt to resolve these oppositions. Instead, I consider where these categories blur, contradict themselves, and become unstable. Central to this inquiry …

The Emergent Wild, Allen Douglas

Theses

This thesis investigates a shift from the structured, approval-driven process of illustration to a more exploratory and intuitive painting studio practice. While my training has emphasized careful planning and technical precision, it has also limited spontaneity and material engagement. In response, I adopt a process-driven approach that privileges improvisation and material agency, repositioning painting as a site in which control is negotiated rather than maintained. This change marks not only a methodological departure but an internal reorientation—from adherence to external standards toward a more self-directed and responsive mode of making. The expansion of my visual universe—through expressive mark-making, increased ambiguity, …

An Evidence-Based Ethical Risk Classification Framework (Ercf) For Ai Policies, Ahmad Almarri

Theses

This thesis develops and evaluates an evidence-based Ethical Risk Classification Framework (ERCF) for analyzing substantive ethical engagement in AI policy documents. The study addresses the gap between high-level AI ethics principles and reproducible computational methods for comparative governance analysis. A secondary dataset of AI ethics policy recordswas used as the starting point. After retrieval, text extraction, and validation, a corpus of 44 policy documents was constructed from an initial 112 records (110 link-bearing entries). Supervised classification was implemented for four core dimensions: Accountability, Bias & Fairness, Privacy, and Transparency. A TF-IDF + Logistic Regression baseline was evaluated using stratified 5-fold …