Physical Sciences and Mathematics Commons^™

Improving Research Software Engineering In Mathematics, Abram Miller

Honors Theses

Research Software Engineering is critical to modern mathematical research, enabling the creation, maintenance, and dissemination of computational tools that bridge theory and practice. However, the field faces systemic challenges, including insufficient funding, lack of institutional recognition, and gaps in training and infrastructure. This thesis investigates these challenges through two approaches: (1) a comparative survey study focused on mathematicians and (2) hands-on contributions to an open-source research software project.

The Improving Research Software Engineering in Mathematics survey, conducted from September 2024 to January 2025, adapts the survey framework developed by Carver et al. in A survey of the state of the …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.

Linear Polydicyclopentadiene Copolymers: Synthesis And Thermal Properties, Connor Lewis

Honors Theses

No abstract provided.

Antiproliferative Effects Of Salvia On Breast Cancers., Allison C. Portaro

College of Arts & Sciences Senior Theses

Breast cancer is the most common cancer diagnosed in female adults, with 2.3 million new cases worldwide in 2020. Plants have been used in traditional medicine and are a potential source of pharmaceuticals. One example is the large Lamiaceae (mint) family. The Salvia genus, commonly known as the sages, is the largest genus in Lamiaceae with almost 900 known species. Salvia lyrata (lyre-leaved sage) has a history of Native American medicinal use to treat cancers and other ailments resulting in this plant being referred to as ‘cancer weed.’ Extracts were produced from the leaves of S. lyrata, S. lyrata …

How Graphene’S Berry Dipole Is Determined By Its Atomic Registry With A Substrate, Hannah K. Isbell

Physics Undergraduate Honors Theses

Since graphene’s discovery in 2004, it has been established as one of the most important materials in recent condensed matter history. Understanding its properties and potential uses has been the subject of intense scientific research around the world. Graphene’s unique physical properties allow it to serve a wide variety of purposes, from its everyday role in the formation of graphite in pencils to its more cutting-edge scientific functions in electronics and energy storage. Gaining a better understanding of graphene’s rich quantum landscape through geometric and topological phenomena, like Berry curvature and Berry dipole, could have significant implications for both theoretical …

Enhancing The University Of Arkansas' Operations Through Data Science, Aura L. Pinto-Avelar

Data Science Undergraduate Honors Theses

As universities navigate financial constraints and resource allocation challenges, data driven financial analysis has become increasingly important. Universities employ various methods to assess financial efficiency, predict future expenditures, and optimize student credit hour distribution. However, the approaches to financial analysis vary widely, with some institutions leveraging advanced predictive modeling and business intelligence tools, while others rely on traditional budgeting techniques and manual forecasting.

This thesis examines how the University of Arkansas' (“Uark”) financial analysis methods compare to those of other institutions and alternative data-driven approaches. Using four years of financial and student credit hour data, this study evaluates cost trends …

Optimizing Fire Station Placement In Sugar Land, Tx: A Socioeconomic Risk-Based Approach, Alicia Gallemore

Data Science Undergraduate Honors Theses

Fire station placement has a critical role in emergency response efficiency and community safety. Traditional optimization models focus on mainly the minimization of response times and the maximization of coverage. However, this approach may overlook potential socioeconomic disparities that can influence emergency demand. This study seeks to expand upon the existing project of zoning a fire station in Sugar Land, TX, by integrating spatial road network analysis and publicly available census data—including population density, median household income, and age-based vulnerability—into a Maximal Coverage Location Problem (MCLP) framework. Using a road network-based travel time with realistic constraints, the goal is to …

Attribute Based Assortment Using Machine-Learning, Hector Negron

Data Science Undergraduate Honors Theses

Retail success is influenced by a store's demographic and environmental context, both of which impact item-level sales performance. This study applies machine learning techniques to optimize item allocation based on club attributes at Sam’s Club locations. By analyzing store- specific factors such as proximity to universities, income levels, and regional preferences, the research identifies patterns that contribute to product demand. The results offer insights into how clubs can enhance inventory decisions, improving sales outcomes while reducing inefficiencies. This study reinforces the value of data-driven retail strategies and presents a practical framework for implementing predictive models in a real-world business context.

Shortage To Surge - Studying The Post-Covid-19 Guitar Retail Market, Jed H. Kim

Data Science Undergraduate Honors Theses

The COVID-19 pandemic was one of the most catalyzing events of the 21st century, leading to supply chain disruptions, lifestyle changes, and a massive shift towards digital technologies. During the COVID-19 lockdown, many people had more free time, and over 16 million individuals learned to play guitar in the first 2 years of the pandemic. According to a study by Fender, 62% of these new guitar learners cited the pandemic as their primary reason for learning the instrument. However, pandemic policies and supply chain disruptions meant that many guitar retailers were unable to satisfy demand, and backorders accumulated. After the …

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Reinforcement Learning, Modeling Markets, And Professional Basketball Free Agency, Jacob Cohn

Computational and Data Sciences (PhD) Dissertations

This dissertation presents a reinforcement learning-based approach to modeling and optimizing decision-making in professional basketball free agency and related economic environments. A Markov Decision Process (MDP) framework is introduced to capture the strategic interactions of NBA teams bidding for free agents under budgetary and roster constraints. To address computational scalability challenges, a reinforcement learning (RL) environment is developed, leveraging Proximal Policy Optimization (PPO) to approximate optimal policies for team decision-making.

Empirical results demonstrate that the RL agent successfully learns strategic bidding behavior that aligns with dynamic programming benchmarks in simplified settings while scaling effectively to larger, intractable environments. The study …

Early Breast Cancer Detection With Ultrasound Data Using Nmf, Rutvi Khamar

Theses and Dissertations

Early detection of breast cancer significantly influences patient outcomes. Dynamic Contrast-Enhanced Ultrasound (DCE-US) has shown promise in early detection by visualizing tumor vascularity and perfusion dynamics in real-time. This study evaluates the efficacy of DCE-US in distinguishing four stages of cancer progression: normal, hyperplasia, ductal carcinoma in situ (DCIS), and invasive cancer, using a transgenic mouse model that mimics human breast cancer. Ultrasound burst pulses, while commonly used to remove unbound contrast agents, can potentially damage human tissues. Using the pre-pulse data helps mitigate this risk, ensuring safer and more reliable measurements. A VEGFR2-targeted microbubble contrast agent was injected, and …

Mitigating Nitrogen Losses: The Role Of Enhanced Efficiency Fertilizers In Sustainable Corn Production, Dipesh Giri

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Nitrogen (N) is essential for crop production, but its inefficient use often leads to environmental losses through nitrate leaching, ammonia volatilization, and nitrous oxide (N₂O) emissions. Enhanced efficiency fertilizers (EEFs), such as nitrification inhibitors (NIs) and urease inhibitors (UIs), have been developed to minimize these losses and improve nitrogen use efficiency (NUE). This research combined a multi-year field experiment and a controlled laboratory study to evaluate how dual-inhibitor fertilizers compare to conventional urea and single-inhibitor formulations in Nebraska cropping systems. The main objective was to understand how nitrogen inhibitors influence N availability, crop performance, and nitrogen losses across different environmental …

Extending Cover Crop Growing Window To Improve The Health Of Environmentally Sensitive Soils, Noshin Ara Tunazzina

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Sloping and sandy soils are highly susceptible to degradation. The health of these soils could be improved with cover crops (CCs). However, CC effectiveness in improving soil health mainly depends on CC biomass production, which is often low in temperate regions. The question is: how can we increase CC biomass production? Lengthening the CC growing window can be a potential strategy. This study assessed how 1) interseeded and terminated early (2-3 weeks before crop planting) CC, 2) interseeded and terminated late (at crop planting) CC, 3) drilled (after harvest) and terminated early CC, 4) drilled and terminated late CC, and …

Capturing The Digital Scene: Applying Routine Activity Theory To Iot Smart Frames, Jordan Bakar

Theses/Capstones/Creative Projects

This project investigates the forensic risks and investigative challenges posed by smart frames, which are WiFi-enabled Internet of Things (IoT) devices used to store, display, and share digital media. These devices often collect and synchronize sensitive media, metadata, and behavioral logs across cloud ecosystems that lack adequate transparency and privacy safeguards. Routine Activity Theory (RAT) provides a criminological framework for examining how the convergence of a motivated offender, a suitable target, and the absence of capable guardianship creates opportunities for misuse and forensic exploitation. Smart frames represent ideal targets because of weak default security configurations, passive data synchronization, and limited …

Two-Player Trick-Taking Games On Bipartite Tournaments., Allan Bagley

College of Arts & Sciences Senior Theses

We consider a two-person game played on a bipartite tournament with equal size parts. The game is modeled after trick-taking card games like bridge or euchre. The two players, South and North, each receive one of the parts as their hand, and the arcs from a vertex in one hand beat the corresponding vertices in the other hand. The game is played over rounds called tricks where the number of tricks is equal to the number of vertices in each player’s hand. At the beginning of the game, one player is designated as the leader, and they play the first …

Learning Educational Technology Prototyping With Generative Ai, Justin Olmanson, Azadeh Hassani, Gretchen K. Larsen

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In this study, we use ethnographic methods, grounded theory, and an iterative analytical approach to explore participant experiences and strategies for engaging generative AI in support of both learning how to prototype educational technologies and learning to code. We examine how ChatGPT and Giuseppe (a scaffolded co-coding interface of our own design) influence students’ approaches to prototyping and programming. This study contributes to the field by: identifying specific challenges and affordances of generative AI in prototyping and educational technology development contexts; and offering insights into how educators, students, and learning technology developers can integrate generative AI in formative educational technology …

Artificial Intelligence Through Young Eyes: A Study Of Students’ Perspectives And Experiences With Artificial Intelligence In Education, Chad Preston Salyer

Ed.D. Dissertations

Artificial intelligence was an emergent and powerful new force in education. The public release of ChatGPT 3.0 in 2022 transformed learning for many students. This phenomenological qualitative study sought to record and analyze student’s perspectives on the influence of artificial intelligence on their learning routines. This study collected data through surveys and interviews with undergraduate students, analyzing patterns of artificial intelligence usage, perceived benefits, and challenges. The findings revealed that most students used artificial intelligence as a primary learning tool and that those students viewed artificial intelligence as beneficial for personalized learning and skill development. However, concerns about over-reliance on …

A Study Of Knots And Quandles, Zhaoqi Wu

Math and Computer Science Honors Theses

We explore the mathematical theory of knots through the lens of algebraic structures known as kei and quandles. We begin by introducing classical knot invariants and then study the fundamental kei of a knot as a tool for distinguishing knot types. We generalize this approach using various kinds of quandles, including Alexander and dihedral quandles, and investigate their associated polynomial invariants. We also examine the connection between quandles and group theory, as well as their algebraic representations in quandle rings. Moreover, we analyze idempotent elements in quandle rings over finite fields, providing both general results and specific examples.

Multimodal Benchmarking For Ncaa Basketball, Brendan Barnett

Honors Scholar Theses

We present the first multimodal, multitask benchmark for NCAA basketball, synthesizing structured statistical features with large language model (LLM)-generated game summaries across 19,739 games spanning four NCAA Division I seasons (2021--2025). We evaluate three model families---XGBoost, deep neural networks, and Transformers---under tabular-only and early-fusion settings to measure the impact of LLM-derived textual embeddings. To assess practical utility, we simulate fixed-stake and Kelly criterion-based betting strategies using historical bookmaker odds, analyzing both profitability and downside risk via Monte Carlo simulation. Our results show that XGBoost with early-fusion achieves the highest return on investment and the lowest risk of loss. This work …

Geometric Algebra For Field Theory In Curved Spacetime, Kevin Rhine

All Graduate Reports and Creative Projects, Fall 2023 to Present

Physics seeks to understand the universe by uncovering the fundamental laws that govern matter, energy, space, and time. At its heart lies the challenge of unification: finding a mathematical framework that consistently describes these interactions across all scales, from the subatomic to the cosmological.

This thesis explores geometric algebra, a mathematical language that unifies algebra and geometry, as a tool for advancing this understanding. By extending this framework to curved spacetimes, where gravity influences the structure of space and time, we investigate its ability to describe physical phenomena such as electromagnetism and general relativity. A notable contribution includes the geometric …

Community Voices, Climate Action Choices: Working Towards A Resilient Monterey County, Lesley A. Solano Alonso

Capstone Projects and Master's Theses

Vulnerable communities in Monterey County face disproportionate environmental and health impacts due to climate change, yet many residents remain unaware of the tools and resources available to support local action. This capstone project was implemented in partnership with Ecology Action (EA) and the Resilient Central Coast (RCC) campaign to increase awareness and engagement with the RCC platform. Serving diverse communities across Monterey County, the project included bilingual outreach efforts, community tabling, educational presentations, and a climate action survey. Over 650 residents were engaged directly, resulting in 99 new household sign-ups on the RCC website, a major milestone for the agency. …

Yield Prediction Of Pv Solar Energy Systems And Its Application In The Energy Grid For Operational Efficiency, Pablo Bustamante

Open Access Theses & Dissertations

The generation of power from photovoltaic (PV) solar panels is influenced by a multitude of factors. These include the tilt and orientation of the solar panels, the latitude of their location, and the prevailing climate and weather conditions. Additionally, shading at specific locations, particularly if the panels are not part of a solar facility, can significantly impact their efficiency. The quality and efficiency of the panels themselves, along with the preventive maintenance of both the solar panels and associated components such as inverters and trackers, are also critical. Finally, the overall system design and installation play a vital role in …

A Profile Wald Test In M-Estimation, Reagan Kesseku

Open Access Theses & Dissertations

Despite the growing popularity of machine learning-based inference, classical statistical inference remains highly relevant in modern data science due to its interpretability and theoretical rigor. Among its core tools, the likelihood ratio test, Wald test, and score test are foundational methods for hypothesis testing within the maximum likelihood framework. Although these tests are asymptotically equivalent under regularity conditions, each offers distinct advantages depending on the context, computational demands, and the availability of parameter estimates. In this dissertation, we introduce a fourth method, the Profile Wald Test (PWT), within the broader M-estimation framework. The PWT is based on profile estimators of …

Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman

Open Access Theses & Dissertations

Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network …

Clinicogenomic Insights For Prostate Cancer Progression, Kelvin Ofori-Minta

Open Access Theses & Dissertations

Prostate cancer (PrCa) remains a critical challenge in precision oncology due to several reasons including its apparent heterogenous condition, recurrence following treatment and rapid progressive forms. Therefore, identifying patients at risk of progression is essential to fast-track therapeutic decisions and improve outcomes. Despite recent advances in genomic and molecular profiling, conventional PrCa risk assessment tools heavily rely on a few clinical parameters, neglecting the prognostic potential of genomic biomarkers in the presence of clinical biomarkers. This study presents a computational pipeline to harmonize and evaluate the prognostic value of clinicogenomic profiles of patients in modelling progression free survival (PFS). PFS, …

Multiscale Integration Of Receptor-Ligand Dynamics Into Discrete And Continuous Tumor Growth Models With Application To Tyrosine Kinase Inhibitor Treatment, Romasa Qasim

Open Access Theses & Dissertations

The epidermal growth factor (EGF) receptor cascade plays a crucial role in the survival and proliferation of tumor cells. Tyrosine kinase inhibitors (TKIs) are a class of drugs that inhibit epidermal growth factor receptors (EGFRs), thereby preventing the downstream signal transduction. Despite their importance, models that link spatial receptor dynamics to tumor growth remain scarce. Further, TKIs act through selective mechanisms, inhibiting active, inactive, or all receptor states, which poses a challenge to traditional modeling approaches.

We propose to numerically study two mathematical models incorporating receptor-dynamics into cancer models to describe the impact of EGFR overexpression and TKIs. The first …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

A Sociocognitive Perspective On The Activation Of Productive Epistemic Resources In College Students' Understanding Of Chemical Equilibrium, Henk Steven Van Den Bogaard

Open Access Theses & Dissertations

Science education, particularly in higher education, has a content coverage problem: it has long been described as "a mile wide, an inch deep" in various consensus documents from the National Academies. In chemistry education, the most common general chemistry curriculum covers topics as chapters based on the popular Sienko and Plane 1960's general chemistry textbook. The limited time available in such fast-paced coverage may limit the instructor's modeling and assessment of key performances in developing a mechanistic understanding of complex chemistry phenomena. In this sociocognitive, qualitative investigation, general chemistry II students' responses to assessment prompts were used to activate cognitive …

Novel Trimethoprim-Based Metal Complexes And Nanoparticle Functionalization: Synthesis, Structural Analysis, And Anticancer Properties, Abbas M. Abbas, Hossam H. Nasrallah, A. Aboelmagd, W. Christropher Boyd, Haitham F. Kalil, Adel S. Orabi

Chemistry Faculty Publications

In this study, we synthesized a novel trimethoprim derivative, 4-(((2-amino-5-(3,4,5-trimethoxybenzyl) pyrimidine-4-yl)imino)methyl)benzene-1,3-diol (HD), by the reaction of trimethoprim with 2,4-dihydroxybenzaldehyde. We then prepared metal complexes of this derivative with Cu(II), Co(II), Ni(II), Ag(I), and Zn(II) and functionalized them with ZnO and Au nanoparticles. Their structures were confirmed through 1H NMR, mass spectrometry, FTIR, conductivity, thermal analysis, magnetic susceptibility, X-ray diffraction, UV-Vis spectroscopy, and TEM, revealing octahedral geometries for all complexes. Surface features were investigated using density functional theory (DFT) analysis. Pharmacokinetic parameters and target enzymes for HD and its complexes were computed using the SwissADME web tool, with the BOILED-Egg model …