Physical Sciences and Mathematics Commons^™

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. …

Optimizing The Peter Kiewit Institute Course Schedule Using Answer Set Programming, Joshua R. Gryzen

Theses/Capstones/Creative Projects

This project introduces a program that automates the process of minimizing conflict between classes that students are likely to take simultaneously at the Peter Kiewit Institute at the University of Nebraska Omaha using Answer Set Programming. The main objectives of this project are to encode the specifics of a schedule regulation for courses pertinent to computer science majors, identify critical conflicts between the courses in a given schedule, and propose an assignment of timeslots. More specifically, a scheduled section is assigned a new timeslot, which is a combination of days, start time, and end time, from the list of timeslots …

Optimization And Personalization Of Fitness Routines Using Mathematical Models, Tyson M. Kerr

Theses/Capstones/Creative Projects

This project aims to develop a mathematical model for generating personalized workout plans tailored to individual fitness goals, preferences, and constraints. By leveraging optimization techniques such as linear programming and genetic algorithms, the model will address key questions, including how to maximize efficiency and consistency in workout plans and which methodologies are best suited for specific fitness objectives. The model involves data collection on user inputs and exercise attributes, defining objective functions for different goals, and integrating constraints like session duration. The implementation will utilize the Python library PuLP to prototype the model, alongside a user-friendly interface for input and …

Phoneme Recognition For Pronunciation Improvement, Matthew Heywood

Theses/Capstones/Creative Projects

This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.

Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …

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 …

Drainage Area Limitations Of Single Watershed, Peak Flow Estimates From Nrcs Methods, Timothy A. Maughan, Rollin H. Hotchkiss, Riley Hales

Nebraska Department of Transportation: Research Reports

Most state Department of Transportation roadway design sections predict peak flow for culvert design using, amongst other approaches, Natural Resources Conservation Service (NRCS) TR-20 technology. Even though this technology is more than 50 years old, there are no clear guidelines for how large a single watershed drainage area may be while remaining appropriate for predicting peak discharge with this method. Our objective was to identify the drainage area where TR-20 peak flow predictions significantly deviate from flow frequency predictions. We developed flow frequency estimates for 130 small-area stream gage sites in rural Nebraska and compared the calculated return period discharges …

An Algebraic-Combinatorial Proof Of A Bézout-Type Inequality For Mixed Volumes Of Three-Dimensional Zonoids, Gennadiy Averkov, Ivan Soprunov

Mathematics and Statistics Faculty Publications

We present a new algebraic-combinatorial approach to proving a Bézout-type inequality for zonoids in dimension three, which has recently been established by Fradelizi, Madiman, Meyer, and Zvavitch. Our approach hints at connections between inequalities for mixed volumes of zonoids and real algebra and matroid theory.

A Comparative Analysis Of Nf-Κb1 Gene Regulatory Sequence Methylation In Normotensive And Hypertensive Kenyans, Aaryan Barlas Piracha

Honors Theses

Accounting for the majority of deaths worldwide, non-communicable diseases (NCDs) present the greatest health challenge of the twenty-first century. Specifically, cardiovascular diseases (CVDs) exceed all other NCDs in annual deaths and especially affect low- and middle-income countries (LMICs). Hypertension, being the primary risk factor for CVD, affects over 75% of adults in LMICs due to inadequate health care and preventative measures. Additionally, epigenetic modifications of DNA are important mechanisms that regulate gene expression; DNA methylation, in particular, affects cytosine residues in cytosine-phosphate-guanine (CpG) islands on regulatory sequences. Previous research in our laboratory analyzed percent methylation at 8 different CpG islands …

Effects Of The Gravity Gradient On The Path Of 1i/‘Oumuamua, Hannah R. Richardson

Honors Theses

In October 2017, the asteroid 1I/’Oumuamua first passed into viewing range [1]. The asteroid is notable for being the first interstellar object to enter the solar system. 1I/’Oumuamua was also unusual in its geometry; it is thought to have an aspect ratio of 6:1 and a length of approximately 400 m [2] [3]. The asteroid was observed to experience a non-Keplerian acceleration estimated to be on the order of 1⇥10−6 m s2 . Several theories have been proposed for the cause of this acceleration, all of which are non-gravitational in nature: volatile outgassing, photon pressure, and solar winds [1][4]. However, …

Composition And Rich/Lean Loading-Dependent Density Measurements Of A Series Of Aqueous Ionic Amines For Co2 Capture, Claudia Nguyen

Honors Theses

As climate change worsens worldwide, there is a drive to develop more efficient and sustainable methods to mitigate its impact through carbon capture methods like carbon dioxide scrubbing. Carbon scrubbing directly captures carbon dioxide (CO2) emissions from industries or power plants before they are released into the environment. The CO2 captured can be utilized for various applications or can be stored underground. However, this method faces numerous challenges, such as the degradation of the solvent or corrosion of equipment. As a result, research has expanded to find a better medium to overcome these issues. In particular, aqueous ionic amines (AIA) …

Toxicity And Biodegradability Of Novel Boronium Vs Conventional Ammonium-Based Antimicrobial Compounds In Wastewater Treatment Systems, Noor Shalan

Honors Theses

Quaternary ammonium compounds (QACs) are highly effective as disinfectants, herbicides, and pesticides; thus, overuse causes elevated levels of residual toxicity in domestic and industrial wastewater. QACs can be toxic to essential bacteria breaking down pollutants in wastewater treatment plants (WWTPs) and can remain untreated in effluent, harming the environment, and contributing to antibiotic resistance, posing risks to human health. Novel boronium-based antimicrobial compounds have demonstrated efficacy in eliminating bacteria, fungi, and viruses. If the boronium compounds exhibit lower residual toxicity, they could offer a promising alternative to QACs. Because these compounds are still in development, their potential toxicity to the …

The Search For Slow Particles And Magnetic Monopoles With Nova, John Clark

Honors Theses

Singular magnetic poles, north or south, have been theorized to exist for hundreds of years. In the modern day, the elusive singular magnetic pole still remains undiscovered. The appearance of this particle would help confirm many Grand Unified Theories, GUTs, and revolutionize our understanding of some of the fundamental forces of the universe. Fermilab's NOvA collaboration is working on ways to screen and detect magnetic monopoles and other slow-moving particles coming from outer space alongside their main mission to study neutrinos. The aim of this work is to determine and improve the Far Detector’s efficiency at identifying slow moving particles. …

St. Cloud Dam: Is The Renewable Energy Worth The Ecological Consequences, Adam Novak

Celebrating Scholarship and Creativity Day (2018-)

The St. Cloud hydroelectric dam is a smaller dam located in central Minnesota on the Mississippi river. The dam causes many environmental issues related to stopping freshwater fish migration, lack of nutrient loading at the bottom of the dam, and a general loss of biodiversity on the Mississippi. Dam removal is an option to solve many of the problems that the dam produces, but it will remove a large amount of renewable energy that powers St Cloud. Do the renewable energy benefits of the St Cloud dam outweigh the ecological benefits of its removal? This poster examines whether the renewable …

What References Are Chatgpt, Gemini, Copilot, And Perplexity Providing For Consumer Health Questions?, Scott Johnson, Catherine Johnson, Ivan Portillo

Library Presentations, Posters, and Audiovisual Materials

Background

With the growing popularity of generative artificial intelligence (AI) models such as ChatGPT, consumers may turn to these tools to easily seek health information. To our knowledge, no study has analyzed the references provided by multiple models for consumer health questions.

Objective

We aimed to analyze the references provided by ChatGPT, Gemini, Copilot, and Perplexity for consumer health questions in order to determine the most frequently appearing references.

Methods

AI generative models ChatGPT 4.0, Google Gemini, Microsoft Copilot, and Perplexity were each asked 30 consumer health questions and prompted to provide the corresponding references. The references were recorded.

The …

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 …

A Machine Learning Analysis Of Factors Leading To Major League Baseball Postseason Berths, Chase S. Foster

Undergraduate Honors Theses

Machine learning is a method that employs statistical algorithms to identify patterns and make predictions from data. This study applies machine learning techniques to analyze data from Major League Baseball (MLB) teams between 1998 and 2024, with the goal of determining which factors strongly influence a team's likelihood of reaching the postseason and in accurately predicting the teams that do and do not qualify for the postseason. Data exploration and unsupervised machine learning methods such as clustering were used to identify underlying patterns in team performance metrics and determine potential significant contributors to team success. Many different supervised learning methods …

Organocatalyst Frameworks For Co2 Reduction To Methanol: Selectivity Control And Mild-Condition Activation, Nehal Asif

Honors Theses

This study investigates the development and application of organocatalysts for the reduction of CO₂ under mild and sustainable conditions. A key focus is the synthesis of an organo-boronic acid–amine catalyst system (derived from thianthren-1-ylboronic acid and diethanolamine) and its subsequent evaluation in reactions with labeled CO₂. Through NMR analyses, including time-resolved monitoring of isotopically labeled substrates, the formation of formyl intermediates and downstream products such as formate and methoxy species was confirmed, demonstrating active CO₂ transformation. Multiple catalyst variants (NA1 series) were screened to assess activity and selectivity, with experimental conditions optimized in terms of temperature, solvent selection, and the …

A Multi-Scale Compartmental Model For Glucose Regulation And Diabetes Treatment, Andrew M. Watts

Honors Theses

Glucose is a fundamental energy source for cellular function, and its regulation is critical for maintaining metabolic stability in the human body. Glucose homeostasis is governed by a network of biochemical processes involving multiple organ systems that coordinate glucose production, storage, and uptake. Disruptions in this regulation contribute to metabolic disorders such as diabetes mellitus, underscoring the need for mathematical models that provide a mechanistic understanding of systemic glucose dynamics. Herein, we report a multi-time scale discrete-time dynamical systems model using compartmental difference equations to describe glucose concentrations across key physiological compartments. By quantitatively modeling glucose transport and metabolism, this …

Can Neutron Star Mergers Alone Reproduce R-Process Enrichment In Ultra-Faint Dwarf Halos?, Christine Gyure

Honors Theses

Neutron star mergers (NSMs) are the only confirmed source of the r-process responsible for heavy element formation. However, there are concerns about the extent to which NSMs can enrich small dwarf galaxies arising from either a strong natal kick velocity or substantial merger time. We present the results of a novel analytic model investigating the impact of these two distributions, represented respectively by a Maxwellian and power law, and their accompanying parameters on chemical enrichment in ultra-faint dwarf halos. We find that our model reproduces the observed enrichment fractions in these small dwarf galaxies for a variety of parameter combinations, …

Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar

Master's Theses

The development of electric Vertical Take-Off and Landing (eVTOL) drones signifies a substantial advancement in urban air mobility, ready to transform transportation models in densely populated regions. These advanced drones, distinguished by their capacity to function in limited spaces and their minimized environmental impact, are set to transform individual, shipping, emergency services, and public safety activities. Nonetheless, like any transformational technology, the implementation of eVTOL systems presents many challenges, especially in the realm of cybersecurity. Adding many devices and entities to an eVTOL network increases the risk of privacy and security attacks. This paper proposes a key-based authentication scheme that …

A Herpetofaunal Inventory Study Of Two National Wildlife Refuges In The Mississippi Delta And Associated Evaluation Of Trapping Efficacy, Andrew Holzinger

Master's Theses

The objectives of this project were to record the diversity and distribution of amphibian and reptile species present at Panther Swamp and Hillside National Wildlife Refuges (NWR) and to quantify trapping efficacy as a result of various methodologies under different habitat variables. The primary methods employed were two varieties of drift fence trap arrays consisting of 100-foot sections of silt fencing with buried 2.5-gallon buckets as pitfall traps and box traps placed at the end of each fence to capture larger organisms. When conditions permitted, aquatic trapping was also employed across the Refuges. A total of 55 species were documented, …

Analyzing Sleep Staging And Cognitive Transitions Using Fmrl Time Series Analysis, Qingwen Zeng

Arts & Sciences Graduate Student Theses and Dissertations

Understanding sleep stages and their underlying cognitive transitions is essential for advancing our knowledge of brain function. While sleep staging has traditionally relied on electroencephalogram (EEG), this study explores the feasibility of classifying sleep stages using only functional Magnetic Resonance Imaging (fMRI) data. We investigate the use of Hidden Markov Models (HMM) and Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) to capture temporal brain-state dynamics from task-free fMRI recordings. A series of pipelines—including sliding window, two-stage majority filtering, and prototype-based mapping via the Hungarian algorithm—were implemented to align model-inferred states with EEG-defined sleep stages. Among the twelve tested pipelines, the …

Biomimetic Design Of Mechanochromic Single-Chain Nanoparticle Polymer Networks, Samuel Robinson

Honors Theses

Hydrogels are a class of polymer networks that show promise in many biomedical applications but are typically limited in use due to their brittle nature. Synthesizing hydrogel networks that are comparable to biological materials has long been an area of interest. In this study, we are working to create a polymer network inspired by the muscular protein, titin. Titin can unfold under mechanical tension, releasing stored length, making the muscle material extensible and durable. To mimic this behavior, we propose creating a polymer network with similar stored length using single chain nanoparticles (SCNPs) cross-linked with phenolphthalein (PP) and β-cyclodextrin (β-CD). …

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 …

An Integrative Approach To Einstein’S Field Equations Of General Relativity For High School Students, Christina M. Aintablian

Senior Honors Theses

Einstein’s Field Equations form the foundation of the theory of General Relativity, describing the interdependence of mass-energy and spacetime curvature, where curvature gives rise to gravity. However, the complexity of these equations makes them difficult for high school students to grasp. The mathematical framework of General Relativity is introduced step by step, beginning with relative motion and culminating in a discussion of complex phenomena that challenge modern physics. Each explanation intertwines core concepts of relativity with pre-established mental models to enhance learning. Through the use of figures, analogies, narratives, and thought experiments, a structured framework is developed to make relativity …

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 …