Physical Sciences and Mathematics Commons^™

Cellular Telemetry For Real-Time River Flow Velocity Data From Fluvial Acoustic Tomography Loggers, Joshua Lee Seymour

Honors Theses

Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents …

Quasipseudometric Value Functions With Dense Rewards, Khadichabonu Valieva

Honors Theses

Goal-conditioned reinforcement learning (GCRL) serves as an extension of reinforce- ment learning (RL) that focuses on goals that can be adjusted, making it useful for many applications, especially in complex robotics tasks. Recent research has established that the optimal value function of GCRL, denoted as Q∗(s, a, g), has a quasipseudometric structure. This finding has led to the development of targeted neural architectures that respect such a structure. However, prior analyses have predominantly focused on sparse reward settings, which are known to increase challenges related to sample complexity. In this work, I with the guidance of my advisor show that …

Cache-Augmented Generation In Rag Pipelines: Fast And Memory-Efficient Approach To Multi-Agent Knowledge Query Systems, Yaju Gopal Shrestha

Honors Theses

This thesis presents an implementation and evaluation of Cache-Augmented Generation (CAG) for knowledge query systems, building upon the approach introduced by Chan et al. (2024). Traditional Retrieval-Augmented Generation (RAG) systems (Lewis et al., 2020) face challenges including high latency, excessive memory usage, and complex infrastructure requirements. By implementing a cache-augmented architecture that preloads relevant knowledge and eliminates real-time retrieval, our approach significantly improves response time while reducing resource requirements. The research demonstrates the effectiveness of CAG through a comprehensive implementation for The University of Southern Mississippi's chatbot system, achieving a 49.02% improvement in response time compared to traditional RAG approaches. …

Chemistry Education At The University Of Southern Mississippi Through Computation And Active Learning Methods, Sadie Pitre

Honors Theses

Education is one of the pillars of society. It evolves alongside scientific progress, shaping how students learn and engage with complex topics. The incorporation of new educational standards at The University of Southern Mississippi (USM) was implemented in two parts: through computational chemistry laboratory exercises for teaching laboratories and through the synthesis of organic molecules for research projects in an upper-level spectroscopy class. The goal of these projects is to improve learning of abstract and/or complex topics beyond the teaching methods traditionally utilized in the undergraduate laboratory and in the classroom.

Computational chemistry, which uses theoretical calculations to model molecular …

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 …

A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari

School of Computing: Dissertations, Theses, and Student Research

The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …

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, …

Simulating Chill: Exploring The Cognitive And Therapeutic Potential Of Cold Vr Environments, Jessica Turner, Piper Hutson, James Hutson

Faculty Scholarship

This study investigates the cognitive and therapeutic potential of immersive virtual reality (VR) environments designed to simulate cold conditions. Through the engagement of participants through multisensory stimuli—including vivid visual representations of the Athabasca Glacier, auditory effects of icy winds, and corresponding haptic feedback—the research evaluates neurological and physiological responses associated with attention, emotional regulation, and stress modulation. Participants experienced virtual scenarios featuring icy winds and snow, activating specific neurological pathways involving the occipital lobe, primary visual cortex, superior colliculus, and insula, thus reinforcing sensory integration. Through predictive coding, the anterior insula and hypothalamus were engaged, prompting thermoregulatory simulations and subconscious …

1785 Parisian Salon Project: New Methods And Practical Innovations In Digital Heritage Reconstruction, James Hutson, Charles O'Brien, Wesley Wolfe, Kayla Kraff, Trent Olsen

Faculty Scholarship

The 1785 Salon Unreal Engine Reconstruction Project represents a significant advance in digital heritage and immersive art historical research by combining generative AI-based asset creation, modular user experience (UX) design, and historically informed workflows. During the Spring 2025 phase, the project achieved major milestones, including the successful development of a replicable pipeline for transforming 2D reference images into period-accurate 3D sculpture models using generative AI and digital sculpting tools. Simultaneously, a robust and adaptable Inspection System was engineered within Unreal Engine, offering granular interaction controls, bilingual (English/French) content integration, dynamic metadata display, and enhanced accessibility. These innovations collectively enabled historically …

Predicting Cardiac Resynchronization Therapy Response: Development And Validation Of A Single Photon Emission Computed Tomography-Based Nomogram, Zhongwei Jiang, Zhongqiang Zhao, Zhuo He, Qiushi Chen, Ju Bu, Chunxiang Li, Dianfu Li, Chang Cui, Weihua Zhou, Huiyuan Qin, Cheng Wang

Michigan Tech Publications

Background: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with drug-refractory heart failure. However, more than thirty percent of patients do not benefit from CRT. This study aimed to develop and validate a novel model based on single photon emission computed tomography (SPECT) phase analysis features to predict CRT response. Methods: We identified 163 CRT patients who received gated resting SPECT myocardial perfusion imaging (MPI) between 2010 and 2020 at The First Affiliated Hospital of Nanjing Medical University. All variables were first processed by univariate logistic regression, and those with a P value < 0.05 were retained. The selected variables were subsequently used in the least absolute shrinkage and selection operator (LASSO) regression to construct a predictive model, which was then represented as a nomogram. Nomogram performance was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs). Internal validation was performed by bootstrapping with 1,000 replicates. Results: Of the 163 patients, 93 (57.1%) responded to CRT during follow-up. Responders had a wider QRS complex duration (QRSd) (164.80 vs. 154.51 ms, P=0.003), fewer premature ventricular contractions (PVCs) (1,392.98 vs. 2,283.60, P=0.003), lower prevalence of non-sustained ventricular tachycardia (NS-VT) (45.2% vs. 77.1%, P< 0.001), and better cardiac function [based on N-terminal pro-B-type natriuretic peptide (NT-proBNP), New York Heart Association (NYHA), and left ventricle (LV) parameters] compared to non-responders. Univariate logistic regression revealed 14 variables significantly associated with CRT response (all P< 0.05). The area under the ROC curve (AUC) value for the nomogram was 0.845 [95% confidence interval (CI): 0.785–0.906; sensitivity: 0.771; specificity: 0.849]. Internal validation yielded a mean AUC of 0.814 (95% CI: 0.777–0.836). The calibration curve demonstrated strong consistency between the predicted and observed outcomes. DCA revealed that the nomogram consistently provides a net benefit over the baseline, demonstrating its high practical value in clinical decision-making. A web-based dynamic nomogram (https:// jzw20000624.shinyapps.io/CRTpredictionmodel/) was developed for clinical application. Conclusions: We developed and validated a SPECT-based prediction model for predicting CRT response, which can assist clinicians in optimizing CRT candidacy preoperatively. Pacing at the latest contraction and relaxation segments, while avoiding scarred regions and optimizing preoperative status, is anticipated to improve CRT response.

Using Machine Learning To Detect Vault (Anti-Forensic) Apps, Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

Research outputs 2022 to 2026

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault …

Using Natural Language Processing And Machine Learning To Detect Online Radicalisation In The Maldivian Language, Dhivehi, Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

Research outputs 2022 to 2026

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. 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 …

Nonlinear Power Function Model Changepoint Detection., Jacob Steven Townson

Electronic Theses and Dissertations

Most work surrounding changepoint analysis focuses on linear models. This dissertation explores changepoint detection in nonlinear power function models, specifically focusing on models where the constant multiplier and power are the parameters to be estimated in addition to the changepoint parameter. The study assumes an asymptotic framework as the number of observations approaches infinity. The study explores various model fitting algorithms, and decides to employ the Newton-Raphson method for parameter estimation, with a custom implementation developed to optimize the process. The research first establishes the strong consistency of estimators for the model without a changepoint. Building on this result, consistency …

Finer Resolution Paleoclimatological Network Analysis Of Hydrological Extremes And Tree-Growth Response For Kentucky, U.S.A., Jordan Sharp

Electronic Theses and Dissertations

As climate change accelerates, southeastern states like Kentucky face increasing environmental and economic challenges. To improve future climate predictions, this study enhances paleoclimate reconstructions with a high-resolution tree-ring network for Kentucky, combining data from both living trees and dendroarchaeological sources. I evaluated the climate sensitivity of tree growth using superposed epoch analysis, static correlations, and spatial field correlations in KNMI Climate Explorer, along with moving correlations against seasonal temperature and moisture variables. Findings reveal that white oak and tulip poplar exhibit significant drought sensitivity across both live and archeological sites. However, I observed a weakening relationship between tree-ring growth and …

The Petm: A Window Into Earth's Future Climate, Jacalyn M. Wittmer Malinowski

Climate Change and the Individual, 2025-26

The Paleocene-Eocene Thermal Maximum (PETM) is not just an event that occurred in Earth’s distant past (~55 million years ago) but is a crucial lesson for today’s meteoric climate change. The PETM offers a historical parallel of rapid flux of greenhouse gases into the atmosphere that caused global warming, ocean acidification, dramatic changes in weather, and extinctions. This global warming event was the result of 20,000 years of “rapid” warming (in geological terms) however, we are experiencing rates of warming that are 5-10x higher than PETM levels. We are not experiencing rapid warming today, in comparison to the past, we …

Bibliography Of Christianity And Mathematics Update, Calvin Jongsma

Faculty Work Comprehensive List

This was a banquet talk given at the Twenty-fourth Biennial Conference of the Association of Christians in the Mathematical Sciences held at Dordt University on May 28-June 1, 2024.

It provides an update on the progress being made in the author’s project of producing a second edition of Bibliography of Christianity and Mathematics. This work is now being developed as a Zotero database with a slightly broader focus on Religious Faith and the Mathematical Sciences and with a much expanded list of books, articles, and blog posts on the topic. The author hopes to make the database public sometime …

Setting Up Students For Success: Analysis Of Effectiveness For Mathematics Placement, Jeff Carvell, Jason N.E. Ho, Dave Klanderman, Sarah Klanderman

Faculty Work Comprehensive List

How do we effectively and equitably place students into math classes in a way that provides them the best chance of success? As many higher education institutions veer away from placement based on standardized testing, many departments are seeking placement alternatives that will properly support students. Additionally, math placement determines not only a student’s mathematics courses but also influences their progress in related fields, including physics, chemistry, engineering, and more. This paper will describe three different existing placement systems at each of our liberal arts institutions as well as the affordances and constraints of each approach. Further, we analyze data …

Iowa Waste Reduction Center Newsletter, May 2025, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Meet the Environmental Assistance Program Staff
--- IWRC Director Joe Bolick joins ISOSWO Board of Directors
--- Keeping Tabs on PFAS
--- IGBC Brewery wins gold
--- Farewell to Paige
--- Compost Operator Renewal Training
--- Industry News

The Impact Of Corporate Profits On Gdi Estimates: Forecasting And Data Revisions, Peterson Haas

Honors Theses

The National Income and Product Accounts (NIPAs) produced by the U.S. Bureau of Economic Analysis (BEA) provide key measures of U.S. economic activity, including gross domestic product (GDP) and gross domestic income (GDI). Although conceptually equivalent, GDP and GDI often diverge due to differences in source data and revision timing. This study focuses on corporate profits—a small but volatile component of GDI that is frequently revised, especially during the BEA’s annual (A1) and benchmark (C1) revisions. I examine how revisions to corporate profits influence GDI revisions and whether they can improve real-time estimates of GDI. First, I document the size …

Comparing Spatial Interfaces Of The Tower Of London Task, Paean Luby

Honors Theses

Executive functioning involves key mental skills like self-control and problem-solving, which are often impaired by brain injuries. The Tower of London (TOL) task is a problem set commonly used to assess planning, but both traditional and digital versions can lack consistency, and, in the case of digital versions, realism and physical engagement. Research shows that 3D tasks, such as a 3D version of the Tower of Hanoi, engage the brain in distinct and meaningful ways, likely due to increased spatial involvement. By merging immersive 3D environments with the consistency of digital tools, virtual reality (VR) has the potential to enhance …

Tracking The Evolution Of Crossings In Memristive Hysteresis Responses, Eric Neuhaus

Honors Theses

A memristive device has an internal resistance that depends on the history of the applied voltage signal. This dependence gives rise to a pinched hysteresis loop in its current-voltage (I-V) characteristic, a property of resistive switching.

Beyond the crossing at the origin, additional crossings can emerge throughout the loop.

Our computational model simulates the current-voltage response of a memristive device under a sinusoidal voltage. We apply techniques from catastrophe theory to perform parametric analysis, assessing how intrinsic parameters influence the emergence of additional crossings. From there, we introduce a voltage transformation parameter to continuously transition the signal to a symmetric …

Privacy-Aware Ai-Based Agricultural Monitoring Using Internet Of Drones, Md Benozir Hossain

Honors Theses

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by …

Tailoring The Morphology Of Block Copolymers Via Solvent-Vapor Annealing, Bayleigh Madelin Loving

Honors Theses

Triblock polystyrene-polyisobutylene-polystyrene (PS-PIB-PS) copolymers have widespread applications from thermoplastic elastomers to protective coatings for ballistic applications. The incompatibility between the soft PIB and hard PS phases leads to phase separation and subsequent formation of morphological domains like lamellae and cylinders. The structures are stabilized by physical crosslinks, which can greatly improve the toughness of the triblock systems by preventing chain pullout during tensile deformation. We tailored the morphologies (achieving different morphologies with the same block copolymer) by changing the annealing conditions of the block copolymers. Solvent vapor annealing was used to selectively activate the mobility of a particular block, causing …

A Spectroscopic Survey Of Atmospheres Of Super-Earths, Luke Brust

Honors Theses

Recent research has made it possible to use spectroscopy to analyze the composition of the atmospheres of exoplanets, planets that orbit other stars. Most projects thus far have focused on the atmospheres of gas giants, as it is less challenging to observe them with available equipment. This project studies the atmospheres of four Super- Earths, planets that have a mass greater than the Earth but smaller than Neptune. This is accomplished by processing the raw spectroscopic data from the Hubble Space Telescope and modeling the atmosphere using the program 𝝉-Rex3. This survey found clear results from two of the selected …

Globally Adaptive Exponential Integrators For Stiff Systems Of Odes, Anzhelika Vasilyeva

Honors Theses

This thesis introduces a novel method for solving systems of Ordinary Differential Equations (ODEs) resulting from the spatial discretization of Partial Differential Equations (PDEs). The proposed approach builds upon an existing technique that employs Krylov projection, which requires evaluating a matrix function at each timestep. The innovation of the new method lies in its reuse strategy, which shifts the perspective from direct matrix function evaluation to polynomial interpolation. Numerical experiments conducted on constant and variable coefficient heat equations, with both smooth and discontinuous initial data, demonstrate the computational time advantage of the new approach. The results indicate that this method …