Physical Sciences and Mathematics Commons^™

Sickle-Cell Genotyping Cost Analysis In Amua And R, Nicholas P. Haley

Senior Honors Theses and Projects

Sickle cell disease (SCD) is a prevalent genetic disorder in the United States, with a significant economic burden due to the high costs of care, especially from chronic red blood cell (RBC) transfusions. These transfusions carry risks such as alloimmunization, which can lead to complications like delayed hemolytic transfusion reactions (DHTRs), further increasing healthcare costs in addition to increasing suGering. This study aims to aid in the evaluation of cost-eGective strategies for SCD management using Markov models, currently within TreeAge Pro software, which is proprietary. A decision model was adapted from the Kacker et al. (2013) study, which analyzed the …

Digital Forensics & Artificial Intelligence: Senior Honors Project Research Report, Noah Shelton Kasir

Senior Honors Theses and Projects

This project studied how current artificial intelligence large language models could be used to learn digital forensics and anti-forensics techniques compared to traditional search engines such as Google Search. This project aimed to answer the question “Can an ordinary person use AI to learn both anti-forensics and traditional digital forensics skills effectively and efficiently?”. The project research was divided into two distinct phases. Phase one consisted of the creation of a fictional case by acting as a layperson using the help of the Microsoft Copilot AI tool. This case consisted of a layperson “suspect” attempting to learn multiple anti-forensics techniques …

The Role Of Leptin In The Regulation Of The Soluble Amyloid Precursor Protein Alpha (Sappa) Levels In Non-Small Cell Lunch Cancer (Nsclc) Cell Media, Benjamin Haddad

Senior Honors Theses and Projects

We previously found that the levels of soluble amyloid precursor protein α (sAPPα) are partly regulated by acetylcholinesterase (AChE) in human non-small cell lung cancer cell lines A549 (p53 wild-type) and H1299 (p53-null). In this study, we hypothesize that sAPPα is partly regulated by leptin, a hormone classically recognized for its anorexigenic role in appetite regulation— yet recently shown to play a role in cancer cell signaling. It was observed that cells treated with leptin exhibited greater concentrations of sAPPα and lower concentrations of Aβ40/42 in the media compared to those of cells left untreated. An opposite effect was observed …

Codecontext: Integrating External Context For Enhanced Source-Code Model Performance In Software Development, Mohammad Arjamand Ali

Senior Honors Theses and Projects

In the ever evolving field of software development, understanding and maintaining complex codebases is crucial. There exist machine learning models and algorithms that aid in this by specifically learning to ‘understand code’, allowing engineers to build applications that help develop and maintain these large codebases. Although existing source-code machine learning models often overlook an important factor: the code's context. Our research focuses on leveraging external contextual information to enhance source-code model performance. We’ve developed a data pipeline that utilizes CodeQL to extract contextual information from the CodeSearchNet benchmark dataset to extend and create an augmented version of the dataset. We …

Fairness-Aware And Culturally Adaptive Machine Learning For Predicting Adolescent Substance Use, Stephanie Nworgu

Senior Honors Theses and Projects

Early initiation of substance use during adolescence poses significant risks to long-term health, educational attainment, and social outcomes, making early identification a critical public health priority. Machine learning models have increasingly been used to predict substance use risk; however, many such models do not explicitly examine whether predictive performance differs across demographic groups. This senior project examines the fairness of logistic regression models used to predict first-time alcohol use among adolescents. Using nationally representative survey data from the Youth Risk Behavior Surveillance System (YRBSS), pooled across the 2017, 2019, 2021, and 2023 survey cycles, this study develops logistic regression–based predictive …

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

Cyber Warfare And The Future Of Conflict, Victor A. Mercado

Graduate Theses/Dissertations

This thesis examines whether cyber warfare now poses a more immediate threat to U.S. national security than weapons of mass destruction (WMD). Cyber operations have become a dominant instrument of contemporary conflict, with malicious actors operating in a persistent “grey zone” that blurs traditional boundaries between war and peace. These operations target military and civilian entities, both directly and often as collateral damage due to the uncontrollable nature of cyber threats. WMDs, despite their catastrophic destructive potential, remain largely constrained by established deterrence frameworks.

The evolving nature of cyber warfare warrants comparison to WMD effects and impact, as cyber capabilities …

Efficient Task Scheduling In Cloud Infrastructures Using Dynamic Score-Based Allocation And Deep Q-Learning, Shadman Sakib

Graduate Theses/Dissertations

Cloud computing has grown rapidly in recent years, mainly due to the sharp increase in data transferred over the internet. This growth makes task scheduling a key and challenging part of cloud systems, as it helps distribute user requests across servers to minimize response time, prevent overloading, and ensure smooth user experience. This thesis proposes two novel approaches for dynamic task scheduling in cloud environments. First, a novel Score-Based Dynamic Load Balancing (SBDLB) strategy is developed, which leverages system parameters to allocate tasks efficiently across virtual machines (VMs) in data centers. SBDLB ensures balanced workload distribution by continuously evaluating VM …

Augmentation Of Quality Of Service, Security, And Trust In Edge-Enhanced Iot Networks Leveraging Blockchain, Kyle Matthew Whitlatch

Masters Theses

The meteoric rise of the Internet of Things (IoT) has led to multiple architectural schemas to handle the data these devices create. Edge-enhancement is a technique where groups of IoT report to a median layer to aggregate the data before relaying to the endpoint. These edges also open opportunities to perform more operations to ensure devices are behaving properly before committing the data to long term storage. By interconnecting these edges with a technology like blockchain, it is possible to have an interconnected and responsive system to ensure the Quality of Service (QoS) of the IoT devices within the architecture …

Constraint Programming For Optimized Degree Paths, Mitchell Lee Skaggs

Masters Theses

This work presents a degree planning tool developed as part of the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS) project which generates complete, valid, and personalized degree paths at any point from admission to graduation. This eliminates tedious calculation and double-checking, allowing advisors to focus on a student’s long-term plans and students to proactively explore potential degree paths. The original research contribution of this work is the use of a unified model for academic requirements to automatically translate complex, real-world curricula into a constraint programming model that can be quickly optimized based on personalized student criteria.

Automatically translating …

Critical Behavior Ofthedilutedquasi-Onedimensionalquantum Ising Model, Logan Bradley Sowadski

Masters Theses

We explore the phase transition in the diluted quasi-one-dimensional quantum Ising model. We begin by giving an introduction to the Ising model followed by derivations of the properties and observables. A brief overview of Monte-Carlo simulations is also given focusing on two algorithms that make simulations for physically realizable systems possible, the Metropolis and Wolff algorithms. We finally discuss the concept of random disorder in the system and the effects this can have on the bulk of the system.

These concepts are then directly applied to a quasi-one-dimensional Ising model. Motivated by recent experiments on the spin-chain material cobalt niobate, …

Overall Tropospheric Noise Over The St. Louis Using Isce And Fringe Processing, Maryann Lee

Masters Theses

Interferometric Synthetic Aperture Radar (InSAR) is a satellite-based technique that is used to measure various aspects such as ground deformation, earthquakes, volcanic activity, and motions of glaciers. Although the technique has proved very useful, measurements errors such as ionospheric and tropospheric noise, and deformation often cause problems with making precise measurements. InSAR is a tool used in geodesy that is used to measure the change of surface deformation over time through a series of repeated SAR acquisitions. These errors in the acquisitions occur from factors of signal decorrelation, atmospheric delays, and phase unwrapping errors that affect the accuracy and precision …

Performance Of Standard Medical Mllms On Ecg Image Data, Prisha Anil

Masters Theses

This work presents a structured benchmarking study of multimodal large language models (MLLMs) applied to electrocardiogram (ECG) interpretation tasks. We evaluate three representative architectures: MedGemma, HuatuoGPT-Vision, and LLaVA-Med, across progressive experimental stages involving text-only structured prompt normalization, text–image fusion with ECG plots, and full multimodal fusion incorporating time-series signals. A standardized five-section cardiology prompt was designed to enforce consistent output structure and SCP-code alignment, enabling reproducible metric computation across models. Quantitative evaluation using BERTScore, token-level F1, and diagnostic accuracy demonstrates that HuatuoGPT-Vision achieves the highest semantic and diagnostic alignment, while MedGemma exhibits superior formatting stability and reproducibility. In contrast, LLaVA-Med …

Multimodal Spatio-Temporal Pest Prediction In Precision Agriculture, V N S Kameswari Sri Sindhu Manchikanti

Masters Theses

Accurate and timely prediction of pest outbreaks is a cornerstone of Agriculture 5.0, which emphasizes intelligent, data-driven, and sustainable decision-making in crop production. This research presents a multimodal deep learning framework that integrates heterogeneous data sources, including weather parameters, satellite-derived vegetation indices, and static and dynamic soil attributes, to forecast pest population dynamics under varying management and ecological conditions. The proposed framework employs modality-specific deep encoders to capture distinct temporal and spatial representations from each data stream and merges them through a late-fusion architecture that learns cross-modal dependencies critical to pest emergence. The design further incorporates treatment-aware and multiclass extensions, …

Synthesis And Analysis Of Materials For Quantum Devices, Mathew Pollard

Doctoral Dissertations

Quantum materials play a pivotal role in the advancement of next-generation technology. Superconducting quantum computing, dissipationless spintronics, or valleytronics offer promising ways forward beyond traditional chip miniaturization. Josephson Junctions (JJs) have already revolutionized quantum information and high precision detectors. Quantum systems, however, are either hard to control, produce, and/or maintain. This calls for a better understanding of microscopic properties and tuning of these quantum states.

In this work, we experimentally investigated growth methods to control the electric and magnetic properties of Topological Insulator (TI) Sb2Te3 through Cr-doping. Our results demonstrate the onset of a Magnetic Topological Insulator (MTI) and have …

Tailoring Selected Aerogels To Targeted Applications, Stephen Yaw Owusu

Doctoral Dissertations

Aerogels are ultra-lightweight, porous solid materials characterized by a three-dimensional nanostructured network. Owing to their exceptional physical and chemical properties, aerogels have garnered considerable attention within the materials science community and were recognized by IUPAC in 2022 as one of the top ten emerging technologies in chemistry. Although numerous aerogels have been synthesized, only a few have been effectively tailored for specific applications. Optimizing the properties of known aerogels for targeted uses remains challenging. This dissertation investigates strategies for tailoring aerogels derived from isocyanate-benzoxazine, benzodiazine, and phenolic resins to meet the requirements of various advanced applications. The approaches employed include …

Machine Learning Models For Location Prediction, Message Routing And Path Planning For Rescue Of Underground Miners, Abhay Goyal

Doctoral Dissertations

Self-rescue during underground mine disasters is vital for miner safety. Evolving hazards and post-disaster conditions demand solutions that enable navigation under severe communication and computational constraints. Centralized systems often fail in such rugged settings, while decentralized methods—particularly Delay Tolerant Networks (DTNs), proven in battlefields and space missions—offer distinct advantages for underground applications. This research addresses five core challenges: (i) predicting miners’ next locations on low-power devices using points of interest and movement sequences; (ii) delivering timely updates on safe routes, evacuation zones, and hazardous areas; (iii) evaluating energy efficiency and comparing graph-based approaches to existing methods; (iv) enabling edge-ready frameworks, …

Generalizations Of Finiteness Conditions And Extension Monads In Algebras With Infinitely Many Or Infinitary Operations, Danielle Christienne Bowerman

Doctoral Dissertations

In this work, we extend the results of finiteness conditions and extension monads found in Insall from finitely many finitary operations to infinitely many finitary operations, as well as touching on infinitary operations. We also examine varieties of algebras, including the notion of strong varieties introduced in Insall, and common constructions of extension monads in varieties of algebras. We see that for finite collections of algebras of the same signature, the extension monad operation on a variety of algebras commutes with the direct product operation, and all retractions from an enlargement or extension monad are trivial. We also see that …

Quantum And Classical Phase Transitions In Disordered Systems, Gaurav Ramesh Khairnar

Doctoral Dissertations

This work examines the effects of quenched disorder on classical and quantum phase transitions. There are countless examples of phase transitions in nature. Here, we study phase transitions in condensed matter with the help of simple models that focus on the essential unifying features of the problems. We are particularly interested in investigating how the presence of disorder changes the behavior of the system in the vicinity of a phase transition.

Here, we discuss three closely related problems regarding phase transitions. First we consider the behavior of highly diluted magnets with varying dilution levels in order to explain the unusual …

Development Of Dual-Scan Nuclear Magnetic Resonance (Nmr) Pulse Programs For Spin-Lattice Relaxation Measurements, Zachary Mayes

Doctoral Dissertations

This research develops and refines the Split Inversion Pulse and Recovery (SIP R) methodology for spin-lattice relaxation (T₁) measurements in NMR spectroscopy. SIP R introduces a two-scan difference technique using a split inversion pulse sequence, where the 180° pulse is split into two 90° pulses phase-shifted with respect to each other. This approach simplifies data fitting, requiring only two parameters to extract T₁ values, compared to the traditional inversion-recovery method, which needs three. The SIP-R-DS and SIP-R-S adaptations extend this framework by incorporating selective NMR resonance excitations, enhancing its applicability to cross-polarization experiments and enabling unobstructed NOE measurements. These adaptations …

Deep Learning And Adaptive Clustering Approaches For Flood Prediction And Efficient Sensor Placement In Missouri, Fahimeh Sharafkhani

Doctoral Dissertations

Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

Epitaxial Growth Of Metal-Organic Framwork Thin Films By Electro-Conversion And Electrodeposition, Xiaoting Zhang

Doctoral Dissertations

Electrochemical epitaxy is a facile and inexpensive soft-solution process to fabricate highly ordered thin films. This research focuses on the epitaxial growth of metal-organic framework (MOF) thin films by electrochemical methods which include electro-conversion and electrodeposition.

First, an electrochemical conversion pathway was invented to achieve epitaxial MOF thin films. Epitaxial single-domain Cu-BTC(111) thin films were manufactured by electrochemical oxidation of Cu₂O(111) films electrodeposited on single-crystal Au(111). The single-crystal-like Cu-BTC(111) thin films with out-of-plane and in-plane order provide well-aligned 3.5 Å triangular windows along the [111] direction. Cu-BTC(111) foils were fabricated from the Cu-BTC/Cu₂O system by electrochemical …

Polybenzodiazine Aerogels: All-Nitrogen Analogues Of Polybenzoxazines - Synthesis, Characterization, And Their Application In Carbon Dioxide Capture, Vaibhav Edlabadkar

Doctoral Dissertations

"The rapidly risen concentration of carbon dioxide in the atmosphere over recent years contributed to global warming and ocean acidification. Carbon capture is viewed as an essential tool from keeping atmospheric CO₂ levels from rising further. This thesis focuses on the development of new materials for high capacity and selective adsorption of CO₂. One such material was based on a newly synthesized tetrahydroquinazoline (THQ), which was designed as an all-nitrogen analogue of main-stream benzoxazine monomers. THQ solutions in DMF gelled at 100 °C via HCl-catalyzed ring opening polymerization to polybenzodiazine (PBDAZ) wet gels, which were dried in …

Applications Of Tetramethyl-Guanidine Supported Cationic Transition Metal Sites Towards C─N Bond Construction Methodologies And [3+2] Cycloaddition Reactions, Suraj Kumar Sahoo

Doctoral Dissertations

"We present a family of divalent cationic bipodal and tripodal M(II) reagents (M = Mn, Fe, Co) supported by superbasic tetramethyl-guanidinyl (TMG) residue framework with applications towards C─N bond construction methodologies. These cationic reagents possess unique characteristics since their enhanced Lewis acidity facilitates in situ elaboration of the primary nitrene-transfer product (aziridine). Indeed, further aziridine-ring opening and cycloaddition of several dipolarophiles (nitriles, alkenes, ketones) permits the three-component synthesis of valuable five-membered N-heterocycles, commonly found in pharmaceuticals and agrochemicals.

We undertake a more systematic study to unravel parameters that make certain divalent metal sites more suitable as catalysts for the in-situ …

Development Of Ligands And Metal Complexes With Weakly Coordinating Apical Elements And Their Application To Nitrene Insertion And Addition To C‒H And C=C Bonds, Meenakshi Sharma

Doctoral Dissertations

"We present a family of triangular coinage metal M₃Cl₃ complexes [M = Cu (I), Ag (I)] supported by antimony [Sb (III)], and bismuth [Bi (III)]-centered highly basic tetramethyl-guanidinyl [TMG₃trphen-E] (E = Sb, Bi) ligand frameworks with their applications as catalysts toward the construction of C‒N bond through aziridination and amination reaction methodologies.

The [(TMG₃trphen-Sb)Cu₃(µ-Cl)₃] catalyst gives better yields of aziridines with styrenes than [(TMG3trphen-Bi)Cu₃(µ-Cl)₃] with PhINTs as a nitrene source. However, the catalytic reactivity of both catalysts is enhanced by an electrophilic nitrene, PhINTces for …

Development Of Catalytic Membranes And Composites For Energy Storage Devices And Nonenzymatic Biosensors, Harish Singh

Doctoral Dissertations

The excessive use of fossil fuels has led to their rapid depletion, causing an energy crisis and environmental issues. Consequently, there's a growing focus on sustainable energy conversion. Electrocatalysts are pivotal in this development, particularly for fuel cells and solar fuel generators, necessitating high-efficiency, cost-effective catalysts for large-scale adoption. Transition metal chalcogenides have emerged as promising electrocatalysts due to their high efficiency, electrochemical tunability, and abundant active sites. This work is divided in two parts. In Part A, we have investigated transition metal chalcogenide and few atom clusters FACs as electrocatalysts for oxygen evolution and reduction, highlighting their structure-property relationships …

Products Of Laser Ablated Actinides And Actinide Comparators In The Presence Of Carbonyl Sulfide Characterized By Rotational Spectroscopy, Joshua Edward Isert

Doctoral Dissertations

As the consumption of energy continues to rise globally, so too has interest in alternative energy sources such as nuclear power. However, a fundamental understanding of the actinide series is needed in order to safely and efficiently utilize these elements. Rotational or microwave spectroscopy, depending on if one is speaking of the physical outcome of the experiment or the region of the electromagnetic spectrum being operated in, is a gas phase molecular study utilized for structural determination. This technique can be utilized to gain an in depth understanding of bonding within the actinide series. However, when studying species that are …

Measuring Resistivity With Low-Cost 3-D Printed Four-Point Probe, Benjamin Siens

SPARK Symposium Presentations

Resistivity is an intrinsic property that determines how strongly electrical current is resisted. Measurement of the resistivity of materials is vital for many fields of science and engineering. This is easily accomplished with a four-point probe. However, commercial probes can be costly. Using 3-D printing and based on the design in Lu et al. (J. Chem. Educ. 2017), a functional four-point probe has been built at a much lower cost in materials. Additionally, the probe design has been simplified for easier construction and better compatibility with Belmont University’s 3- D printers. ITO glass was used to determine the accuracy of …

Fisheries Research Report No. 345: Metropolitan Monitoring Program 2024, Alissa C. Tate, Claire B. Smallwood, Karina L. Ryan

Fisheries Research Reports

Ongoing monitoring of recreational fisheries in the Metropolitan zone is essential to ensure the sustainable use of fisheries resources. Surveys of shore- and boat-based recreational fishers within the Metropolitan zone of the West Coast bioregion provide information on human dimensions and catch-per-fisher as well as mean length and weight of key recreationally caught species. For shore-based fishers, estimates of fishing effort, catch and harvest are also presented for key nearshore species. Information in this report is used to support ongoing assessments for key nearshore species and inform the development and evaluation of harvest strategies and other management actions, such as …