Physical Sciences and Mathematics Commons^™

Biological And Chemical Evaluation Of Amide-Based Cjoc42 Derivatives As Potential Gankyrin-Inhibitors For The Treatment Of Breast, Lung, And Liver Cancer, Benita Britton-George

Theses and Dissertations

Gankyrin is an ankyrin-repeat protein overexpressed in certain cancers and is responsible for cell growth and proliferation. Gankyrin is important to the development of many types of cancer, including breast, lung, and liver cancer. Consequently, gankyrin and its various protein-protein interactions, are prospective therapeutic targets for inhibiting the progression of certain cancers. In 2016, the first small molecule inhibitor of gankyrin, cjoc42, was discovered and subsequently demonstrated modest antiproliferative activity against liver cancer cells. Previous work from our lab demonstrated that replacing the sulfonate ester of cjoc42 with an amide group significantly improved gankyrin binding while enhancing antiproliferative activity against …

Neural Congruency Contrastive Learning Framework Validation Using Artificially Created Eeg Data For Dyslexia Research, Jacqueline M. Torres

Theses and Dissertations

Analysis of electroencephalogram (EEG) recordings in children with dyslexia has been commonly used to explore the neural mechanisms underlying reading disorders. Yet, challenges such as low signal-to-noise ratio (SNR), high inter-subject and inter-trial variability, and the inherently multivariate nature of EEG signals hinder the isolation of neural components elicited during reading diagnostic tests. To mitigate these challenges, the neural-congruency analysis framework was recently proposed, leveraging traditional machine learning optimization methods to incorporate domain knowledge about the congruency of neural responses across participants (i.e., consistent neural responses among proficient readers). However, the application of deep learning techniques, specifically contrastive learning, remains …

Investigation Of The Binding Mechanisms Between Naphthenic Acids And Humic Acid Using Raman Spectroscopy And Computational Modeling, Jaelyn N. Palmer

Theses and Dissertations

The oil sand regions in Alberta, Canada have proven to be costly to the environment despite its economic benefits. The toxins produced during oil processing are sent to tailing ponds to be recycled, though often leaking into nearby waterways and soil. Naphthenic acids, NAs, are one of the most common toxins produced and have shown to be toxic to aquatic life, animals, and humans. As there is dissolved organic matter in tailing ponds and surrounding environments, tailing ponds often lead to NAs coming in contact with organic material. Humic acid, HA, is a major soil component that has been suspected …

Qualitative Analysis Via Network Models Of Human Dynamin-1 Throughout Gtp Hydrolysis, Kevin Brian Schulman

Theses and Dissertations

This thesis aims to elucidate the molecular mechanisms by which nucleotide binding dictates the intrinsic dynamics and to understand the functional conformational changes of Human Dynamin-1 during the GTP hydrolysis cycle through the use of Elastic Network Models (ENM). A comprehensive understanding of this process is crucial given dynamin's role in clathrin-mediated endocytosis (CME). The research utilized both experimentally derived crystal structures and AlphaFold-predicted structures of dynamin, specifically examining the GTPase and Bundle Signaling Element (BSE) domains across nucleotide-free, GTP-bound, GDP-bound, and transition states. The essential features of the research methodology involved a multi-scale computational approach. Qualitative analyses were performed …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Into Thick Air: Adapting Cultural Practices Into Solutions For Modern Wicked Problems, Syed Muhammad Erzum Naqvi

Theses and Dissertations

Into Thick Air argues that cultural practices can inspire solutions

to challenging modern problems. This project employs the Basant

festival as a case study to critically reimagine kite flying as a

collective practice with the potential to contribute to the reduction

of air pollution in Lahore, a city consistently ranked among those

with the poorest air quality globally. By building kites from fabric

treated with nanoparticle coatings, Into Thick Air transforms a

traditional community activity into an accessible, citizen-driven

method for removing pollutants from the air. The treated kites

empower Lahoris to take charge of their environment while

encouraging collective …

Purification Of Recombinant His6-Gfp-Preptin Fusion Protiens, Apoorva Kollaram

Theses and Dissertations

Preptin, a 34-amino-acid peptide derived from proinsulin-like growth factor II, has emerged as a promising therapeutic candidate for both Type 2 diabetes mellitus and osteoporosis. Its dual functionality—stimulating insulin secretion without inducing hypoglycemia and promoting osteoblast proliferation—positions it as a unique alternative to traditional sulfonylureas and osteogenic agents. In this study, we developed a purification protocol for green fluorescent-preptin fusion proteins. These fusion proteins will be used to investigate the functional significance of residues Pro17, Val18, Gly19, Trp27, Lys28, and Gln29 in rat preptin, which shares high sequence similarity with the human form. Site-directed mutagenesis was performed using NEB Q5 …

Qlorax: Heuristic-Guided Fine-Tuning Of Llama-2 For Domain Adaptation In Entrepreneurship, Gaurob Saha

Theses and Dissertations

This thesis presents a study on the fine-tuning of large language models (LLMs) for domain-specific applications using limited data. We fine-tuned the LLaMA-2 (7B) model on a curated entrepreneurial dataset containing 3,545 human-written question-answer pairs, of which 3,095 were used for training and 450 were reserved for evaluation. A complete fine-tuning and evaluation pipeline was developed, which included clustering human-written answers, generating centroid-based summaries for each cluster, and evaluating the model's generated responses through cosine similarity.

Training was carried out over five epochs, with model performance evaluated after each epoch. The fine-tuned model demonstrated strong semantic alignment with human-written content, …

Exploratory Data Analysis (Eda) And Predictive Machine Learning (Ml) For Buildings’ Energy Fault Detection, Akshith Nukala

Theses and Dissertations

Building energy load fault detection is a critical challenge in energy usage analysis. It helps uncover energy wastage, machinery/appliance degradation or inefficiency, and failures or faults in buildings’ HVAC (heating, ventilation, and air conditioning) systems. Early identification of machinery failure and energy wastages due to operational maintenance negligence in large sites such as campus buildings is indispensable for achieving energy efficiency. This is crucial for saving patrol and minimizing the response time to restore the building appliances or systems to their optimal state.

Advancements in state-of-the-art AI/ML data-driven algorithms and techniques enabled us to build accurate, efficient and scalable fault …

Exploring Brent-Kung Adders In Balanced Ternary Cmos Logic, Astrea J.C. Rojas

Theses and Dissertations

The modern computer operates on a 64-bit architecture. These devices can store large numbers and precise decimals, but more advanced devices are needed to support progressing technologies every day. A more efficient system with higher speeds and larger operable numbers would be a key to optimization of computation as we know it. The ternary device, operating in base-3, has the potential to be that optimization. However, binary technology has such precedent and research that it is a difficult gap to span to compare the ternary system to the modern binary system. With a more advanced adder and optimized gates using …

An Analysis Of Coal Ash Pollution In The State Of Georgia, Mick W. Etchison

Theses and Dissertations

This social science paper will explore the factors contributing to the prevalence of coal ash in specific regions of Georgia and its impact on local ecosystems and human health. It provides an analysis of the current environmental and political issues surrounding the containment and spread of coal ash throughout the State. Coal ash is primarily found in the central, southwestern, and northeastern regions where coal-fired power plants and coal ash storage sites are located. The paper will then discuss the political, economic, and legal reasons behind the lack of effective regulation and mitigation of coal ash spread. Coal ash illustrates …

Multitask Learning For Named Entity Recognition And Relationship Extraction, Adrienne D. Hembrick

Theses and Dissertations

Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP), involving the identification of structured information from unstructured text. Two core components of IE—Named Entity Recognition (NER) and Relation Extraction (RE)—are widely used to extract key concepts and the relationships between them across various domains. However, the sequential dependency of RE on the output of NER makes it vulnerable to error propagation: inaccuracies in entity recognition can negatively affect downstream relation extraction.

To mitigate this issue, Multitask Learning (MTL) has been proposed as an approach that jointly models NER and RE, aiming to improve overall performance and reduce …

Synthesis Of Novel Schiff Bases And Their Metal Complexes, Ryan J. Lukkarinen

Theses and Dissertations

Presented in this thesis is the synthesis and characterization of multiple thiosemicarbazones and dithiocarbazates, as well as their corresponding metal complexes. Chapter 1 is an introductory chapter exploring the significance and potential applications of this work. Chapter 2 focuses on thiosemicarbazone synthesis and characterization. Chapter 3 describes the synthesis and characterization of dithiocarbazates. Chapter 4 details metal complex synthesis using the previously described ligands. Chapter 5 summarizes the conclusions of the research and future work that may stem from it.

Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods, Prince Kofi Asare

Theses and Dissertations

Unplanned hospital readmissions represent a significant challenge for healthcare systems, contributing to substantial financial burdens and highlighting gaps in patient care coordination. In the U.S., approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing billions annually. Social determinants of health, such as income, housing stability, and social support, account for up to 80% of health outcomes, yet their integration into predictive models remains underexplored. This study introduces a novel Bayesian framework for predicting 30-day readmission risk, combining Gaussian Process models with spike-and-slab priors and Bayesian Lasso regression with Laplace priors. Utilizing Markov Chain Monte Carlo methods, the approach …

Automorphism Groups Of N-Graded Vertex Algebras Associated With Cyclic Leibniz Algebras With Small Dimensions, Alexander M. Keene

Theses and Dissertations

A fundamental problem in the study of vertex (operator) algebras V is the determination of the group of (grading-preserving) N-graded vertex algebras associated with cyclic Leibniz algebras of dimensions 2 and 3 that were classified by C. Barnes, E. Martin, J. Service, and G. Yamskulna in [1].

In each case examined, investigation of the automorphism group relies on the key fact that the action of an automorphism σ is determined solely by its value at a single basis element b. Furthermore, we employ a result in [19] by H. Li and G. Yamskulna which states that we can determine the …

Centralized Deep Reinforcement Learning For Homogeneous Multi-Component Maintenance Optimization, Joseph W. Wittrock

Theses and Dissertations

This thesis explores an application of reinforcement learning (RL) in maintenance optimization. Recent advances in hardware-accelerated computation and deep learning have made RL a powerful tool for solving optimization problems which are too complex for traditional methods. Maintenance optimization involves improving the efficiency and effectiveness of maintenance activities through data-driven approaches, ultimately reducing costs and increasing asset availability. Making informed maintenance decisions is crucial to long-term sustainability.

A desirable maintenance policy maximizes a utility signal while minimizing the cost of maintenance. Techniques in sequential decision making such as dynamic programming (DP) and RL have found success in optimizing these maintenance …

High Throughput, Solid Sample Analysis With Cone-Based Mass Spectrometric Ionization Sources, Emily J. Wiggins

Theses and Dissertations

The widespread presence of environmental and consumer-product contaminants (e.g. trace metals, opium alkaloids) has underscored the need for rapid, reliable, and high-throughput analytical techniques that are capable of handling complex solid samples. While traditional mass spectrometric (MS) methods tend to have high sensitivity and accuracy, they often require labor-intensive and time-consuming sample preparation steps that limit throughput and field applicability. This thesis investigates the use of 3D-Printed Cone Spray Ionization-Mass Spectrometry (3D-PCSI-MS) as an ambient ionization technique for the direct screening of contaminants from solid matrices, eliminating the need for traditional sample preparation and allowing for real-time analysis.

In …

Knowledge And Ontology Enhanced Approach To Natural Language Understanding (Koe-Nlu) In Computational Social Media And Healthcare, Naga Usha Gayathri Lokala

Theses and Dissertations

Natural Language Understanding (NLU) faces both opportunities and challenges as the amount of social media and healthcare data grows. This is particularly evident in context-sensitive applications such as evaluating cognitive health, identifying mental health symptoms, and monitoring drug abuse. Even though traditional NLU models work well for processing language in a wide range of areas, they often lack the ability to understand language in a specific domain, reason in context, and incorporate structured external knowledge. This dissertation talks about the Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU), a new framework that is meant to make NLU systems …

Modification Of Nanoparticles For Designed Interfaces, Tony Lee Neely, Jr.

Theses and Dissertations

The work contained herein, is focused on the design, synthesis, and characterization of polymer nanocomposite interfaces and the property enhancement afforded from said interface design. Through the use of reversible addition fragmentation chain transfer (RAFT) polymerization for the grafting of polymer chains to silica nanoparticles, the surface of silica nanoparticles can be manipulated to tune the properties of the nanocomposite as a whole.

In the first part of this work, heterogeneity is introduced onto the surface of silica nanoparticles via a sequential RAFT polymerization to afford a bimodal brush system. A densely grafted, short brush population is polymerized from the …

Optimal Data Splitting Methods, Sujay Mudalgi

Theses and Dissertations

In predictive modeling, effective data splitting is crucial for creating statistically representative training and validation sets. The state-of-the-art data splitting methods are based on minimizing the energy distance between the split subsets. However, there are a number of limitations in the existing methods, which this dissertation aims to address. First, the existing methods were computationally inefficient. Thus, Chapter 2 proposes a method to scale up these approaches for big data. Here, we introduce scalable Twinning (s-Twinning), which significantly improves the execution speed of data splitting without sacrificing accuracy. Second, the existing methods did not consider the predictive relationship in the …

Unraveling The Reactivity Of Nitrate Ester Explosives: Insights From Femtosecond Time-Resolved Mass Spectrometry And Computational Chemistry, Erica Britt

Theses and Dissertations

This dissertation uncovers the ultrafast molecular mechanisms underlying the decomposition of nitrate ester explosives—nitroglycerin (NG), ethylene glycol dinitrate (EGDN), and amyl nitrate—through the integration of femtosecond time-resolved mass spectrometry and advanced computational chemistry. Although these compounds play fundamental roles in both military and civilian contexts, the precise molecular dynamics governing their pronounced reactivity and impact sensitivity have remained inadequately characterized. Our research establishes that low O–NO2 bond dissociation energies are the primary driver of ultrafast fragmentation and high reactivity in all three esters. The distinctive molecular architectures of these esters dictate their explosive properties: NG’s tri-nitrate configuration leads to exceptional …

Predictive Inference For Ion Concentration With Machine Learning And Bayesian Methods, Alexandra B. Ulbing

Theses and Dissertations

Ultraviolet--visible (UV--Vis) spectroscopy produces high-dimensional signals that are strongly collinear, shift with concentration, and exhibit heteroskedastic, non-Gaussian noise. These features make supervised regression from spectra to ionic concentrations statistically challenging and limit the reliability of methods that assume linear structure or homoscedastic errors.

This dissertation develops two complementary frameworks for prediction and uncertainty quantification in UV--Vis spectroscopic regression: (1) frequentist stacked ensembles combined with distribution-free conformal prediction, and (2) Bayesian hierarchical modeling and Bayesian stacking. Together, they provide a unified view of model-based and distribution-free uncertainty across nickel and nickel--cobalt datasets.

The frequentist component builds ensembles of Functional Data Analysis …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat Jannat

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Facile Synthesis Of Gold, Copper, And Silicon Nanostructures By Laser Ablation In Liquid, Nicholas Simpson

Theses and Dissertations

Metal nanoparticle (NP) synthesis is a continually evolving area of research, owing to the widespread application of NPs in fields such as catalysis, drug delivery, and biosensing. Compared to bulk metal material, NPs contain a larger proportion of surface atoms and exhibit unique optical properties, thus making them ideal for applications where maximizing surface interactions is key. Moreover, their compositions can be altered in several different ways, as evidenced by metal alloy, core/shell, supported, and defective NPs. While conventional synthesis methods are capable of producing NPs in large quantities, they often require conditions of high temperature and pressure, strong reducing …

Dosimetric Validation Of An Electron Monte Carlo (Emc) Algorithm For Small Cutouts And Extended Ssd’S: Assessing Emc In Small Field Dosimetry, Anil Basavaaraju

Theses and Dissertations

This thesis validates the accuracy and limitations of Electron Monte Carlo (eMC) simulations in Eclipse treatment planning systems for small-field electron dosimetry and extended source-to-surface distances (SSDs). Systematic measurements were performed using a microDiamond detector across various electron energies and field sizes. The study examines the discrepancies between measured and calculated (eMC) dose distributions, validating challenges due to lateral scatter disequilibrium and extended air gaps. Our findings provide systematic data for eMC dose calculations and demonstrate that eMC performs within clinical tolerances, with the ultimate goal of enhancing the precision and safety of radiation treatments.

Multiphysics Modeling Of Material Response To High-Intensity X-Ray And Laser Pulses: Heating, Ablation, And Plasma Expansion, Youssef Abouhussien

Theses and Dissertations

This dissertation presents a computational framework to investigate material response under high-intensity X-ray fluxes produced by an exo-atmospheric nuclear detonation and laser-material irradiations, with a focus on heating, ablation, and plasma expansion phenomena relevant to satellite vulnerability and high-energy-density environments. A hybrid Monte Carlo and Two-Temperature Model (MC-TTM) was developed to simulate X-ray and laser energy deposition and thermal relaxation in metals and semiconductors across a range of X-ray and laser pulse durations from femtoseconds to nanoseconds. Results demonstrate distinct thermal behavior between materials, with ablation thresholds and phase transitions captured in good agreement with experimental data.

In parallel, a …

Development, Synthesis And Characterization Of Advanced Low- Cobalt And High-Nickel Cathode Materials For Lithium-Ion Batteries, Arjun Patel

Theses and Dissertations

This dissertation focuses on the improvement of structural and electrochemical stability of active materials for lithium-ion batteries. It explores the modification strategies to mitigate the issues related to Ni-rich cathode materials, along with the continuous production of cathode precursors using a slug flow reactor to improve the quality of the product.

The First part of the dissertation examines the effect of substitution of cobalt with aluminum and iron on the electrochemical stability of Ni-rich lithium nickel cobalt manganese oxide (NCM) materials. This study investigates how Al and Fe doping work and how they impact the performance and stability of these …

Leveraging Distributed Semantics From Deep Learning Architectures For Literature-Based Discovery, Clint A. Cuffy

Theses and Dissertations

Literature-based discovery (LBD) is a scientific process that introduces methods to automatically identify novel insights between non-interacting sets of literature. To date, numerous statistical and machine learning-based methods have been applied in the biomedical domain to find treatments for diseases such as Raynaud's disease, Parkinson's disease, and Multiple Sclerosis. However, the lack of standardized practices and creation of bespoke methodologies produces a scenario where the adoption of LBD remains challenging in real-world systems. Our work addresses these concerns through the improvement of five critical areas: 1) error propagation within LBD's a priori dependent tasks, 2) exploring the integration of modern …

Establishing A Conventional Linac-Based Electron Flash Beam, Justin Defrancisco

Theses and Dissertations

Problems: A rediscovered radiotherapy technique, FLASH, involves increasing the dose rate to >40 Gy/s. For a given dose, FLASH is shown to decrease the normal tissue complication probability without affecting the tumor control probability. More preclinical studies are necessary to confirm and enable these desirable properties on humans. Current preclinical investigations have diverse constraints, requiring FLASH apparatuses with flexible parameters and accurate control. However, there are limited FLASH platforms available, the existing ones are costly. Our institution is not in possession of any such infrastructure. Even if available, dosimetry and beam control in FLASH are extremely lacking. There is …

Modeling The Impacts Of The Current And Projected Temperatures On Spongy Moth Population Dynamics, Adrienne B. Spring

Theses and Dissertations

The spongy moth (Lymantria dispar) is an invasive forest pest that has caused significant ecological damage across the United States. Its invasion front is shaped by a number of factors, including temperature in both the northern and southern regions. With ongoing climate change, areas that were previously uninhabitable may become increasingly favorable for moth population establishment and expansion, while other areas may experience thermal stress limiting persistence. This study develops a temperature-driven population model to analyze how temperature affects the spongy moth population dynamics along the invasion front. This model incorporates temperature effects on fecundity, stage-specific survival rates, …