Physical Sciences and Mathematics Commons^™

Development Of Salinomycin Analogs As Potential Anti-Cancer Agents, Pavan Goud Juluri

Theses and Dissertations

Salinomycin, a poly-ionophore antibiotic originally isolated from Streptomyces albus, exhibits antimicrobial activity against Gram-positive bacteria. Over the years Salinomycin has been studied in the effects it has on cancer, specifically cancer stem cells. Poly(ADP-ribose) polymerase (PARP) is an enzyme involved in DNA repair. Inhibiting PARP has been explored as a strategy in cancer treatment, particularly in cancers with defective DNA repair mechanisms, such as those with BRCA mutations. The benzo-thiazoles, and -imidazoles are identified as inhibitor scaffolds, which compete with nicotinamide in the binding pocket of human poly- and mono-ADP-ribosylating enzymes. Benzothiazole, and benzimidazole based compounds particularly with modification at …

Investigation Of The Phase Space Of Tungsten Oxides Nanostructures Via Resistive Hot Wire Oxidation For Efficient Electrochemical Energy Storage And Conversion Applications., Krishna Kc

Theses and Dissertations

The synthesis of tungsten oxide (WO3−δ, 0 ≤ δ ≤ 1) nanostructures with controlled phase and morphology remains a significant challenge, particularly for applications in energy conversion and storage. This dissertation presents an eco-friendly and time-efficient method for synthesizing tungsten oxide nanostructures with tailored stoichiometry and crystal phases through the Resistive Hot Wire Oxidation (RHWO) technique. In this method, a tungsten wire is heated via resistive heating in an oxygen environment, and by precisely controlling the growth conditions, we successfully synthesized WO2.72, WO2.76, WO2.80, WO2.83, WO2.90, and WO2.98 phases with controlled morphologies using simple instrumentation. This approach eliminates the need …

Developing A Multimodal Approach To Channel Characterization On Youtube, Shadi Shajari

Theses and Dissertations

YouTube has become a dominant arena for global information sharing, where creators and audiences interact through content, comments, and engagement dynamics. Understanding how these interactions shape a channel’s identity requires a comprehensive characterization of both audience behavior and content structure. This dissertation develops a unified framework for characterizing YouTube channels through multimodal analysis that integrates behavioral modeling, dimensionality reduction, clustering, and content-based characterization to provide a holistic view of audience and editorial patterns. The framework begins by analyzing the structural relationships within co-commenter networks, where users who repeatedly comment on the same videos are connected to capture patterns of interaction …

Improving Surface Analysis Through The Correct Use Of Low Energy Ion Scattering And X-Ray Photoelectron Spectroscopy: Best Practices, Common Errors, And Multimodal Approaches For Material Development, Joshua W. Pinder

Theses and Dissertations

Surface analysis is the study of the atoms at surfaces and their connectivity, including their relative depth distribution, concentration, chemical state, and crystallinity. These characteristics govern many important material properties. X-ray photoelectron spectroscopy (XPS) is one of the most widely used surface analysis techniques. Proper interpretation of XPS spectra including the appropriate use of backgrounds, peak shapes, peak positions, and charge compensation, with appropriate constraints on fitting parameters provide valuable information about elemental composition, concentration, depth distribution, and chemical speciation of surface atoms. However, despite widespread use, much of the XPS literature contains poor interpretations and irreproducible results which undermine …

Topological Data Analysis Of New York City Taxi Trip Data Using Advanced Dimensionality Reduction And Clustering Techniques – Uncovering Structure And Timeliness, Mahalakshmi Sakthivel

Theses and Dissertations

In this project, we used Topological Data Analysis (TDA) to explore the shape and structure of high-dimensional data and the timeliness dimension of information quality through topological data analysis, with the long-term goal of automatically computing timeliness values that reflect how useful data items are for decision making. The project followed a two-phase approach: in the first half, we employed the Kepler Mapper library along with techniques like Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), T-SNE (t-Distributed Stochastic Neighbor Embedding) and Customized Embedding to analyze and visualize complex datasets. In the second phase, we specifically applied our …

Densest Subgraph Discovery On The Cpu, Hunter Gerard Gareau

Theses and Dissertations

The Densest Subgraph Discovery (DSD) problem is a prevalent problem in the field of graph mining, aiming to find the cohesive subgraph. Given a graph �� = (��,��) and an integer �� ≥ 2, the goal is to find a vertex sub- set �� ⊆ �� whose induced subgraph �� (��) maximizes the ��-clique density, defined as the number of ��-cliques per vertex. Larger val- ues of �� capture higher-order connectivity patterns beyond edges, enabling the discovery of more cohesive structures. There have been many solutions to this problem. However, one avenue that other graph mining problems have gone down …

Infinite Line, Infinite Knowledge: The 'Spera' And Organized Chaos In Lambert's Encyclopedia, The 'Liber Floridus', Ava Romano

Theses and Dissertations

The Liber Floridus is a medieval encyclopedia renowned for its program of circular diagrams, or sperae. Inside this manuscript of 190 chapters, these diagrams frame and embody written knowledge, revealing a connection between encyclopedism and life in the Benedictine monastery by creating a coherent visual organization of chapters.

Simulation Of Quantum Walks For Secure Data Access Patterns, Arslan Ahmad Janjua

Theses and Dissertations

Quantum computing is revolutionizing computational science, offering fundamentally new approaches to information processing that surpass classical limitations. One of the most versatile and powerful tools in this emerging field is quantum walks. Quantum walks are quantum analogs of classical random walks that leverage superposition and interference to explore complex spaces efficiently.

This thesis will explore how discrete-time scattering quantum walks can be simulated and analyzed to investigate patterns of secure data access. By modeling quantum walks on a variety of graph structures and studying the dynamics of marked vertices, the project aims to demonstrate how quantum interference and graph topology …

Structural Characterization Of Peptide Nanoaggregates At Varying Concentrations And Their Impact On Cellular Responses, Malika Rozmetova

Theses and Dissertations

Peptide nano-assemblies can regulate cellular pathways in a structure-dependent manner. Here, we show that the enzyme-responsive peptide NapFFK(NBD)Yp forms distinct nanoscale assemblies at different concentrations, triggering differential ER stress and extracellular vesicle release in MDA-MB231 cells. These results highlight the importance of supramolecular organization in peptide–cell interactions.

Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz

Theses and Dissertations

This thesis develops and evaluates a deep learning-based prediction model capable of identifying intended limb movement from surfaced electromyography (sEMG) signals using sequence learning techniques. sEMG signals change over time due to multiple factors such as muscle fatigue or user variability. Traditional prosthetics control methods rely on static feature extraction, ignoring how signals change over time, thereby limiting their ability to capture the temporal changes of muscle activity. As a result, these approaches often lead to poor accuracy, robustness, and generalization. Limited experimental validation has been conducted on sequence-based machine learning approaches using temporal sEMG data from publicly available datasets …

Evaluating Hybrid Quantum-Classical Models For Image Classification In The Nisq Era, Utku Binkanat

Theses and Dissertations

This thesis presents a systematic empirical evaluation of quantum machine learning performance under noisy intermediate-scale quantum (NISQ) era constraints. Through 670 controlled experiments, it evaluated quantum kernel support vector machines and variational quantum classifiers against classical baselines on MNIST binary and multiclass classification tasks with systematic variation of problem difficulty, feature dimensionality (4, 8 qubits), and training set size (n ∈{100, 250, 400, 500, 2000, 4000}). Statistical rigor was ensured through five random seeds per condition and comprehensive significance testing. During the testing with binary datasets, classical methods (SVM, logistic regression, k-NN, neural networks) achieved 85.9% to 99.6% accuracy with …

A Pipeline For Creating Obfuscated Binary Samples To Train Ai-Powered Detection Models, Luka R.H. Wilmink

Theses and Dissertations

The analysis of binary files is a critical component of antivirus software and is one of the most important tools for incident response teams across the industry. In the field, malware is often obfuscated, a practice in which the compilation process is transformed with different techniques to hinder decompilation and reverse engineering. Artificial Intelligence and Machine Learning techniques can assist, but models need to be trained on well constructed datasets first. This paper outlines a pipeline for creating such a dataset and builds a proof-of-concept machine learning classification model. All associated data and code are supplied in the project GitHub …

Propensity Score Matching Accounting For Longitudinal Trends Before Baseline With Group-Based Trajectory Modeling, Dustin R. Bastaich

Theses and Dissertations

Propensity score matching is used in observational studies to balance baseline attributes between a treatment of interest and a control group. Propensity score matching typically relies on baseline variables, but longitudinal trends in patient characteristics can also influence treatment decisions and subsequent health outcomes. This dissertation extends standard approaches by explicitly incorporating longitudinal trajectories of key variables into the propensity score estimation process.

Trends in a longitudinal variable prior to baseline were characterized using group-based trajectory modeling. A two-step modeling approach was implemented where trajectory groups of a key variable were first estimated and then included as covariates in the …

Identifying Mobility Hub Suitability In The Mid-Sized United States Urban Area Using Weighted Overlay And Percentile Based Local Peak Analysis, Eric Asplund

Theses and Dissertations

IDENTIFYING MOBILITY HUB SUITABILITY IN THE MID-SIZED UNITED STATES URBAN AREA USING WEIGHTED OVERLAY AND PERCENTILE‑BASED LOCAL PEAK ANALYSIS

By Eric Asplund

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Urban and Regional Planning at Virginia Commonwealth University.

Virginia Commonwealth University, 2026.

Major Director: Dr. Ivan Suen, Ph. D., Associate Professor, Faculty of Urban and Regional Studies and Planning

Shared mobility hubs are increasingly posited in transportation planning as interventions supporting multimodality, sustainability, and equitable access, but guidance on evaluation of potential hub locations remains uneven, particularly in mid-sized United States cities where …

Environmental Monitoring In Shallow Lakes: Public Health Threats In Urban Waters, Kyleigh E. Johnson

Theses and Dissertations

Lakes are an integral part of urban and suburban communities, providing ecological, recreational, and public health benefits. Many of these lakes are located in densely populated areas and are subject to urban influences on water quality. Algal toxins and Escherichia coli are two common recreational concerns. Algal toxins come from harmful algal blooms that are influenced by nutrient availability and temperature, while bacteria are often introduced through run-off and animal waste. Sampling every 3-4 weeks from May-October 2025 allowed for the assessment of seasonal patterns and site-specific differences across nine lakes in the Richmond area. The highest bacterial concentrations appeared …

High-Resolution Mapping Of Soil Moisture Variation Using Uas Thermal And Multispectral Imagery, Jackline Amma Timah

Theses and Dissertations

In agricultural landscapes, soil moisture regulates hydrologic partitioning, nutrient transport and water quality, land-atmosphere energy exchange that shapes local climate, and ecosystem resilience. However, traditional monitoring approaches, such as in-situ sensors and satellite imagery, often lack the spatial resolution required to capture fine-scale soil moisture variability. This study evaluated whether unmanned aerial system (UAS)-derived thermal, multispectral, and terrain variables can capture fine-scale spatial variability in volumetric water content (VWC) within an SRB in central Illinois.

High-resolution imagery was collected and paired with 50 field-measured VWC observations. Land surface temperature (LST), vegetation indices (NDVI and NDRE), spectral bands, and slope were …

Characterizing A Low-Gradient Stream In Central Illinois As Gaining Or Losing And Observing Seasonal Changes In This Behavior, Eric Timothy Brunner

Theses and Dissertations

While the seasonal behaviors of low-gradient streams have been studied, the behavior of surface water and groundwater interactions within low-gradient streams in glaciated terrain is not fully understood. A third order, low-gradient glacial stream with a saturated riparian buffer in central Illinois was studied to determine the direction of flow between the surface water and groundwater. The goals of this study were to determine 1) whether the stream was gaining or losing water, and 2) whether the gaining or losing nature of the stream-aquifer system changes seasonally. At both an upgradient and downgradient location, two wells were installed: one in …

An Application Of Molecular Dynamics To Hed Plasma Physics: Point And Group Defect Diffusion In Coulomb Crystals, Levi Webb

Theses and Dissertations

Molecular dynamics (MD) simulations are used to explore the microscopic behaviors of complex systems that cannot be easily studied experimentally. In particular, the microphysics of high energy density (HED) astrophysical plasma environments remain inaccessible by modern experiment---even in laser facilities, these plasmas exist only on short timescales that do not lend easily to investigation. The diffusion of point and group defects through crystallized plasmas in the cores of white dwarfs and neutron star crusts, called Coulomb crystals, has potentially macroscopic implications for the evolution of these bodies. Thus, I employ the widely-used MD code LAMMPS to simulate small Coulomb crystal …

Type Ii Diabetes Treatment Comparison Via Compartment Modeling, Abigail M. Collins

Theses and Dissertations

Type II diabetes mellitus affects one in ten adults worldwide, yet the effects of treatment type and adherence level on developing complications and quality of life have not been well characterized at the population level, and mathematical modeling offers a structured way to examine these dynamics. This thesis adapts the Boutayeb et al. (2004) model to incorporate dynamic treatment types and levels of adherence, producing nine scenarios in which complication development rate and complication recovery rate differed, to compare peak complications and quality of life across treatment and adherence conditions. Using a system of ordinary differential equations and compartment modeling, …

Assessing Glacial Mass Change And Its Impact On Water Resources In Parts Of The Himalayas Using Satellite Remote Sensing, Kriti Panta

Theses and Dissertations

This study investigates long-term changes in terrestrial water storage and their impact on hydrological processes in glacier-fed regions of Nepal. The study focuses on the central to eastern Himalayan region, including major river basins within the Koshi system. Multiple datasets, including the Gravity Recovery and Climate Experiment (GRACE) and GRACE follow-on (GRACE-FO) missions satellite data, the Global Land Data Assimilation System (GLDAS) products, and in situ streamflow data, were used for the period 2002–2019. Terrestrial Water Storage Anomaly (TWSA) derived from GRACE data shows a strong decreasing trend over time, indicating significant water storage loss across the study area. Soil …

Modeling The Impact Of Projected Water Demand On Groundwater Flow And Transport In The Mahomet Aquifer, Christabel Chinonye Abugu

Theses and Dissertations

The Mahomet Aquifer is a vital groundwater resource in east-central Illinois, supporting municipal, agricultural, and industrial water use. With projected changes in water demand driven by population growth and future socioeconomic conditions, assessing the long-term response of the aquifer is critical for sustainable groundwater management. Increased pumping to meet future demand may contribute to groundwater-level decline, intensified cones of depression, and altered groundwater-flow paths relevant to contaminant migration. This study addressed two research questions: (1) how projected changes in groundwater demand affect groundwater levels in the Mahomet Aquifer, and (2) how projected demand influences groundwater-flow paths relevant to contaminant migration. …

De Novo Protein Binding To Zinc Oxide Through Biomineralization Pathways, Jean-Mark A. Francis

Theses and Dissertations

De novo proteins are structurally distinct from proteins found in nature and thus capable of having their amino acid sequence modified to accomplish tasks such as increasing protein-nanoparticle binding without unfolding or decomposing. Zinc Oxide nanoparticles are functionally distinct from their bulk counterparts and are widely used as semiconductors in a variety of fields such as medicine and agriculture. With demand for these nanoparticles increasing, environmentally sustainable methods of Zinc Oxide nanoparticle synthesis are being investigated as an eco-friendly alternative to currently utilized but environmentally hazardous chemical and physical techniques. This research investigates the binding characteristics between Zinc Oxide nanoparticles …

Dimension Reduction Involving Exogenous Variables With Applications In Manufacturing And Healthcare, Linxi Li

Theses and Dissertations

High-dimensional data analysis presents diverse challenges, including the curse of dimensionality, the complexities of working with datasets that combine large feature spaces with limited sample sizes, and difficulties in identifying meaningful relationships among variables. As datasets grow in size and complexity across different fields, it is increasingly important to develop practical approaches for extracting essential information from such data. Dimension reduction methods address these challenges by alleviating the effects of high dimensionality, enhancing the ability to reveal hidden patterns, and uncovering latent structures within the data to support further analysis. Some methods reduce dimensionality while preserving all relevant information, offering …

Plumbed 3-Manifolds And Neumann Moves, Noah J. Pope

Theses and Dissertations

We give a constructive proof that every weakly negative definite plumbing tree can be transformed into a negative definite one by a finite sequence of Neumann moves. The argument combines Neumann’s plumbing calculus with the diagonalization algorithm of Duchon, Eisenbud, and Neumann, which extracts the eigenvalues of the framing matrix directly from the combinatorics of the tree. We show that any positive eigenvalues are supported on linear branches and can be eliminated systematically via controlled applications of Neumann moves. This provides an explicit algorithm reducing weakly negative definite plumbing trees to negative definite ones.

A Mathematical Frameworks For Singular, Nonlinear Phenomena: Applications To Nematocyst Firing And Inhomogeneous Nls With Coulomb Potential, Abdulrahman Alharbi

Theses and Dissertations

Nematocysts are specialized cellular organelles found in all cnidarians, including corals and jellyfish, as well as in some single-celled protists such as dinoflagellates. These organelles display remarkable diversity in morphology and function, playing roles in prey capture and defense. The firing of a nematocyst is one of the fastest accelerations in nature, yet the underlying physical mechanisms remain not fully understood. In this work, we address key questions: how sufficient force is generated to overcome the fluid boundary layer, whether fluid–structure interaction models can reproduce observed dynamics, and what mechanisms trigger discharge.

Our research investigates models based on osmotic pressure …

Machine Learning-Based Spatio-Temporal Modeling Of Climate Dynamics And Desertification In The Sahara–Sahel Region, Stephen M. Tivenan

Theses and Dissertations

Arid climate classifications are threshold-dependent and easily interpretable mappings that are widely used in ecological, agricultural, and climate-related studies. These classifications inform scientific understanding, support policy and land management decisions, and provide an intuitive summary of environmental conditions. Despite their usefulness, traditional arid climate classifications often fail to quantify uncertainty, incorporate spatial context, or account for complex relationships among relevant environmental variables. Existing approaches to uncertainty assessment have largely relied on comparing classifications across multiple datasets or alternative formulas, but these methods generally overlook important spatial dependence and latent structure in the data.

This dissertation develops three machine learning-based statistical …

Medical Linear Accelerator Water Tank Measurements Optimization For Commissioning, Tyler R. Laugh

Theses and Dissertations

Beam commissioning is a critical component of linear accelerator (LINAC) implementation, requiring accurate measurement of percent depth dose (PDD), beam profiles, and output factors across a wide range of field sizes. Conventional commissioning workflows often require multiple detectors and can be time-intensive, particularly for small-field dosimetry where measurement accuracy is highly sensitive to detector characteristics. This study evaluated the performance of a plastic scintillation detector (BP-PSD) for full water tank commissioning and its potential to improve efficiency while maintaining dosimetric accuracy.

Beam data were acquired for field sizes ranging from 1 × 1 cm² to 40 × 40 cm² using …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Absolute Quantification And Identification Of Rna From Rna-Lipid Nanoparticles Using High Resolution Mass Spectrometry, Jason C. Funderburk

Theses and Dissertations

RNA therapeutics are a rising drug category with potential use for a range of conditions encompassing infectious diseases to therapies for cancer, diseases, and genetic disorders. RNA-lipid nanoparticles (RNA-LNPs) are the prominent delivery method for these therapeutics approved products include mRNA vaccines and polyneuropathy treatments. The emergency use authorizations and orphan drug status of current RNA-LNP drugs has allowed approval without finalization of the regulatory analytical procedures for quality monitoring. The objective of the study was to develop an LC-MS assay to simultaneously measure identity and concentration of two therapeutically relevant intact RNA constructs extracted from RNA-LNPs to enhance quality …