Physical Sciences and Mathematics Commons^™

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …

Engineering Protein Linkers For Optimized Telsam-Darpin Fusion And Crystallization With Electric Field-Based Polymer Orientation, Maria Jose Pedroza Romo

Theses and Dissertations

Fusing a variant of the sterile alpha motif domain of the human translocation ETS leukemia protein (TELSAM) to a protein of interest significantly enhances crystallization and diffraction quality. TELSAM is a pH-dependent, polymer-forming crystallization chaperone that, when covalently fused to a protein, forms a well-ordered crystal lattice. However, the success of this method depends on two factors: 1) the design of the linker connecting TELSAM to the target protein, which influences both crystal packing and target orientation, and 2) the prevention of TELSAM polymer flipping, a phenomenon that disrupts lattice periodicity and impairs structure determination. To address the first challenge, …

Satellite-Based Identification Of Mesoscale And Submesoscale Eddies And Fronts In The Gulf Of Mexico, Ethan Cruz

Theses and Dissertations

Mesoscale and submesoscale processes in the Gulf of Mexico (GoM) are fundamental in shaping the region’s physical and biogeochemical ocean dynamics. Advances in high-resolution satellite observations now allow detailed detection of submesoscale (< 25 km radius) and mesoscale eddies and ocean fronts, which drive heat, nutrient, and carbon fluxes. This dissertation integrates two complementary investigations: (1) a comparison of eddy-tracking algorithms: Contour Tracing (CT) and Temperature Thresholding (TT), applied to Optimum Interpolation Sea Surface Temperature (OISST, 1/4°) and Operational Sea Surface Temperature and Ice Analysis (OSTIA, 1/20°) products; and (2) a comparative evaluation of three ocean front detection algorithms: Canny Edge Detection (Canny), the Cayula–Cornillon Algorithm (CCA), and the Belkin–O’Reilly Algorithm (BOA), across multiple satellite-derived parameters, including sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a (Chl-a), and altimetry-derived absolute dynamic topography (ADT). The eddy-tracking analysis shows that the higher-resolution OSTIA consistently detected more eddies than OISST, with TT performing best during winter when SST gradients were strongest. The front detection analysis, incorporating datasets from the Surface Water and Ocean Topography (SWOT) and Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) missions, was evaluated under diverse conditions, including flood and drought periods, Loop Current phase changes, and extreme weather events. A machine learning–based Gaussian Mixture Modeling (GMM) approach was also developed, providing a novel framework for dynamic front detection. By benchmarking traditional and machine learning methods, this integrated study identifies optimal algorithm–dataset combinations for resolving submesoscale and mesoscale features in the GoM, with applicability to other regions.

Oxidation Resistance And Osmotic Energy Harvesting Applications Of Reduced Graphene Oxide Forward Osmosis Membranes, Michael Appiah-Kubi

Theses and Dissertations

Reduced graphene oxide (RGO) forward osmosis (FO) membranes have emerged as promising candidates for efficient wastewater management and osmotic energy harvesting, due to their enhanced chemical stability and superior FO performance for low-energy waste brine treatments, such as volume reduction of oxidative chromium brine for cost-effective disposal. This dissertation first examines the oxidation resistance of RGO forward osmosis membranes against chromate Cr (VI). Our findings reveal that both the water flux and the reverse salt flux of RGO membranes are suppressed, accompanying an enhanced reverse flux selectivity after exposure to an acidic medium of Cr (VI) (pH 2.0-7.0). Meanwhile, the …

Acoustic Characterization And Modeling Of Heated Supersonic Tactical Jet Aircraft And Launched Rockets, Logan Troy Mathews

Theses and Dissertations

Understanding and accurately modeling the acoustic sources and propagation of high-intensity tactical jet aircraft and rocket noise is essential for vehicle design, environmental impact assessment, and mitigation strategies. Despite progress in the understanding of these sources, significant challenges persist in both predictive modeling and noise mitigation. This dissertation advances both the characterization of full-scale supersonic tactical jet aircraft noise sources and the development of modern empirical prediction models for rocket launch noise. Across multiple measurement campaigns and modeling efforts, the work integrates advanced array signal processing, high-fidelity field measurements, and improved empirical methods to expand the physical understanding of source …

Magnetic Ordering And Dynamics In Assemblies Of Magnetite Nanoparticles Throughout The Superparamagnetic Transition, Johnathon Michael Rackham

Theses and Dissertations

Magnetic nanoparticles are used in nanotechnologies and biomedical applications, such as drug targeting, hyperthermia treatment, MRI contrasting agents, and bio-separation of compound solutions. Magnetite (Fe3O4) nanoparticles stand to be effective in these roles due to the non-toxic nature of magnetite and its strong magnetization. To this end, a greater understanding of the magnetic behavior of the individual magnetite nanoparticles is needed when a collection of them is used. This research seeks to discover the local magnetic ordering and dynamics of fluctuations of ensembles of magnetite nanoparticles at various stages of the magnetization process, throughout the superparamagnetic blocking transitions, and for …

Evaluating Adjustment And Proficiency Disparities In Virtual Reality, Mohammad Jahed Murad Sunny

Theses and Dissertations

The rapid integration of VR in various application domains necessitates a deeper understanding of how levels of user experience impact user performance and task efficiency. This study investigates the relationship between experience with VR, expertise in 3d computer gaming, and physiological skills across multiple performance metrics, such as task-completion time, task load, accuracy, manipulation speed, and related spatial requirements. In a comprehensive analysis of multiple levels of VR experience and 3d computer-game expertise, we identified key trends that indicate increased experience in both domains significantly enhances task efficiency while at the same time reduces perceived workload and improves task accuracy. …

Hindcasting The Occurrence Time Of Major Earthquakes Using Machine Learning And Time Series Analysis, Rubidha Devi D

Theses and Dissertations

An earthquake is an intense shaking of the ground that typically occurs when tectonic plates move beneath the surface of the Earth. Scientists analyze historical seismic records and geophysical and atmospheric signs to build models estimating the probability of major earthquakes by detecting patterns and anomalies in data such as ground deformation and seismic waves. This study would help in predicting earthquakes to minimize risks of people and buildings.

The present study investigates the devastating earthquakes along the Chilean subduction zone in South America, linking non-seismic data to machine learning predictions of Outgoing Longwave Radiation (OLR) and Relative Humidity (RH) …

Bridging Scales In Epidemic Modeling From Individuals To Networks And Ecosystems, Saiprasasd Vr

Theses and Dissertations

Epidemics have shaped the trajectory of human history, repeatedly exposing vulnerabilities in healthcare systems and influencing socio-economic dynamics on a global scale. The COVID-19 pandemic, in particular, underscored the pressing need for flexible and responsive modeling framework capable of adapting to dynamic and evolving epidemic scenarios. Motivated by these challenges, this thesis explores the development of innovative epidemic models that integrate classical mathematical modeling techniques, network-based frameworks, and modern data-driven approaches. Each chapter addresses a distinct dimension of epidemic spread, from vaccination dynamics and community coupling to ecological complexity and adaptive human behavior, culminating in a model-free paradigm suitable for …

Evaluating Nfl Quarterback Decision Making Through Tracking Data, Jesse N. Melville

Theses and Dissertations

Quarterback decision-making is one of the most critical determinants of success in the National Football League (NFL), yet traditional statistics fail to account for the spatial, temporal, and contextual factors influencing each play. This thesis proposes a data-driven methodology for evaluating quarterback decisions using NFL player tracking data from the 2022 season. We replicate and extend the DeepQB framework by training deep learning models to estimate expected completion probabilities and expected yards gained for each receiver at the moment of the throw. Our extension applies the approach to a newer dataset with additional contextual features, enabling a more detailed evaluation …

A Dendrite Equivalence Relation On Loop Spaces, Spencer Arnesen

Theses and Dissertations

In Definition 2.0.3 we define an equivalence relation on paths such that two paths into a space X are related if doing the first, then the second backwards factors as a loop through a dendrite. This equivalence relation induces a group structure L(X) on a large subset of the loop space of X, namely all loops that have a unique reduced representative. We show that certain continuous maps induce a homomorphism between dentropy groups and that projection from three-dimensional real space to two-dimensional real space does not induce a homomorphism on dentropy groups.. We show that dentropy groups are locally …

Computational Modeling Of Magnetic Field Interaction With Superconductors Under High-Field And High-Dissipation Conditions: A Tdgl Approach Applied To Nb And Nb3sn, Aiden Victor Harbick

Theses and Dissertations

Superconducting radiofrequency (SRF) cavities are essential components in modern particle accelerators, enabling the efficient acceleration of charged particles for various applications in physics, medicine, materials science, and beyond. The performance of these cavities is significantly influenced by the properties of superconducting materials, such as niobium (Nb) and triniobium-tin (Nb3Sn), and the defects and surface features present within the material. This dissertation presents a computational study focused on understanding the behavior of SRF cavities, using a sample-specific time-dependent Ginzburg-Landau (TDGL) framework to simulate their performance under realistic material conditions. The research integrates experimental data and density functional theory (DFT) calculations to …

Mathematics Student Teachers' Curricular Reasoning During Planning, Erika Breann Miller

Theses and Dissertations

Teaching is a complex practice, in part because of all the decisions teachers make daily. This is especially true for student teachers who have little to no prior teaching experience to inform them about the decisions that need to be made in the classroom and how to reason through all those decisions as they plan and enact the lessons. Secondary mathematics student teachers were introduced to the Instructional Pyramid (Dingman et al., 2021) which organizes and maps teacher’s mathematical reasoning onto four elements of a classroom—teacher, student, mathematics, and curriculum—to examine which curricular reasoning aspects they use while planning mathematics …

A Novel High-Order Absorbing Boundary Condition For Elastic Multiple Scattering Problems, Jordan Gregory Sheppard

Theses and Dissertations

Elastic waves are waves that propagate by causing deformations in an elastic medium such as metal, rock, or rubber. The main characteristic of elastic wave motion is the coexistence of multiple types of waves at once in the medium, including compressional and shear wave components. This type of motion can be used to study the impacts of physical phenomena such as earthquakes. In this paper, we discuss our research on the time-harmonic scattering of elastic waves in a medium by multiple circular cylindrical obstacles that the wave cannot penetrate. Using the Helmholtz decomposition, we break down the vector-valued physical system …

Topological Homotopy Groups, Tyler Evans

Theses and Dissertations

In general, the homotopy groups of topological spaces fail to be topological groups when endowed with the quotient topology. In this thesis, we show that for Peano continua with Abelian fundamental group, the fundamental group endowed with the quotient topology is indeed a topological group. However, this result does not extend to higher homotopy groups; that is, there exists a Peano continuum whose nth homotopy group, endowed with the quotient topology, is not a topological group. Additionally, a new characterization of the quotient topology in terms of a limit operator is introduced.

Extreme Artifacts Removal With Curriculum Learning, Sushant Gautam

Theses and Dissertations

Restoring severely blurred images remains a significant challenge in computer vision, impacting applications in autonomous driving, medical imaging, and photography. This thesis introduces a novel training strategy based on curriculum learning to improve the robustness of deep learning models for extreme image deblurring. Unlike conventional approaches that train on only low to moderate blur levels, this method progressively increases the difficulty by introducing images with higher blur severity over time, allowing the model to adapt incrementally. Additionally, perceptual loss and hinge loss were integrated during training to enhance fine detail restoration and improve training stability. Various curriculum learning strategies were …

Topnet R1: A Multi-Stage Ai Framework For Topic Discovery In Scientific Abstracts, Md Elias Hossain

Theses and Dissertations

Scientific abstracts are rich sources of knowledge, yet extracting meaningful topics remains challenging due to limitations in existing topic modeling techniques. Traditional methods often struggle with interpretability, scalability, and contextual understanding. To overcome these issues, we introduce TopNet R1, a multi-stage ensemble framework that integrates traditional topic models with contextual embeddings from Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 4 (GPT-4) large language models (LLMs). Top- Net R1 operates in three phases: (1) topic generation using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), and Hierarchical Dirichlet Process (HDP); (2) LLM-assisted pattern recognition …

Exploring The Use Of A Random Forest In Correcting Gfs Tropical Cyclone Landfall Errors, Victoria Grace Maxwell

Theses and Dissertations

While Tropical Cyclone (TC) track forecasting has improved over the years it has been slow. These improvements can be attributed to increased computer capability and better model inputs. Machine learning has been used for forecasting different TC track and intensity. The study here looks to use a Random Forest to predict the TC latitude/longitude “landfall” at 72-hr and quantify improvements to the GFS forecast. Principal Component Analysis was used to reduce the dimensionality of the meteorological variables. Stepwise regression was used to determine the principal components that were important for forecasting landfall. These variables were then tested with a multivariate …

Unboxing The Black Box: Graph-Based Interpretability In Transformer Models, Ramak Nassiri

Theses and Dissertations

This thesis introduces a novel interpretability framework for transformer encoder models by hypothesizing that their internal embedding updates define a state transition system. We propose that transformers implicitly learn token-to-token influence dynamics, which can be analyzed using graph-theoretic methods. To validate this, we construct transition graphs from embedding changes and rank token importance using the PageRank algorithm. We develop two transformer models from scratch: a self-supervised model that incorporates serotype tokens to learn contextualized embeddings, and a supervised model initialized with these embeddings. Cosine similarity analysis of their token influence patterns reveals strong structural alignment, with values exceeding 93%. This …

Soil Ethics In Practice: Harvesting Conservation Choices Among Puerto Rican Coffee Farmers, Patricia Marie Cordero-Irizarry

Theses and Dissertations

Coffee farming in Puerto Rico has declined over recent decades, partly due to deteriorating soil quality. However, the decision-making process behind farmers’ adoption of soil conservation practices remains unclear, particularly the overlooked role of soil ethics, which emphasizes the moral principles guiding human relationships with the soil. This mixed-methods study examined coffee farmers’ adoption of soil conservation practices by integrating the Values-Beliefs-Norms (VBN) theory and a newly developed Soil Ethics Survey (SES) instrument for the quantitative component, while using Decision-Making Theory and Social Cognitive Theory to qualitatively explore how farmers’ experiences and reflections shape their soil ethics. Quantitative findings revealed …

Understanding How Nws Meteorologists Tailor Hazardous Weather Messaging To Core Partners And Identify Local Vulnerabilities, Allison Camille Harvey

Theses and Dissertations

Over the past few decades, the National Weather Service (NWS) has continued to improve impact-based decision support services (IDSS) efforts to strengthen communication with core partners. This study addressed how a new spatially hazard-specific vulnerability tool called the Brief Vulnerability Overview Tool (BVOT) may impact how NWS meteorologists tailor messaging to their core partners. It was found that relationship building between NWS meteorologists and core partners is key to enhancing trust, especially during blue sky days. This was done by learning core partners’ needs, thresholds, and communication styles when presenting weather information. Core partners also noticed cues from NWS meteorologists …

Augmenting Healthcare Communication: Context-Aware Ai Frameworks For Clinical Decision Support And Automated Summarization, Subash Neupane

Theses and Dissertations

Healthcare communication is plagued by fragmented medical knowledge, patient misunderstanding of care plans, and clinician burnout from documentation burdens. These inefficiencies cost the U.S. healthcare system over $300 billion annually due to preventable nonadherence and administrative waste [47, 19]. While Artificial Intelligence (AI) technology like Large Language Models (LLMs) offer potential solutions, existing systems fail to deliver personalized, context-aware guidance or automate documentation without sacrificing accuracy. This dissertation addresses these gaps through three novel context-aware AI frameworks such as MedInsight, ClinicSum, and ClinicDuo. MedInsight leverages a multi-source context augmentation approach to synthesize patient centric medical responses by integrating Electronic Health …

Estimating Reliability Of Electric Vehicle Charging Ecosystem Using Principle Of Maximum Entropy, Himanshu Tripathi

Theses and Dissertations

This thesis addresses the challenge of estimating electric vehicle (EV) charging system reliability against unpredictable threats like cyberattacks and extreme weather, where traditional methods fail. We utilize the Principle of Maximum Entropy (PME), a statistical tool that provides unbiased risk estimates using limited information. Applied to the EV charging ecosystem, our case study shows how PME models stress factors to predict failures and optimize maintenance. This approach extends beyond EVs to other complex systems with scarce data, such as smart grids or healthcare devices. By linking uncertainty directly to reliability, PME offers a universal method to improve decision-making under unpredictable …

Determining The Toxic Threshold And Other Use Characteristics For Industrial Hemp Seed Oil Used For Wood Product Protection, Daylan M. Barnes

Theses and Dissertations

Plant-based wood treatments have been utilized for many years, with varying success. Oils such as those extracted from linseed (flax), tung, and hemp plants have been used for thousands of years as finishes on wood. These are all drying oils, which dry to a film when exposed to air. Hemp seed oil has a high fatty acid content and can take a long time to dry on wood products but is valued in woodworking for being easy to use, food-safe, and having little to no odor. In this study, cold-pressed hemp seed oil, blended with a soybean carrier oil, is …

Autonomous Generation Of Ids Rules From Threat Intelligence, Azim Bazarov

Theses and Dissertations

Signature-based Intrusion Detection Systems (IDS) detect malicious activities by matching network or host activity against predefined rules. These rules are derived from Cyber Threat Intelligence (CTI), which includes attack signatures and behavioral patterns obtained through automated tools and manual threat analysis, such as sandboxing. The CTI is then transformed into actionable rules for the IDS engine, enabling real-time detection and prevention of threats. The constant evolution of cyber threats necessitates frequent rule updates, which delay deployment time and weaken overall security readiness. Recent advancements in autonomous agentic systems powered by Large Language Models (LLMs) offer the potential for automatic IDS …

A Mathematical Theory Of Epitaxial Growth, Brock C. Price

Theses and Dissertations

In this dissertation we investigate several PDE models of Epitaxial growth. These models are fourth-order PDE’s featuring exponential nonlinearities as well the p-Laplacian and 1-Laplacian. The presence of the exponential nonlinearity is what provides the main mathematical difficulty. Theexponent in particular does not have enough estimates to guarantee any compactness. Because of this, one has to allow the inclusion of a singular portion to the exponent in the sense of the Lebesgue decomposition theorem. In thefirst chapter weinvestigate a related epitaxial growth, with transition rates of the Metropo lis variety and a linear exponent. The metropolis rates induce an extra …

Unraveling Quantum Properties Of Novel Two-Dimensional Systems Using A First-Principles Approach, Binod Regmi

Theses and Dissertations

We used first-principles density functional theory (DFT) to investigate the structural, electronic, and magnetic properties of an oxygen crystal in a copper slab, a ferrimagnetic electride with highly tunable magnetic anisotropy energy (MAE), and the Fermi surface analysis of different kagome metal phases. The Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional was used in the Vienna ab initio simulation package (VASP) for all these calculations. Because of dimensionality reduction, the electronic properties of copper change significantly at the two-dimensional (2D) scale due to whichwe observe more reconstruction in the geometry of the Fermi surface. Dimensionality reduction also influences charge transport. Moreover, we present …

Extracting Magnetic Exchange Interactions From Magnetic Pair Distribution Function Data Via Monte Carlo Simulations, Emma Zappala

Theses and Dissertations

The magnetic pair distribution function (mPDF) is a powerful tool used to analyze diffuse magnetic scattering data in real-space. Presently, mPDF is usually used to model real-space spin configurations of materials, offering important insights into the local magnetic structure, but not to directly probe the magnetic interactions giving rise to the magnetic structure in the first place. Using Monte Carlo simulations, a robust algorithm to simulate the magnetic structure of materials, we can connect the underlying magnetic exchange parameters to the spin configurations from experimental mPDF data. We prove this concept using a simple two-dimensional Ising model and then demonstrate …

Transcendental Formulas For The Coefficients Of Ramanujan's Mock Theta Functions, Gradin Anderson

Theses and Dissertations

Ramanujan's 1920 last letter to Hardy contains seventeen examples of mock theta functions, including the famous third order function f(q), which resemble modular forms of weight 1/2. This list has since been expanded by individuals such as Gordon and McIntosh and the McKay--Thompson series arising in moonshine theory. In 1964, Andrews---improving on a result of Dragonette---gave an asymptotic formula for the coefficients of f(q) and conjectured an exact formula: a conditionally convergent series that closely resembles the Hardy--Ramanujan--Rademacher formula for the partition function. A key breakthrough came in 2001, when Zwegers wrote f(q) as the holomorphic component of a vector-valued …

Classification And Analysis Of Families Of Graphs With Focus On Nordhaus-Gaddum Bounds, Sibi Pranav Muthuprakash

Theses and Dissertations

Nordhaus-Gaddum type problems focus on bounding characteristics of graph and its complement. There are certain families of graphs which yield optimal upper bounds on various Nordhaus-Gaddum characteristics of graph. In this paper, we will talk about bounding combinatorial algebraic connectivity, normalized algebraic connectivity, the Cheeger constant, and the isoperimetric number of a graph and its complement. We will identify some optimal or near optimal families of graphs and conduct an analysis on these families that will help to bound these characteristics. We will also prove and conjecture some bounds that can help in formulating stronger bounds on these characteristics of …