Physical Sciences and Mathematics Commons^™

Discovery Of Newly Resolved Filament And Extraplanar Gas In M33 With The Local Group L-Band Survey, Emma Taylor Kleiner

Dissertations, Theses, and Capstone Projects

The proximity of the Local Group allows for unparalleled resolution of the structure and kinematics of the ISM while simultaneously observing the overall rotation of the galaxy. These nearby galaxies present the opportunity to probe stellar feedback processes in various galactic environments. M33 in particular, offers insight into the nature of the extended neutral gas and its relationship with the star-forming disk and the surrounding CGM, by way of its classic star-forming spiral and relatively undisturbed system. Outside of M33’s distinguished warp, the galaxy offers little evidence of satellites and shows few signs of interaction, begging the question of how …

Edge Co-Occurrence Regularization For Node Classification, Kadir Altunel

Theses

We propose a simple yet effective regularization technique for node classification on graphs that leverages edge-based label co-occurrence patterns. We first train an MLP on node features to produce class probability distributions, then compute a fixed penalty matrix from edge-based co-occurrence statistics of these predictions. This penalty matrix, which captures unlikely class combinations on connected nodes, is then used to regularize GNN training without further updates. We evaluate this approach across multiple homophilic datasets (Cora, CiteSeer, PubMed, ogbn-arxiv) and heterophilic benchmarks (Chameleon, Squirrel, Actor, Roman-Empire) using three GNN architectures: GCN, GraphSAGE, and H2GCN. Results show consistent improvements on homophilic graphs, …

Sound Detection Of Memory Leaks In Llvm Ir Programs Using Accumulation Analysis, Robert Blacha

Theses

Resource leaks occur when a limited resource such as memory is allocated by a program and needlessly held past the point of use. Leaks can lead to a degradation of services which can be specifically triggered with malicious behavior, for example abusing a memory leak in a program to cause a server to slow down and crash for a denial-of-service attack.

Prior work has demonstrated that accumulation analysis provides a sound detection of resource leaks with a working implementation for programs written in Java. While useful, current implementations are limited to programs written in Java, which has a garbage collector, …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Pypitfall: Dependency Chaos And Supply Chain Vulnerabilities In Python, Jacob Mahon

Theses

Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting downstream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. PyPitfall, a quantitative analysis of vulnerable dependencies across the PyPI ecosystem, is introduced. The dependency metadata of 378,573 PyPI packages is analyzed. 4,655 packages that explicitly require a known vulnerable package version and 141,044 packages that permit a vulnerable …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Simulating The Movement Of A Major League Fastball, Max M. Moss

Graduate Masters Theses

The orientation of the seams on a baseball may seem arbitrary in nature; however, they play an enormous role in the aerodynamics of the ball during its short flight to home plate. This thesis aims to model the movement of a four-seam fastball as accurately as possible using the fourth order Runge-Kutta method. The model incorporates the effect of the seams on the path of the ball by utilizing the oscillating cross-sectional area, which varies for each angle of rotation. The simulated results demonstrate a strong agreement with the observed pitch trajectories, accurately reproducing both induced vertical break and horizontal …

Role Of Technical Assistance In Pollution Prevention In Massachusetts Craft Breweries, Shubhechchhya Regmi

Graduate Masters Theses

This study aims to examine pollution from Massachusetts craft breweries, and if it can be lessened through a pollution prevention program. This study analyzed data from two assessments with the aim of analyzing the role of technical assistance in pollution prevention across eight impact categories. The first assessment was conducted on 29 MA breweries between 2022 and 2024, and 23 were assessed a second time (in summer 2024 and summer 2025). Craft breweries face several constraints in adopting pollution prevention practices the pollution prevention program known as BetterBev helps to bridge this gap by providing technical assistance, identifying inefficiency practices …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …

Perceptual And Geometric Advances In Crowd Simulation, Bilas Talukdar

Dissertations

Simulating realistic crowd motion remains a fundamental challenge in computer graphics and multi-agent systems, as it requires modeling both physically plausible interactions and perceptually natural behaviors. Existing crowd simulation methods typically employ simplified geometric abstractions, most commonly circular agent representations, and model navigation using either analytical interaction formulations (e.g., force, velocity, or constraint-based methods) or learned policies derived through reinforcement learning. Despite their effectiveness, these approaches often overlook detailed geometric structure and do not explicitly account for perceptual realism. This dissertation addresses these challenges by improving the realism of virtual crowd simulation through two key advancements: perceptual preference learning and …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Differential-Geometric Methods For Neural Signed Distance Fields: Parameterized Surface Extraction And Curvature Regularization For Cad Models, Haotian Yin

Dissertations

Neural signed distance fields have emerged as a powerful framework for representing three-dimensional geometry through continuous and differentiable neural functions. Their flexibility, resolution independence, and compatibility with gradient-based optimization make them especially attractive for surface reconstruction and geometric learning. However, despite these advantages, two fundamental challenges remain for engineering-grade applications. First, higher-order geometric properties such as curvature are difficult to model reliably during training and often require computationally expensive second-order differentiation. Second, while neural signed distance fields provide implicit surface representations, they do not directly yield a globally consistent forward map or parameterization for downstream geometric processing.

This dissertation addresses …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Disentangling Non-Thermal Electron Injection And Decay In Solar Flares Using Multi-Wavelength Radio Observations, Brian Eugene O’Donnell

Dissertations

The broadband microwave imaging spectroscopy capability provided by the Expanded Owens Valley Solar Array (EOVSA) allows new diagnostics of high-energy processes in solar flares, providing spatially and temporally resolved spectra rich in information about the acceleration and transport of energetic electrons.

In this work, injections and transport of energy and particles into the solar corona during flares are studied. This is accomplished through the development and use of the PIP_Decomp Fitter, an automated fitting tool made by the author to fit injection and precipitation/decay parameters using the spatially resolved radio spectra obtained by EOVSA. These tools are used to study …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Anonymity And Accountability In Secure Messaging, Erin Kenney

Dissertations

Encypted messaging has become more and more prevalent as time moves on, and its benefits in assuring privacy cannot be overstated, but it also brings along with it concerns on how to moderate platforms where all messages are hidden. Message Franking, followed by Traceback systems, addressed these concerns by allowing the sender of a message to be proven when reported, even for forwarded messages in the case of Traceback, however these systems damage the privacy guarantees that originally motivated encrypted messaging to begin with.

In practice, even without those concerns encrypted messaging alone is not enough to prevent the most …

Parameter Density Estimation For Cardiac Electrophysiology Models Using Data Consistent Deep Learning, Michael Luo

Dissertations

Mathematical models of biological rhythms and excitable systems can provide insights into mechanisms underlying cardiac electrical dynamics. However, estimating the parameters of these models from experimental observations is often difficult due to noise, heterogeneity, and unobserved variables. For example, in an electrocardiogram (ECG) recording, information about the electrical properties of different regions of the heart is compressed into a single voltage trace. Additionally, variability within these signals may contain important information about population heterogeneity, regional differences in electrophysiology, and time-dependent modulation.

This dissertation develops, explores, and evaluates methods that perform feature-based distributional inference for complex nonlinear dynamical systems. The objective …

Streamlined Biomedical Image Processing Pipelines, Jiehyun Kim

Graduate Doctoral Dissertations

This dissertation focuses on advancing carotid artery analysis through a series of visualizations and deep learning tools for calcified plaque assessment and related biomedical imaging tasks. Accurate plaque evaluation is essential, but current workflows depend on slow, clinician-dependent manual review. To address these limitations, this work introduces the CACTAS framework, a set of tools and methods that enable fast and reliable plaque segmentation for clinicians.

The first study, the CACTAS-Tool, provides a web-based labeling tool that enables clinicians to label plaque directly in three dimensions through a streamlined one-click interface. This tool significantly reduces the effort required to generate high-quality …

Static Data-Race Detection For Gpu Programs: Behavioral Types With Partial Completeness Guarantees, Zhen Rong Liew

Graduate Doctoral Dissertations

GPUs are essential to modern computing but notoriously difficult to program correctly. Static analysis tools can verify data-race freedom, but their over-approximations produce spurious reports of data races that do not occur, limiting their practical usefulness. This dissertation establishes when Memory Access Protocols (MAPs), a compositional abstraction modeling memory access behavior between synchronization barriers, can be simultaneously sound and complete. We first prove that MAP-based analysis is sound and complete for well-typed Jaminan programs---those without data-dependent array indexing. This result establishes the theoretical boundary: completeness is achievable when data-dependent control flow and indexing are absent. Jaminan extends MAPs with symbolic …

Small Molecule Antioxidants As Potential Therapeutics For Preeclampsia, Maxim Mastyugin

Graduate Doctoral Dissertations

The major goal of this work was to develop a therapeutic intervention against preeclampsia (PE). Preeclampsia affects up to 5-7% of all pregnancies and has no causative treatments. It is associated with oxidative stress caused by a prolonged ischemic state and increase in reactive oxygen species (ROS) in the placenta. The cardinal symptoms include high blood pressure, kidney dysfunction, and in severe cases, eclampsia. With no treatment for the underlying causes available, we offer small molecule antioxidants which may scavenge ROS in the placenta arising from the initiating event of PE, the oxidative stress. By reducing placental ROS, these antioxidants …

Computer-Aided Development Of Novel Antioxidants, Rita Bernadett Vlocsko

Graduate Doctoral Dissertations

Physiological redox homeostasis is a fine balance between prooxidants and antioxidants that are integrated elements of several reduction-oxidation mechanisms at molecular, organellar, cellular and tissue levels. When this equilibrium is disrupted and prooxidants become dominant, the body relies on endogenous and exogenous antioxidants to counterbalance the dysregulation and ultimately prevent the progression of oxidative stress. Reactive species (reactive oxygen species, reactive nitrogen species, or reactive sulfur species) are significant contributors to prooxidant activity. Over the years, substantial knowledge has been accumulated regarding their origin and roles in disease development, leading to the discovery and development of antioxidants that effectively target …

Classical Motion Of Bounded Orbits In Two-Body Problems, Dina F. Stein

Physics and Astronomy Undergraduate Senior Theses

The study of bounded and periodic motion in classical systems is of central importance in celestial mechanics, atomic models, and the theory of integrable systems. Central-force problems are of particular importance due to their rich geometric structure and the existence of conserved quantities that strongly constrain orbital behavior. In this thesis, we investigate the classical motion of bodies subject to two central-force potentials: the inverse-square and the pseudo-harmonic. For each system, explicit solutions for the radial and angular equations of motion are derived and analyzed. The resulting trajectories are classified according to whether they are bounded, unbounded, open, closed, or …

Transportation Deserts And Structural Mobility Access In New York City, John Cruz

Student Theses

This study examines structural mobility access across New York City census tracts by constructing a tract-level Mobility Access Index (MAI) that integrates employment accessibility, hospital accessibility, and first-mile subway walking burden using MTA GTFS transit data, NYC Taxi and Limousine Commission trip records, and US Census American Community Survey demographic estimates. Three OLS regression models, supplemented by Lasso, Elastic Net, and Random Forest specifications, test whether structural access gaps are associated with short-distance connector trip intensity and per-worker connector cost burden. Results show that MAI varies substantially across tracts, with high access concentrated in Manhattan and along major subway corridors. …

Forecasting Interborough Express Ridership Using Network-Based Station Typologies And Direct Demand Models, Fomba Kassoh

Student Theses

Forecasting ridership for new transit infrastructure is difficult in the absence of observed outcomes, particularly under domain shift between an existing system and a proposed corridor. This study develops a station-level direct demand modeling (DDM) framework to forecast average weekday ridership for the proposed Interborough Express (IBX) in New York City — a 14-mile circumferential rapid transit corridor connecting Brooklyn and Queens. The approach pairs unsupervised learning with supervised estimation in a common feature space defined by transit service, accessibility, and built-environment characteristics. K-means clustering identifies latent station typologies (node–place regimes), and IBX stations are projected into this topology to …

Measuring Modulation Envelope Of A Pound Drever Hall Feedback Loop, Josh S. Lee

Physics and Astronomy Undergraduate Senior Theses

From improving qubit readout to real world medical technology, fast and ultrasensitive electrometry has many real world applications. The primary goal of the overarching project is to improve ultrasensitive charge detection using methods involving cavity-embedded Cooper pair transistor (cCPT) devices. This project hopes to accomplish this by using the gate-tunability of the cCPT to reduce the 1/f-noise, a type of low-frequency noise that can interfere with the cCPT’s measurement operability. By using a Pound-Drever-Hall (PDH) feedback loop-inspired scheme that can adjust the cCPT gate, this project hopes to improve the stability of the cCPT’s resonant frequency against the 1/f-noise, in …

Traveling Wave Fronts For Sir Epidemic Models With Nonlocal Dispersal And Delayed Effects, Ashley Evette Embry

Theses and Dissertations

This dissertation investigates traveling wave solutions for two classes of delayed nonlocal dispersal susceptible--infected--recovered (SIR) epidemic models incorporating biologically realistic mechanisms such as delayed infectivity, delayed dispersal, nonlocal transmission, demographic turnover, and nonlinear incidence effects. These models extend classical spatial epidemic frameworks by allowing long-range population movement through nonlocal dispersal operators and incorporating temporal memory into both diffusion and transmission processes. The primary objective is to establish the existence of traveling wave solutions connecting disease-free equilibria to endemic states and to characterize threshold conditions governing epidemic propagation. The simultaneous presence of nonlocal dispersal, multiple delays, and non-monotone nonlinear incidence terms …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Commissioning And Validation Of An Electron Treatment Planning System: Following Mppg.5.B Guidelines, Hailey N. Reaux

LSU Master's Theses

Purpose: The purpose of this work was to commission and validate the electron treatment planning system ElectronRT (eRT) (.decimal, Sanford, FL) for clinical use following the recommendations outlined in the American Association of Physicists in Medicine (AAPM) Medical Physics Practice Guideline (MPPG) 5.b (2022). The system’s ability to accurately calculate electron dose distributions was evaluated under normal and oblique beam incidence, varying field sizes and source-to-surface distances (SSDs), and irregular surface geometries.

Methods: The eRT beam model was configured using commissioning data from an Elekta Infinity linear accelerator at Mary Bird Perkins Cancer Center (MBPCC). Validation measurements were performed for …