Physical Sciences and Mathematics Commons^™

Developing An Understanding Of Concavity Built On Covariation, Jade E. Gregg

Theses and Dissertations

This study investigated how six students developed a quantitative understanding of concavity founded in covariational reasoning through a series of task-based teaching interviews. A key finding was the importance of multivariational reasoning--the coordination of more than two variables--in helping students reason deeply about concavity in context. Common difficulties, including confusion about negative rates and overreliance on visual representations, served as productive opportunities for learning when addressed through carefully designed tasks and discussions. The findings extend existing frameworks of covariational reasoning and highlight instructional approaches that can support students in developing a robust, quantity-based understanding of concavity.

Distributed Computation Of Graph Structures By Mobile Agents, Prabhat Kumar Chand

Doctoral Theses

This thesis investigates how mobile agents with no centralised control can be employed in anonymous networks to perform efficient distributed graph computations. The network is modelled as a simple, undirected, anonymous graph with n nodes and m edges, where nodes are memoryless and indistinguishable, and edges represent bidirectional communication links or traversal paths for the agents. The mobile agents are uniquely identifiable, possess limited local memory, and operate under a local communication model, in which communication is restricted to agents colocated at the same node. Under this computational model, we explore how mobile agents can collaborate effectively to solve global …

No-Boundary Wave Function For Chain-Inflation, Sutikshna Singhdeo

University Departments

This thesis explores the fundamental boundary conditions of the universe within the framework of canonical quantum cosmology, critically examin-ing the mathematical prescriptions that dictate its initial state. Central to this work is a comparative analysis of the Hartle-Hawking No-Boundary proposal and Vilenkin's Tunneling solution. While the Hartle-Hawking Eu-clidean path integral elegantly replaces the initial singularity with a smooth geometric cap, semiclassical approximations reveal a severe phenomeno-logical flaw: it exponentially favors a low-energy, empty universe over the high-energy state required to trigger slow-roll inflation. To resolve this, the study evaluates Vilenkin's Tunneling solution, which enforces an outgoing-wave boundary condition that naturally …

Scattered Light Noise At Ligo Livingston Observatory Induced By Microseismic Ground Motion, Debasmita Nandi

LSU Doctoral Dissertations

Gravitational waves are “ripples” in the fabric of spacetime and create a minuscule change in the distance between two points. After publishing his theory of general relativity in 1915, Albert Einstein predicted the existence of gravitational waves as a consequence of the theory. Almost 100 years after the theoretical prediction, gravitational waves were detected for the first time in 2015 by the Advanced LIGO detectors in Livingston, Louisiana, and Hanford, Washington. Gravitational waves have a very small amplitude, making it very challenging to detect them. Different types of noise sources either of instrumental or environmental origin can reduce the astrophysical …

Influence Of Supermassive Black Holes On The Star Formation Activities Of Their Host Active Galaxies, Ananthamoorthy Bannanje

University Departments

Active galactic nuclei (AGN) release substantial energy into their surrounding environn1ent, influencing the physical properties of the host galaxy and regulating the accretion of matter onto the central 'supermassive black hole (SMBI-D.' This process is known as ?GN feedback.' While AGN feedback is consideied an essential component in simulations to reproduce several observed properties of galaxies and clusters" direct observational evidence for its immediate and long-term impact on 'star formation' (SF) in both strong- and weak-jetted systems remains sparse and often contrasting. The primary limitation arises from the lack of high-resolution observations required for spatially resolved investigations of SF down …

On Universal C∗ Algebras Associated To Operator Spaces And Generalized Crossed Products, Sayan Kansa Banik

Doctoral Theses

In my thesis, we study generalized crossed product constructions of the group \( C^* \)-algebra \( C^*(G) \) with respect to certain completely positive maps, where \( G \) is assumed to be a discrete amenable group. We also investigate the universal \( C^* \)-algebra \( \mathcal{E}_\alpha \) introduced by Hirshberg, which is constructed from \( C^*(G) \) and a pure injective homomorphism \( \alpha \colon G \to G \). In particular, we analyze its relationship with Exel's construction of generalized crossed products associated with the endomorphism of \( C^*(G) \) induced by \( \alpha \), together with an appropriate …

Detecting And Correcting Systematic Image Distortion In Digital Atomic-Resolution Images From Crystals: Projected Symmetry Of Graphite Calibration Samples For Scanning Prove Microscopy And Subperiodic Crystals For Aberration-Corrected Transmission Electron Microscopy, Tyler Michael Bortel

Dissertations and Theses

Though it is an impressive technology, Scanning Tunneling Microscopy (STM) produces images commonly plagued by small systematic errors, often called "drift". These effects have a variety of causes, most notably nonlinear behavior of piezoelectric actuator tubes responsible for the positioning of the scanning probe or sample. One result of these distortions is a potential incorrect classification of the symmetry groups and projected Laue classes of recorded images from crystals—made possible by using recently developed objective methods. An implementation of a known method for detecting and correcting these errors is presented. This method operates strictly in Fourier space so as to …

Pseudodifferential Absorbing Boundary Conditions For Waves, Lauren Taylor

Mechanical Engineering Theses

Absorbing boundary conditions (ABCs) are required to truncate the computational domain when performing Finite Element Analyses of exterior acoustic problems where physical domain is unbounded. ABC is applied on a fictitious boundary containing the scatterer and ideally allows outgoing waves to leave without non-physical reflections. Preventing artificial reflections is essential to benefit from the accuracy of the numerical method used. Otherwise, ABC acts as a reflective surface, and artificial reflections distort the solution in the entire domain which is not recoverable by any type of refinement. Pseudodifferential ABCs were used to describe the Dirichlet-to-Neumann map which maps known boundary values …

Open Neighbourhood Edge Degree And Metric Descriptors Qspr And Multi Criteria Analysis Of Chemicals And Antiviral Drugs, Gayathri A Ms

Theses and Dissertations

Chemical Graph theory offers powerful computational methods for evaluating chemical structures, significantly impacting cheminformatics and drug analysis. It provides a robust mathematical framework to represent, model, and analyse molecular systems through topological indices and graph invariants. These indices serve as effective numerical descriptors, encapsulating significant structural information that enhances the prediction of physicochemical and biological properties. This thesis utilizes mathematical modeling, algorithmic computation, and decision making process to derive extensive insights to drug discovery and molecular analysis.

The thesis introduces a new class of topological indices ONE1, ONE2, ONE3, ONE4, ONE5,ONE6 and ONE7 based on open-neighbourhood-edge-degree and it evaluates the …

Cooperative Evolution For Discovering Scalable Spiking Neural Network Architectures, Catherine C. Rodriquez

LSU Master's Theses

SNNs are a foundational model in neuromorphic computing, where efficient architectures must be discovered for a wide range of applications. Evolutionary methods, such as EONS, offer a flexible approach to this search but become increasingly difficult to scale as task complexity grows due to the rapidly expanding combined topology–parameter search space and associated memory demands. To address this challenge, we propose a co-evolutionary ensemble framework in which a population of candidate SNNs is evolved with fitness defined by each network’s marginal contribution to group performance. Grounded in cooperative game theory and difference evaluation functions from multiagent systems, this formulation provides …

Analytic Solutions To Oceanographic Kinematic Property Equations, James Turbett

Theses and Dissertations

With the recent advent of large submesoscale drifter deployments, interpreting the timeseries of kinematic properties (KP) - divergence, vorticity, shear, and normal strain rates observed therein is a pressing issue. The evolution equations for the four KP are derived from the two-dimensional momentum conservation equations. The resulting equations, a nonlinear coupled system of ordinary differential equations, capture the submesoscale motion of drifters along fixed ocean surfaces, with time and space scales on the order of days and kilometers. This thesis builds theory around KP timeseries behavior by solving for the analytic solutions in the particular cases that one KP is …

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr

LSU New Orleans Theses and Dissertations

This thesis asks how best to forecast real datacenter operational telemetry. Can pretrained foundation time-series forecasters do it zero-shot, or do trained classical baselines perform best? On a single-site benchmark of eight audited targets and four horizons (32 slices), the zero-shot foundation forecasters Moirai, Chronos, and TimesFM win 26 of 32 slices against multivariate classical baselines. However, the foundation wrappers operate per-channel on the target history, while classical baselines consume the full feature tensor. When the same classical models are retrained univariate, the foundation lead persists (27 of 32 slices) and a 3-seed rerun of a classical challenger reproduces it; …

Numerical Range Of Rank-1 Partial Isometries And Elliptical Range Theorem, Lucas Saone

Theses

The Elliptical Range Theorem states that the numerical range of a 2x2 complex matrix A is an elliptical disc with foci at the eigenvalues of A and center at tr(A)/2. Many proofs have been offered for this theorem. In this paper, we provide another one. The strategy we will use is to decompose the matrix A into the sum of rank-1 operators of the form YxX*, where X and Y are in 2-D complex vectors. When analyzing the numerical range of a rank-1 operator of this form, three results can happen: i) the numerical range forms a disc at the …

Prediction Of Satellite Temperature During An Orbit Of A Cubesat, Michael Reynolds

Honors Theses

This thesis develops a thermal-simulation strategy for predicting CubeSat component temperatures, applied to Jag-Sat-1, a CubeSat developed at the University of South Alabama and deployed from the International Space Station in 2022. The orbit was reconstructed from two-line element (TLE) data using simplified general perturbations (SGP4) propagation, and spacecraft attitude was recovered from onboard gyroscope measurements. Sunlight, penumbra, and umbra intervals were computed geometrically, and the external radiative environment — direct solar, Earth infrared, and albedo heat fluxes — was modeled using orientation-dependent view factors. These time-varying fluxes drove a transient finite-element thermal simulation of the full satellite geometry in …

The Interwoven Dynamics Of Climate, Fire, Resilience And Orangutan Survival In Central Kalimantan’S Peat Swamp Forest, Putu Pujiantari

Graduate Theses and Dissertations

Tropical peat swamp forests are globally significant carbon reservoirs and biodiversity strongholds, yet they face escalating threats from climate change, fire, and anthropogenic disturbances. This dissertation investigates the interwoven dynamics of climate, fire, forest resilience, and orangutan survival in Central Kalimantan, Indonesia, a region which was transformed by the failed Mega Rice Project and recurrent catastrophic wildfires for decades. Using a multi-scale approach that integrates remote sensing time-series analysis (2001–2024),nlandscape-scale disturbance mapping (2005–2025), and fine-scale movement ecology, this research tests three core hypotheses: (1) moisture availability is the dominant climatic driver of wildfire activity, operating through non-linear thresholds; (2) post-fire …

Beyond Semantic Matching: Integrating Graph Structures, Boolean Logic, And Interactive Aggregation In Modern Retrieval, Quan Mai

Graduate Theses and Dissertations

Modern Natural Language Processing (NLP) and Information Retrieval (IR) systems have achieved remarkable success in semantic understanding; however, they continue to struggle with complex logical reasoning, structural interactions, and dynamic aggregation. This dissertation presents a comprehensive framework to enhance the structural, logical, and interactive capabilities of language models and retrieval systems across four progressive studies. First, we address the challenge of modeling dynamic conversational logic in online debates. We introduce a Sequence Graph Network (SGN) that captures the temporal and interactive exchange of ideas—such as counterarguments and reinforcements—by updating node features sequentially through a novel Sequence Graph Attention (SGA) layer. …

Potassium Fertilization Timing And Critical Stalk Potassium Concentration As Tools To Improve Corn Production, Gabriela Andrade Leite Mengez

Graduate Theses and Dissertations

Potassium (K) is a common yield-limiting nutrient in Mid-Southern U.S. corn (Zea mays L.) production, emphasizing the importance of effective diagnostic tools to support fertilizer decision-making. This study integrated two approaches to improve K management in irrigated corn systems: (i) evaluating K fertilization timing to identify in-season opportunities for correcting K deficiency, and (ii) developing a corn stalk-based diagnostic test to assess end-of-season K and nitrate-nitrogen (NO₃-N) status and fertilizer adequacy. Seven (2024-2025) and eight (2023-2024) field trials were conducted across Arkansas for the K application timing and stalk testing studies, respectively. For the K timing study, fertilizer-K (112 kg …

Localizing And Repairing Backdoor-Sensitive Layers In Pre-Trained Language Models For Secure Fine-Tuning, Sunanda Das

Graduate Theses and Dissertations

Task-agnostic backdoor attacks can contaminate pre-trained language models (PLMs) in a way that survives downstream adaptation, even under full fine-tuning, making it difficult for practitioners to trust third-party checkpoints. Existing defenses often rely on privileged assumptions (e.g., access to poisoned data or trigger/target knowledge), thereby limiting their applicability in realistic settings. We present DiSec (Disentanglement of potentially adversarial weights for Secure fine-tuning), a robust and label-efficient purification framework that uses only clean auxiliary text and does not rely on downstream supervision or attack signatures. DiSec elicits model-internal signals from this clean data to separate suspicious parameter components that are inconsistent …

Influence Of Stormwater Flooding Events On The Mobile County School District Student Population, Kadence Kali Johnston

Honors Theses

Mobile County, Alabama, receives among the highest annual rainfall totals in the United States resulting in frequent stormwater flooding that intersects with existing socioeconomic vulnerabilities. Within the Mobile County Public School System (MCPSS), 77% of students are economically disadvantaged and over 10% experience homelessness, creating heightened risk for educational disruption during flood events. This study investigates how stormwater flooding influences school attendance, closures, and academic performance across MCPSS, providing insight into how climate-driven hazards exacerbate educational inequities. The research integrates flood frequency, school performance metrics, and GIS flood-zone mapping and teacher perceptions to understand how flooding affects student learning opportunities. …

Establishing Ferromagnetism In Van Der Waals Antiferromagnets Mps3, Dinesh Upreti

Graduate Theses and Dissertations

Recent discoveries of magnetism in low dimensions not only have gained fundamental scientific interest but also holds significant potential in numerous technological applications, such as spintronics. This has led to a wide range of efforts in the synthesis of new layered materials or tuning their existing magnetic properties, which has been instrumental in understanding the novel physical concept and development of new technological frontiers. Transition metal thiophosphate, MPS3, is a class of layered antiferromagnet family. The rich magnetic/crystal structure with delicate choice of transition metal (M) together with intriguing optical, electronic, and ferroelectric properties have placed them as a model …

Enhancing Deep Learning Image Reconstruction In Breast Ct With Task-Driven Training And Physics-Informed Models, Megan Ariel Murphy

Dissertations (1934 -)

Dedicated breast X-ray computed tomography (breast CT) is a developing imaging modality that has potential as an alternative to mammography. Unlike traditional mammography, breast CT offers high-resolution, fully 3D anatomical detail, which aids in the detection of microcalfications and subtle lesions that are potential indicators of cancer. To be effective for screening, breast CT must limit radiation exposure by reducing CT view angles. However, traditional image reconstruction methods yield noisy, artifacted images in this sparse-view CT setting. This dissertation develops image restoration and reconstruction methods that address these limitations by integrating physics-informed iterative methods with machine learning tools, and a …

Multi-Angle Dynamic Light Scattering Experiments With Real-Time Autocorrelation Using Field-Programmable Gate Arrays, Spiros Simotas

Theses and Dissertations from DePaul University

Dynamic light scattering (DLS) determines particle size from temporal fluctuations in coherently scattered light. Here a field-programmable gate array (FPGA) computes intensity autocorrelations in real time, and an automated diffractometer with custom 3D-printed cuvette, laser, homing, and GT2-gear mounts enables multi-angle measurements. Square-wave timing tests at 1 Hz, 100 Hz, and 10 kHz verified the correlator response. Calibrated latex spheres were then measured at seven scattering angles from 32.5◦ to 44.5◦. Each autocorrelation function was fit to the standard single-exponential form g(τ ) = B + βe−2Γτ with weights from analytic per-lag Poisson uncertainties σg = pg/N based on coincidence …

Towards Understanding Users' Theory Of Recommender Systems, Mohammed Muheeb Faizan Ghori

Theses and Dissertations from DePaul University

Recommender systems have become deeply embedded in digital platforms such as streaming services, social media, and e-commerce applications, shaping how users discover information, consume content, and interact with online environments. Despite their widespread adoption, these systems are frequently perceived as opaque ‘black boxes’, leaving users uncertain about how recommendations are generated, why certain content is prioritized, and whether their interactions meaningfully influence recommendation outcomes. Existing research at the intersection of recommender systems and human–computer interaction has largely focused on improving transparency, explainability, and recommendation quality from a system-centered perspective. However, limited research has examined how users conceptualize recommender systems and …

Statistical Evaluation Of A New Orleans Historic Rainfall Dataset (1840–1893), Arielle C. Filostrat

LSU Master's Theses

This study evaluates the climatic credibility of a newly identified historical rainfall dataset for New Orleans, Louisiana, spanning 1840–1893. The New Orleans Historical Dataset (NOHD) is compared to a modern observational record from the Audubon rain gauge (1893–2024) to determine whether nineteenth-century rainfall observations are consistent with known precipitation behavior in the region. Establishing the reliability of early records is critical for extending climatological baselines and improving understanding of long-term rainfall variability.

Historical rainfall observations were first evaluated through archival review using the New Orleans Medical and Surgical Journal and other nineteenth-century meteorological records to verify notable rainfall events. Daily …

Chaotropic And Kosmotropic Effects Of Brines On Protein Structure: Habitability Across The Solar System, Shelby Dawn Osborne

Graduate Theses and Dissertations

Salts exhibit unique and specific effects on biomolecular structure depending on the physical nature of the salt (ion density, formation of hydration shells, direct binding to protein). This thesis implements biochemical, thermodynamic, and computational methods to investigate how the nature of the salt effects protein structure, which aids in defining the habitability of various brine solutions. By using simple proteins and solar system relevant salts, we inspect the physical reactions of proteins to these saltwater environments to understand which salts stabilize or destabilize protein structure. Several methods of experimentation are utilized to provide an understanding of the interaction relevant to …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

Ethos: A Computational Framework For Material Culture Research, Chelsea L. Grafe

Department of Textiles, Merchandising and Fashion Design: Dissertations, Theses, and Student Research

Museum catalog records make large collections searchable, but they do not always preserve the reasoning behind the claims they contain. A record may present a quilt's identity as settled while leaving the reasoning behind that claim invisible, making it difficult for later researchers to check, question, or reuse. This project asks whether museum catalog data can be analyzed in a way that keeps each analytical claim connected to the evidence that supports it. To test this question, the project applies a material-first computational framework named Ethos. Ethos formalizes an object-centered inquiry sequence in which material dependencies establish where analysis begins …

Improving Vector Embedding Generation Throughput For Neural Information Retrieval Models, Conrad Tkacz

Theses and Dissertations from DePaul University

For decades, advancements in information retrieval technologies have changed how individuals discover data with computer systems. More recent advancements in artificial intelligence (AI) have also made the benefits of neural information retrieval systems available to millions. These information retrieval systems help empower future discoveries, though this is not the case for scientific data and High-Performance Computing (HPC) systems. HPC systems can generate enormous amounts of data, and no tools are currently available that can ingest data at the rates required to efficiently build an information retrieval system to explore scientific data. This thesis explores the current state of constructing a …

Automated Machine Learning And Satellite Image Data Fusion For Monitoring Water Quality In Mosaicked Landscapes, Sahar Rezaei

Graduate Theses and Dissertations

Despite the ecological, economic, and social importance of inland water bodies, they are increasingly impacted by human and natural drivers. Harmful algal blooms (HABs) and total suspended solids (TSS) are key indicators of water quality, and elevated concentrations pose significant ecological and public health risks. The spatiotemporal dynamics of these variables remain insufficiently understood in major river systems, including the Upper Mississippi River (UMR) and the Illinois River Basin (IRB). While in situ measurements provide accurate observations, they are costly and offer limited spatial and temporal coverage, restricting basin-scale monitoring. Remote sensing offers a powerful alternative; however, single-sensor approaches are …