Physical Sciences and Mathematics Commons^™

Nanoporous Gold Nanoparticles And Model Membrane Systems Applied To The Study Of Lipopolysaccharide (Lps), Dhanbir Lingden

Dissertations

Lipopolysaccharide (LPS), a key structural component of Gram-negative bacteria, is a major trigger of sepsis and septic shock, the systemic inflammatory conditions responsible for millions of deaths annually. Even at concentrations as low as 1 ng/mL, LPS can activate the immune system and initiate inflammatory cascades. According to the Global Burden of Disease Study 2017, sepsis accounted for 48.9 million cases globally, with 11 million deaths, emphasizing the urgent need for improved diagnostic and therapeutic strategies.

Accurate and rapid detection of LPS is vital for early intervention and improved clinical outcomes. While traditional detection methods are still widely used, modern …

Variable Importance, Knockoff Filters, And Improving False Discovery And False Negative Rates, Nicholas Ehlman

Dissertations

Tree ensemble methods such as Random Forests and Boosted Trees have introduced a range of variable importance statistics, offering powerful tools for feature selection. The advent of knockoff filters marked a significant advancement by combining the use of these variable importance statistics with the ability to control the False Discovery Rate (FDR). However, achieving a low FDR frequently comes at the cost of a high False Negative Rate (FNR), limiting the power of such approaches. In this work, we propose a novel method for leveraging knockoff variables to keep both FDR and FNR low. While this method does not have …

Enzymatic Characterization Of Bacterial Enzymes And Inhibitors As Potential Antibiotics With New Mechanisms Of Action, Emma Helene Kelley

Dissertations

Bacteria have become increasingly resistant to antibiotics, therefore there is an urgent need for new drug classes of antibiotics to help fight antibiotic infections. To this end, our research is focused on inhibitors of dizinc metalloenzymes N-succinyl-L,L-diaminopimelic acid desuccinylase (DapE), an enzyme in the lysine biosynthesis pathway, N-acetyl-L-ornithine deacetylase enzyme (ArgE), an enzyme in the arginine biosynthesis pathway, and sodium-dependent NADH: ubiquinone oxidoreductase (Na+-NQR) enzyme, a respiratory complex enzyme as promising drug targets. DapE, ArgE, and Na+-NQR and are only present in bacteria, including ESKAPE pathogens that can cause potentially deadly infections, thus inhibitors of DapE, ArgE, and Na+-NQR offer …

Development Of Novel Force Fields For Metal Ions, Madelyn Smith

Dissertations

Many biological proteins require the presence of a metal ion in order to properly function. However, metal ions’ complex nature charge poses several challenges to accurately and efficiently simulate computationally. For example, metal ions can exhibit multiple oxidation states, electronic state degeneracy, flexible coordination numbers, and significant polarization effects. To address these problems, this dissertation aims to enhance force fields for modeling metal ions in molecular dynamics simulations. First, a comprehensive set of van der Waals radii for metal ions is derived, demonstrating the importance of using physically meaningful parameters in force fields. Second, a comprehensive set of atomic and …

The Impact Of Substrate Structure And Catalyst Identity On The Lewis-Acid Catalyzed Carbonyl-Olefin Metathesis Reaction, Cory William Schneider

Dissertations

The formation of carbon-carbon double bonds is one of the most important transformations in organic chemistry, as they are integral to the structural scaffold of many organic molecules. Thus, the development and understanding of reactions that form such bonds is crucial. Lewis-acid catalyzed carbonyl-olefin metathesis is one such reaction. Like many others, this reaction is sensitive to both the identity of the catalyst, as well as the structure of the substrate. In this dissertation, I present empirical and theoretical works investigating how the catalyst identity and the substrate direct the reactivity of the Lewis-acid catalyzed carbonyl-olefin metathesis reaction.

Testing For Broad Alternatives In Stratified Contingency Tables, Nan Mi

Dissertations

In medical and social sciences fields, data are measured in terms of discrete categories. The primary question of interest involves the relationship between a set of factors and a set of response variables under studies. Moreover, the distribution of the response variables may be influenced by another set of variables called confounders. The data from such studies are summarized in 3-way tables. The hypothesis we are interested in can be expressed in terms of "no partial association" between the sub-populations and the response levels.

The methods for testing the association or independence in a 2x2 contingency table have been developed, …

Probing Shielding Tensor Components Of Amino Acids Using Nuclear Magnetic Resonance, Shiva Agarwal

Dissertations

Chirality is fundamental to terrestrial life. While most amino acids exist as nonsuperimposable mirror images, amino acids in terrestrial life are homochiral, with the L-enantiomer being ubiquitous. The detection of an excess of L-amino acids in carbonaceous meteorites suggests that extraterrestrial processes may have contributed to this enantiomeric excess (ee). One proposed mechanism, the magnetochiral model, provides a potential explanation for this phenomenon in stellar environments characterized by strong magnetic and electric fields and the presence of relativistic leptons. According to this model, subtle differences in the electronic environments of chiral amino acids under such conditions …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Abel Inversion Comparison Of Geant4 Simulation And Ozone Production Using Cavity Ringdown Spectroscopy In Nitrogen/Oxygen Mixtures In The Presence Of Alpha Radiation, Sidney John Gautrau

Dissertations

The effects of radioactive materials on atmospheric gases have been a topic of interest for years. Radioactive materials ionize the surrounding air, and subsequent reactions lead to molecules such as ozone and nitrogen oxides. The presence of these species above background levels can be used as a marker for radioactive materials which has desirable defense applications like remote detection of radioactive materials. The molecules created in the presence of radioactive materials have been quantified in literature using G-values, which is the number of molecules of a product produced per 100 eV of deposited energy. In this work, Cavity Ringdown Spectroscopy …

Simulating Interactions Between (Remote) Internal Waves And The Background Flows And Topography Of The U.S. West Coast, Oladeji Siyanbola

Dissertations

This dissertation focuses on the simulation of remotely and locally generated semidiurnal internal tides (ITs) and near-inertial waves (NIWs), and how they interact with the California Current System (CCS) and the U.S. West Coast (USWC) topography. In Chapter II, we force Regional Ocean Modeling System (ROMS) simulations of the CCS with tides and remote internal waves (IWs) originating from as far as Hawaii, using a realistic global HYbrid Coordinate Ocean Model (HYCOM) simulation. To allow for optimal wave energy influx and minimize boundary reflections from the interior of the domain, we conduct boundary sensitivity tests on tide and IW forcing …

Solution Of Preconditioned Nonsymmetric Saddle Point Systems Through Modified Conjugate Gradient Iteration, Samson Ayo

Dissertations

In this dissertation, we present an iterative method (Preconditioned Nonsymmetric Saddle Point Conjugate Gradient) for simultaneously solving forward ($A{\bf x}={\bf b}$) and adjoint ($A^T{\bf y}={\bf g}$) linear systems. Our approach involves constructing an augmented nonsymmetric saddle point matrix that has a real positive spectrum and developing a conjugate gradient-like iteration for this matrix. We investigate the use of Schur Complement preconditioners with block-diagonal factorization computed by an incomplete QR factorization of $A$ to speed up the convergence of our method and compare the results to the preconditioned generalized least squares residual (GLSQR) and quasi-minimal residual (QMR) methods. We develop quadrature …

Resilient Learning For Anomaly Detection In Smart Living Systems, Sahar Abedzadeh

Dissertations

Cyber-Physical Systems (CPS) rely on anomaly-based detection methods to ensure the integrity and security of critical infrastructures such as smart grids, smart water metering systems, and advanced metering infrastructures (AMI). Anomaly detection methods are commonly used to identify deviations from normal system behavior by establishing learned profiles and thresholdbased distinctions between benign and anomalous events. However, conventional frameworks often fail to account for adversarial data poisoning attacks, unlabeled unsafe events, and environmental noise—factors that distort training data, degrade detection accuracy, and increase false alarms. This dissertation proposes a resilient learning framework that mitigates these biases by integrating quantile regression, M-estimation …

Nonparametric Finite Mixture Of Ising Graphical Models, Manal Hamadi Alloqmani

Dissertations

Statistical applications in fields such as bioinformatics, genomics, speech processing, image processing, and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. This formalism gives a nice framework for capturing complex dependencies among the random variables and building a large-scale model for high-dimensional data. Recently, high-dimensional data are more assumed to come from one population and follow a parametric or …

Utilizing Drainage Induced Pfas Enrichment In Cascade Generated Foam For Efficient Removal At Wastewater Treatment Plants, Ethan Samuel Coffin

Dissertations

Observed trends in municipal solid waste landfills reveal a distinct disparity between per- and polyfluorinated alkyl substances (PFAS) composition entering in waste, mostly as diPAP and FTOH, and leaching out as FTCA and PFCA. These patterns are elucidated by compiling PFAS compositions in paper, textiles, and carpet, with known precursor transformations that generate FTCA and PFCA in leachate. Landfill leachate is commonly discharged to wastewater treatment plants (WWTP), which are ineffective at degrading PFAS and only serve as key conduits and discharge points for PFAS to the environment. Foam fractionation has been identified as a promising technology for concentrating and …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa

Dissertations

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), enable direct communication between the brain and external devices without the involvement of peripheral nerves or muscles. Among various BCI paradigms, motor imagery (MI)–based BCIs are particularly appealing due to their intuitive, cue-independent nature, allowing users to issue control commands at will. MI–BCIs hold substantial promise for improving the quality of life of individuals with motor impairments, as well as enhancing hands-free control for healthy users. However, their widespread adoption remains limited by challenges such as low signal-to-noise ratio, inter- and intra-subject variability, and the need for frequent calibration. These challenges …

Quadratic Stochastic Processes: Algebraic Structures And Their Applications, Taimun Saleh Qaisar

Dissertations

This research focuses on the algebraic structures of the Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠). In this work, we first study 𝜉𝑎- Quadratic Stochastic Operators (𝑄𝑆𝑂𝑠) linked to the partition P3. We simultaneously discuss the dynamics of the obtained 𝑄𝑆𝑂𝑠. Moreover, algebraic structure of the associated genetic algebra is studied. Further, we build Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠) using the given Markov processes. Consequently, we obtain an ordinary differential equation for the resultant Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠). Besides, we apply the solution of this ordinary differential equation for the option pricing problem. Thereafter, we construct Quadratic Stochastic Processes (𝑄𝑆𝑃𝑠) in three-dimensional space by …

Insights On The Cranial Anatomy And Osteohistology Of Cyonosaurus, Alexander Acker

Dissertations

This study investigates the cranial anatomy and life history of the small-bodied gorgonopsian Cyonosaurus. Previous research on gorgonopsians has disproportionately focused on larger members of the clade, leaving smaller forms relatively understudied. This gap has led to several unresolved questions, the most pressing of which concerns gorgonopsian ontogeny. Specifically, it remains unclear whether small-bodied gorgonopsians represent genuinely diminutive adult individuals or juveniles of larger species. The specimen NHCC LB1087 presents an ideal opportunity to address this issue and gain new insights into the biology of small-bodied gorgonopsians. The first chapter focuses on cranial anatomy. Due to the specimen’s preservation, many …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …