Physical Sciences and Mathematics Commons^™

An Efficient Detection And Deep Clustering Based Pipeline For Reliable Rodent Ultrasonic Vocalization Analysis, Sabah S. Anis

Theses and Dissertations

Ultrasonic vocalizations (USVs) are critical for understanding rodents' emotional states and social behaviors. However, manual analysis of USVs is time-consuming, subjective, and prone to errors. This thesis presents an automated pipeline that addresses these challenges by performing efficient USV detection and clustering. The proposed approach significantly reduces the time and effort needed to analyze USV data while improving accuracy and reproducibility.

To address this gap, we introduce ContourUSV, a five-step pipeline for USV detection. First, it begins with generating spectrograms from audio recordings, which are then pre-processed to enhance the contrast between USVs and background noise. Key steps include median …

Post-Selection Inference In Regression Models, Qinyan Shen

Theses and Dissertations

This dissertation develops new methodology for valid statistical inference following model selection in various regression settings. We first consider the logistic regression model for a binary response that can only be observed at the group level, as in group testing, and is subject to testing error. With the true responses only partially observed, we employ the expectation-maximization (EM) algorithm to account for missing information in the response data and simultaneously conduct variable selection using the LASSO-penalized log-likelihood function. After variable selection, we extend an existing post-selection inference method based on the polyhedral lemma \parencite{lee2016exact} to make inferences on selected covariates, …

Methods And Applications For Bayesian Semiparametric Survival Analysis, Zile Zhao

Theses and Dissertations

Survival analysis is a cornerstone of biomedical and clinical research and plays an important role in fields as diverse as engineering, actuarial science, and sociology. In this dissertation, we develop new semiparametric Bayesian methodology for three problems from survival analysis: 1) adjustment for treatment crossover in randomized controlled trials (RCTs), 2) multilevel modeling of clustered survival outcomes when the cluster size is also informative, and 3) divide-and-conquer Bayesian inference for massive survival data. Our methods are semiparametric in the sense that we assume the covariates have a linear effect with regard to the log-hazard or the log- survival time; however, …

Quinone-Mediated Extracellular Electron Transfer In Escherichia Coli During Glucose Oxidation Metabolism, Megan Danielle Whisonant

Theses and Dissertations

The growing global demand for energy, ongoing reliance on fossil fuels, and increasing water pollution from industrial and anthropogenic sources present significant environmental challenges. In response to these issues, renewable and sustainable energy sources offer substantial potential for reducing dependence on fossil fuels and ensuring access to clean water. Microbial electrochemical systems (MESs) have emerged as promising, eco-friendly solutions for energy-efficient wastewater treatment and bioremediation. A key challenge in MESs development is facilitating effective electron transfer between microorganisms and electrode surfaces. The first chapter of this thesis discusses the fundamentals of MES types, explains mechanisms of extracellular electron transfer, and …

The Potential Importance Of Microenvironment On Polymer Bound Chemical Reagents, Nathan Joseph Halsteter

Theses and Dissertations

In this thesis will provide a brief history of polymer bound reagents, their uses, and advancements. The importance of polymer microenvironments will be explored using a model kinetic resolution reaction utilizing polymer bound reagents to observe how changes in a polymer’s microenvironment effects the yield and selectivity of a reaction. It was found that there are copious factors that affect polymer bound reagents, and no direct conclusion could be drawn from the study.

Spatio-Temporal Modeling & Goodness Of Fit Testing For Ecological Fire Data, Jedidiah Olof Lindborg

Theses and Dissertations

An analysis of fire data sets resulting from controlled burns was performed. Spatio-temporal models were applied to the data sets to determine which covariates are significant in predicting fire temperature. The data sets were censored and only contain temperatures above 300C, due to a limitation in the measuring device. To handle the censoring, an extrapolation was used to reconstruct the temperatures below 300C. Models were created and run for a data set with the extrapolated temperatures and a data set with all censored temperatures removed. Several aspects of the model were evaluated, such as the chosen hyperparameters, the spatial covariance …

Structure-Activity Relationships Of Isothiourea And Silicon Phthalocyanine Catalysts, Christian J. Harrison

Theses and Dissertations

Efficient organic synthesis often relies on improving catalyst designs and investigating reaction mechanisms to accomplish feats such as high stereoselectivity, low catalyst loadings, and performing sustainable chemistry. Within the field of organocatalysis, intermolecular interactions between a catalyst and a substrate are a large contributing factor to the control of a reaction, including rate and selectivity. Investigating mechanisms can provide information on the type of intermolecular interactions occurring in the reaction, aiding future reaction and catalyst development. This dissertation will explore two recent projects and how probing intermolecular interactions through simple modifications provides information useful for new catalyst designs and properties. …

Regression With Atypical Data: Measurement Error, Periodicity, And Non-Normality, Nicholas W. Woolsey

Theses and Dissertations

Regression is a ubiquitous and fundamental method that can be found in any ele- mentary statistics course. The simplicity and self evidently useful nature of linear regression beguiles a non-negligible portion of researchers into disrespecting assump- tions required by these models, namely in terms of accuracy of covariates and the underlying nature of the data. This disregard can at best lead to meaningless results and at worse cause significant misunderstandings in scientific pursuit.

In this dissertation we strive propose remedies to violations of specific assump- tions. Namely the assumptions that covariates are either observed without measure- ment error or they …

Reactivity Of Redox-Active Carboranyl Diphosphines And Carboranyl Diphosphoniums: Metal-Free Pathways For Small Molecule Activations And Transformations, Amanda Lynn Humphries

Theses and Dissertations

Transition metal catalysis has been an integral part of synthetic chemistry for its ability to selectively activate strong substrate bonds, promoting numerous crucial transformations. Despite their utility, the precious metals that are often employed in these catalytic reactions suffer from several drawbacks (including high toxicity, low natural abundance, high costs, and low sustainability), increasing the motivation to design suitable metal-free alternatives. Towards this end, several ambiphilic main-group systems have been devised to show metal-like reactivity, such as carbenes, borylenes, and Frustrated Lewis Pairs. This manuscript will describe the ambiphilic reactivity of carborane-based phosphorus compounds, highlighting the unique metallomimetic reactivity that …

Characterizing Dissolved Organic Matter Composition In A Southeastern United States Watershed, Gwendolyn Marcelette Hopper

Theses and Dissertations

Dissolved organic matter (DOM) has important roles in many biogeochemical cycles. DOM is primarily composed of dissolved organic carbon (DOC), nitrogen, and phosphorus, and the processing of DOM is important for nutrient regulation and carbon cycling. In the coastal plains of the southeastern United States, there are many rivers and streams known as “blackwaters” with exceptionally high concentrations of chromophoric dissolved organic matter (CDOM) leached from terrestrial soils, coinciding with a region experiencing some of the nation’s highest rates of development. As changes in land use and climate continue to occur, DOM composition from natural to anthropogenically impacted systems can …

Determination Of Structure-Function Relationships In Hybrid Catalysts On Metal Oxide Supports, Joseph John Kuchta

Theses and Dissertations

Transition metal catalysis has revolutionized chemical synthesis, enabling efficient and selective transformations critical to the pharmaceutical, agricultural, and materials industries. However, traditional methods often rely on expensive, non-sustainable metals and harsh reaction conditions that generate significant waste. This dissertation explores a series of innovations aimed at bridging the gap between homogeneous and heterogeneous catalysis through the development of hybrid catalysts. These systems combine the molecular precision of homogeneous catalysts with the stability and practicality of heterogeneous supports, offering a pathway to more sustainable and scalable catalytic processes.

The first area of focus investigates nickel-based hybrid catalysts as a cost-effective and …

Quantifying Biomass Burning Impacts On Soil Nox Emissions And Reactive Nitrogen Cycling, Olivia Rae Steinbeck

Theses and Dissertations

Nitrogen oxides (NOx = NO + NO₂) are trace gases that play a critical role in the atmosphere by influencing air quality, oxidation chemistry, and the deposition of fixed nitrogen. Historically, NOx emissions have been primarily linked to anthropogenic sources such as fossil fuel combustion, industrial activities, and agriculture. However, as successful legislation has significantly reduced these emissions, the relative importance of natural NOx sources has become an increasing concern. Soil NOx emissions are of particular interest due to their strong temperature dependence and their connection to global climate change. Rising global temperatures are expected to accelerate soil NOx emissions …

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

Functional Data Analysis On Life Expectancy And Healthcare Expenditure, Hagen Sanchez

Theses and Dissertations

This study analyzes the life expectancy for 237 countries from 1950-2023 and the healthcare expenditure for 50 countries from 1970-2022, and how life expectancy and healthcare expenditure relate to each other. Functional Principal Components Analysis was used to analyze the life expectancy and healthcare expenditure for each of the countries. Additionally, the regions of the countries were analyzed to identify any regional trends for the life expectancy data. Due to missing data and the structure of the healthcare data, multiple imputation methods and Principal Component Analysis techniques were explored for the healthcare data. Furthermore, a simulation study was conducted to …

Explorations In Extremal Combinatorics: Graph Spectra, Codegree Squared Sum Of Hypergraphs And Lin-Lu-Yau Ricci Curvature, George Henry Brooks

Theses and Dissertations

This dissertation explores a variety of extremal problems in spectral graph theory, hypergraph Tur\'an theory in the $\ell_2$-norm, and Lin-Lu-Yau Ricci curvature. We begin by studying the maximization of linear combinations of eigenvalues in relation to the adjacency matrix of a graph. For a graph $G$ on $n$ vertices, we order its eigenvalues $\lambda_1 \geq \dots \geq \lambda_n$. First, we study a generalization of the maximum spread. Specifically, we consider the $(i,j)$-spread, defined as $\lambda_{i+1}-\lambda_{n-j}$, and aim to maximize that quantity over all simple graphs on $n$ vertices. We establish asymptotic bounds for all $i,j\geq 0$ and identify infinitely many …

Autoregressive Modeling Of Dna Molecule Shapes Accompanied By An Empirical Assessment Of The Ljung-Box Test, David William Custer

Theses and Dissertations

The assumption of independence rarely holds in real-world data. Correlated observations are ubiquitous, especially in sequential contexts where time series models are essential for capturing temporal dependence. This study analyzed six groups of damaged and undamaged DNA sequences, where an "F" in the middle of a sequence indicates damage. One biological aim is to understand how DNA regenerates with the assistance of proteins that recognize damaged regions. Motivated by empirical support for AR(2) modeling, we fit autoregressive models to the first three principal component scores of each DNA group, capturing the dominant structure in the data. We conducted model diagnostics, …

Mo Isotopes In The Aleutian And Costa Rican Arcs: Source Variability And Juvenile Crust Formation, Ekaterina Rojas Kolomiets

Theses and Dissertations

Chemical and physical transfer between the surface, lithosphere and asthenosphere at subduction zones leads to arc magmatism and continental crust (CC) production. Subduction zones magmas and associated volcanic products sample a variety of magmatic sources that contribute to their chemical compositions in various proportions. This dissertation contributes to the study of subduction zones magmatism by assessing the relative role of subduction inputs (sediments, altered oceanic crust – AOC – and serpentinite) as flux agents in the arc magmatism and in CC formation in subduction zones under the scope of Mo isotope systematics.

I present the first Mo isotope analyses form …

Exploring The Self-Assembly Of Functional Bis-Urea Macrocycles: M-Terphenyl Bis Urea Macrocycles And Triphenylamine Bis-Urea Macrocycles, Gamage Isuri Pramodya Wijesekera

Theses and Dissertations

Porous materials have gained significant attention due to their diverse applications in catalysis, separation, and molecular storage. Among them, self-assembled macrocycles represent a unique class of functional materials that leverage non-covalent interactions to form well-defined architectures with pores of controlled size. This thesis explores the design, synthesis, and self-assembly of bis-urea macrocycles in both solid-state and solution-phase environments, with a particular focus on supramolecular polymerization mechanisms and the emergence of kinetically trapped states.

The first chapter of this thesis presents a detailed overview of self-assembled porous materials, categorizing them based on pore dimensions and discussing their significance in catalysis, separation, …

Two Dimensional Van Der Waals Magnet, Cheongheon Lee

Theses and Dissertations

Conventional charge-based memory has reached its limits in improving energy efficiency while scaling down size. Consequently, memory utilizing 2D magnetic materials has emerged as an alternative. 2D Van der Waals (VdW) magnets are ultrathin magnets with unique properties that make them optimal for this purpose. Utilizing the weak interlayer coupling via VdW interaction has enhanced the possibility of integrating diverse materials to create heterostructures. The integration of spintronics requires not only stable magnetization but also an effective switching mechanism. Various external influences have been experimentally tested, such as the proximity effect, external magnetic field, gate voltage, and temperature dependence. This …

First Detrital Zircon Provenance Data From Oligocene-Pleistocene Sediment In The Beaufort-Mackenzie Basin, Arctic Ocean, Joseph Martina

Theses and Dissertations

Sediment, runoff, and nutrient input to the Arctic Ocean plays a critical role in both physical and biological processes that impact regional and global systems. The Beaufort-Mackenzie Basin, situated in the Arctic Ocean offshore northwestern Canada, contains more than 14 km of clastic strata and provides a record of erosion and sediment transport from the Cretaceous to present. Pliocene sedimentation rates within the basin jump nearly 2000% relative to underlying units, but the cause of this shift remains unknown. We used cuttings from the N. Issungnak L-86 well, focusing on the Oligocene-lower Miocene Kugmallit and Mackenzie Bay sequences and the …

Elevating Next Generation Wireless Devices Towards Contactless Sensing For Healthcare Applications, Aakriti Adhikari

Theses and Dissertations

There is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained …

The Role Of Symmetries In Atomic, Electromagnetic, And Particle Physics, Joshua Martin O'Connor

Theses and Dissertations

Symmetries in atomic, electromagnetic, and weak interaction physics are explored to understand symmetry-breaking extensions of the Standard Model of particle physics. Lorentz-violating field theories are extremely interesting theoretically, since they possess many new features that are absent in Lorentz-invariant models. We outline the formalism and experimental status of the Lorentz- and CPT-violating Standard Model Extension, in both the classical and quantum regimes. Processes such as vacuum Cerenkov radiation, which are kinematically forbidden when Lorentz symmetry is exact may become allowed when this symmetry is weakly broken. Particle decays, such as pion and kaon decays, although allowed in Lorentz-invariant theories, are …

Causal Neuro-Symbolic Artificial Intelligence: Synergy Between Neuro-Symbolic And Causal Artificial Intelligence, Utkarshani Jaimini

Theses and Dissertations

Understanding and reasoning about cause and effect is innate to human cognition. In everyday life, humans continuously engage in causal reasoning and hypothetical retrospection to make decisions, plan actions, and interpret events. This cognitive ability allows us to ask questions such as: “What caused this situation?”, “What will happen if I take this action?”, or “What would have happened had I chosen differently?” This intuitive capacity to form mental models of the world, infer causal relationships, and reason about alternative scenarios, particularly counterfactuals, is central to our intelligence and adaptability. In contrast, current machine learning (ML) and artificial intelligence (AI) …

A Neuro-Symbolic Ai Approach To Scene Understanding In Autonomous Systems, Ruwan Tharanga Wickramarachchige Don

Theses and Dissertations

Effectively understanding scenes requires a unified representation of scene data and background knowledge. A neuro-symbolic AI approach to scene understanding leverages such a unified representation to enable advanced expression, inference, and labeling of scenes, improving the perception of autonomous systems.

Scene understanding remains a central challenge in the machine perception of autonomous systems. It requires the integration of multiple sources of information, background knowledge, and heterogeneous sensor data to perceive, interpret, and reason about both physical and semantic aspects of dynamic environments. Current approaches to scene understanding primarily rely on computer vision and deep learning models that operate directly on …

On A Conjecture On Covering Systems And An Irreducibility Question On Sparse 0,1-Polynomials, Alexandros Kalogirou

Theses and Dissertations

In 1952, H. Davenport posed the problem of determining a condition on the minimum modulus $m_0$ in a finite distinct covering system that would imply that the sum of the reciprocals of the moduli in the covering system is bounded away from 1. In 1973, P.~Erd\H os and J.~Selfridge indicated that they believed that $m_0$ > 4 would suffice. We provide a proof that this is the case in Chapter 2. Chapters 3 and 4 are dedicated to showing that $0,1$-polynomials of high degree and few terms are irreducible with high probability. Formally, let $k\in\mathbb{N}$ and $F(x)=1+\sum_{i=1}^kx^{n_i}$, where $ 0

A Spatial Scan Statistic For Group Testing Data, Vincent Onyame

Theses and Dissertations

Group testing involves pooling specimens from multiple individuals and offers an efficient means to surveil low-prevalence pathogens, but poses challenges for spatial cluster detection when only pooled results are observed. In this thesis, we develop a spatial scan statistic tailored to group-testing data with variable pool sizes. The statistic compares a null hypothesis of a homogeneous infection rate across all clusters to an alternative hypothesis that infection probabilities differ inside and outside a candidate cluster, with both models fitted by maximum likelihood estimation. We approximate the null distribution of the maximum likelihood ratio test via Monte Carlo simulation.

Through a …

Some Likelihood-Based Methods For Clustering Functional Data, Tong Shan

Theses and Dissertations

The analysis of functional data is an increasingly relevant part of statistics. The exploratory data analytic method of cluster analysis plays a very important role in different fields. Over the years, researchers have developed many clustering approaches, striving to achieve more accurate and efficient clustering. In Chapter 1, we aim to improve the accuracy of our outcome when clustering functional data. To achieve this goal, we use a predictive likelihood function which serves as an objective function to optimize in order to determine the most appropriate clusters. To optimize the objective function over the space of clustering partitions, we produce …

On Berge Pancyclicity For Uniform Hypergraphs, Teegan Cole Bailey

Theses and Dissertations

An $n$-vertex graph $G$ is \textit{hamiltonian} if it contains a length $n$ cycle as a subgraph. A stronger notion of hamiltoncity is pancyclicity. A graph $G$ is \textit{pancyclic} if $G$ contains a cycle of length $k$ for every $3\leq k \leq n$. Dirac's classical result on hamiltonicity states that if an $n$-vertex graph $G$ has minimum degree $\delta(G)\geq \frac{n}{2}$, then $G$ is hamiltonian. Under the same minimum degree condition as Dirac, J. A. Bondy showed that $G$ must also be pancyclic or the exceptional graph $K_{\frac{n}{2}, \frac{n}{2}}$. In 1971, Bondy proposed a so called meta-conjecture claiming that \textit{``almost any nontrivial …

Multi-Task Deep Learning Approach For Segmenting And Classifying Competitive Swimming Activities Using A Single Imu, Mark Shperkin

Theses and Dissertations

Competitive swimming performance analysis has traditionally relied on manual video review and multi-sensor systems, both of which are resource-intensive and impractical for everyday training use. This study investigates whether a single wrist-worn inertial measurement unit (IMU) can be used to automatically segment and classify swimming activities with high accuracy. We propose a multi-task deep learning pipeline based on the MTHARS (Multi-Task Human Activity Recognition and Segmentation) architecture introduced by Duan et al. to perform stroke classification, lap segmentation, stroke count estimation, and underwater kick count estimation. Data were collected from eleven collegiate-level swimmers wearing left-wrist-mounted IMUs, each performing five 100-yard …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …