Physical Sciences and Mathematics Commons^™

Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry

Theses and Dissertations

Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …

Photoluminescence Of Beryllium-Related Defects In Gallium Nitride, Mykhailo Vorobiov, Mykhailo Vorobiov

Theses and Dissertations

This study explores the potential of beryllium (Be) as an alternative dopant to magnesium (Mg) for achieving higher hole concentrations in gallium nitride (GaN). Despite Mg prominence as an acceptor in optoelectronic and high-power devices, its deep acceptor level at 0.22 eV above the valence band limits its effectiveness. By examining Be, this research aims to pave the way to overcoming these limitations and extend the findings to aluminum nitride and aluminum gallium nitride (AlGaN) alloy. Key contributions of this work include. i)Identification of three Be-related luminescence bands in GaN through photoluminescence spectroscopy, improving the understanding needed for further material …

Graph Coloring Reconfiguration, Reem Mahmoud

Theses and Dissertations

Reconfiguration is the concept of moving between different solutions to a problem by transforming one solution into another using some prescribed transformation rule (move). Given two solutions s₁ and s₂ of a problem, reconfiguration asks whether there exists a sequence of moves which transforms s₁ into s₂. Reconfiguration is an area of research with many contributions towards various fields such as mathematics and computer science.
The k-coloring reconfiguration problem asks whether there exists a sequence of moves which transforms one k-coloring of a graph G into another. A move in this case is a type …

Symmetry Analysis Of The Canonical Connection On Lie Groups:Co-Dimension Two Abelian Nilradical With Abelian And Non Abelian Complement, Nouf Alrubea Almutiben

Theses and Dissertations

We consider the symmetry algebra of the geodesic equations of the canonical
connection on a Lie groups. We mainly consider the solvable indecomposable four,
five and six-dimensional Lie algebras with co-dimension two abelian nilradical, that
have an abelian and not abelian complement. In this particular case, we have only
one algebra in dimension four namely; A4,12 , and three algebras in dimension five
namely; A5,33, A5,34, and A5,35 In dimension six, based on the list of Lie algebras in
Turkowski’s list, there are nineteen such algebras namely; A6,1- A6,19 that have an
abelian complement, and there are eight algebras that …

Penalized Interpolating B-Splines And Their Applications, Kylee L. Hartman-Caballero

Theses and Dissertations

One of the most studied data analysis techniques in Numerical Analysis is interpolation. Interpolation is used in a variety of fields, namely computer graphic design and biomedical research. Among interpolation techniques, cubic splines have been viewed as the standard since at least the 1960s, due to their ease of computation, numerical stability, and the relative smoothness of the interpolating curve. However, cubic splines have notable drawbacks, such as their lack of local control and necessary knowledge of boundary conditions. Arguably a more versatile interpolation technique is the use of B-splines. B-splines, a relative of Bézier curves, allow local control through …

Optimizing Deep Inspiration Breath Hold Technique For Personalized Breast Cancer Radiotherapy, Srinidhi Rajagopalan

Theses and Dissertations

The purpose of this research is entrenched in advancing the practice of 3D Conformal Radiation Therapy (3D CRT) for breast cancer patients by meticulously evaluating the impact of different Deep Inspiration Breath Hold (DIBH) techniques on dosimetric parameters. This study aims to ascertain the quantitative benefits of varied breathing techniques, primarily focusing on the 'Ultimate DIBH' method's efficacy in sparing critical organs such as the heart and lungs.

The comprehensive analysis of different DIBH techniques in this research is not merely an academic exercise but is geared towards substantiating the hypothesized advantages of these techniques with empirical data. By generating …

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Measuring The Properties Of Rod-Shaped Bacteria By Single-Entity Electrochemistry, Ashley Tubbs

Theses and Dissertations

Antibiotic-resistant bacteria pose a significant and escalating threat to hospitals around the world, necessitating the development of rapid and sensitive detection methods. Single-entity electrochemistry has emerged as a promising approach for detecting and identifying such bacteria, and monitoring the efficacy of antibiotics in real-time. Herein, we employ the translational diffusion equation for circular cylinders to predict the collision frequency of rod-shaped bacteria, informing our experimental setup. Our work demonstrates that lab-fabricated Pt ultramicroelectrodes can sensitively detect bacteria at femtomolar concentrations under migration-controlled conditions. Further, we present a method implemented in MatLab to automate the analysis of step-like signals observed in …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Large Language Models, Prompting, And Synthetic Data Generation For Continual Named Entity Recognition, Charles I. Cutler

Theses and Dissertations

With the ever-growing amount of textual data, the task of Named Entity Recognition (NER) is vital to Natural Language Processing (NLP), a field which focuses on enabling computers to understand and manipulate human language. NER enables the extraction of information from unstructured text. Accurate information extraction is crucial for applications ranging from information retrieval to systems for question-answering. To ensure that NER models are robust to changes in data distributions and capable of recognizing new entity types, one may consider expanding the capabilities of an existing model. Continual learning is a paradigm within machine learning. It studies the objective of …

Asymmetric Cuh-Catalyzed Reductive Coupling Of Allenamides With Carbonyl And Imine Electrophiles, Stephen Collins

Theses and Dissertations

The carbon scaffold of many drugs and natural products contain multiple stereogenic centers bearing heteroatoms. As a result of this, chemists have long sought methods to efficiently install these multi-heteroatom functionalities. Reductive coupling reactions have been extensively studied over the past decades, and the allylation of carbonyls via the reductive coupling approach has been a key method for generating chiral allylic alcohols. This work utilizes inexpensive Cu for the asymmetric reductive coupling of allenamides with carbonyls or imines to simultaneously install two heteroatoms (either oxygen and nitrogen or nitrogen and nitrogen, respectively) onto the product. These molecules have a polarity …

Mathematical Modeling And Analysis Of Inflammation And Tissue Repair: Lung Inflammation And Wound Healing In Corals Under Stress, Quintessa Hay

Theses and Dissertations

A variety of insults, including tissue injury and/or exposure to pathogen, elicit an immune response in many organisms. An improperly regulated immune response can result in deleterious effects to the organism. Here we present models for lung injury in young and old mice and models for wound healing in coral reefs.

It is well known that the immune response becomes less effective in older individuals. This is of particular interest in pulmonary insults such as ventilator induced lung injury (VILI) or lung infection. We extended a mathematical model for the inflammatory response to VILI and used experimental data to select …

Minimal Sets, Union-Closed Families, And Frankl's Conjecture, Christopher S. Flippen

Theses and Dissertations

The most common statement of Frankl's conjecture is that for every finite family of sets closed under the union operation, there is some element which belongs to at least half of the sets in the family. Despite its apparent simplicity, Frankl's conjecture has remained open and highly researched since its first mention in 1979. In this paper, we begin by examining the history and previous attempts at solving the conjecture. Using these previous ideas, we introduce the concepts of minimal sets and minimally-generated families, some ideas related to viewing union-closed families as posets, and some constructions of families involving poset-defined …

I. Preparation Of Strategic Synthetic Building Blocks For World Health Organization Essential Medicines. Ii. Applying The Principles Of Process Intensification To The Synthesis Of Fluoroquinolone Antibiotics., Jeffrey M. Noble

Theses and Dissertations

The synthesis of active pharmaceutical ingredients is often a time-consuming and expensive process. Due to the nature of pharmaceutical manufacturing, these costs are carried forward to the final purchase price by the consumer. Although there are many factors which are involved in the final costing of a pharmaceutical, the cost associated with the synthesis is the primary one which can be managed by chemists and engineers.

This work discusses the synthetic routes of four active pharmaceutical ingredients as well as the synthetic techniques and equipment used with an emphasis on how the application of specific techniques can be applied to …

Material Extrusion-Based Additive Manufacturing: G-Code And Firmware Attacks And Defense Frameworks, Haris Rais

Theses and Dissertations

Additive Manufacturing (AM) refers to a group of manufacturing processes that create physical objects by sequentially depositing thin layers. AM enables highly customized production with minimal material wastage, rapid and inexpensive prototyping, and the production of complex assemblies as single parts in smaller production facilities. These features make AM an essential component of Industry 4.0 or Smart Manufacturing. It is now used to print functional components for aircraft, rocket engines, automobiles, medical implants, and more. However, the increased popularity of AM also raises concerns about cybersecurity. Researchers have demonstrated strength degradation attacks on printed objects by injecting cavities in the …

Variability In Causal Effects On A Binary Outcome And Noncompliance In A Multisite Randomized Trial, Xinxin Sun

Theses and Dissertations

Noncompliance to treatment assignment is widespread in randomized trials and presents challenges in causal inference. In the presence of noncompliance, the most commonly estimated effect of treatment assignment, also known as intent-to-treat (ITT) effect, is biased. Of interest in this setting is the complier average causal effect (CACE), the ITT effect among compliers. Further complication arises when the outcome variable is partially observed.

My research focuses on estimating the distribution of a site-specific CACE in a multisite randomized controlled trial (MRCT) by maximum likelihood (ML). Assuming compliance missing at random (MAR). We express the likelihood as an integral with respect …

Investigating The Dynamics And Fragmentation Of Nitroaromatic Radical Cations Through Femtosecond Time-Resolved Mass Spectrometry And Computational Chemistry, Hugo Andres Lopez Pena

Theses and Dissertations

Chemists have sought to control molecular dissociation with lasers for decades. Effective control of unimolecular dissociation was only achieved with the development of high-intensity ultrashort pulsed lasers and coherent control techniques that operate on timescales faster than vibrational energy redistribution. In view of this, our lab has specialized in the study of polyatomic radical cations using femtosecond time-resolved mass spectrometry (FTRMS). The interest in radical cations stems from the fact that they are highly reactive species that contribute to many physical, chemical, and biological processes. For instance, radical cations participate in shock initiation of detonated energetic materials used as explosives …

Development Of Tangible Code Blocks For The Blind And Visually Impaired, Hyun Woo Kim

Theses and Dissertations

The fields of Science, Technology, Engineering, and Mathematics (STEM) have been growing at an accelerating rate in recent times. Knowing how to program has become one key skill for entering all of these STEM fields. However, many students find programming difficult. The block based programming language, Scratch, was specifically designed to lower hurdles to learning how to program for sighted students. Unfortunately, although very effective and widely used in K12 classrooms, Scratch, similar to other block based languages, is inaccessible to students who are blind and visually impaired (BVI). This thesis is part of a larger project to make the …

Asymmetric Cuh-Catalyzed Reductive Coupling Of Allenamides With Carbonyl Electrophiles & Development Of Nanocatalysts For Heterogeneously Catalyzed Buchwald-Hartwig Amination, Raphael K. Klake

Theses and Dissertations

Many drugs and natural products contain multiple stereogenic carbons bearing heteroatoms throughout their carbon framework. Therefore, methods that can efficiently install multiple heteroatoms on a molecule are valuable. Reductive coupling reactions have been extensively studied, and the allylation of carbonyls via the reductive coupling approach has been a key method for generating chiral tertiary and secondary allylic alcohols. This work utilizes inexpensive Cu for the asymmetric reductive coupling of allenamides with carbonyls to simultaneously install two heteroatoms (oxygen and nitrogen) on the product. These molecules have a polarity profile that make them difficult to make using traditional methods. Herein, we …

Synthesis And Application Of Redox-Active Covalent Organic Frameworks In Rechargeable Batteries, Mohammad K. Shehab

Theses and Dissertations

Synthesis and Application of Redox-Active Covalent Organic Frameworks in Rechargeable Batteries

Mohammad K. Shehab

Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, United States

Abstract

In recent years, lithium-ion batteries (LIBs) have been considered the dominant energy storage devices for portable electronics and electric vehicles due to their high energy density, low self-discharge rate, and long cycle life. In LIBs, the traditional positive electrodes employed are mainly derived from metal-containing inorganic compounds composed of cobalt, iron, nickel, or manganese (LiCoO₂, LiMn₂O₄, and LiFePO₄) coupled with graphite as the negative electrode. Despite …

Mechanisms Of Emulsion Destabilization: An Investigation Of Surfactant, Stabilizer, And Detergent Based Formulations Using Diffusing Wave Spectroscopy, Jordan N. Nowaczyk

Theses and Dissertations

Conventional approaches for studying emulsions, such as microscopy and macroscopic phase tracking, present challenges when it comes to establishing detailed mechanistic descriptions of the impact of emulsifier and stabilizer additives. Additionally, while a combination of sizing methods and macroscopic phase tracking can provide insights into droplet size changes and concentration, the use of multiple measurements can be cumbersome and error-prone. It is the focus of this work, to present a new method for studying water in oil (W/O) emulsions that involves using diffusing wave spectroscopy (DWS) to examine the impact of three different surface stabilizing additives at varying concentrations. By …

Using Superatomic Clusters And Charge Transfer Ligands To Control Electronic Characteristics Of Phosphorene Nanoribbons And Phosphorene Monolayer, Ryan Lambert

Theses and Dissertations

Phosphorene is a two-dimensional electron poor p-type semiconductor with high carrier mobility and great promise for applications in electronics and optoelectronics. As the main theme in this dissertation, the following work represents different investigations of various electronic properties associated with phosphorene. Most notable are the findings on charge transfer doping with metal-chalcogenide superatoms which displays novel control of the two most important properties of a semiconductor – the band gap energy and the nature of carriers. By tuning the width of the gap and p-/n-type character of conduction, we gain control over a material’s capacity to play a certain role …

Selected Problems In Graph Coloring, Hudson Lafayette

Theses and Dissertations

The Borodin–Kostochka Conjecture states that for a graph G, if ∆(G) ≥ 9 and ω(G) ≤ ∆(G) − 1, then χ(G) ≤ ∆(G) − 1. We prove the Borodin–Kostochka Conjecture for (P5, gem)-free graphs, i.e., graphs with no induced P5 and no induced K1 ∨P4.

For a graph G and t, k ∈ Z+ at-tone k-coloring of G is a function f : V (G) → [k] such that |f(v) ∩f (w)| < d(v,w) for all distinct v, w ∈ V(G). The t-tone chromatic number of G, denoted τt(G), is the minimum k such that G is t-tone k-colorable. For small values of t, we prove sharp or nearly sharp upper bounds on the t-tone chromatic number of various classes of sparse graphs. In particular, we determine τ2(G) exactly when mad(G) < 12/5 and also determine τ2(G), up to a small additive constant, when G is outerplanar. Finally, we determine τt(Cn) exactly when t ∈ {3, 4, 5}.

Enhancing Neuromorphic Computing With Advanced Spiking Neural Network Architectures, Paolo Gabriel Alejandro Cachi Delgado

Theses and Dissertations

This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel's Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, …

Effects Of Slip On Highly Viscous Thin-Film Flows Inside Vertical Tubes (Constant Radius, Constricted And Flexible), Mark S. Schwitzerlett

Theses and Dissertations

Viscous liquid film flows in a tube arise in numerous industrial and biological applications, including the transport of mucus in human airways. Previous modeling studies have typically used no-slip boundary conditions, but in some applications the effects of slip at the boundary may not be negligible. We derive a long-wave model based on lubrication theory which allows for slippage along the boundary. Linear stability analysis verifies the impact of slip-length on the speed, growth rate, and wavelength of the most unstable mode. Nonlinear simulations demonstrate the impact of slip-length on plug formation and wave dynamics. These simulations are conducted for …

Rainbow Turan Methods For Trees, Victoria Bednar

Theses and Dissertations

The rainbow Turan number, a natural extension of the well-studied traditional
Turan number, was introduced in 2007 by Keevash, Mubayi, Sudakov and Verstraete. The rainbow Tur ́an number of a graph F , ex*(n, F ), is the largest number of edges for an n vertex graph G that can be properly edge colored with no rainbow F subgraph. Chapter 1 of this dissertation gives relevant definitions and a brief history of extremal graph theory. Chapter 2 defines k-unique colorings and the related k-unique Turan number and provides preliminary results on this new variant. In Chapter 3, we explore the …

Face Anti-Spoofing And Deep Learning Based Unsupervised Image Recognition Systems, Enoch Solomon

Theses and Dissertations

One of the main problems of a supervised deep learning approach is that it requires large amounts of labeled training data, which are not always easily available. This PhD dissertation addresses the above-mentioned problem by using a novel unsupervised deep learning face verification system called UFace, that does not require labeled training data as it automatically, in an unsupervised way, generates training data from even a relatively small size of data. The method starts by selecting, in unsupervised way, k-most similar and k-most dissimilar images for a given face image. Moreover, this PhD dissertation proposes a new loss function to …

Investigations In The Semi-Strong Product Of Graphs And Bootstrap Percolation, Kevin J. Mccall

Theses and Dissertations

The semi-strong product of graphs G and H is a way of forming a new graph from the graphs G and H. The vertex set of the semi-strong product is the Cartesian product of the vertex sets of G and H, V(G) x V(H). The edges of the semi-strong product are determined as follows: (g₁,h₁)(g₂,h₂) is an edge of the product whenever g₁g₂ is an edge of G and h₁h₂ is an edge of H or g₁ = g₂ and h₁h_{2 …}

Innovations In Drop Shape Analysis Using Deep Learning And Solving The Young-Laplace Equation For An Axisymmetric Pendant Drop, Andres P. Hyer

Theses and Dissertations

Axisymmetric Drop Shape Analysis (ADSA) is a technique commonly used to determine surface or interfacial tension. Applications of traditional ASDA methods to process analytical technologies are limited by computational speed and image quality. Here, we address these limitations using a novel machine learning approach to analysis. With a convolutional neural network (CNN), we were able to achieve an experimental fit precision of (+/-) 0.122 mN/m in predicting the surface tension of drop images at a rate of 1.5 ms^-1 versus 7.7 s^-1, which is more than 5,000 times faster than the traditional method. The results are validated on real images …

Model-Based Imputation Of Below Detection Limit Missing Data And Group Selection In Bayesian Group Index Regression, Matthew Carli

Theses and Dissertations

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as …