Physical Sciences and Mathematics Commons^™

Enabling High-Rate Thermoplastic Composite Manufacturing Through Structure-Processing-Property Relationships Of Polyphenylene Sulfide, Lina Ghanbari

Dissertations

In this dissertation, relationships between polyphenylene sulfide (PPS) chain structure and melt processing parameters are linked to thermoplastic composite (TPC) properties and mechanical performance. Deviations from linear PPS structure and the formation of branching/crosslinked moieties upon exposure to TPC manufacturing steps presents an opportunity to systematically uncover the interdependencies of melt-state thermal history on polymer rheology, crystallization, crystal structure, and translate these properties to the continuum level. This work first explores unreinforced PPS and how the formation of non-linear chain architectures yield altered rheological states when held at melt processing temperatures. These hindered chains exhibit unconventional crystallization behavior and non-uniform …

Design, Synthesis, And Optimization Of Allosteric Inhibitors Of Hiv-1 Integrase, Krunal H. Patel

Dissertations

The human immunodeficiency virus type 1 (HIV-1) infection remains a global health crisis, necessitating the development of innovative antiviral strategies. During the integration step, HIV-1 integrase (IN) interacts with viral DNA and the cellular cofactor LEDGF/p75 to effectively integrate the reverse transcript into the host chromatin. Recently, a novel class of antiretroviral agents called Allosteric Inhibitors of HIV-1 Integrase (ALLINI) compounds has emerged as a promising avenue in the fight against HIV-1. While originally designed to inhibit IN-LEDGF/p75 interactions, these compounds have been shown to also impact late-stage viral maturation severely through IN multimerization. Induction of IN multimerization interferes with …

Perovskite Materials For Optoelectronics Applications: Solar Cells And Photodetectors, Abdul Kareem Kalathil Soopy

Dissertations

Perovskite materials have emerged as promising candidates for optoelectronic applications, particularly in solar cells and photodetectors, due to their remarkable properties such as high carrier mobility, tunable bandgap, and low-cost fabrication. This thesis explores two novel approaches, namely monovalent doping and bulk as well as surface passivation, to enhance the performance and stability of perovskite-based devices.

The primary goal of this research is to enhance the properties of perovskite materials to improve their optoelectronic performance, aiming to broaden their use in solar cells and photodetector devices. Specifically, this study seeks to evaluate the effectiveness of Cu doping and Zinc porphyrin …

Mapping The Invisible: Machine Learning And Geovisualization Methods For Groundwater Management, Khalid Galal Elhaj

Dissertations

Groundwater is a vital global resource, yet mapping and managing aquifers remains challenging due to costs and complexities. This doctoral dissertation pioneers novel data-driven methodologies harnessing machine learning and hydrogeology domain knowledge to sustainably characterize and monitor aquifer systems. The overarching hypothesis investigated is that time series clustering of historical well hydrographs can enable low-cost delineation of aquifer boundaries by detecting response similarities between wells sharing the same aquifer. To test this, three interconnected studies were conducted. First, a specialized time series clustering framework was developed incorporating a global temporal alignment index and custom dissimilarity metrics tuned for hydrogeology. The …

Mathematical Modeling For Dental Decay Prevention In Children And Adolescents, Mahdiyeh Soltaninejad

Dissertations

The high prevalence of dental caries among children and adolescents, especially those from lower socio-economic backgrounds, is a significant nationwide health concern. Early prevention, such as dental sealants and fluoride varnish (FV), is essential, but access to this care remains limited and disparate. In this research, a national dataset is utilized to assess sealants' reach and effectiveness in preventing tooth decay, particularly focusing on 2nd molars that emerge during early adolescence, a current gap in the knowledge base. FV is recommended to be delivered during medical well-child visits to children who are not seeing a dentist. Challenges and facilitators in …

Finding Combinatorial Patterns In Real Valued Omics Data, Kenneth Smith

Dissertations

Precision medicine is a healthcare approach which tailors disease prevention and treatment to an individual, based on their genetics, environment, lifestyle, and physiological state. These factors interact to produce biological changes that can be measured to produce data called omics, and include genomics, lipidomics, and proteomics. Despite the abundance of omics data and analysis techniques, researchers still struggle to identify biological findings that replicate across data sets and translate into clinical applications. In this dissertation, we employ combinatorial optimization techniques to improve upon three steps in the precision medicine analysis pipeline: 1) data cleaning, 2) community detection, and 3) feature …

A Holistic And Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives, Martin Keagan Wynne Brown

Dissertations

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. Police reports generated by officers' response to 911 calls remain an untapped resource for identifying such incidents. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, advanced machine learning, and ensemble techniques to detect behavioral health cases within police reports. In this dissertation, we develop tools and frameworks to automatically detect behavioral health cases from …

Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder

Dissertations

Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …

My Legacy Document: A Story Told Through A Pedagogical Lens Of Three Landscapes. School, Work, And Faith, Debra Williams

Dissertations

Too often professionals, when answering the question “Who are you?”, resort to listing what they do. Although individuals are gifted, experienced, and qualified, as these persons influence and significantly impact others, one could get lost as a person and be unfulfilled. That’s a problem.

Positioning myself as the study of research, utilizing autoethnography as the chosen methodology, my doctoral journey is chronicled. Three landscapes, school, work, and faith, serve as the areas of emphasis.

Establishing Practical Equivalence Of Factor Loadings In Multigroup Confirmatory Factor Analysis, Christopher Edward Shank

Dissertations

This dissertation compares the performance of equivalence test (EQT) and null hypothesis test (NHT) procedures for identifying invariant and noninvariant factor loadings under a range of experimental manipulations. EQT is the statistically appropriate approach when the research goal is to find evidence of group similarity rather than group difference; despite this, the conventional approach to measurement invariance analysis relies upon NHT. EQT has proved effective for invariance detection using global model-data fit statistics in simulated and real-world data (Counsell et al., 2020) but its use in partial measurement invariance (PMI) analysis for evaluation of factor loading differences between groups has …

Synthesis And Characterization Of Heavy-Metal Oxoanion- And Phosphonate-Binding Nanojars, Pooja Singh

Dissertations

Nanojars are an emerging class of anion-binding and extraction agents. These supramolecular coordination complexes (2 nm wide) exhibit exceptional anion binding strength that points to their possible application in the recovery of toxic or valuable anions from water bodies. Nanojars of the formula (TBA)2[XO3/42−⸦{CuII(μ-OH)(μ-pyrazolate)}n] (TBA = tetrabutylammonium cation; XO3/42− = anion; X = Se, Te, Cr, Mo, W, HP, HAs, HV; n = 26 – 36) are synthesized from Cu2+, OH−, pyrazole and the TBA salts of various anions. Nanojars possess hydrophobic exteriors with an array of pyrazolate ligands and hydrophilic interiors with a …

Cybersecurity Education And Continuous Learning Towards Uae Ncsp Fulfilment, Saleh Hamad Aldaajeh

Dissertations

This dissertation delves into enhancing cybersecurity education by aligning academic curricula with national cybersecurity strategic plan (NCSP) objectives, emphasizing the crucial role of Higher Education Institutions (HEIs) in developing a skilled cybersecurity workforce. Analyzing ten NCSPs, it identifies strategic themes and gaps between national goals and HEI offerings. The study reviews NCSP guidelines, international cybersecurity indices, and literature, including the NICE-NIST framework, to develop a framework that bridges the educational gap, improving learning outcomes and arming students with vital skills, knowledge, and competencies. Furthermore, it introduces a platform for continuous cybersecurity learning, employing micro-credentials, blockchain technology, and AI-driven systems. Based …

An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan

Dissertations

Autonomous Vehicles (AVs) are transforming next-generation autonomous mobility. These vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance overall mobility. They achieve higher levels of Autonomous Driving (AD) by integrating sensors, communication technologies, computation, and Artificial Intelligence (AI). Apart from these advancements, significant gaps remain in integrating heterogeneous technologies and disciplines essential for optimizing AD. Therefore, the current solution approaches lack the capability to exploit intelligent road infrastructures and effectively orchestrate perceptual services for complex driving scenarios. The main objective of this dissertation is to address these challenges by proposing a novel end-to-end intelligent framework …

On The Projections And Unitary Groups Of Unital C*-Algebras, Fouzia Shaheen

Dissertations

H. Dye proved that the unitary group in a factor determines the algebraic type of that factor. Al-Rawashdeh, Booth and Giordano established that, for a large class of simple unital C*-algebras, an isomorphism between the unitary groups induces an isomorphism between their K₀-ordered group and 1-groups. Then using the results of Dadarlat-Elliot-Gong and Kirchberg-Phillips, the C*-algebras are isomorphic. Dye introduced special projections P_i,j (a) of the matrix algebra M_n(A), and he used it as a main tool to establish his results in the case of von Neumann factors. Precisely, in case of von Neumann algebra, …

Enhancing Health Analytics: Secure And Private Federated Learning Solutions, Nisha Thorakkattu Madathil

Dissertations

Federated Learning (FL) is a collaborative method allowing individuals to train a model jointly without sharing their local datasets. It utilizes decentralized data sources to protect privacy, making it particularly promising in medical contexts where data confidentiality is paramount. FL facilitates the use of diverse datasets from various healthcare organizations while upholding patient confidentiality. It also plays a crucial role in advancing medical research and healthcare services while adhering to data distribution and compliance requirements. The primary challenges within federated healthcare encompass privacy preservation among sensitive distributed data, ensuring efficient communication, addressing data heterogeneity, and ultimately guaranteeing model accuracy. To …

Multi-Modality Transformer For E-Commerce: Inferring User Purchase Intention To Bridge The Query-Product Gap, Srivatsa Mallapragada

Dissertations

The rapid growth of e-commerce has necessitated the development of sophisticated product retrieval systems that can effectively match user queries with relevant products. However, the semantic gap between queries and products remains a significant challenge, as traditional retrieval methods often fail to capture the nuances of user purchase intentions. E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge that are untapped in the current product search algorithms. This dissertation presents learning strategies that leverage the query-product transaction logs to enrich the pipeline of our proposed multi-modal transformer model, which transforms initial user queries into pseudo …

Unraveling Biases And Customer Heterogeneity In E-Commerce Recommendation Systems, Sachin Sharma

Dissertations

This research explores the biases present in AI algorithms within e-commerce recommendation systems, focusing on how these biases prioritize popular, sponsored, and private-label products over actual customer preferences. We extend the responsible AI discourse by critically examining these biases and their implications for fairness in e-commerce. To strengthen the current understanding of AI fairness in the fields of information systems and computer science, we aim to challenge the assumption that AI fairness is objective and the same for everyone. We examine how individual differences, such as equity sensitivity and exchange ideology, contribute to users' varied perceptions of AI fairness. Through …

Variability In Semidiurnal Surface And Internal Tides In Global Ocean Model Simulations, Harpreet Kaur

Dissertations

This dissertation focuses on semidiurnal (D2) surface and internal tides. Chapter 2 investigates the transition of M₂ barotropic Kelvin waves into Hybrid Kelvin-Edge (HKE) waves and the associated generation of internal tides at widening shelves using theory and a realistic global baroclinic Hybrid Coordinate Ocean Model (HYCOM) simulation. To understand the effect of complex, realistic bathymetry on the HKE wave transition, we perform quasi-realistic barotropic HYCOM simulations of the Celtic Sea/Bay of Biscay shelf areas. We conclude that the HKE wave transition is most likely masked by the effects of complex bathymetry and offshelf baroclinic fluxes cannot be exclusively …

How Can A Cloud Computing It Framework Be Created And Applied Effectively In The Online Printing Industry?, Stefan Meissner

Dissertations

This research aims to design a cloud computing IT framework for the online printing industry based on a detailed literature review, the development of proof of concepts (PoC), and the conduction of a focus group. The framework can be adopted by the online printing industry or by vendors of print-specific applications to optimize their products for the online printing industry. The author has been working in the online printing process optimization and automation since 2007. During this time, he got deep insight into many industry-specific applications, their architectural design, and their challenges being used in the context of online printing. …

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Development Of Novel Protein Digestion And Quantitation Methods For Mass Spectrometic Analysis, Yongling Ai

Dissertations

Proteins are the workhorses of biology, playing multifaceted roles in maintaining cellular function, signaling, and response to environmental cues. Understanding their abundance and dynamics is pivotal for unraveling the complexities of biological processes, which underpins the foundations of molecular and cellular biology. Accurate measurement of protein quantities provides insights into cellular homeostasis, facilitates the discovery of biomarkers, and sheds light on the molecular mechanisms of diseases, bridging the gap between the molecular intricacies of proteins and their functional consequences in health and disease. The evolution of protein quantitation methodologies, from classical colorimetric assays to sophisticated mass spectrometry-based approaches, has expanded …

Biophysical Factors Affecting Habitat Suitability For Crassostrea Virginica, Jason D. Tilley

Dissertations

Oyster reefs provide a variety of important ecosystem services. However, the mortality rate of eastern oyster, Crassostrea virginica, the dominant species that produces oyster reefs in the northern Gulf of Mexico, is increasing at an alarming rate due to a variety of abiotic and biological factors. I examined how biophysical factors, including the less-studied fatty acid profiles of the suspended particulate matter on which oysters feed, influenced morphometric condition of C. virginica.

I sampled suspended particulate matter (SPM) and oysters in-situ in the western Mississippi Sound, which historically supported the majority of oyster production in Mississippi waters. Sampling …

Early Carbonate Diagenesis Of The Miocene Dam Formation In Southwestern Qatar, Mohammed Al-Musawi

Dissertations

The timing and mechanisms of platform-scale dolomitization have been the subject of a long and contentious scientific debate known as the “dolomite problem.” The current study provides new insights through an investigation of the Miocene mixed carbonate-siliciclastics-evaporites Dam Fm in southwestern Qatar. Petrographical, mineralogical, and geochemical data are integrated from two research cores, the Dam-01 and Dam-02. The Dam Fm provides an ideal site to investigate dolomitization because these rocks are: (i) relatively young, (ii) have not been deeply buried, (iii) exhibit no evidence of diagenetic resetting, and (iv) characterized by alternating meter-scale depositional cycles that help constrain the relative …

Making Data Meaningful: Stakeholder Perceptions On Data Visualization And Data Management Practices Within A Multi-Tiered System Of Supports (Mtss), Domenick Saia

Dissertations

Data-driven decision-making and collaboration are core pillars of a multi-tiered system of supports (MTSS); however, timely and accessible data use, as well as data literacy and visualization literacy skills, are challenges school leaders and educators face related to implementing such frameworks. I hypothesized efficient data management systems and data visualization tools enable school teams to predict student learning outcomes, readily communicate, and better understand student data. The purpose of this study design was to highlight a need for more efficient data structures that allow school stakeholders to balance their roles within an MTSS framework more effectively. The context of this …

New Methods For Stereoselective Glycosylation In Application To Significant Biomedical Targets, Melanie L. Shadrick

Dissertations

Glycosyl halides have been utilized for glycosylation reactions since the early studies by Arthur Michael, nearing the end of the 19^th century. Koenigs and Knorr then utilized silver salts to activate glycosyl bromides and chlorides to create synthetic glycosides. Many efforts to improve the outcome of reactions with glycosyl halides have emerged. The key emphasis has traditionally been placed on reaction rates, product yields, and stereocontrol. Recently, our lab reported that silver(I) oxide-mediated Koenigs-Knorr glycosylation reaction can be dramatically accelerated in the presence of catalytic acid additives. Methods to improve glycosylation was explored using mannosyl and glucosyl bromides. However, …

Binding Interactions Of Biologically Relevant Molecules Studied Using Surface-Modified And Nanostructured Surfaces, Palak Sondhi

Dissertations

This research focuses on the field of surface nanobioscience, wherein different nanosurfaces that will be used as working electrodes in the electrochemical cell are manufactured and surface modified to understand the critical binding interactions between biologically significant molecules like proteins, carbohydrates, small drug molecules, and glycoproteins. This research is essential if we are to determine whether a synthetic molecule can serve as a therapeutic candidate or diagnose a disease in its early stages. In order to fully understand the binding interactions, the study begins with defining some of the fundamental concepts, principles, and analytical tools for biosensing.

Afterwards, we addressed …

A Novel Multi-Model Patient Similarity Network Driven By Federated Data Quality And Resource Profiling, Alramzana Nujum Navaz

Dissertations

Smart and Connected Health (SCH) is revolutionizing healthcare by leveraging extensive healthcare data for precise, personalized medicine. At its core, SCH relies on the concept of patient similarity, which involves the comparative analysis of newly encountered patients with those who exhibit comparable similarities from the existing patient cohort. Yet, this approach faces significant challenges, including data heterogeneity and dimensionality. Our research introduces a multi-dimensional Patient Similarity Network (PSN) Fusion model tailored to handle both static and dynamic features. The static data analysis focuses on extracting contextual information using Bidirectional Encoder Representations from Transformers (BERT), while dynamic features are captured through …

Electrical, Optical, And Thermal Properties Of Snse Based Materials With High Thermoelectric Performances, Najwa Qasem Al Bouzieh

Dissertations

This thesis conducts a thorough exploration of the characteristics and prospective applications of Tin Selenide (SnSe), a pivotal semiconductor for advancing contemporary electronics and optoelectronics. The investigation mainly focuses on comprehending the alterations in SnSe's properties when doped with elements such as Hafnium, Zinc, Bismuth, Germanium, Sodium, Iodine, and Silicon. 2D-SnSe allotropes, when doped with Hafnium, have exhibited remarkable optical characteristics, especially in the δ-SnSe allotrope, rendering it adaptable for varied optical uses like solar cells and LEDs. Additionally, evaluations of elasticity show improved resilience and augmented in-plane stiffness owing to Hf doping, occasionally reducing ductility. The work uniquely emphasizes …

Automation Of Crack Detection And Quantification In Civil Infrastructure Facilities Using Deep Learning Techniques, Luqman Ali

Dissertations

Cracks are the earliest signs of structural deterioration that reduce the lifespan and reliability of structures and can lead to severe damage. Assessment and monitoring of the facilities are required for lifetime maintenance and failure prediction. Structure condition information can be obtained manually, i.e., through subjective visual inspection and evaluation by human experts. Manual inspection techniques are labor-intensive, time-consuming, and inspector-dependent, i.e., vulnerable to the inspector’s perceptiveness. Automatic crack detection is crucial at the earliest stage to avoid further structure degradation and allow fast intervention. Deep Learning algorithms have become more popular in crack detection systems in recent years. However, …

A Data-Efficient Approach For Effective Diagnosis Of Left Ventricular Hypertrophy From Echocardiography Modality, Moomal Farhad

Dissertations

Left Ventricular Hypertrophy (LVH) is a medical condition characterized by the thickening and enlargement of the left ventricle (LV) of the heart. Accurate and timely diagnosis of LVH is vital for clinical prognosis and treatment decisions. Echocardiography has emerged as the gold standard for diagnosing LVH due to its ability to independently predict long-term risks such as heart failure and stroke. Echocardiography, a non-invasive and cost-effective imaging technology, is instrumental in assessing various aspects of heart health. Among the critical diagnostic calculations made possible by echocardiography, the determination of ejection fraction and heart chamber size is paramount in assessing LVH. …