Physical Sciences and Mathematics Commons^™

Reclaiming Healing Spaces: A Phenomenological Study On The Transformative Power Of Outdoor Therapy From The Lived Experiences Of Black Clinicians Working With Black Clients, Lynn Murphy

Dissertations

This phenomenological study involved assessing the experiences of Black therapists who engaged Black clients in outdoor therapeutic contexts. The study was founded on the existing literature that shows the quality of the therapeutic relationship is pivotal for client retention and the Western standards that have historically favored treatment within indoor environments. To contextualize this research, a comprehensive literature review was commenced, covering topics such as the decolonization of therapy, the historical and present-day relationship between Blacks and the outdoors in the United States, sedentary lifestyles, the psychological benefits of time spent in nature, various types of outdoor therapy, and the …

On Near-Linear Cellular Automata Over Near Spaces, Abdul-Rahman M. Nasser

Dissertations

Cellular Automata can be considered as examples of massively parallel machines. They are computational mathematical objects consisting of a grid of cells, each of which can exist in a finite number of states. These cells evolve over discrete time steps according to a set of predefined rules based on the states of neighboring cells. The notion of cellular automata was first introduced by Ulam and von Neumann and then popularized by John H. Conway in the 1970s with one of the most famous examples being The Game of Life.

This research builds on and generalizes the work of Tullio Ceccherini-Silberstein …

Alternative Adjacency Matrices And Spatial Analysis, Jaeseong Hwang

Dissertations

Spatial analysis is essential for comprehending the spatial distribution of diseases and various phenomena across geographic regions. This study investigates the utilization of alternative adjacency matrices in spatial analysis, with a specific focus on implementing Poisson regression models. This study intricately explores the methodology behind constructing alternative weight matrices, specifying weight matrices, and comparing the performance of Poisson models using five different weight matrices.

The popular Poisson model model is described, and five different definitions of weight matrices are defined, which are the following: binary weight matrix, inverse distance weight matrix using Euclidean distance, Graph distance matrix, Path matrix, and …

Quasi-Monte Carlo Estimation For Functional Generalized Linear Mixed Models., Ruvini Kumari Jayamaha Hitihamilage

Dissertations

Functional Data Analysis (FDA) is a topic of growing interest in the statistics community and is applied in a wide range of fields such as Anthropology, Epidemiology, Meteorology, Neurology and Engineering. The data in FDA are smooth curves or surfaces in time or space which can be conceptualized as functions. Because of the smooth nature of the data and the measurements are highly correlated, making the classical methods such as univariate or multivariate analysis are infeasible for such data. Functional data Analysis (FDA) deals with these kinds of more detailed, complex, and structured data.

In this dissertation, we propose a …

A Microgenetic Learning Analysis Of Contextuality In Reasoning About Exponential Modeling, Elahe Allahyari

Dissertations

This work explores the complex cognitive processes students engage in when addressing contextual tasks requiring linear and exponential models. Grounded within Piagetian constructivism and the Knowledge in Pieces (KiP) epistemological perspective (diSessa, 1993, 2018), this empirical study in a clinical setting develops a Microgenetic Learning Analysis (MLA) of the reasoning of 14 students from an Algebra II course. It reveals the critical role of cognitive disequilibrium as an essential cognitive state for conceptual development and the process of reorganizing knowledge systems. The study uncovers the fluctuations in students’ reasoning patterns and the significant impact on students’ reasoning patterns of task-specific …

An Experimental Study Of Supervised Machine Learning Techniques For Minor Class Prediction Utilizing Kernel Density Estimation: Factors Impacting Model Performance, Abdullah Mana Alfarwan

Dissertations

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled focus on one aspect of the larger problem of student retention and dropout prediction in higher education (HE): identification.

This study differs from current literature by implementing an experimental design approach with simulated student data that closely mirrors HE situational and student data. Specifically, this study tested the predictive ability of the four ISML classification models (CLS) under experimentally …

Empirical Exploration Of Software Testing, Samia Alblwi

Dissertations

Despite several advances in software engineering research and development, the quality of software products remains a considerable challenge. For all its theoretical limitations, software testing remains the main method used in practice to control, enhance, and certify software quality. This doctoral work comprises several empirical studies aimed at analyzing and assessing common software testing approaches, methods, and assumptions. In particular, the concept of mutant subsumption is generalized by taking into account the possibility for a base program and its mutants to diverge for some inputs, demonstrating the impact of this generalization on how subsumption is defined. The problem of mutant …

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

On The Ubiquity, Properties And Evolution Of Small-Scale Magnetic Flux Ropes In The Heliosphere, Hameedullah Farooki

Dissertations

The solar wind is a plasma constantly blowing out from the Sun with a large-scale magnetic field having significant local complexity at small scales. Small-scale magnetic flux ropes (SMFRs), plasma structures with twisted field lines, are an important element of this complexity. This dissertation contributes several studies that further our understanding of SMFRs. The first study applies machine learning to measurements from Wind labeled by the presence of SMFRs and magnetic clouds (MCs). MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties, whereas SMFRs appeared to be …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Information Theoretic Bounds For Capacity And Bayesian Risk, Ian Zieder

Dissertations

In this dissertation, the problem of finding lower error bounds on the minimum mean-squared error (MMSE) and the maximum capacity achieving distribution for a specific channel is addressed. Presented are two parts, a new lower bound on the MMSE and upper and lower bounds on the capacity achieving distribution for a Binomial noise channel. The new lower bound on the MMSE is achieved via use of the Poincare inequality. It is compared to the performance of the well known Ziv-Zakai error bound. The second part considers a binomial noise channel and is concerned with the properties of the capacity-achieving distribution. …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

The Effects Of Free Volume And Processing On The Development Of Gas Separation Membranes, Jacob Schekman

Dissertations

Structure, thermal, mechanical, gas transport, and free volume properties of thiol-ene based systems are investigated and discussed. In the pursuit of generating low energy-cost polymer membranes for gas separation, it became apparent that UV-curing of thiol-ene materials presented several routes toward achieving this goal. Network structure plays a vital role in determining the gas transport properties of membrane materials. UV photopolymerization techniques provide a means to rapidly vitrify network morphologies which can be tuned depending on choice of monomer. Thiol-ene monomers offer a broad range of precursor materials from which to choose for the design of functional membrane materials.

Chapter …

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 …

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 …

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 …

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. …

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 …

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 …

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 …

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 …

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 …

First Principles Investigation Of Energy Harvesting Materials For Green Environment, Mehreen Javed

Dissertations

The cutting-edge research of materials enables the discovery of novel energy harvesting materials. In this project the structural, electronic, magnetic, thermodynamic, thermoelectric, and optical properties of different energy harvesting materials are studied. The main objective of this work is primarily to study thermoelectrically efficient half-heuslers and photovoltaically active perovskites. Variant schematics of innovative compounds with defect introduction are investigated. The compositionally altered compounds designed by introducing crystallographic defects in terms of substitutional or interstitial dopants, offer new trends of material properties. To accomplish the task, Density Functional theory based computational packages (VASP and Wein2K) are used. Using defect and strain …

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 …

Interactions Of Lewis Acids And Carbonyls In The Presence Of Ligands Or Additives: How Solution Behavior Differs And The Implication On Catalytic Systems, Sophi Rose Todtz

Dissertations

Lewis acids have proven extremely valuable to the field of organic synthesis. Their diverse application in reactions of carbonyl-containing substrates has caused a multitude of efforts towards characterization of solution interactions between Lewis bases and Lewis acids. Though Lewis initially described a 1:1 interaction between one acid and one base, our lab has previously employed in-situ IR spectroscopy to characterize 2:1, 3:1, and 4:1 solution structures under catalytic conditions. These insights have played a critical role in our understanding of catalyst behavior in carbonyl-containing systems, allowing us to describe byproduct inhibition in the carbonyl-olefin metathesis mechanism. The research presented in …