Digital Commons Network^™

School Counselor Burnout And Self-Efficacy And Their Impact On The Degree Of Implementation Of A Comprehensive School Counseling Program, Rebecca Pierre-Louis

Counseling & Human Services Theses & Dissertations

School Counselors are educators who are vital in supporting students’ educational development. School Counselors do this by delivering a comprehensive, developmentally appropriate, and prevention-oriented school counseling programs that provide direct, indirect, and responsive services to support students’ career, academic, and social/emotional development. School Counselors do this while often experiencing an expanded definition of their role within a school building. While research supports the benefits of comprehensive school counseling programs to schools and students, school counselors often face large student caseloads. As well, they are tasked with many non-school counseling duties, which hampers the delivery of a comprehensive school counseling program. …

Enhancing Data Usability For People With Visual Impairments, Yash Prakash

Computer Science Theses & Dissertations

Human-Data Interaction (HDI) focuses on how individuals engage with, analyze, and extract insights from data. For blind and visually impaired (BVI) users, interacting with data, whether searching for relevant information from structured data (e.g., web data items) or interpreting visualizations to draw insights (e.g., data charts), presents significant challenges. These challenges arise from the complexity and sheer volume of data which cannot be effectively handled by assistive technologies like screen readers and screen magnifiers. Despite its importance, data usability, the ease, efficiency, and satisfaction with which BVI individuals can interact with the data, has received less attention compared to data …

Flux Trapping Sensitivity On The Metallurgical State Of Superconducting Radio Frequency Niobium, Bashu Dev Khanal

Physics Theses & Dissertations

Trapped magnetic flux in superconducting radio-frequency (SRF) niobium cavities due to the incomplete Meissner effect is a significant source of rf losses. The relationship between flux expulsion and flux trapping sensitivity to the metallurgical state of 1.3 GHz and 3.0 GHz SRF niobium cavities fabricated from standard and cold-worked Nb sheets and subjected to various heat treatments has been studied. Flux expulsion increased with higher heat treatment temperature in all cavities. Nb cavities made from cold-worked sheets showed better flux expulsion after 800 ◦C/3 h compared to the standard SRF-grade Nb cavities. Similar flux expulsion was observed after annealing at …

Computational Investigation Of Energetic Materials: Influence Of Electronic And Steric Properties On Sensitivity And Decomposition Mechanisms, Elizabeth Ruth Zengel

Chemistry & Biochemistry Theses & Dissertations

Developing novel high energy density materials (HEDMs) requires knowledge of the causes and mechanisms of detonation. These chemical events are almost instantaneous and involve the release of a large amount of energy, which limits the experimental studies that can be performed on them. Computational methods including density functional theory (DFT) and molecular dynamics (MD) simulations have been used to investigate trigger bonds, those which break to initiate detonation. These bonds are commonly found within explosophores, substituents that increase the explosive potential of a molecule.

The Wiberg bond index (WBI) is an estimation of orbital overlap and bond strength between two …

Analysis Of Multi Grade Deep Learning, Ronglong Fang

Mathematics & Statistics Theses & Dissertations

Multi-Grade Deep Learning (MGDL) is a training framework that incrementally builds deep neural networks. It does this by dividing the training process into multiple “grades,” where each grade sequentially trains a shallow neural network to learn the residue from the previous one, using the outputs of prior grades as input. This approach progresses from shallow to deep architectures. This dissertation offers a comprehensive theoretical and numerical analysis of the MGDL methodology.

We first demonstrate that MGDL can effectively learn target functions within the sum-composition learning format. In this context, MGDL approximates high-frequency components by composing multiple low-frequency functions. This unique …

Towards Improving Computational Modeling Of The Lumbar Spine - Impact Of Material Properties And Laminotomy On Spinal Biomechanics, Isaac K. Kumi

Mechanical & Aerospace Engineering Theses & Dissertations

Spinal ligaments play a crucial role in maintaining the mechanical stability of the lumbar spine. These dense, collagenous tissues not only resist excessive motion but also distribute loads between spinal components to protect neural structures. Traditionally, the mechanical response of ligaments has been modeled using linear elastic assumptions, which treat the tissue as having a constant stiffness regardless of loading history. While this simplifies analysis, it fails to capture the time-dependent behaviors, such as creep, stress relaxation, and hysteresis, that are characteristic of biological tissues.

Viscoelastic modeling may provide a more physiologically accurate representation by incorporating both elastic and viscous …

Unfolding Particle Detector Effects And Solving Qcd Inverse Problem With Generative Ai, Tareq Saeed Alghamdi

Computer Science Theses & Dissertations

Advancements in artificial intelligence (AI) have revolutionized high-energy physics by enabling generative models to address key detector-related Challenges. This work explores the generative model to mitigate smearing, acceptance, and inefficiency in particle detectors, enhancing experimental precision.

We present a generative model-based framework to model and correct detector distortions. Using the Jefferson Lab CLAS g11 experiment as a case study, our approach successfully unfolds detector effects in multi-particle final states while preserving multidimensional correlations despite complex reaction mechanisms. A key focus is addressing the acceptance problem—accurately modeling detector acceptance without computationally expensive simulations. By training generative model-based framework on simulated detector …

Examining The Impact Of A Utility Value Intervention On Self-Regulated Learning Engagement, Wanda D. Brooks

STEMPS Theses & Dissertations

The purpose of this study was to examine the impact of inserting a motivation intervention into a self-regulated learning training in an introductory biology course to increase academic performance and persistence to degree completion in science. The study was an experimental design with pre- and post-tests, with treatment and comparison groups. The training was in the form of three self-regulated learning cycles presented to students on a Blackboard Organization site where students were asked to record self-regulated learning activities in a journal during training. The primary aim of the intervention was to increase engagement in the self-regulated learning training by …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Intel Oneapis Esimd, Joseph Wassell

Computer Science Theses & Dissertations

The growing popularity of Computational Fluid Dynamics (CFD) simulations among engineers necessitates the use of GPU acceleration for increased efficiency. NASA FUN3D offers GPU accelerated CFD simulations using unstructured grids across the speed regime from incompressible to hypersonic flows involving reentry. This work focuses on the generalized multi-color point implicit solver used in FUN3D, accounting for roughly half of the run time. Specifically, this work focuses on developing three optimized multi-color linear-solver kernels for the Intel Data Center Max 1550 GPU that is available on the Argonne Leadership Computing Facility’s (ALCF) exascale machine, Aurora. These optimized kernels work for a …

Toward More Challenge-Engaging Stem Students: A Narrative Inquiry Into Student Persistence In The Face Of Failure, Stephanie R. Fahey

Educational Leadership & Workforce Development Theses & Dissertations

Science, Technology, Engineering, and Mathematics (STEM) postsecondary graduates greatly contribute to advances in technology and research, and STEM fields promise graduates careers with positive long-term outcomes. Many postsecondary students initially choose STEM majors; however, less than half of those students graduate with a STEM degree. High STEM attrition is a problem that continues to vex educators and researchers. One attributable factor to high STEM attrition is students adversely reacting to initial academic failure in challenging STEM educational contexts. However, there are a lack of studies that investigate the individual experiences of students who persist despite academic failure to better understand …

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

Two Analytic Issues Arising From Models In Probability And Materials Science, Giang Vu Thanh Nguyen

Mathematics & Statistics Theses & Dissertations

This dissertation explores two distinct topics centered on mathematical models in probability theory and materials science. The first part investigates a series of functions derived from an adaptive algorithm designed to address the score-based secretary problem, a classic challenge in probability theory. This problem involves making immediate decisions to select the best candidate from a sequence of interviews. The algorithm aims to maximize the probability of selecting the optimal candidate based on observed scores. We prove two fundamental analytic properties of this sequence of functions as a theoretic support of the algorithm: first, the functions in the sequence each possess …

Graduate Students’ Perspectives On Universal Design For Learning (Udl) 3.0 Guidelines: A Q-Methodology Study, Taylor C. Rodriguez

Educational Leadership & Workforce Development Theses & Dissertations

The purpose of this study was to explore graduate students’ perspectives of Q-methodology to understand their insights on the newly updated Universal Design for Learning (UDL) 3.0 guidelines and how they feel these guidelines support their academic journey. A Q-sort instrument consisting of 36 statements aligned with UDL 3.0 principles was administered to graduate students at the master’s and doctoral levels. Thirty-two participants completed the Q-sort activity, and factor analysis revealed four distinct viewpoints reflecting varied perceptions of UDL’s relevance and impact within graduate education.

Findings indicate areas of consensus among students, particularly regarding the importance of developing student autonomy …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

The Influence Of Monsoon Variability On The Circulation Of The Near-Surface Indian Ocean And The Depth-Integrated Chlorophyll, Marufa Ishaque

OES Theses and Dissertations

The Indian Ocean experiences a strong semiannual reversal of monsoon winds, which determines the weather and climate of Asia, including freshwater fluxes between the atmosphere, land, and ocean. Studies and model projections suggest that the timing and intensity of the seasonal monsoon have already started to change, with more dramatic changes likely in the future. However, the impact of these changes on the Indian Ocean circulation system, including the inter-basin salt/freshwater transport between the Bay of Bengal and the Arabian Sea, remains unclear. To better understand how monsoon variability affects Indian Ocean circulation patterns, a Regional Ocean Modeling System simulation …

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation, Hengameh Fakhravar

Engineering Management & Systems Engineering Theses & Dissertations

Driven by the growing need in the 21st century for integrating rigorous statistical analysis into engineering research, there is a movement to develop an integrated statistical engineering science within statistics and quality communities (Hoerl & Snee, 2010; Anderson-Cook et al., 2012). Systems Statistical Engineering research seeks to integrate the Causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer’s Viable System Model (1972, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. Cotter & Quigley (2018) set forth the Bayesian systemic …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

A Methodology For Model-Based Certification, Jay Albert Silverman

Engineering Management & Systems Engineering Theses & Dissertations

A need for a single source of knowledge for design decisions has led to the development of a new field of systems engineering, model-based systems engineering (MBSE) (Delligati, 2014). System certification is defined as affirming that regulatory requirements for a system have been met (Goodwin & Juzaitis, 2006). System certification is a specific application of system validation. System verification can be defined as ensuring that the system of interest (SOI), as designed, is in accordance with the design inputs/requirements. System validation can be defined as ensuring that the SOI, as designed, meets the defined system needs (Wolfgand, Katz, & Wheatcraft, …

Computed Tomography Radiomics-Based Cross-Sectional Detection Of Mandibular Osteoradionecrosis In Head And Neck Cancer Survivors, Serageldin Kamel, Laia Humbert-Vidan, Zaphanlene Kaffey, Sarah Mirbahaeddin, Abdulrahman Abusaif, David T A Fuentes, Kareem Wahid, Cem Dede, Mohamed A Naser, Renjie He, Ahmed W Moawad, Khaled M Elsayes, Melissa M Chen, Adegbenga O Otun, Jillian Rigert, Mark S Chambers, Andrew Hope, Erin Watson, Kristy K Brock, Katherine Hutcheson, Lisanne Van Dijk, Amy C Moreno, Stephen Y Lai, Clifton D Fuller, Abdallah S R Mohamed, Md Anderson Head And Neck Cancer Symptom Working Group

Faculty, Staff and Student Publications

Purpose: This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).

Materials and methods: CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions. Correlation analysis (r > 0.95) reduced features for model training. A random Forest (RF) classifier with Recursive Feature Elimination identified discriminative features. Explainability was assessed …

Service With A Smile Or Salesperson Mirroring? Understanding The Flow Of Emotional Contagion In Sales Encounters, Vinh Quoc Trong Luong

Theses and Dissertations in Business Administration

This study examines the directionality of emotional contagion in sales interactions, addressing a critical gap in understanding whether emotions flow primarily from the salesperson to the customer, from the customer to the salesperson, or bidirectionally. While prior research emphasizes customer-driven emotional flow or bidirectional alignment, this study challenges these assumptions by employing categorical Cross-Recurrence Quantification Analysis (CRQA) to assess temporal emotional synchronization in sales dialogues. Leveraging automated sentiment analysis and multi-agent AI evaluation for performance metrics, the research analyzes 166 sales interactions to quantify emotional influence dynamics. Results reveal that salespeople predominantly lead emotional exchanges, exhibiting stronger and more stable …

Formation Control Between Leader And Migratory Follower Tissues Allows Coordinated Growth, Toru Kawanishi, Takamichi Sushida, Tony Y-C Tsai, Hiroyuki Takeda, Sean G Megason

2020-Current year OA Pubs

Coordinated growth of multiple tissues is fundamental to shaping our body, but the underlying mechanisms remain underexplored. In zebrafish embryos, midline tissues composed of the notochord, floorplate, and hypochord elongate synchronously with their lengths aligned. We show that floorplate and hypochord cells collectively migrate posteriorly along the nascent notochord extracellular matrix as it extends posteriorly, maintaining the tripartite configuration. Fibroblast growth factor-mediated migration in a spatially graded manner causes cell stretching, which triggers Yap-dependent proliferation and controls floorplate and hypochord growth. Supported by mathematical modeling, we further suggest that their growth is fine-tuned by mechanical tethering to the notochord via …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

2-Adic Quantum Mechanics, Continuous-Time Quantum Walks, And The Space Discreteness, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

The authors show that a large class of 2-adic Schrödinger equations is the scaling limit of certain continuous-time quantum Markov chains (CTQMCs). Practically, a discretization of such an equation gives a CTQMC. As a practical result, new types of continuous-time quantum walks (CTQWs) on graphs using two symmetric matrices are constructed. The transport between nodes in one direction is described by one matrix, while the transport between nodes in the opposite direction. This construction includes, as a particular case, the CTQWs constructed using adjacency matrices. The final goal of this work is to contribute to the understanding of the foundations …

Optimal Quantization For Nonuniform Discrete Distributions, Russel Cabasag, Samir Huq, Eric Mendoza, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

This paper explores the process of optimal quantization for several types of discrete probability distributions. Quantization is a technique used to approximate a complex distribution with a smaller set of representative points, which is important in fields such as data compression and signal processing. We begin by examining two specific nonuniform distributions over a finite set of values and identify the best representative points for different levels of approximation. We then extend our analysis to two infinite discrete distributions: one supported on the reciprocals of natural numbers and another on the natural numbers themselves. For these distributions, we compute the …

Squeezing Information From Radio Surveys To Probe The Primordial Universe, Dionysios Karagiannis, Roy Maartens, Shun Saito, José Fonseca, Stefano Camera, Chris Clarkson

Physics Faculty Research & Creative Works

A major goal of cosmology is to understand the nature of the field(s) which drove primordial Inflation. Through future observations, the statistics of large-scale structure will allow us to probe primordial non-Gaussianity of the curvature perturbation at the end of Inflation. We show how a new correlation statistic can significantly improve these constraints over conventional methods. Next-generation radio telescope arrays are under construction which will map the density field of neutral hydrogen to high redshifts. These telescopes can operate as an interferometer, able to probe small scales, or as a collection of single dishes, combining signals to map the large …

Preface, Special Issue For The 16th International Conference On Graph Transformation (Icgt 2023), Maribel Fernández, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

This special issue contains six extended versions of papers presented at the 16th International Conference on Graph Transformation (ICGT 2023), held in Leicester, UK, on 19–20 July 2023. The conference was part of STAF 2023 (Software Technologies: Applications and Foundations) and was held under the auspices of the European Association for Theoretical Computer Science (EATCS), the European Association of Software Science and Technology (EASST), and the IFIP Working Group 1.3, Foundations of Systems Specification.