Digital Commons Network^™

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 …

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) …

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 …

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 …

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 …

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, …

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 …

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 …

Mission Engineering Competencies Workshop Report: Roles, Responsibilities, And Skills, James D. Moreland, Jr. (Editor), Thomas Irwin, Robert-Allen Baker

Center for Mission Engineering (CME) Publications

This report summarizes the findings of a two-day Old Dominion University Mission Engineering Workshop involving the U.S. Navy, U.S. Marine Corps, U.S. Air Force, National Aeronautics and Space Administration, the University of New South Wales, and Old Dominion University, focused on defining the roles, competencies, and skills essential to Mission Engineering. It presents a structured competency framework and offers recommendations to guide workforce development, training, and cross-agency collaboration in support of complex, mission-driven systems.

Unraveling Nonperturbative Qcd With Transverse Momentum Hadronic Structures, Tommaso Rainaldi

Physics Theses & Dissertations

The main purpose of this thesis is to investigate the theoretical foundations of the factorization theorems that involve transverse quark and gluon momentum distributions, and to better understand the interface between foundational questions and phenomenological applications. Many of these applications involve using transverse parton momentum dependent (TMD) correlation functions as tools for probing the complex nonperturbative structures of the hadrons.

We first introduce the concepts of collinear and TMD factorization in a toy-model theory and, by leveraging the comparative simplicity of such a model, we test the range of validity and limitations of the factorization approach. At the same time, …

The Relationships Between Expectancy-Value Beliefs, Communal Value Beliefs, Family Identity, And Academic Persistence Among First- And Continuing-Generation College Students, Douglas W. Snuffer

STEMPS Theses & Dissertations

First-generation college students (students for whom neither parent has a 4-year college degree) have lower college graduation rates than their continuing-generation peers (Cahalan et al., 2021; Fry, 2021). First-generation students may experience barriers to higher education, leading to lower levels of academic achievement motivation for college. Two of these barriers may include 1) cultural mismatch (Stephens, Fryberg, et al., 2012) with colleges via communal value (a value set that emphasizes community and family over the self) and 2) a strong sense of family identity, which lends itself to perceived family obligations (Fuligni et al., 2007). To examine the relationships between …

Using Radium Isotopes To Compare Nutrient Inputs Via Submarine Groundwater Discharge To Offshore Export On The West Florida Shelf, Andrew L. Lindgren

OES Theses and Dissertations

Radium isotopes have been used to trace submarine groundwater discharge (SGD) and associated nutrient and metal inputs into the coastal ocean. They have also been used to estimate rates of diffusive mixing and export fluxes of dissolved constituents offshore. However, use of both approaches in tandem to determine the fate of groundwater inputs after they enter the coastal ocean is still relatively unexplored. Radium isotopes and nutrients were measured in two cross-shelf cruises on the West Florida Shelf in different seasons and in quarterly sampling of offshore wells. Through 226Ra mass balance and 223Ra-derived diffusive mixing rates, inputs of nutrients …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …

Specialization Or Diversification? Creators’ Strategies On User-Generated Content Platforms, Ziwei Ye

Theses and Dissertations in Business Administration

Recent advancements in digital platforms have reshaped content creation and distribution. User-generated content (UGC), created and shared by internet users, is transforming entertainment, communication, and information sharing. The rise of UGC has fueled the growth of the "creator economy"—an ecosystem of creators, users, and advertisers facilitated by platforms such as YouTube and TikTok. While prior research has primarily explored how UGC platforms incentivize content quantity and quality, this study advances the literature by examining how creators' content strategies influence consumer attention and how platform mechanisms shape this relationship, offering new insights into the interplay between creator behavior and platform design. …

Evidence For Distal Bolide Impact And Tsunami Deposits In The Upper Atlantic Coastal Plain Of Moore County (North Carolina, Usa) Generated By The Eocene Chesapeake Bay Bolide Impact, G. Robert Ganis, Ralph H. Willoughby, David J. Cicimurri, G. Richard Whittecar, Steven J. Hageman

OES Faculty Publications

Beds interpreted as Eocene bolide-generated impact and tsunami deposits occur at Paint Hill in the Upper Atlantic Coastal Plain of Moore County, North Carolina, USA. These strata, herein named the Mount Helicon Formation, consist of four distinct beds comprising about one meter of total section. Bed 1 (the basal bed) is approximately 43 cm thick and consists of sandy carbonaceous clay with carbon glass and rock fragments and contains 14-18 parts per billion (ppb) iridium (interpreted as bolide impact ejecta). Bed 2 is approximately 9 cm thick and consists of non-cohesive silt-size particles and loosely bound sand-size accretionary lapilli-like masses …

Relationship Between Local Fiscal Effort And High School Graduation Rate, Zachary Michael Haney

Educational Leadership & Workforce Development Theses & Dissertations

Education is an investment in human capital, but the cost to provide this public service is significant. Understanding the return on this investment is essential for informed and effective financial decision-making. While previous school finance studies have examined the relationship between funding and various indicators of student achievement and student attainment, most have focused on spending practices rather than the level of investment itself. Although the literature affirms the need for educational resources, further research is required to determine which types of investment most positively affect student outcomes. Therefore, funding must be analyzed prior to budgetary allocation to evaluate its …

"Exploring The Training Data Landscape For Ai Based Threathunting For Protecting Intellectual Property", Manzi Siibo, Christopher Kreider

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of the effectiveness that would result in the integration of AI into traditional threat hunting systems. To do so, 10-15 scholarly articles and data sets were evaluated to see the results of AI and machine learning threat hunting versus traditional systems. With so many proven benefits of this integration, this paper also explores how it impacts the protection of Intellectual property which is some of the most important forms of information that threat hunting systems aim to protect.

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …