Digital Commons Network^™

A Parallel Fuzzy Logic Framework For Surgical Skill Evaluation Via Instance Segmentation And Deepsort Tracking, Mohsen M. Mohaidat

Dissertations

Manual evaluation of suturing skills during laparoscopic training is often subjective and labor-intensive, resulting in the lack of scalable and consistent feedback for trainees. This study proposes an automated framework that not only significantly reduces the need for in-person assessment by experts but also ensures scalability, thereby addressing the objectivity and cost-effectiveness limitations. While low-cost laparoscopic box trainers have become increasingly popular for residency training, performance assessment still depends on expert supervision. The proposed system aims to alleviate these limitations.

This study introduces a novel automated framework incorporating an optimized DeepSORT algorithm for classifying, localizing, and tracking surgical tools using …

Uncovering Best Practices For Professional Learning: A Focus On Dialogic Discourse, Belinda Jaquez

Dissertations

The education system rests in a paradox with an ever-changing world and classrooms frozen in time. Though technological advancements, societal changes, and policy reforms are continuously reshaping the world, classrooms have remained anchored in traditional teaching and learning practices. This study examined the relationship between professional learning programs and the quality of classroom instruction with a focus on implementing more collaborative and dialogic approaches to both student and professional learning. This research involved a multifaceted program evaluation of the learning environment centered on the learning experiences of both students and teachers. A utilization-focused evaluation approach was implemented to ensure the …

A Phenomenological Qualitative Study Of Sense Of Community In An Inner-City Parochial School: Expanding The Seats At The Table., Mary Andree Darnall

Dissertations

St. Angela’s School is an elementary school in the Austin area of Chicago. This phenomenological qualitative study examined Sense of Community (SOC) at St. Angela’s, operationalized as “The feeling that St. Angela School is welcoming, psychologically and physically safe, that I matter, I am respected, and that I belong.” As an inner-city, parochial school, St. Angela’s has seen extensive changes in the neighborhood and the student population, both in ethnicity and enrollment. The last two principals requested a study of its SOC to identify solutions as diminished enrollment and teacher turnover have threatened its continued existence.

Thirty-three individuals …

Identifying Success Characteristics Of First-Generation College Students: A Framework For Early Intervention In K-12 Education., Christopher Harmon

Dissertations

This dissertation examines how K–12 educators can better support first-generation college students by identifying the characteristics that promote success and developing early interventions using Wagner et al.’s (2006) 4 C’s model: Competencies, Conditions, Culture, and Context. Guided by Michael Quinn Patton’s Utilization-Focused Evaluation (UFE), it closely investigates how factors such as family background, academic preparation, finances, and social support shape first-generation students’ capacity to persist and excel in college. The research draws on interviews with first-generation doctoral students, offering a detailed, first-hand account of their educational journeys from early schooling through advanced degrees. Notably, the findings highlight the importance of …

Probing Shielding Tensor Components Of Amino Acids Using Nuclear Magnetic Resonance, Shiva Agarwal

Dissertations

Chirality is fundamental to terrestrial life. While most amino acids exist as nonsuperimposable mirror images, amino acids in terrestrial life are homochiral, with the L-enantiomer being ubiquitous. The detection of an excess of L-amino acids in carbonaceous meteorites suggests that extraterrestrial processes may have contributed to this enantiomeric excess (ee). One proposed mechanism, the magnetochiral model, provides a potential explanation for this phenomenon in stellar environments characterized by strong magnetic and electric fields and the presence of relativistic leptons. According to this model, subtle differences in the electronic environments of chiral amino acids under such conditions …

Testing For Broad Alternatives In Stratified Contingency Tables, Nan Mi

Dissertations

In medical and social sciences fields, data are measured in terms of discrete categories. The primary question of interest involves the relationship between a set of factors and a set of response variables under studies. Moreover, the distribution of the response variables may be influenced by another set of variables called confounders. The data from such studies are summarized in 3-way tables. The hypothesis we are interested in can be expressed in terms of "no partial association" between the sub-populations and the response levels.

The methods for testing the association or independence in a 2x2 contingency table have been developed, …

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

The Role Of Excitatory Neuromodulation In Managing Variability Of Neural System Output, Omar Itani

Dissertations

Neural systems can generate consistent outputs across a population despite substantial variability in the underlying components of individuals. This dissertation aims to identify mechanisms through which neuromodulation influences the relationship between parametric and output variability in neural systems. Through a combination of theoretical analysis, computational modeling, and data-driven approaches, the research addresses how excitatory neuromodulation can shape population-level activity variability and identifies key patterns that govern the production of consistent neural population output despite underlying parameter variability.

The theoretical foundation is established by considering how excitatory neuromodulation affects population variability in simplified neuronal models. Two fundamental patterns of variability reduction …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Does The Value Last? Exploring The Longitudinal Value Of A Research Experience For Preservice Science Teachers, Elizabeth A. Leong

Dissertations

Little attention has been given to the preparation of secondary preservice science teachers (PST) to meet the vision for science teaching and learning outlined in A Framework for K-12 Science Education. Existing literature suggests PSTs would benefit from authentic engagement in scientific research. But research experiences for preservice science teachers or preRETs are rare (Krim et al., 2019). This longitudinal qualitative case study explores the value retained one year after participation in a preRET, which engages PSTs in scientific research and education-focused professional learning. This study utilizes the preRET Value Creation Model (preRET VCM) (Authors, 2024), which overlays the Value-Creation …

Striking The Balance: The Relationship Between Government Funding And Nonprofit Financial Health, Mehrnoush Jamshidi

Dissertations

ABSTRACT

The nonprofit sector plays a vital role in society, partnering with the government to provide essential services across various fields, including education, healthcare, social services, arts and culture, and environmental protection. Salamon (1987) highlighted the cooperative relationship between nonprofits and the government, where nonprofits contribute expertise and community-based knowledge, while the government offers financial stability. Through this partnership, the government helps address “voluntary failures,” such as philanthropic insufficiency, particularism, and paternalism, which can limit a nonprofit’s ability to serve the public effectively. Given this close working relationship, understanding the financial health of nonprofits is crucial for ensuring …

Post Racial Reckoning: Montessori Instructors' Perspectives On Teaching Through An Antibias Antiracist Lens, Jasmine E. Williams

Dissertations

Prior to the racial reckoning of 2020, Montessori teacher education programs (MTEPs) have only narrowly focused on social justice education (Aronson et al., 2020; Bartolomè, 2004; Cook, 2015; Cross, 2005; D’Cruz, 2022; King, 1991). Although MTEPs began to respond differently post-reckoning, support for implementing practices like antibias antiracist (ABAR) instruction was often absent. This study sought to understand how the racial reckoning and efforts to include an ABAR lens impacted the teaching practices of instructors in MTEPs. While ABAR-related studies exist in general teacher education (Blanchard et al., 2018; Madkins & Nazar, 2022; Martinez-Alba, Herrera, & Hersi, 2022), few focus …

Can You Hear Me Now? Student Perspectives Of 3d Phenomena-Based Science Teaching Practices, Leshea A. Moncrease

Dissertations

This dissertation presents findings of a self-study that investigated how student input, through reality pedagogy, may inform 3D phenomena-based teaching practices. 3D phenomena-based teaching practices are not new, but teachers are trying to determine how to use this framework to meet the needs of all students. 3D phenomena-based lessons use phenomena to drive instruction using core ideas, crosscutting concepts, and science and engineering practices. The goal of using 3D phenomena-based teaching practices is to create 21^st century critical thinkers and problem-solvers who engage in scientific habits. The acquisition of student perceptions through the analysis of student interviews, reflective journal …

Student Engagement In Ela Choice-Based Core Curriculum: A Case Study Of The Dramatic Writing For Film, Television, And Theater Course, Brian Thomas Clements

Dissertations

This paper describes a case study examining student feelings towards engagement in choice-based core ELA courses and teacher feelings towards student engagement specifically within the Dramatic Writing for Film, Television, and Theater course offered in the state of Georgia. The context of this study focuses on the impact that student agency, choice, and voice have on student engagement from their own perspective and how Invitational Education Theory connects to this impact and the course itself. The case study examines the course, its students, and teachers in two schools in a large, suburban metro Atlanta school district. Through interviews and observations, …

Using Iready To Support Math Intervention After The Covid-19 Pandemic, Marvin Prather

Dissertations

The purpose of this study is to research the use of iReady for assisting in growing MAP RIT scores at STEAM Elementary School. This study will use quantitative methods. An ex post facto research design will be used with a causal-comparative approach, which means that a study will investigate potential cause-and-effect relationships between variables by examining existing groups or conditions that have already occurred, without actively manipulating any factors, essentially looking at “after the fact” to see if differences between groups can be linked to a potential causal variable The study examined the effect of the i-Ready Math program on …

Perspectives On Using Multimedia In Social Studies For Student Engagement In Marginalized Youth: A Case Study, Dorrian Swinger

Dissertations

Abstract

Marginalized youth are currently facing an academic crisis in schools. Research has shown that marginalized youth are statistically more likely to drop out of school and not graduate from high school (defined as the academic crisis in this study). One reason for these high drop-out rates is disengagement in what is taught and how content is delivered in school. When students cannot relate or connect to their studies, it causes disengagement and uninterest in school. The disengagement can lead to a lack of academic success, which creates negative motivation to drop out of school. The more marginalized youth are …

An Endangered Profession: Elevating The Lived Experiences Of Current And Retired Black Teachers In Georgia To Confront Retention Challenges And Reimagine The Legacy Of Teaching, Thelma Sharese Colbert

Dissertations

Black teachers have always played a critical role in shaping the educational journeys and cultural identities of students of color, yet their presence in U.S. public schools continues to decline. This study investigates what compels Black/African American educators to remain in the profession despite systemic barriers, racism, and marginalization. It centers the voices of Black/African American teachers in Georgia, a state with a long and complex history of educational inequity.

Ten participants, all self-identified Black/African American educators with at least three years of experience, shared their lived experiences through semi-structured interviews, pre-interview surveys, and observational field notes. Participants were recruited …

The Impact Of Flipped Classroom With Active Learning Model Of Instruction In Teaching Stoichiometry In Chemistry Among Black And Brown Students, Lawrence Femi Akintokun

Dissertations

Students pursuing professions in STEM sometimes encounter difficulties in General Chemistry, a semester-long foundational subject. A fundamental subject in this course is stoichiometry, which pertains to the mathematical connections between elements and compounds in chemical processes. This fundamental notion is essential for comprehending advanced chemistry subjects, although it may provide challenges for students because of its dependence on algorithms and prior knowledge. Research has predominantly concentrated on the conceptualization of stoichiometry through models endorsed by educators, rather than on student involvement. This project sought to investigate the interactions of Black and Brown students with essential stoichiometry ideas in General Chemistry …

Abel Inversion Comparison Of Geant4 Simulation And Ozone Production Using Cavity Ringdown Spectroscopy In Nitrogen/Oxygen Mixtures In The Presence Of Alpha Radiation, Sidney John Gautrau

Dissertations

The effects of radioactive materials on atmospheric gases have been a topic of interest for years. Radioactive materials ionize the surrounding air, and subsequent reactions lead to molecules such as ozone and nitrogen oxides. The presence of these species above background levels can be used as a marker for radioactive materials which has desirable defense applications like remote detection of radioactive materials. The molecules created in the presence of radioactive materials have been quantified in literature using G-values, which is the number of molecules of a product produced per 100 eV of deposited energy. In this work, Cavity Ringdown Spectroscopy …

A Mixed Methods Study On Teacher Knowledge And Its Influence On Reducing Math Anxiety In Students, Shannon E. Cruz

Dissertations

Math anxiety has been a cause for concern in many people's lives before research commenced on its impact on math performance and learning. The dissertation in practice was focused on how a teacher’s lack of preparation and tools to support students with math anxiety could be changed in the field of education. Using a design thinking-based process, the student researcher identified a research question about how professional development about math anxiety helps teachers understand and support affected students while analyzing two null hypotheses regarding teacher understanding and ability to support students. Once the knowledge gap was identified, the student researcher …

Does Universal Screening Increase The Proportion Of Culturally, Linguistically, And Economically Diverse Students In Gifted Education?, Leona Alexander

Dissertations

The purpose of this quantitative study was to examine the impact of implementing a universal cognitive screening tool on the proportional representation of culturally, linguistically, and economically diverse (CLED) first-grade students in Gifted and Talented Education (GATE) programs. This study employed a comparative interrupted time series design utilizing a difference-in-differences (DiD) regression model along with additional statistical analyses (two-proportions z-tests and Mann-Whitney U tests) due to limitations in the sample sizes.

Participants included first-grade students (N=425) from two demographically and socioeconomically similar districts in a Rocky Mountain region. One district implemented the universal screener (intervention group n=368), and the other …

Temporal Dynamics In Spatial Random Field Theory: A Methodological Advance In Fmri Data Analysis, Theophilus Barnabas Kobina Acquah

Dissertations

Functional magnetic resonance imaging (fMRI) is a noninvasive tool for studying neural correlates of cognition by measuring task-evoked brain activity. Accurate interpretation of fMRI data depends on modeling the hemodynamic response (HR), which varies across brain regions and conditions. Brain plasticity adds complexity as functional changes during development and aging affect cognition, emotion, and behavior. BOLD signals display complex temporal dynamics influenced by both neural and physiological factors, challenging conventional models. This highlights the need for adaptive frameworks that account for temporal dependencies and spatial heterogeneity in neural activity detection. This current study enhances fMRI data analysis by integrating temporal …