Digital Commons Network^™

Monitoring And Short-Term Forecasting Of Atmospheric Air Pollutants Using Deep Neural Networks, Prasanjit Dey

Dissertations

Accurate forecasting of near-surface atmospheric air pollutants such as PM2.5, NO2, SO2, CO, and O3 remains a critical scientific and societal challenge. This difficulty arises from several factors, including nonlinear pollutant dynamics, sparse ground monitoring net works, heterogeneous satellite observations, and strong cross-pollutant interdependencies. Substantial advances have been achieved in temporal deep learning, probabilistic modeling, satellite-based estimation, physics-informed methods, and foundation models. However, these paradigms have largely evolved in isolation. As a result, existing systems are often station-dependent or pollutant-specific and optimized for a single forecasting task. This limits their robustness and generalizability across regions and heterogeneous data regimes.

This …

Examining The Relationship Between Trauma-Informed Safety Drill Practices And School-Level Chronic Absenteeism In Maine Secondary Schools, Tina Nightingale

Dissertations

School safety practices can influence students' perceptions of safety, emotional well-being, and engagement in the educational environment; however, limited research has examined whether trauma-informed safety drill practices are associated with measurable educational outcomes. The purpose of this quantitative, non-experimental, correlational study was to examine the relationship between trauma-informed safety drill practices and school-level chronic absenteeism in Maine middle and high schools. Guided by trauma-informed care, adolescent neurodevelopment, and Protection Motivation Theory, the study investigated whether higher levels of trauma-informed implementation of safety drills, as measured by the Trauma-Informed Safety Drill Practices Index (TISDPI), were associated with lower rates of chronic …

Being Desi At School: Exploring The Racialized School Experiences Of South Asian American Youth, Prabha Kaundinya

Dissertations

Schools in America have been central areas for racialization, and as an immigrant, racial-ethnic minority population, South Asian American (SAA) students have been subjected to a variety of racialization processes (S. J. Lee et al., 2017; Rice, 2017, 2019; Subedi, 2013). However, what is known about SAA educational experiences is often based on celebratory stereotypes that proclaim their uniform success while neglecting attention towards the diversity of their needs (including academic, social-emotional, or mental health needs; Saran, 2015; Ngo & Lee, 2007; P. Patel & Patel, 2017; Pedraza & Guillaume, 2023; N. Sharma et al., 2020). This dissertation uses a …

Strategies For Extending The Service Life Of Prestressed Concrete I-Shaped Beams, Sanjoy Kumar Bhowmik

Dissertations

Prestressed concrete (PSC) I-shaped beams are widely used in bridge construction because of their structural efficiency and durability. The service life of these beams is reduced and the maintenance frequency is increased due to the distress during fabrication and subsequent deterioration. Although several mitigation strategies have been proposed and implemented, beam end cracking during fabrication remains a major concern. The causes and mitigation strategies for beam end cracking have been studied for decades, but there have been no comprehensive studies utilizing beam end strains during fabrication and lifting at prefabrication plants under normal operational conditions. Moreover, existing maintenance and repair …

Deployment-Aware Deep Learning For Computer Vision: Efficient Architectures From 3d Segmentation To Mixed Reality, Bahar Uddin Mahmud

Dissertations

Deep learning has become the dominant approach for solving vision-centric problems; however, its successful deployment in real-world applications remains limited by high computational cost, data dependency, and insufficient integration with practical and human-centered environments. While state-of-the art deep learning models often achieve impressive performance in controlled settings, they frequently fail to generalize or operate efficiently under deployment constraints such as limited resources, complex data modalities, and real-time interaction requirements. These limitations motivate the need for a deployment-oriented deep learning framework that balances accuracy, efficiency, and practical usability.

This dissertation investigates the design and deployment of efficient deep learning architectures for …

Long-Covid And Cognition, Mychael Bruce

Dissertations

COVID-19 is an evolving virus that can harm the body’s respiratory and cardiovascular systems. Directly related to COVID-19 are individuals who do not recover from their initial infection, making them long haulers. Long-COVID is transitioning from a range of isolated phenomena to higher prevalence rates in patients who contracted COVID-19. Many individuals report issues with cognitive function, referred to as ‘brain fog’, that affect memory, attention, and even language skills. This literature review addresses the available research on Long-COVID and how persistent infection correlates with cognitive difficulties. It aims to explain the neurobiology of Long-COVID and how Long-COVID interacts within …

Walking The Path Of Conocimiento: Understanding And Navigating Workplace Conflict Through A Chicana Feminist Lens, Elisa A. Ortega-Schultz

Dissertations

The growing presence of Latinas in the U.S. workforce underscores the need for deeper understanding of their workplace experiences. Latinas represent one of the fastest growing segments of the labor force; however, research consistently shows that this growth is not matched by equitable access to leadership, compensation, or culturally responsive workplace environments. Latinas continue to experience persistent barriers related to pay inequity, career advancement, cultural belonging, and overall well-being.

Conflict frameworks that account for the unique experiences of Latinas are needed, as dominant approaches rooted in neutrality, procedural efficiency, and Western individualism often fail to account for the embodied, relational, …

Quantum And Conventional Informatics Studies Of Synthesis Energetics And Defect Formation In Nitride Crystal Epitaxy, Andrew Steven Messecar

Dissertations

Machine learning is a valuable approach for the processing and analysis of complex information. By estimating relationships from recorded data, machine learning methodologies can be effective strategies for pattern recognition, enabling investigations and technological applications based thereon. The potential for improved understanding of high-dimensional data has drawn interest towards machine learning from across the sciences, including the research and development of new and improved material systems. In the context of experimental materials research, much of the reported efforts to incorporate machine learning into conventional practice have been primarily focused on either the enhanced analysis of characterization experiment data or the …

Well-Being, Resilience, And Trauma During Migration: A Mixed Methods Study Of Venezuelan Migrants In Transit, Margaret S. Port

Dissertations

A substantial body of research has examined the psychological impact of experiences preceding and following migration, yet comparatively little attention has been given to the psychological toll of the migration journey itself. Moreover, due to the relative novelty of the Venezuelan migrant crisis and the community’s arrival in the United States, the psychological impact of Venezuelan migrants’ unique migration experience has been underexamined. This study utilized an explanatory sequential mixed methods design to investigate the relationships between well-being, resilience, and trauma among Venezuelan migrants in Chicago, Illinois, while also exploring their lived experiences of the migration journey. Study participants (n …

Perceptual And Geometric Advances In Crowd Simulation, Bilas Talukdar

Dissertations

Simulating realistic crowd motion remains a fundamental challenge in computer graphics and multi-agent systems, as it requires modeling both physically plausible interactions and perceptually natural behaviors. Existing crowd simulation methods typically employ simplified geometric abstractions, most commonly circular agent representations, and model navigation using either analytical interaction formulations (e.g., force, velocity, or constraint-based methods) or learned policies derived through reinforcement learning. Despite their effectiveness, these approaches often overlook detailed geometric structure and do not explicitly account for perceptual realism. This dissertation addresses these challenges by improving the realism of virtual crowd simulation through two key advancements: perceptual preference learning and …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Differential-Geometric Methods For Neural Signed Distance Fields: Parameterized Surface Extraction And Curvature Regularization For Cad Models, Haotian Yin

Dissertations

Neural signed distance fields have emerged as a powerful framework for representing three-dimensional geometry through continuous and differentiable neural functions. Their flexibility, resolution independence, and compatibility with gradient-based optimization make them especially attractive for surface reconstruction and geometric learning. However, despite these advantages, two fundamental challenges remain for engineering-grade applications. First, higher-order geometric properties such as curvature are difficult to model reliably during training and often require computationally expensive second-order differentiation. Second, while neural signed distance fields provide implicit surface representations, they do not directly yield a globally consistent forward map or parameterization for downstream geometric processing.

This dissertation addresses …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Vergence Task-Based Neural Pathways With Binocularly Normal Vision And Comorbid Persistent Post-Concussive Symptoms -Convergence Insufficiency, Ayushi Sangoi

Dissertations

Binocular dysfunctions are more prevalent in the persistent post-concussive symptoms (PPCS) population than in the general population. The most prevalent binocular disorder is convergence insufficiency (CI), affecting 3-17% of the general population and up to 10 times as many people with PPCS. CI makes it difficult to fuse or maintain fusion on targets at near, and its symptoms include double or blurry vision and headaches when performing close-range tasks such as reading, which can exacerbate PPCS symptoms. Given controversy over the subjectivity and effectiveness of diagnostic tools and symptom surveys for both PPCS and CI, understanding why CI has high …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Significant Crash Characteristics Associated With E-Scooter And E-Bike Crashes, Aimee Jefferson

Dissertations

Micromobility devices—namely e-scooters and e-bikes—have rapidly gained popularity in the United Sates, rising from 35 million annual shared rides in 2017 to over 133 million in 2023 (NACTO 2024). But also increasing is the number of injuries associated with these devices; however, most research emphasizes injury and demographic patterns rather than crash characteristics that would inform prevention strategies. Most existing crash research also relies on small sample sizes, lacks nuance distinguishing between involved parties (motorists, pedestrians, single device), and fails to distinguish between bicycle and micromobility crash patterns despite micromobility devices often being instructed to use conventional bicycle facilities. These …

Glass Transition Temperature Of Plga Nanoparticles And The Application In Drug Delivery, Guangliang Liu

Dissertations

The glass transition temperature (T_g) of poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles plays a crucial role in governing molecular mobility, diffusion, and consequently, drug release kinetics. However, the interaction among residual surfactant, drug effect, nanoscale confinement, and release medium on T_g remains insufficiently characterized. This study aims to bridge this gap by correlating the thermal behavior of PLGA nanoparticles with their drug release behavior under physiologically relevant conditions.

In the present study, PLGA nanoparticles were synthesized using both nano-emulsion and surfactant-free nano-precipitation approaches. The influence of residual surfactants - poly(vinyl alcohol) (PVA) and didodecyldimethylammonium bromide (DMAB) - was systematically …

Membrane-Engineered Nanotherapeutic Platforms For Drug Delivery: Hollow Fiber Membrane Synthesis Of Lipid Nanoparticles, Biomimetic Nanocarriers, And Nanobubbles, Zhixiang Liu

Dissertations

Lipid-based nanocarriers have emerged as a cornerstone technology for RNA therapeutics, enabling effective intracellular delivery for applications ranging from vaccination to gene regulation. However, current manufacturing approaches, particularly microfluidic-based platforms, face inherent limitations in scalability, throughput, and structural tunability due to their reliance on confined channel geometries and restricted mixing architectures. Addressing these challenges requires fundamentally new strategies that decouple nanoparticle formation from traditional microscale flow constraints while maintaining precise control over physicochemical properties.

This dissertation presents a comprehensive framework for the design, engineering, and application of advanced lipid-based nanocarriers, centered on a hollow fiber membrane (HFM)—assisted nanopore-mediated assembly platform. …

Transcranial Ac Modulation Of Cerebellar Nuclear Activity In Awake Animals, Nuran Kavakli

Dissertations

Entrainment of cerebellar nuclear (CN) cells via cerebellar transcranial alternating current stimulation (ctACS) has been reported in animals under ketamine/xylazine anesthesia. Our main objective was to demonstrate modulation of CN activity in unanesthetized, freely moving animals using ctACS. Multi-channel carbon-fiber electrodes were implanted into the interpositus nucleus for recording multi-unit (MU) activity, and thin-film electrodes were implanted subcutaneously over the posterior cerebellum for stimulation. A frequency-domain-based metric was developed to quantify modulation from MU signals. The results demonstrated modulation in a wide range of frequencies (4 Hz-300 Hz) as in anesthetized animals. In contrast, the amplitude of the peak in …

Generative Ai And Student Engagement, Motivation, And Learning: Exploring The Use Of Generative Ai In Higher Education Online Courses, Jeffrey L. Simmons

Dissertations

ABSTRACT

The rapid emergence of Generative AI (GenAI) large language models (LLMs) such as ChatGPT in higher education has left many institutions unprepared to assess their potential benefits and risks. This mixed-methods study examined how ChatGPT and Google’s NotebookLM were integrated into online course development and delivery at a small private nonprofit university in California, and assessed their perceived impact on student engagement, motivation, and learning.

Data were collected from post-course Likert-scale surveys completed by students across three online master’s-level courses and from pre-course prediction surveys by instructors in the two NotebookLM courses. Quantitative analyses included one-sample t-tests against a …

For My Oklahomies: Educators’ Perspectives On Policy, Public Discourse, And Professional Wellbeing, Cassandra Herndon

Dissertations

This critical qualitative study examines how ideologies rooted in ultra-conservative movements shape the professional lives of Oklahoma K–12 educators, and how they maneuver, resist, or accommodate ideological forces within a climate of surveillance and professional delegitimization. Grounded in critical social theory and drawing on Foucault (1977) and Leonardo (2003), the study applies a recontextualized model of transformational resistance (Hannegan-Martinez et al., 2024) to analyze educators’ acts of agency within a restrictive policy landscape.

Drawing on semi-structured interviews with eight Oklahoma K–12 teachers and administrators, this study employs critical narrative inquiry alongside reflexive thematic and critical policy analysis to ask: (1) …

Learning Through Lowriding: Knowledge In The Garage, Bianca Elisa Arellano

Dissertations

Lowriding culture has long been situated at the margins where state-sanctioned criminalization processes delegitimize the intellectual agency of lowriders, rendering their knowledge systems institutionally illegible. This dissertation argues that such epistemic invisibility denies lowriders the agency to define their own intellectual contributions, and it reclaims lowriding as a site of profound resourceful innovation. Grounded in Chicana feminist epistemologies, the study theorizes how nepantla—the process of navigating and living at the margins—works alongside rasquachismo, the working-class sensibility through which constraint becomes a site of knowledge production, to frame lowriding in Chicanx San Diego communities as an embodied tension where subversive, resourceful …

Decision-Making In Early-Stage Startups: How Venture Capitalists And Angel Investors Evaluate Uncertainty, Andrew M. Pence

Dissertations

The purpose of this dissertation is to examine how venture capitalists (VCs) and angel investors (AIs) make investment decisions regarding early-stage startups. Using an Interpretative Phenomenological Analysis (IPA) framework—consisting of three core components: phenomenology, hermeneutics, and idiography—this study draws upon three decision-making models: the Rational Actor Model (RAM), the Heuristics and Biases model (H&B), and the Naturalistic Decision-Making model (NDM) to explore what motivates VCs and AIs to choose, for instance, “Company A over Company B.” Semi-structured interviews were conducted with 10 experienced venture investors to examine how they evaluate risk, assess founders and teams, and navigate the persistent uncertainty …

A Praxis Of Transformative Care: Centering Historically Resilient Students At A Pwi, Tanaisha Coleman

Dissertations

Campus cultural centers frequently treat student resilience as an individual struggle rather than a collective responsibility, obscuring systemic failures. At a predominantly white institution (PWI), historically resilient students, those from diverse backgrounds who have historically endured the structural load of institutional exclusion, carry the compounded weight of navigating systemic barriers while driving institutional change. This qualitative study disrupts traditional narratives of surviving and thriving to examine how undergraduate students constituting a student advisory committee (SAC) coconstructed critical resilience rooted in transformative and collective care.

Using community-based participatory research (CBPR), this study positions students as coarchitects of their institutional experiences. Over …

Heat Shock Factor 1 Signaling: A Novel Pathway Implicated In Rett Syndrome Pathophysiology, Sonia Gonzalez

Dissertations

Rett syndrome (RTT) is a severe neurodevelopmental disorder that is associated with loss-of-function mutations in the methyl-CPG binding protein 2 (MECP2) gene. There are limited therapeutic options to address the wide range of social, cognitive, motor, and respiratory symptoms that patients experience throughout their lifetime. To identify novel therapeutic targets, we leveraged a rare form of RTT known as MeCP2-mutation-negative or atypical RTT. Traditionally, RTT is first diagnosed clinically and then confirmed through the identification of mutations in the MECP2 gene. However, ~5% of RTT patients are found to be MeCP2-mutation-negative (atypical), despite sharing overlapping symptomology with typical RTT. We …

Elementary Students' Science Attitudes And Teachers' Implementation Of The Ngss Science Practices: A Mixed Methods Study, Michelle Laborn

Dissertations

It has been recorded in the literature that student science interest drops as students enter and transition to middle and high school. This dissertation takes up the areas of student science interest and the teaching of the science practices in the context of elementary education. This study seeks to understand student science interest in elementary students in the years before entering middle school where a drop of interest is frequently reported. It is hypothesized that an increased use or implementation of the NGSS science practices by classroom teachers would generate higher science interest in elementary students. In a framework of …

Do International Alumni Give Differently? A Study On The Alumni Philanthropic Perspective Of Trust And Key Relational Factors Affecting Attitude Towards Giving, John Held

Dissertations

Philanthropy plays a key role in the funding of institutions of higher education. Previous research has focused on domestic populations, exploring the antecedents and elements of philanthropy in higher education. The literature has yet to examine differences in these elements between domestic and international, or distance extremes, amongst alumni populations. This study investigates the perceptions and motivating factors influencing alumni who experience significant geographic separation from their Higher Education Institution (HEI) and their philanthropic support for their alma mater. The conceptual model includes factors at the organization-to-organization, person-to-organization, and person-to-person levels and their moderating effects on the relationship between trust …