Digital Commons Network^™

Deterministic Black-Box Context-Free Grammar Inference: Scaling From Small To Big Languages, Mohammad Rifat Arefin

Computer Science and Engineering Dissertations

Context-free grammars are essential for a number of software engineering tasks like program understanding, debugging, and grammar-based testing. However, formal language descriptions (i.e. grammars) are often unavailable, outdated, or inaccessible because parsers are closed-source or cannot be instrumented. Black-box grammar inference addresses this gap using only valid sample programs and parser acceptance decisions. The problem is challenging because finite samples rarely exercise all language features or their combinations, while the inference process cannot inspect the parser’s internal representation of syntax. This dissertation develops deterministic techniques for black-box context-free grammar inference. The first tool, TreeVada introduces the structural assumption that balanced …

Cross-Layer Supervisory Control For Low-Altitude Uav Swarm Networks, Nitin Singh Rathore

Computer Science and Engineering Theses

Low-altitude unmanned aerial vehicle (UAV) swarms are increasingly used in applications such as aerial sensing, disaster response, and communication support, where reliable operation under dynamic and uncertain conditions is essential. In these environments, performance degradation arises from multiple sources, including external disturbances, sensing uncertainty, and communication impairments. Although these effects originate from different layers of the system, such as dynamics, observation, and networking, they often manifest as similar tracking or coordination errors. Conventional control approaches, which rely primarily on error-driven feedback, do not explicitly account for the underlying cause of these deviations, limiting their effectiveness in multi-agent settings. This thesis …

Effects Of Metals On Persulfate Activation For The Decomposition Of Pfas, Malu Deav Devadasan

Civil Engineering Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants widely used in industrial and consumer products due to their unique physicochemical properties. Their resistance to degradation, potential for bioaccumulation, and associated adverse health effects have prompted increasing regulatory attention, including the establishment of drinking water standards for six PFAS compounds by the U.S. Environmental Protection Agency in 2024.

Current PFAS treatment technologies are often energy-intensive and costly or generate secondary waste streams requiring further management. Meanwhile, this research investigated transition metal-catalyzed advanced oxidation processes for PFAS decomposition in water under ambient conditions. Previous studies demonstrated effective degradation of perfluorocarboxylic acids …

Gaussian Process Regression–Based Uncertainty Quantification For Unmanned Aircraft System Traffic Management And Advanced Air Mobility Applications, Aakarshan Khanal

Mechanical and Aerospace Engineering Dissertations

Uncertainty quantification has gained significant attention in recent years as a research area in dynamical systems. Mathematical representations of the physical system, combined with an understanding of model uncertainties, enable the propagation of uncertainty in temporal space, which allows us to make informed decisions. However, what if the true dynamics of the system is unknown or too complex to define explicitly? In such cases, the system’s behavior can instead be inferred or learned from observed input–output data rather than from an analytical or physics-based model. To this end, this dissertation focuses on developing a data-driven framework for nonparametric dynamics modeling, …

Trustworthy Multimodal Ai For Medical Imaging: Enhancing Diagnosis, Reasoning, And Human-Agent Interaction In Extended Reality, Jai Prakash Veerla

Computer Science and Engineering Dissertations

The transition from traditional microscopy to digital pathology has digitized diagnostic data, yet clinical workflows remain constrained by two-dimensional screens and passive, opaque analysis tools that fail to capture the spatial complexity of biological systems. While Foundation Models now promise to reason across histology and genomics, a critical disconnect persists between the richness of this data and the limited cognitive bandwidth of clinicians, who currently lack the immersive interfaces and trustworthy agents necessary to utilize it effectively. This dissertation presents a unified framework for "Embodied Agentic AI," establishing a pipeline that augments physician capabilities through immersive visualization, robust security, and …

Beyond Permissions: A Privacy And Security Analysis Of Sensor And Network Activity On Xr Devices Using Xrmonitor, Gayatri Sravya Siripurapu

Computer Science and Engineering Theses - Archive

Standalone virtual reality headsets integrate multiple sensors and cameras whose runtime behavior is not transparently documented, creating potential privacy and security risks that are difficult to audit using existing tools. The relationship between software feature activation and the underlying hardware engaged at runtime remains opaque, limiting the ability of users and researchers to assess what data is being collected and when.

This thesis presents XRMonitor, a unified framework for monitoring and analyzing sensor activation behavior and network traffic on Meta Quest devices. The framework is motivated by the lack of transparency in how standalone XR devices engage their hardware sensors …

Artificial Intelligence Adoption In The Workplace. An Exploration Of Augmentation, Oyinkansola O. Sodiya

Management Dissertations

As collaborative work with artificial intelligence (AI augmentation) gains interest, it is crucial to investigate factors that affect how employees perceive and use AI tools at work. Drawing on task-technology fit and technology adoption theories, this dissertation examines the ways in which task dimensions, organizational contexts, and individual differences affect the perceived usefulness of working with AI tools. This dissertation demonstrates that task-technology fit is fundamental. Employees in jobs with high information processing demands are likely to positively perceive the usefulness of AI augmentation relative to employees in jobs with high interpersonal demands. Employees with more proactive personalities perceive greater …

Toward Interpretable Multi-Omics Multimodal Biomedical Artificial Intelligence, Yanjun Lyu

Computer Science and Engineering Dissertations

The complexity of human disease arises from biological processes that unfold across multiple scales, from molecular variation through cellular function, tissue organisation, brain phenotypes, each of which is associated with distinct measurement modalities, regularities, and characteristic. Contemporary biomedical artificial intelligence has brought the opportunity to reveal the complexity with in; however, its methodological default, in which models are trained on most readily available modality, does not adequately engage with the multi-scale connected structure by which biological meaning is constituted. The research area of multi-omics and multi-modal AI for biomedicine remains at an early exploratory stage, and the work presented in …

Pre-Training And Interpretability In Deep Learning Models, Weizhi An

Computer Science and Engineering Dissertations

In the evolving field of artificial intelligence, the efficacy of deep learning models is often gated by the quality of their training and the clarity of their decision-making processes. This dissertation addresses these crucial challenges by focusing on two key areas: enhancing pre-training strategies and improving model interpretability. Our approach is twofold, integrating novel pre-training methodologies that embed domain-specific knowledge early in the model training process, and developing advanced techniques for disentangling and clarifying the decision-making mechanisms within these models. The first direction of our research employs MoDNA, a motif-oriented pre-training framework specifically designed for DNA language models. By leveraging …

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …

Flow Injection Determination Of Fluoride For Total Organic Fluorine Analysis, Memona Zulafiqar

Chemistry & Biochemistry Theses

Poly- and perfluoroalkyl substances (PFAS) are anthropogenic chemicals that have gained increasing attention due to their association with a wide range of adverse health effects. These compounds are highly diverse, with approximately 15,000 PFAS reported. Their exceptional environmental stability and strong tendency to bioaccumulate result in environmentally relevant concentrations that typically occur at very low levels, often in the ng L⁻¹ range. This chemical diversity presents a major analytical challenge, as reference standards are available for only a limited number of PFAS. Consequently, conventional targeted analytical methods frequently quantify less than 1% of the total PFAS present, leading to a …

Reliability-Based Performance Evaluation Of Slurry Wall For Leakage Control At Chakaria Landfill, Bangladesh, Shishir Bhandari

Civil Engineering Theses

Landfill leachate threatens groundwater quality in low-lying coastal Bangladesh, where a shallow water table and a discontinuous natural clay barrier heighten contamination risk beneath municipal solid waste sites. At the Chakaria Landfill in Cox's Bazar District, a cement-bentonite slurry wall was proposed as a vertical cutoff to control seepage, but its reliability under the site's high-groundwater condition had not previously been evaluated in probabilistic terms.

This study evaluated the wall's performance using a two-dimensional SEEP/W finite-element seepage model, a validated response surface (RSM) equation relating seepage rate to wall permeability, thickness, depth, and a Hasofer-Lind first-order reliability (FORM) analysis built …

Integrative Approaches And Data Analysis For Single-Cell Rna Sequencing Data, Teng Long

Computer Science and Engineering Dissertations

The rapid growth of single-cell RNA sequencing and transcriptomic datasets has created major computational challenges in causal discovery, representation learning, and biologically faithful data generation. To address these challenges, this dissertation presents three complementary deep learning frameworks for the analysis and modeling of transcriptomic data. Together, these methods form an integrative computational toolkit for understanding complex biological systems from high-dimensional and heterogeneous gene expression data.

First, this dissertation introduces DAG-VAERL, a causal discovery framework that integrates variational autoencoders, graph neural networks, reinforcement learning, and attention mechanisms to infer directed acyclic graphs for gene regulatory network analysis. DAG-VAERL improves causal structure …

Synthetic Contact Zones: Meeting Nature And Animals In Video Games, Sarah C. Mcpherson

English Theses

Due to economic and environmental barriers, encounters with animals and nature have become increasingly inaccessible for many people, while video games have emerged as a common site for simulated environmental interaction. This thesis conceptualizes video games as synthetic contact zones, or digital spaces where representations of humans, animals, and environments interact, conflict, and shape one another. Drawing on the theories of Mary Louise Pratt, Donna Haraway, and Alenda Y. Chang, this project analyzes how video games model relationships between players, animals, and ecological systems through interactive mechanics and environmental storytelling. Explorations of Red Dead Redemption 2, Blair Witch, …

Enhancing The User Experience Of Colorado Bend State Park Through The Implementation Of Cultural Landscape Analysis, Hunter C. Lynch

Landscape Architecture Masters & Design Theses

This design thesis investigates how landscape character and sense of place can be more clearly expressed through a cultural landscape approach to design at Colorado Bend State Park, a state park in Texas, with a focus on guiding future trail and campsite development in the currently undeveloped eastern portion of the park.The park’s dramatic canyons, limestone bluffs, spring-fed systems, and upland prairies form a series of distinct, yet interconnected landscape conditions shaped by the interaction of physical structure, ecological systems, and cultural associations. While these characteristics define the site's identity and are reflected in portions of the existing park, they …

Voice And Student Engagement With Writing: Integrating Perspective, Personality, And Style, Sara S. Martin

English Dissertations

The rise of generative AI has many teachers worried about the future of writing classes and students’ desire to learn. However, instructors can motivate students to write by valuing what AI can never replace: their voices. Previous definitions of voice took the concept for granted and left it either vague or reductive, unhelpful for structured lessons. I propose a new, more teachable definition of voice as perspective, personality, and style. Perspective takes priority because it belongs solely to the individual and the unrepeatable moment of composition. Personality infuses passages with emotion, forming a bond between author and reader. Style, the …

Enhancing The Performance Of Disk-Based Key-Value Stores: From Learned Index Acceleration To I/O-Efficient Hybrid Caching, Sujit Maharjan

Computer Science and Engineering Dissertations - Archive

The exponential growth of data in modern computing environments has rendered the efficient extraction of information from massive datasets a critical systemic requirement. Key-value (KV) storage systems serve as the backbone for these operations; however, their performance is consistently bottlenecked by two primary functional requirements: identifying the data's location and managing the physical cost of accessing the storage device. Data locations are typically identified via an index, while disk I/O is minimized through caching. This dissertation presents LearnedStore, TurboIndex, and ReadBooster, which break these performance bottlenecks by introducing architectural modifications to the index and cache. LearnedStore accelerates operations by adapting …

Adaptive Synchronization In Digital Twin–Enabled Iot Systems: A Unified Framework For Energy, Fidelity, And Latency Trade-Offs, Uzma Zehra

Computer Science and Engineering Theses

Digital twin technology has emerged as a foundational paradigm for enabling real-time monitoring, analysis, and control in Internet of Things (IoT) systems by maintaining virtual representations of physical processes. Its effectiveness, however, critically depends on timely and accurate synchronization between distributed sensing devices and their corresponding digital counterparts. Frequent synchronization improves reconstruction fidelity and system responsiveness but incurs significant communication energy consumption and network latency. In contrast, infrequent synchronization conserves communication resources but can lead to stale or inaccurate digital twin states, particularly in environments with rapidly changing dynamics. These opposing effects give rise to a fundamental trade-off among energy …

Performance Evaluation Of Modified Moisture Barrier For Subgrade Stabilization Of Pavements On Expansive Soils, Md Tamim Shahriar

Civil Engineering Theses

Expansive soils inflict an estimated nine to fifteen billion dollars in annual damage to infrastructure across the United States, surpassing the combined losses from earthquakes, floods, hurricanes, and tornadoes (Nelson and Miller, 1997; Jones and Jefferson, 2012). In Texas, eighteen of twenty-five TxDOT districts contend with pavement failure rooted in moisture-driven volume change of subgrade clay, consuming roughly twenty-five percent of the agency's annual budget for maintenance and repair (Sebesta, 2002; Wanyan et al., 2010). Conventional remediation approaches, including soil replacement, chemical stabilization with lime or cement, and prewetting, are either prohibitively expensive, unsuitable for high-sulfate soils, or confined to …

Generative Imaging For Computational Pathology, Md Jillur Rahman Saurav

Computer Science and Engineering Dissertations

Hematoxylin and eosin (H&E) staining remains central to cancer diagnosis, providing morphological information essential for pathological assessment. Immunohistochemistry (IHC) and newer multiplexed imaging technologies complement H&E by revealing molecular information critical for accurate tumor subtyping and treatment decisions. In practice, however, H&E and IHC are obtained from different consecutive sections that are not spatially aligned, comprehensive multiplexed panels are expensive and tissue-consumptive, and not all stains are available at every clinical site, limiting comprehensive molecular profiling and the full diagnostic potential of these technologies in clinical practice. This dissertation addresses these gaps through three complementary generative deep learning studies in …

Forecasting Precipitation In Cuba Using Graph Based Deep Learning, Taufiqul Islam

Earth & Environmental Sciences Theses

Daily precipitation forecasting remained a challenging problem in regions characterized by strong spatial heterogeneity, nonlinear atmospheric dynamics, and intermittent rainfall behavior. Cuba represented a particularly complex case due to the combined influence of tropical cyclones, easterly waves, mesoscale convective systems, and orographic effects, which produced highly variable rainfall patterns in both space and time. Conventional statistical and machine-learning models typically treated stations independently and therefore overlooked spatial dependencies that strongly influenced rainfall variability across the island.

This study developed a spatiotemporal deep-learning framework for daily precipitation forecasting across 40 spatial nodes in Cuba using data from 1979 to 2023. The …

Computational Study Of Rotating Detonation Combustors, Aditya Balasubramaniam

Mechanical and Aerospace Engineering Theses

Rotating detonation combustors (RDCs) are pressure-gain combustion devices that sustain one or more continuously rotating detonation waves, offering potential thermodynamic and performance advantages over conventional deflagration-based systems. Their behavior depends strongly on combustor geometry and operating conditions. Understanding these effects is therefore essential for the design and optimization of practical RDCs. Accordingly, this thesis numerically investigates annular RDCs with two primary objectives: (1) to evaluate the effects of propellant mass flux and (2) to assess the influence of annular width on detonation-wave dynamics and combustor performance.

A finite-volume framework is used to solve the compressible reactive Euler equations with hydrogen–air …

Scalable Quantum Network Routing Through Reinforcement Learning And Resource Optimization, Tasdiqul Islam

Computer Science and Engineering Dissertations

Long-distance quantum communication depends on distributing high-delity entanglement across quantum repeaters. Entangled states are fragile: they decohere in memory, are consumed when used, and lose delity after each swap. Quantum routing therefore diers from classical routing: an algorithm must decide not only the path, but when to generate, store, swap, and consume entanglement before they lose their usefulness. This dissertation studies scalable resource allocation and routing for quantum networks under delity, memory, and concurrency constraints. It rst addresses re- peater deployment with heuristics that nd near-optimal locations while cutting com- putation from days to seconds versus integer linear programming (ILP). …

Early Detection And Objective Assessment Of Neonatal Hypoxic-Ischemic Encephalopathy Severity, Soheila Norasteh

Bioengineering Dissertations

Hypoxic–ischemic encephalopathy (HIE) is a neonatal brain injury caused by reduced oxygen and blood flow to the brain around the time of birth. It remains a major cause of neonatal mortality and long-term neurodevelopmental impairment worldwide. Therapeutic hypothermia is the standard treatment for moderate-to severe HIE and improves outcomes when initiated within the first six hours of life. Therefore, accurate assessment of injury severity during this period is essential. Cur rently, HIE severity is determined primarily through neurological examination and classified as mild, moderate, or severe. However, these examinations are subjective, cannot provide continuous monitoring of brain function, and may …

Reliable And Label-Efficient Learning For Open-World Visual Perception And Robot Learning Under Uncertainty, Zongyao Lyu

Computer Science and Engineering Dissertations

Modern learning systems deployed in open-world environments must make reliable decisions despite predictive uncertainty, previously unseen classes, limited annotations, and distribution shifts. This dissertation develops methods for reliable and label-efficient learning in visual perception and robot control.

First, this work studies uncertainty in object detection by representing semantic and spatial predictions probabilistically. A deep-ensemble framework aggregates detections into class-probability distributions and probabilistic bounding boxes, while a subsequent extension combines deep ensembles with Monte Carlo dropout to further investigate predictive uncertainty. Second, this dissertation addresses open-set recognition, where classes absent during training may appear at inference time. An empirical study shows …

Array Signal Processing And Machine Learning In 5g/6g Networks, Roopesh Kumar Polaganga

Electrical Engineering Dissertations - Archive

This dissertation investigates advanced methodologies in Array Signal Processing (ASP) and Machine Learning (ML) to enhance the performance, efficiency, and intelligence of next-generation wireless networks, with a primary focus on 5G and emerging 6G systems. As wireless networks face rapid traffic growth, increasingly heterogeneous service requirements, and more complex propagation environments, conventional design and optimization approaches become insufficient to meet evolving demands in reliability, capacity, spectral efficiency, and energy efficiency. On the network intelligence side, this work develops data-driven frameworks for causal discovery, scheduler enhancement, session-duration prediction, and Radio Resource Control (RRC) state optimization using real-world telecommunication network data. On …

A Qualitative Interpretive Meta-Synthesis On The Lived Experiences Of Labor Trafficking Survivors, Paola T. Chavez Reyes

Social Work Theses

Across the globe, an estimated 27.6 million people are in forced labor. Despite this widespread prevalence, labor trafficking (LT) in the United States remains difficult to identify and is often overlooked. This study aims to synthesize LT experiences of survivors and providers accounts at the point of recruitment and during victimization to inform identification efforts. A qualitative interpretive meta-synthesis was conducted using directed content analysis guided by Social Cognition Theory. Peer-reviewed studies documenting LT survivor experiences from 2013-2025 were systematically analyzed to identify the interaction between cognitive processes, environmental influences, and behavioral responses. Across seven studies, the findings reveal that …

Improving Asthma Management In Schools: A Comprehensive Approach To Education, Environmental Enhancements And Asthma Action Plans, Yesenia Trevino

Doctor of Nursing Practice (DNP) Scholarly Projects - Archive

Abstract

Introduction

Asthma contributes to school absenteeism, especially in underserved communities. This project aimed to enhance asthma management at a South Texas elementary school through staff and student education, monthly air filter changes, and personalized Asthma Action Plans (AAPs).

Methods

A quality improvement project at a South Texas elementary school involved 40 asthmatic students over eight weeks, using the Plan-Do-Study-Act (PDSA) framework. It included staff and student education, air filter maintenance, and facilitation of Asthma Action Plans (AAPs). Pre/post surveys, custodial logs, AAP audits, and attendance were analyzed, with paired t-tests used to assess changes (N=32).

Results

There was 71.9% …

Empowering Patient Engagement In Effort To Reduce Readmissions, Derinda Hugaboon, Msn, Rn

Doctor of Nursing Practice (DNP) Scholarly Projects - Archive

Background: Hospital readmissions among Medicare beneficiaries remain a major quality concern and are often associated with social determinants of health (SDOH) and care transition gaps. Targeted discharge interventions that address self‑management needs and follow‑up barriers may reduce avoidable readmissions. Objective: To reduce readmissions by implementing a bundled intervention addressing social determinants of health (SDOH), care‑transition gaps, and inadequate post‑discharge primary care follow‑up. Methods: Medicare Advantage (MA) members aged ≥45 were included. A total of 249 members accounted for 275 overall admissions during the project period; 191 admissions (69.5%) received at least one intervention component (informational discharge packet, face‑to‑face encounter, and/or …

Neighborhood Embeddings And Scalable Learning For Optimal Transport And Unbalanced Optimal Transport, Muhammad S. Rana

Mathematics Dissertations - Archive

Dimensionality reduction techniques are developed from the assumption that high-dimensional data often arises from low-dimensional structures embedded into the high-dimensional ambient space. Classical dimensionality reduction methods rely on the Euclidean distance, which may fail to capture the geometric structures of the datasets. This dissertation includes alternative metrics for dimensionality reduction techniques and challenges in applying these techniques.

First, we investigate the Wasserstein distance based neighbor embeddings for dimensionality reduction methods and compare the classification and clustering performance with the classical Euclidean based methods. The Wasserstein distance models the data as probability distributions and compares two distributions applying optimal transport (OT) …