Digital Commons Network^™

Zero-Knowledge Proofs For Verifiable Machine Unlearning, Bidur Niroula

Open Access Theses & Dissertations

Machine unlearning aims to remove the influence of deleted data from a trained machine learning model. This is important because privacy regulations such as GDPR, CCPA, and PIPEDA give individuals the right to request the deletion of their personal data. Since unlearning can be expensive, a dishonest server may skip it or return an incorrect model and this motivates verifiable machine unlearning. Existing cryptographic approaches prove that the server executed a specific training or unlearning algorithm on the committed dataset, so the proof is tied to the algorithm's trajectory. Empirical verifiers avoid this cost but are known to be circumventable …

Log Anomaly Detection With Parameter-Efficient Tiny Language Models: From Centralized Fine-Tuning To Privacy-Preserving Federated Learning, Isaiah Thompson Thompson Ocansey

Open Access Theses & Dissertations

System logs are a primary source of information for identifying faults, misconfigurations, and security incidents in computing infrastructure. As these logs grow in volume and complexity, manual inspection becomes impractical, and automated detection methods are needed. This thesis investigates how compact language models can be applied to the task of log anomaly detection under two operational conditions: when log data is centralized and when it is located at different data sites.

In the first part, we propose LogTinyLLM, which applies parameter-efficient fine-tuning through Low-Rank Adaptation and adapter-based techniques to adapt tiny language models for identifying contextual anomalies in log sequences. …

Penalty Approach To Constrained Optimization Problems In Water Reservoir And Energy Generation Management, Edwin Horacio Trejo

Open Access Theses & Dissertations

The growing demand for resilient and sustainable energy generation has driven interest in Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. Operating these systems effectively requires making water release decisions that satisfy physical and regulatory constraints while maximizing energy production. Prior work by Vega (2024) developed a Dynamic Outlier Filter Long Short-Term Memory (DOF-LSTM) architecture for forecasting reservoir release patterns. That predictive work is valid and addresses an important component of the HFPVH decision pipeline. However, the constraint system in that work operates externally to the learning process, and the behavior of penalty-based constraint enforcement had not been studied independently in this context. …

Molecular Mechanisms Of Pfas Induced Protein Dysfunction: Implications For Neurodegenerative Risk, Daisy Lee Wilson

Open Access Theses & Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread human exposure and well-established links to adverse health outcomes. However, despite increasing epidemiological and experimental evidence, the molecular mechanisms underlying PFAS-induced neurotoxicity remain incompletely defined. This dissertation investigates PFAS toxicity across biological scales, integrating protein biophysics with neuronal cell models to elucidate pathways relevant to neurodegeneration. At the molecular level, we demonstrate that PFAS disrupts the structure and function of globular proteins, including the impairment of α-lactalbumin's calcium-binding capacity and of β-lactoglobulin's retinol binding, through concentration-dependent conformational destabilization. By applying these findings to a neuronal model, we show that …

Hvsr-Based Resonance Analysis And Fault Zone Characterization In The El Paso Region, Alexandro Dominguez

Open Access Theses & Dissertations

Horizontal to Vertical Spectral Ratio (HVSR) analysis offers an efficient method for characterizing near-surface structure, resolving sediment thickness, and identifying fault-controlled basin geometry in regions with strong impedance contrasts. This study applies HVSR techniques to continuous ambient-noise recordings collected across the El Paso region, a tectonically active segment of the southern Rio Grande Rift where accurate subsurface mapping is essential for seismic hazard assessment. Ambient-noise windows were processed, spectrally smoothed, and stacked to obtain stable HVSR curves, from which fundamental resonance frequencies and corresponding sediment-thickness estimates were derived. Spatial patterns in these resonance frequencies reveal pronounced basin asymmetry, abrupt lateral …

From Covid-19 To Influenza: Transforming Public Health Surveillance Through Freeze-Drying Innovations, Rui Dong

Open Access Theses & Dissertations

Public health surveillance increasingly relies on wastewater-based epidemiology (WBE) as a non-invasive, community-level early warning system for infectious diseases like COVID-19 and influenza. However, detecting minute viral concentrations in complex wastewater matrices remains a significant analytical challenge. Traditional concentrating methods, such as ultrafiltration, are limited by high performance variability and significant viral signal loss due to necessary pretreatment steps, such as pre-clarification, which inadvertently discard solid-associated viral fractions. To address these limitations, this dissertation introduces and evaluates freeze-drying as a novel, pretreatment-free concentrating method for wastewater viral surveillance. By sublimating water, freeze-drying uniquely retains both liquid and solid-phase viruses within …

Age And Characteristics Of Quaternary Fluvial And Dune Deposits Along The Arkansas And Cimarron Rivers In Osage And Pawnee Counties, Oklahoma, Barry A. Duncan

Graduate Theses and Dissertations

Quaternary aeolian and fluvial deposits along the Arkansas and Cimarron Rivers in Osage and Pawnee counties of central Oklahoma, record Late Pleistocene and Holocene fluvial landscape history in the eastern Osage Plains. Sediment supply, climate variability, channel incision, and channel migration control this landscape evolution. Sediment supply of both streams is dominantly sand. Late Pleistocene and Holocene precipitation have varied with periods of drought alternating with increased precipitation and warming. The study area is near the eastern boundary of substantial aeolian activity with drought and aeolian reworking of fluvial sand more severe to the west. This study examines the sediment, …

Impaired Tunability Of Brain State And Motor Dysfunction In A Mouse Model Of Rett Syndrome, Victoria Kindler Norman

Graduate Theses and Dissertations

Rett syndrome (RTT) is a rare neurological disorder, caused by disrupted function of the MECP2 gene, resulting in impaired cognitive and motor functions. Previous studies suggest that although MECP2 has important functions throughout the body, the etiological origins of RTT-related dysfunction should be sought within the brain. However, it remains a mystery how neural population dynamics are altered in the RTT brain and how these alterations relate to motor dysfunction. Previous studies using an RTT mouse model point to abnormal correlations among firing rates of neurons. Here we hypothesize that such disrupted neural activity correlation could be caused by abnormal …

Three Essays On Workplace Ethics And Ai: Conceptualizing, Scale Development, And Validation Of Self-Value Actualization, And Ai-Enabled High-Performance Work Systems, Mansura Nusrat

Open Access Theses & Dissertations

This dissertation advances understanding of workplace ethics and responsible AI integration across three interconnected essays unified by social cognitive theory. My first essay introduces self-value actualization (SVA), a novel construct defined as the dynamic, integrated psychological process through which individuals continually strive to align and fulfill their core moral values through observable professional behavior. Drawing on social cognitive theory and emotional intelligence theory, I conceptualize SVA as a higher-order self-regulatory orientation comprising three mutually reinforcing processes: value congruence, moral reflection, and adaptive morality. I developed and validated a 9-item SVA scale across four studies using expert panels, exploratory factor analysis, …

Understanding Machine Learning Model Behavior Under Fairness And Privacy Constraints, David Anthony Sanchez

Open Access Theses & Dissertations

Machine learning systems deployed in high-stakes domains are increasingly expected to satisfy demands beyond predictive accuracy-including fairness across demographic groups, protection of sensitive information, and explanations that human stakeholders can inspect and trust. This thesis investigates how those demands can be met through learning frameworks that explicitly govern the relationship between data and models, arguing that trustworthiness is a design problem rather than a post hoc correction. The thesis is organized around three studies, each targeting a distinct point of data-facing control. The first develops CondFairGen, a fairness-aware conditional generator for tabular data that improves subgroup equity by dynamically reweighting …

Environmental Movements In El Paso And Ciudad Juarez: Possibilities And Limitations Of Cross-Border Organization At The U.S.-Mexico Border, Vanessa Maria Almada

Open Access Theses & Dissertations

At the U.S.-Mexico border, the anthropogenic and nationalistic construction of the landscape has accelerated urbanization and unequal development without establishing effective binational institutions for governance over a shared commons. Borderlanders, then, contest unsustainable development with the added complexity of navigating a barrier which creates diverse socioeconomic and political realities that challenge cross-border cooperation. This qualitative research analyzes environmental organizations in El Paso and Ciudad Juarez. Through ethnographic study of mobilization in response to environmental legislation and administrative action in each city, the immediate barriers to cross-border cooperation and limitations of nationalistic institutions in the borderlands can be better understood.

Bayesian Deep Learning For Photovoltaic Power Forecasting: A Probabilistic Framework For Uncertainty Quantification And Grid Reliability Optimization, Pablo Abraham Bustamante

Open Access Theses & Dissertations

The global energy landscape is undergoing a profound transformation, driven by the urgent need to decarbonize power systems, enhance energy security, and meet growing electricity demands. Solar photovoltaic (PV) power has emerged as a critical component of future energy infrastructure due to its abundance, scalability, and cost-effectiveness. However, PV generation is inherently variable and weather-dependent, introducing significant uncertainty into grid operations and complicating the task of balancing supply and demand. Accurate forecasting of PV power generation-particularly on day-ahead and hour-ahead horizons-has become a strategic necessity for grid stability, economic efficiency, and environmental sustainability. PV output is influenced by numerous factors, …

Geospatial Analysis Of The Influence Of Wastewater Disposal On The Induced Seismicity In Oklahoma, Usa, Kristine Nagy

Graduate Theses and Dissertations

Seismic records over the past decade and a half have indicated a significant increase of earthquakes in the state of Oklahoma and the evidence has strongly suggested the rise is likely caused by substantial wastewater disposal operations, a subsequent process of oil and natural gas extraction. This study investigated the relationship between wastewater disposal via injection wells, and its influence on inducing earthquakes in Oklahoma by employing Geographic Information System (GIS) tools for geospatial data integration and spatial analysis using United States Geological Survey (USGS) earthquake data and Underground Injection Control (UIC) Class II well data sourced from the Oklahoma …

Quantifying The Short-Term Hydrogeologic Responses Of Highway Construction In Hot Springs, Arkansas, Billy Jack Summerford

Graduate Theses and Dissertations

In late 2020, excavation for the Highway 5 Bypass in Hot Springs, Arkansas exposed seepage faces along roadcut walls. Groundwater levels at nearby wells decreased by up to 20.9 ft and have remained ~20 ft below pre-construction values on average. This study quantifies changes in storage in the unconfined aquifer within the Hot Springs thermal recharge zone associated with the seepage faces using a numerical groundwater model calibrated to pre-construction head at well ARb4. Model scenarios are used to evaluate whether construction-induced seepage or drought best explains the decrease from late 2020 to early 2021. The numerical groundwater simulation represented …

The Impact Of Quantum Computing On Ransomware, Byron Denham

Graduate Theses and Dissertations

Ransomware is one of the most pervasive and dangerous threats to cybersecurity today. Attacks involving ransomware have been responsible for the disruption of critical services in many fields including healthcare, education, and government. In the current paradigm, crypto ransomware relies on asymmetric cryptography to perform its operations in a way that ensure the victim’s only chance for file recovery is by paying the attacker’s requested ransom payment. However, advancements in quantum computing threaten the asymmetric cryptography that ransomware relies on. It has been shown that, once developed, a sufficiently powerful quantum computer will have the ability to break asymmetric cryptography …

Modeling Causal Interactions Across Brain Functional Systems For Population-Specific Disease Analysis, Alissen Moreno

Theses and Dissertations

Functional brain connectivity provides critical insight into neural mechanisms underlying neurodegenerative and affective disorders. Traditional neuroimaging studies rely on undirected or region-specific connectivity measures developed predominantly using racially homogeneous cohorts, limiting fairness and generalizability across diverse populations.

We propose a population-aware framework modeling directed causal interactions across large-scale brain functional subnetworks. Resting-state fMRI data from the HABS-HD cohort were used to construct subject-level causal connectivity matrices via ICA-LiNGAM, aggregated into interpretable system-level hyper-connectomes representing interactions among eleven canonical brain subsystems. These features trained nonlinear models for Alzheimer’s disease stage classification and trait worry prediction.

Results demonstrate that MLP models outperform …

Enhancing Walleye (Ogaa) Recruitment: Evaluating Assisted Reproduction As A Mitigation Strategy, Douglass Keiser

Biology Graduate Theses

Over the past several decades, cold- and cool-water fish populations across the Upper Midwest, including Minnesota and Wisconsin, have shown troubling signs of decline. Among the most affected is the walleye/Ogaa (Sander vitreus), a species deeply embedded in the ecological balance, cultural identity, and economic vitality of the region, and central to the lifeways, treaty-reserved rights, and resource stewardship of Tribal Nations, including Ojibwe communities across the Upper Midwest. Despite ongoing stocking efforts and harvest regulations, many lake systems have experienced persistent declines in natural recruitment, prompting resource agencies to investigate the underlying causes. One such system is …

Agentcite: Trustworthy Ai Agents For Information Verification Across Referenced Documents, Eymen Yigit

Master's Theses

This thesis presents AgentCite, a multi-agent framework for automated verification of referenced numerical data in research documents. The framework employs a hybrid design in which LLM-based agents handle document understanding and evidence retrieval, while deterministic components manage structured parsing and value comparison, improving consistency and reproducibility while reducing token consumption and execution time compared to fully agentic approaches.

AgentCite consists of three autonomous agents: a Negotiator, a Main Document Agent, and a Source Documents Agent, coordinated through a fixed tool-call pipeline. The Negotiator orchestrates verification by extracting tabular data from the main document, retrieving evidence from per-source vector stores, and …

Fret Studies On The Binding Of Fibroblast Growth Factor (Fgf) Proteins To The Hard Corona Of Cuins2-Based Quantum Dots, Colette Robinson

Graduate Theses and Dissertations

Fibroblast Growth Factor (FGF) has been shown to be an important protein in angiogenesis, which is critical in developmental biology and wound healing but has also been implicated in cancer metastasis. FGF interacts with an FGF Receptor (FGFR) to signal the onset of angiogenesis but the detailed mechanism of angiogenesis, and particularly its regulation, is still largely unknown. Imaging the interaction of FGF with FGFR in biologically-relevant environments, such as directly in cells, is key towards furthering this understanding and may eventually aid in the development of better cancer treatments. CuInS2/ZnS quantum dots (QDs) are an attractive fluorescent probe to …

High Throughput Phenomics Pipeline For Pulse Crop Nutritional Breeding, Amod Udayanga Madurapperumage

All Dissertations

Dry pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and chickpea (Cicer arietinum L.) are major pulse crops valued for their high nutritional composition and importance to global food systems. Pulses are rich in carbohydrates, protein, and essential minerals, making them ideal whole foods and critical contributors to food and nutrition security. Due to these advantages, pulse breeding programs are increasingly focusing on enhancing nutritional traits, such as protein quality, amino acid balance, and micronutrient density, through the process of biofortification. However, improvement of agronomic traits remains equally essential. Characteristics such as plant height, standability, stress tolerance, …

Learning Global Context For Sparse Activity Recognition In Lengthy Recordings With Limited Dataset Size, Zeyu Tang

All Dissertations

This dissertation describes methods to analyze lengthy recordings of data in order to detect sparsely occurring activities. The narrative below describes the progression of research that led to the development of these methods and their generalization into a unified framework. My research started with designing models for dietary monitoring, including detecting meals from day-long recordings and detecting intake gestures from meal-length recordings. Both tasks share some common characteristics: (a) the target event takes only a small portion of data recordings, and (b) there is global context within full-length data recordings that can help a model make better decisions. After finishing …

Galois Action And Arithmetic In Algebraic Number Fields, Jared Kettinger

All Dissertations

This dissertation explores the arithmetic of numerous algebraic objects living within an algebraic number field from submonoids of the integers up to localizations of the ring of integers. We begin with a study of factorization in proper orders using an element-theoretic approach. In Chapter 2, by defining a natural generalization of the Davenport constant, we are able to determine the elasticity of certain orders whose integral closure is a unique factorization domain. In Chapter 3, using ideal-theoretic analogues, we are able to significantly broaden the scope of our results and the literature on factorization in orders. In particular, we give …

Integrating Incentive Design And Spatial Prioritization For Climate-Smart Forestry Adoption In South Carolina, United States, Miah Maye Pormon

All Dissertations

Forests provide essential ecosystem services, including carbon sequestration, water regulation, timber production, and habitat provision. However, increasing development pressures and land-use changes threaten forest persistence and long-run ecosystem service provision. Climate-smart forestry (CSF) practices, such as improved forest management and extended rotation, offer opportunities to enhance carbon storage, forest resilience, and economic livelihoods. The effectiveness of these practices depends on forest owners’ participation and the strategic allocation of financial resources or incentives. This dissertation develops an integrated framework that combines behavioral economic analysis and mapping to improve the design of current incentive programs. The first chapter employs the Contingent Valuation …

Geography, Income, And The Incidence Of A Massachusetts Carbon Tax, Susan Stratton Sayre

Economics: Faculty Publications

I use spatial microsimulation to estimate the geographic incidence of the consumer-facing portion of various revenue-neutral carbon tax designs in Massachusetts. I identify a substantial urban-rural disparity in impacts that is reduced, but not eliminated, by proposed revenue-targeting mechanisms. I further demonstrate that seemingly small variations in the definition of rural households can have substantial impacts on urban-rural equity and the distribution of gains and losses. Proposed policies are highly progressive in the aggregate with most low-income households receiving more in rebates than they pay in taxes, but notable numbers of low-income households are likely to experience significant losses. Methodologically, …

Low Methane Emissions From Nutrient-Rich Former River Channels In A Hydrologically Managed Dryland Delta, Siena Stassi, Jude A. Benavides, Chu-Lin Cheng, Marcelo Gomes Da Silva, Nicholas E. Ray, Nathan O. Barros, Rafael M. Almeida

School of Earth, Environmental, & Marine Sciences Faculty Publications

Anthropogenic stresses in dryland regions can alter hydrology, salinization, and biogeochemical processes. How these pressures influence methane (CH4) cycling and emissions from aquatic ecosystems remains poorly understood. Over the course of 1 year, we measured diffusive and ebullitive CH4 emissions from resacas—shallow, hydrologically managed distributary channels prevalent throughout the Lower Rio Grande Valley along the US–Mexico border. Despite warm, nutrient-rich waters, CH4 emissions from resacas were low, and more comparable to rates observed in estuarine systems. CH4 fluxes were positively related to temperature but negatively related to salinity and rainfall. Elevated salinity, driven by evaporation and source water from the …

Ecological Insights And Management Implications Of The Global Migratory Connectivity Of Green Turtles, Jaime Restrepo, Dina Nisthar, Harris Wei-Khang Heng, Lily K. Bentley, Corrie Curtice, Sarah Deland, Ei Fujioka, Patrick N. Halpin, Sarah K. Poulin, Roldán A. Valverde

School of Earth, Environmental, & Marine Sciences Faculty Publications

Aim

Green turtles are a widely distributed and highly migratory species; despite extensive data on their movement, there is no species-specific global synthesis on the subject. Based on three decades of published literature and building on previous global analyses, we developed a global network model of migratory connectivity for green turtles to better understand their spatial biology.

Location

Global.

Time Period

1990–2022.

Major Taxa Studied

Green Sea Turtle (Chelonia mydas).

Methods

We conducted a structured literature review extracting georeferenced information on the movement of green turtles from 1990 to 2022, aggregating this information into a single connectivity model, …

The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing

All-Inclusive List of Electronic Theses and Dissertations

This dissertation evaluates whether a reusable assurance architecture, the Quality Assurance Machine (QAM), can provide effective product and process quality assurance for ML-enabled software platforms. The QAM is a system-level SQA architecture that turns plans and policies into versioned configurations, executes them in controlled environments, and produces preserved run evidence that supports traceability, auditability, and controlled change. The study follows Design Science Research and evaluates the instantiated artifact using eight assurance requirements (AR1–AR8) synthesized from standards-based guidance, including IEEE 730 and ISO/IEC/IEEE 15026. A four-year longitudinal evaluation combines two methods. First, operational evidence from routine regression and release-validation runs, defect …

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

Enhancing Traffic Safety Through Ai-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems, Razan Alsulieman

Dissertations

Drunk driving remains a major threat to road safety worldwide, contributing significantly to traffic injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and effectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone- based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based …

Discrete-Event Simulation: A Leslie System Model For Transportation Demography, Md Zobaer Ahammad

Electronic Theses and Dissertations

Transportation systems are influenced by demographic change, household formation,and patterns of vehicle ownership. These factors affect long-run transportation demand and congestion levels within urban infrastructure. Understanding how demographic dynamics interact with transportation behavior is therefore important for analyzing the long-term evolution of transportation systems. This thesis develops a modeling framework that integrates discrete-event simulation with a Leslie-type matrix model to study transportation–demography interactions.Simulation outputs are aggregated to construct a transition matrix describing changes in transportation states. This matrix acts as a linear (or affine) transformation on the transportation state vector, allowing the system to be analyzed as a discrete linear …