Digital Commons Network^™

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 …

Towards Generalizable Representation Learning Across Domains, Hossein Kashiani

All Dissertations

Despite remarkable progress in deep learning, a major challenge remains: machine learning models often struggle to generalize to unseen domains under distribution shift. In real-world settings, data often differ from training conditions due to changes in lighting, sensor type, image resolution, and style. These differences can significantly degrade performance, highlighting the need for representations that are both robust and generalizable. This thesis addresses this challenge by developing a set of frameworks for generalization across domains in anomaly detection, deepfake detection, and vision-language image recognition. For anomaly detection, this thesis introduces ROADS, a robust prompt-driven framework for multi-class unified anomaly detection. …

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 …

Smart Medical Support System And Swin Transformer Framework For Breast Cancer Detection And Segmentation In Mammograms, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Accurate and reliable breast cancer detection from mammographic images remains a critical challenge due to subtle lesion appearance, high intra-class variability, and class imbalance inherent in clinical datasets. To address these issues, this study proposes Swin-BreastNet, an explainable and optimization-driven deep learning framework for binary classification of benign and malignant breast lesions from full-field digital mammograms. The proposed approach leverages the hierarchical Swin Transformer model to effectively capture fine-grained local texture patterns and long-range contextual dependencies through Shifted Window Multi-head Self-Attention (SW-MSA). A key novelty of this work lies in the integration of Harris Hawks Optimization (HHO) for automated hyperparameter …

Public Health Responsible Ai Capability (Ph-Raic) Framework: A Conceptual Model For Integrating Ai Into Public Health Agencies, Arnob Zahid, Ravishankar Sharma, Rezwan Ahmed

All Works

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We …

Assessing Geomorphic Change From Large Wood Additions In An Intensively Monitored Watershed, Alexander Walt

All Graduate Theses and Dissertations, Fall 2023 to Present

Rivers across the American West are under stress. Over time, human activities have decreased riparian vegetation and removed naturally occurring large wood from fallen trees and beaver dams. Overgrazing, wetland drainage, and artificial barriers like levees and berms have further changed these ecosystems, leaving behind simple channels that lack varied habitat for aquatic species.

Efforts to restore rivers have made some progress, but many projects are small in scale and focus more on reshaping the river rather than restoring the natural processes that keep it healthy. This study evaluates a different approach—Low-Tech Process-Based Restoration (LTPBR)—which works with nature to rebuild …

A New Approach To Generate Combinatorial Patterns In Logical Analysis Of Data And Its Application To Predict College Retention, Salihah Ahmed E. Jaafari

Theses and Dissertations

Student retention and degree completion remain central challenges for higher-education institutions, with significant implications for student success, institutional effectiveness, and public accountability. While advances in predictive analytics have enabled earlier identification of students at risk of withdrawal, many commonly used machine learning approaches suffer from limited interpretability, constraining their practical usefulness for advising, intervention, and policy decision making. This dissertation addresses the problem of predicting student persistence by developing and evaluating optimization based, interpretable classification models within the Logical Analysis of Data (LAD) framework. Building on existing LAD formulations, this research introduces two novel pattern generation models, the Best Term …

Design And Simulation Of Anti-Resonant Hollow-Core Fiber For Sensing Applications, Pravallika Kante

Theses and Dissertations

This thesis presents the design and simulation of an 8-tube single-ring anti-resonant hollow-core fiber for sensing applications, with particular emphasis on methane gas detection at the fundamental absorption wavelength of 3.3 µm. Conventional solid-core silica optical fibers exhibit strong multi-phonon material absorption beyond 2.5 µm, rendering them fundamentally unsuitable for efficient light guidance and direct gas sensing at mid-infrared wavelengths. Anti-resonant hollow-core fibers overcome this limitation by guiding light predominantly through an air-filled hollow core via the anti-resonant reflecting optical waveguide mechanism, in which the thin silica glass walls of the cladding tubes act as Fabry-Pérot etalons that confine the …

Final Silver Bow Creek Conservation Area Materials Management Plan, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Revised Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

2024 Annual Operations And Maintenance Report 2024 Bres Annual Summary Report For Work Completed In Quadrant 4, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Privacy Protection In Machine Learning: Methods For Structured And Unstructured Data, Karuna Bhaila

Graduate Theses and Dissertations

As machine learning models become increasingly integrated into data-driven decision-making, the protection of sensitive information throughout the model lifecycle is a paramount concern. As these models process and memorize sensitive, proprietary, or personal data, they risk leaking information through their outputs or internal states, especially in domains such as healthcare and finance. The protection of data in machine learning has thus been a crucial field of study. Within this paradigm, researchers have studied theoretical and application-oriented mechanisms for realizing privacy protections for various data formats. Nonetheless, privacy in machine learning still has many open problems, especially with the emergence of …

Developmental Mathematics As A Potential Barrier To Degree Completion In Community Colleges, Cynthia Bernice Fletcher

Graduate Theses and Dissertations

Developmental mathematics is often seen as a barrier to student progression in community colleges, especially for students pursuing Associate of Science degrees requiring math coursework. This quantitative, ex post facto, non-experimental study examined how developmental mathematics factors predicted Associate of Science degree completion at a two-year community college in the West South-Central United States. Specifically, it assessed how academic performance in developmental mathematics, placement method, math pathway, and number of developmental math courses related to Associate of Science degree completion, as well as differences across student subgroups. Archival institutional data were used for first-time-in-college students across four cohorts: 2018, 2019, …

Genre Prediction Using Rnns And Llm-Enhanced Video Game Review Data, Gabriel Young

Graduate Theses and Dissertations

LLMs (Large Language Models) are powerful tools for engaging with textual data, carrying many advantages over classical NLP (Natural Language Processing) and ML (Machine Learning) approaches. However, a classical ML model can still be faster, more efficient to run, and accessible than an LLM. We seek to gain the benefits of LLM text comprehension and preserve them in a classical ML model, a hybrid approach. The LLM operates on text to surface relevant information and associations in our problem space, then the ML model trains on the LLM output. The model may learn from the LLM and provide a more …

Barriers To Climate Change And Sustainability Action: Nursing Education And Practice, Dawn Marie Smith

Dissertations

Climate change is one of the most pressing public health emergencies of our time and nurses can have a great impact in their current practice and in the education of future nurses (The Alliance of Nurses for Healthy Environments, n.d.; American Nurses Association, 2023; Health Care without Harm, 2025). Deaths due to rising temperatures, vector-borne illness, and food insecurity related to drought and extreme weather are on the rise (WHO, 2024). It has been estimated that globally over 250,000 additional deaths will be attributed to climate related effects between 2030 and 2050 (Watts et al., 2020; WHO, 2023).

A primary …

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 …

Development Of A High-Throughput Framework For Studying Choice-Movement Dynamics In Freely Moving Rats Across Motivational And Pharmacological States, Atanu Giri

Open Access Theses & Dissertations

This thesis develops and applies high-throughput behavioral assays and analysis pipelines to study how external contingencies and internal state shape rodent decision-making. First, it introduces RECORD (Reward-Cost in Rodent Decision-making), a modular platform that combines 3D-printed arenas, microcontroller-based control, and closed-loop trial structure to deliver graded rewards and costs in a foraging-like environment. RECORD supports scalable data collection across multiple arenas and a software stack for parsing, databasing, and extracting spatiotemporal behavioral features and psychometric choice functions. Using this framework, the thesis quantifies how animals integrate sucrose reward magnitude with aversive light cost, revealing structured individual differences and sex-dependent movement …

Prediabetes Prediction Before Disease Onset Using Multimodal Health Data: A Machine Learning Approach, Luisa Veronica Gracia Mazuca

Open Access Theses & Dissertations

Prediabetes is a critical health condition that increases the risk of developing type 2 diabetes. The hemoglobin A1c (HbA1c) test diagnoses patients with prediabetes, but the disease has already caused metabolic alterations. Early detection is essential for timely interventions, and machine learning models offer a promising approach to identify prediabetic individuals through the analysis of biomarkers such as cytokines. We compared four classifiers-logistic regression, decision tree, random forest, and k-nearest neighbors - using cytokines (TNF-α, MCP-1, IL-1β, IL-6, IFN-γ), age, BMI, and waist-to-hip ratio (WHR). Models were evaluated using stratified 5-fold cross-validation and ROC-AUC. K-Nearest Neighbors (k-NN) achieved the highest …

Exploratory Proxy Occupational Health Risk Assessment On Pm2.5 Exposure Among Outdoor Personnel At International Ports Of Entry, Josdell Maria Guerra Ruiz

Open Access Theses & Dissertations

Despite growing public health concerns on PM2.5 exposure and its respiratory health effects, no documented evidence has been found on the application of proxy methods to perform occupational health risk assessments for PM2.5 exposure among outdoor personnel. The purpose of this research study was to conduct a descriptive and predictive occupational health risk assessment for cancer and non-cancer respiratory health outcomes related to PM2.5 exposure among outdoor personnel at targeted international ports of entry (IPOE). A total of 61 samples were collected, for 24 hours, at two targeted IPOEs (every sixth day) over 12-month period. Samples included measurements on PM2.5 …

The Effect Of Temperature And Hydroperiod On Rotifer Hatching Diversity And Phenology In Ephemeral Playas, Brent Samuel Hogue

Open Access Theses & Dissertations

As climate continues to change, weather events such as heat waves and precipitation are expected to become more extreme, and organisms living in these ephemeral habitats will experience unprecedented conditions that may test their limits. However, some organisms that live in these habitats are adapted for resiliency in the face of fluctuating conditions. The focal taxa of this study are rotifers, which have the ability to produce diapausing eggs, that are stored in sediment egg banks. Diapausing eggs will hatch once suitable conditions occur in the habitat, while the rest remain stored in the sediment. This strategy assists in the …

Metabolomics In The Dryland Critical Zone: The Role Of Soil Metabolites In Phosphorus Acquisition, Kalpana Kukreja

Open Access Theses & Dissertations

Earth's Critical Zone (CZ) plays a key role in sustaining life through carbon, water, and nutrient cycling from the top of the canopy to the groundwater. A key feature of critical zone research is the integration of multiple interdisciplinary techniques. An emerging set of techniques that can add to this approach is the field of metabolomics, the quantification and study of comprehensive metabolite profiles. In this dissertation, I apply metabolomics to address knowledge gaps in dryland critical zone ecosystems, specifically how soil metabolite dynamics influence biogeochemical processes, and with a particular emphasis on phosphorus (P) cycling in the Chihuahuan Desert. …

Computational Studies Of Alpha-Lactalbumin Binding To Perfluorodecanoic Acid, Randhal Smith Ramirez Orozco

Open Access Theses & Dissertations

This thesis investigates the structural and thermodynamic interactions between Perfluorodecanoic acid (PFDA) and bovine alpha-lactalbumin (ALAC), a critical milk protein essential for infant nutrition. The primary objectives were to computationally determine the most probable binding sites of PFDA to ALAC and to quantify the strength of these interactions alongside the structural changes they induce. This study addresses a critical gap in understanding how persistent environmental contaminants like PFDA are recruited and transported by nutritional proteins within the milk matrix. The methodology integrated high-throughput molecular docking with long-range, molecular dynamics (MD) simulations to provide a dynamic characterization of the ALAC-PFDA complex. …

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 …

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. …

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.

Comparative Case Studies Of How General Chemistry Ii Students Explain Entropically-Driven Phenomena, Raymundo Aragonez

Open Access Theses & Dissertations

Understanding how students conceptualize entropy remains a central challenge in chemistry education. Often referred to as a "driving force" of chemical reactions, the dispersal of energy as a process proceeds is indicative of a process's spontaneity or thermodynamic favorability. Students frequently default to discussing phenomena in deterministic terms, potentially reflecting the language choice and heuristics selected by the instructor such as "disorder". In this study, assessment prompts about entropy from a convenience sample of general chemistry textbooks were characterized as to their potential to elicit three-dimensional learning, and students' responses to assessment prompts from a curriculum aligned with three-dimensional learning …

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, …

Soil Property Responses To Push-Pull Cropping In East Africa, Grace Mercy Amboka, Mattias Jonsson, Celina Apel, David Meinhof, Adomas Liepa, Frank Chidawanyika, Andargachew Detebo, Felipe Librán-Embid, Emily A. Martin, Ingolf Steffan-Dewenter, Marcell K. Peters, Jie Zhang, Michael Thiel, Michael Otim, James Mugisha, Ghebremedhin Belay Bahta, Fredah Maina, Alice Murage, Benjamin Feit, A. Sigrun Dahlin

All Peer-Reviewed Publications

Push-pull technology is increasingly promoted in sub-Saharan Africa, particularly for pest management and enhancing crop productivity. However, its influence on soil properties remains understudied, despite its potential implications for soil health and sustainable soil fertility management. This study examines soil properties in push-pull and conventional non-push-pull cropping systems. Soil samples were collected from push-pull and conventional plots in Ethiopia, Kenya, Rwanda, and Uganda. We examined the associations between soil physicochemical properties and cropping systems, along with key components of push-pull, namely Desmodium coverage and plot age, and manure and mineral fertiliser application. Overall, there were a few differences in soil …

Soil Microbial Responses To Artifical Light At Night, Randall L. Walker

Open Access Theses & Dissertations

Global change, driven by both natural processes and human activities, has significantly transformed ecosystems. Urbanization and artificial light at night (ALAN) are key contributors, disrupting natural light cycles and influencing biological rhythms, biodiversity, and ecosystem functions. Thus far, there remains a gap in knowledge of how soil ecosystems are impacted by ALAN. This study uses a laboratory incubation to investigate the effects of ALAN on soil microbial communities and functions across urban and natural ecosystems. Briefly, soil was collected from eight sites around El Paso, Texas and neighboring New Mexico representing urban and rural light exposure. Soil was then subjected …