Digital Commons Network^™

Re: Approval Letter For The Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area 2019-2023 Data Summary Report And Response To Epa Comments (Dated September 24, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2022 Insufficiently Reclaimed Sites Sampling: Bres No. 16 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2022 Insufficiently Reclaimed Sites Sampling: Bres No. 96 – Washoe Dump Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final 2022 Unreclaimed Sites Sampling: Ur-06 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Misodice: Multi-Agent Imitation From Mixed-Quality Demonstrations, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

We study offline imitation learning (IL) in cooperative multi-agent settings, where demonstrations have unlabeled mixed quality - containing both expert and suboptimal trajectories. Our proposed solution is structured in two stages: trajectory labeling and multi-agent imitation learning, designed jointly to enable effective learning from heterogeneous, unlabeled data. In the first stage, we combine advances in large language models and preference-based reinforcement learning to construct a progressive labeling pipeline that distinguishes expert-quality trajectories. In the second stage, we introduce MisoDICE, a novel multi-agent IL algorithm that leverages these labels to learn robust policies while addressing the computational complexity of large joint …

A Study Of Configuration Management Database (Cmdb) Adoption In It Service Management (Itsm) Implementations Within Nj Community Colleges, Fredrick Dande

All-Inclusive List of Electronic Theses and Dissertations

This study examines the adoption of Configuration Management Databases (CMDBs) in IT Service Management (ITSM) implementations within New Jersey (NJ) community colleges. Despite the well-documented benefits of CMDBs—such as faster issue resolution, improved compliance, and greater visibility across IT infrastructures—implementation success rates remain low. As technology continues to enhance production capabilities and expand access to information, the need for centralized configuration visibility has become critical. A CMDB provides a single system of record for IT assets and services, helping organizations manage outages, assess changes, maintain compliance, and improve asset tracking. This research used an online survey to collect data from …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

Error Reduction Methodology And Data Simulation For Interval Data, Ranik Christopher Jelinek

Undergraduate Honors Capstone Projects

Chronic kidney disease (CKD) is a progressive condition affecting hundreds of millions of individuals worldwide. However, clinical datasets often record continuous laboratory measurements as categorical intervals rather than precise numerical values. This interval-censored structure presents methodological challenges for standard regression-based classifiers. This study compares three strategies for handling interval-valued predictors prior to fitting a logistic LASSO model: (1) midpoint imputation, which replaces each interval with its arithmetic center; (2) ordinal encoding, which maps intervals to integer ranks; and (3) a Monte Carlo simulation approach, which repeatedly samples uniformly from each observed interval and averages predictions across replications. Using a 10-fold …

Copula-Based Tests For Assessing The Association Between Genetic Variants And Mixed Phenotypes, Martin Amoah

Open Access Theses & Dissertations

High-dimensional omics studies increasingly involve heterogeneous data types and phenotypes, which traditional association methods struggle to model jointly due to incompatible marginal distributions and complex dependence structures. This thesis develops a unified copula-based framework for assessing associations between genetic variants and mixed phenotypes by decoupling flexible marginal models from their joint dependence structure. While previous copula-based approaches in this setting have focused largely on continuous and binary traits, we extend these methods to a broader class of phenotype pairs. Specifically, we introduce new association tests for bivariate outcomes involving ordinal–continuous, nominal–continuous, and survival–continuous combinations. The proposed methodology derives joint density …

Modeling Aerosol Transport During High Particulate Matter Episodes In El Paso-Juarez Region And Mitigating Mining-Related Emissions Of Rare Earth Elements (Rees) To The Air, Suzan Aranda Luna

Open Access Theses & Dissertations

This dissertation examines the use of the HYSPLIT model to develop a methodology for the transport and dispersion of air masses affecting particulate matter (PM) concentrations in the El Paso Region, and bioleaching experiments as an alternative to mitigating rare-earth concentrations in the air. Chapter 2 describes the study methodology, 3 and 4 cover the modeling methodology using two-representative high PM2.5 episodes, occurring on February 28, 2024, and June 19, 2024, respectively. These sections encompass the analysis of backward trajectories using four different meteorological datasets to build trajectory frequency maps, the exploratory model analysis of modeled outputs from lower to …

Mbse For Process Analytical Technology- Bwon Analysis Case Study, Arnaldo Garcia Cervantes

Open Access Theses & Dissertations

Volatile Organic Compound (VOC) emissions from industrial sources, particularly Benzene, present significant environmental and public health challenges. Regulatory frameworks, such as the U.S. Environmental Protection Agency’s Benzene Waste Operations NESHAP (BWON), mandate strict monitoring of control devices, specifically carbon adsorption canisters, to prevent emission breakthrough. However, current industry practices rely heavily on manual Method 21 testing, a labor-intensive process that creates lagging indicators and increases the risk of non-compliance events. This thesis proposes the design and development of an on-line, automated fugitive emissions monitoring system tailored for carbon canisters using Model-Based Systems Engineering (MBSE). Utilizing the Object-Oriented System Engineering Method …

Intelligent Predictive Frameworks Under Data Scarcity And Uncertainty, Solayman Hossain Emon

Open Access Theses & Dissertations

Modern predictive systems frequently operate under conditions of limited annotated data, high uncertainty, and the need for reliable decision-making. When the predictive models expand across heterogeneous data types (e.g., spatial, temporal streams), the challenge lies not only in accurate prediction but also in adapting in data distributions shifts or label scarcity. To address these issues, this thesis explores an Intelligent Predictive Framework that operates robustly under data scarcity and uncertainty across two distinct domains: medical imaging (spatial) and time-series forecasting (temporal). In the first part of this work, a semi-supervised mean teacher (MT) paradigm is tailored for medical image segmentation …

A Unified Framework For Embedding-Based Synthetic Data Generation With High Cardinality Categorical Features, Cesar Iram Vazquez

Open Access Theses & Dissertations

High-cardinality categorical variables remain difficult to model in tabular data, where classical encoders encounter sparsity, susceptibility to leakage, and the loss of meaningful relational structure. This dissertation develops a unified framework for learning, evaluating, and synthesizing representations of such variables using both traditional encoders and modern embedding methods, including Word2Vec, FastText, Node2Vec, TF–IDF/SVD, and supervised entity embeddings. The framework is applied across three benchmark datasets (Adult, PetFinder, Breast Cancer) and a hierarchical educational case study (IPEDS/CIP). Embedding quality is examined through both downstream predictive performance and structure-focused diagnostics that quantify neighborhood behavior and geometric coherence. To assess whether synthetic data …

Algebraic Approach To Data Processing: Techniques And Applications, Julio Urenda

Open Access Theses & Dissertations

In many areas of human knowledge, symmetries and invariances play an important role. In fundamental physics, starting with Relativity Theory, new physical theories have been formulated in terms of invariances and of the corresponding transformation groups – i.e., in terms what a mathematician would call an algebraic approach. In engineering, devices like wind tunnels, which are based on scale-invariance, enable us to test smaller-scale models of the actual designs. In biological sciences, symmetries and invariances are extremely important in analyzing the shape and functioning of living beings, from mammals to viruses. Invariance and symmetry – in the form of fairness …

Integrating Manager Perspectives Into The Evaluation Of Wetland Revegetation Strategies, Loren F. Taylor

All Graduate Theses and Dissertations, Fall 2023 to Present

Ecological restoration is the process of assisting the recovery of an ecosystem that has been degraded and is dependent on complex and context-specific decisions. The decision-making process influencing restoration is rarely examined alongside ecological processes. There is a need for research that incorporates the social, economic, institutional, and logistical considerations that shape real-world practices, as well as ecological processes. Decisions made by restoration practitioners and land managers (hereon “manager”) are challenged by the lack of information shared regarding the practical constraints (e.g., cost, labor, specialized equipment) they face. Revegetation, particularly in wetlands, poses challenges due to site conditions and degradation …

Machine Learning Applications: Cell Tracking And Nonparametric Estimation Of Non-Smooth Divergences, Mina Mahbub Hossain

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis brings together two important research directions: how to compare different sets of data more accurately, and how to better understand how brain cancer cells move and change shape.

In the first part, we look at a problem in statistics: measuring how different two data sources are from each other. Traditional methods often make strong assumptions, which may not always hold in real situations. Our approach avoids those assumptions by using an ensemble method, a way of combining many weak estimators into one stronger result. This makes the method more flexible and reliable, especially when dealing with complex or …

Social Media Mining For Extracting The Experience Of Neurodivergent Individuals On Twitter (X) And Reddit, Kartik Thakkar

All Graduate Theses and Dissertations, Fall 2023 to Present

Neurodiversity refers to the natural neurological variations in the human brain such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), dyslexia, dyspraxia, Tourette syndrome and other neurological disorders. it’s estimated that around 15% to 20% of the world’s population is neurodivergent. This means a significant portion of people experience neurological differences in how they think, learn, and interact with the world.

Social Media has become an important space for neurodivergent individuals to share their experiences, build communities, and seek support. This thesis explores how the online venues like X (Twitter) and Reddit offer themselves as digital spaces where …

Using Luminescence Of Quartz Temper From Archaeological Pottery To Infer Wildfire Intensity In The Southwestern Us, Brooklyn D. Dib

All Graduate Theses and Dissertations, Fall 2023 to Present

The size and frequency of wildfires have increased recently, impacting ecosystems, communities, and human health. This research uses the heat-sensitive luminescence signals of quartz sand temper within archaeological pottery to record past wildfire conditions. Pottery sherds, which are fragments of archaeological pottery, from the forest floor in sites in New Mexico and Arizona were collected from different fire contexts (no fire exposure, historical fires, prescribed burns, and modern wildfires). These forests are primarily Ponderosa Pine with a grassy understory. The interior sand grains of each sherd sample were analyzed using optically stimulated luminescence, which dates when the mineral grains were …

Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano

Graduate Theses and Dissertations

In this dissertation, we explore the potential of machine learning and deep learning techniques to enhance the performance and robustness of applications across two major domains. By addressing the challenges within these fields, we demonstrate that we can leverage learning algorithms to obtain substantial improvements in accuracy and robustness. First, we tackle a problem in the field of predictive health maintenance. We propose a novel auto encoder and neural network based methodology to predict failure times in complex aviation systems to learn to distinguish between normal and abnormal operational behavior, and use this information to inform the neural network to …

Agricultural Practices’ Impact On Soil Health Indicators In Mid-South U.S. Crop Production Systems, Katherine Suzanne French

Graduate Theses and Dissertations

Soil health and regenerative agriculture are concepts gaining popularity across global agriculture systems. The effect of sustainable farming practices such as nutrient management, cover crops, adoption of no-tillage, and residue retention on crop yield, as well as environmental resilience, is increasingly being studied. This research aimed to evaluate the impacts of (i) cover cropping and nutrient management, and of (ii) soil sampling depth and timing on indicators of soil health in various mid-southern irrigated row crop systems. Soil health indicators like soil organic matter (SOM), carbon dioxide respiration (CDR), beta-glucosidase enzyme activity (BG), permanganate oxidizable carbon (POXC), and the soil …

Predicting Stock Price Movement With Llm-Enhanced Tweet Emotion Analysis, An Vuong

Graduate Theses and Dissertations

Accurately predicting short-term stock price movement remains a challenging task due to the market’s inherent volatility and sensitivity to investor sentiment. In this thesis, we present a published paper that discusses a deep learning framework integrating emo- tion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta’s LLaMA 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Two Topics In Survival Analysis: Restricted Distance Covariance Test For Non-Proportional Hazard And A New Estimation For Dropout Rate, Ruizhe Yin

Graduate Theses and Dissertations

When treatment effects change over time, standard statistical methods, such as the log-rank test and the Cox proportional hazards model, may give misleading results. This dissertation presents the restricted distance covariance (rdcov) test, a nonparametric method that compares survival curves between groups within a chosen study period [0, τ] using right censored data. The statistic measures the dependence between pre-specified group labels and survival times using pairwise distances from Kaplan-Meier estimates. Our method does not rely on the proportional hazards assumption, and it equals zero only when survival functions are identical across groups. Thus, this test can be applied to …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

Sediment Burial Negatively Impacts The Growth Of Seagrass Posidonia Sinuosa, Chanelle Webster, Nicole Said, Natasha Dunham, Simone Strydom, Kathryn Mcmahon

Research outputs 2022 to 2026

Burial disturbances affect foundation plant species in marine ecosystems. Deposition of dredge spoil can bury seagrass meadows yet we have limited threshold information to predict the trajectory of impact from burial or possible recovery. To investigate the response to burial by dredge spoil, established seagrass ramets of Posidonia sinuosa were collected from a population in Western Australia and exposed to cutter suction dredge spoil sediment. Plant responses were measured during a burial phase after 2, 4 and 8 weeks to assess the influence of duration to burial depths (0, 1, 4, 8 and 16 cm). Sediments were then removed, and …

Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

Insecticide-Treated Net Use And Elimination Of Malaria In Sub-Saharan African Countries: Assessing The Global Technical Strategy Using An Evolutionary Game Approach, Laxmi, Tamer Oraby, Michael G. Tyshenko, Ina Danquah, Samit Bhattacharyya

School of Mathematical & Statistical Sciences Faculty Publications

Background: Malaria continues to be a major public health challenge in Sub-Saharan Africa (SSA), where the majority of the countries have not met the World Health Assembly's endorsed Global Technical Strategy (GTS) milestones in 2020 for malaria reduction. Insecticide-treated net (ITN) usage is a well-established and effective intervention, often outperforming other measures such as indoor residual spraying (IRS). However, multiple survey studies have reported improper use of ITNs across various SSA countries. This misuse likely poses an important barrier to the intervention's success, although it remains a largely untested hypothesis.

Methods: We developed a behaviour-incidence model and statistical analysis of …

Is Noise Exposure Associated With Impaired Extended High Frequency Hearing Despite A Normal Audiogram? A Systematic Review And Meta-Analysis, Sajana Aryal, Monica Trevino, Hansapani Rodrigo, Srikanta K. Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Understanding the initial signature of noise-induced auditory damage remains a significant priority. Animal models suggest the cochlear base is particularly vulnerable to noise, raising the possibility that early-stage noise exposure could be linked to basal cochlear dysfunction, even when thresholds at 0.25-8 kHz are normal. To investigate this in humans, we conducted a meta-analysis following a systematic review, examining the association between noise exposure and hearing in frequencies from 9 to 20 kHz as a marker for basal cochlear dysfunction. Systematic review and meta-analysis followed PRISMA guidelines and the PICOS framework. Studies on noise exposure and hearing in the 9 …

Development Of A Handy Tool For The Selection Of Urban Stormwater Best Management Practices, Aaron T. Kenny

Civil & Environmental Engineering Theses & Dissertations

The City of Norfolk, Virginia faces substantial stormwater management challenges due to shallow groundwater, tidal influence, dense urban development, and limited right-of-way. These constraints limit the applicability of many Best Management Practices (BMPs) and require the early identification of feasible practices before detailed hydrologic modeling. This thesis introduces a decision-support tool that quickly and systematically identifies and prioritizes BMPs that are both feasible and well-suited to Norfolk’s Municipal Separate Storm Sewer System (MS4) program, streamlining early-stage selection and saving time and resources.

The tool implements a two-stage methodology. First, feasibility gates are applied using catalog attributes derived from the Virginia …