Digital Commons Network^™

Modeling Non-Normal Distributions With Mixed Third-Order Polynomials Of Standard Normal And Logistic Variables, Mohan D. Pant, Aditya Chakraborty, Ismail El Moudden

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous data associated with many real-world events often exhibit non-normal characteristics, which contribute to the difficulty of accurately modeling such data with statistical procedures that rely on normality assumptions. Traditional statistical procedures often fail to accurately model non-normal distributions that are often observed in real-world data. This paper introduces a novel modeling approach using mixed third-order polynomials, which significantly enhances accuracy and flexibility in statistical modeling. The main objective of this study is divided into three parts: The first part is to introduce two new non-normal probability distributions by mixing standard normal and logistic variables using a piecewise function of …

Pediatric Cancer Incidence, Temporal Trends, And Mortality In The United States By Health Disparities Indicators, Seer (1973-2014), Prachi P. Chavan, Laurens Holmes Jr.

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pediatric cancer incidence has been increasing in the United States, despite improvement in pediatric cancer survival. This steady increase in incidence trends is not completely understood but maybe associated with social and environmental factors. In this study we aimed to assess the cumulative incidence, temporal trends, and mortality rates in pediatric cancer. Additionally, we examined sub-group variability in both incidence and mortality rates. Methods: Data from Surveillance, Epidemiology, and End Results (SEER) −18 from 1973–2014 were used for the purpose of analysis in this study. Age-adjusted incidence rates were used to assess temporal trends in cancer among children aged < 1–19 years. Univariable and multivariable binomial regression models were used to examine the association between race and cancer mortality while adjusting for potential confounders. Results: There were 92,594 cancer diagnoses during this period. White children comprised 74,758, (80.7%), black children 10,030, (10.8%), and other races 6648, (7.2%). Overall the age-adjusted cumulative incidence was slightly higher among white children (16.4%) than black children (12.4%) and other (13.0%). Children aged 15–19 years and those in metropolitan regions were more likely to be diagnosed with pediatric cancer. Relative to females, males were 16% more likely to die from the disease [adjusted Risk Ratio (aRR): 1.16, 95% Confidence Interval (CI): 1.09–1.22]. Additionally, compared to white children, black children had higher mortality rates [(aRR): 1.37, 99% CI: 1.23–1.52]. Conclusions: There is an increasing trend in pediatric cancer incidence; while white children have the highest incidence, black children and males indicated a survival disadvantage, indicative of racial and sex variability in overall pediatric cancer in the United States.

Every Publication Needs An Author Verified Tl;Dr Statement, Sarah Oerther, Daniel B. Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …

Application Of Machine Learning And Large Language Models In Healthcare For Data Prediction And Summarization, Chiazam Chisom Izuchukwu

College of Graduate Studies: Theses & Dissertations

This study aims to examine the use of machine learning (ML) and large language models (LLMs) in healthcare to enhance disease prediction, clinical decision-making, and information management. Five supervised ML models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and Naïve Bayes (NB)—on three different computing platforms—Google Colab, Databricks, and Snowflake—were employed for disease classification. Data preprocessing included treating missing values, encoding categorical variables utilizing one-hot-encoding, feature scaling when needed, and tackling class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) before an 80-20 train-test separation. Models were created with Scikit-learn (Google Collab), Spark MLlib (Databricks), and …

Synthesis And Characterization Of A Biobased Polymer From Tung Oil And Pine Rosin & Characterization Of Polyhydroxybutyrate., Arijanet P. Osumah

College of Graduate Studies: Theses & Dissertations

This thesis presents two independents but thematically aligned research projects focused on the development and evaluation of sustainable polymers. The first involves the synthesis and characterization of a biobased polymer derived from tung oil and pine rosin. Tung oil, which is a triglyceride was chemically modified to produce tung oil diglyceride (TDG), which subsequently reacted with abietic acid-rich pine rosin to yield tung oil abietate (TOA), a biobased monomer. The TOA monomer was polymerized with butyl methacrylate (BMA) and divinylbenzene (DVB) using di-tert-butyl peroxide (DTBP) as a thermal initiator. The resulting polymer was thoroughly characterized using Fourier transform infrared spectroscopy …

Enhancing Adhd Diagnosis In College Students Using Multimodal Integration Of Nicats And Iva-2 Tools, Rushmila Shabneen

College of Graduate Studies: Theses & Dissertations

This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with multimodal metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS), which monitors facial expressions, eye movements, and computer interactions. Preliminary results show that combining these tools improves ADHD detection, reduces false …

Assessment And Health Benefit Of Vitamin C Using A549 Cell, Yetunde Adepoju

College of Graduate Studies: Theses & Dissertations

Vitamin C (ascorbic acid) is a vital micronutrient recognized for its antioxidant capacity and essential bodily functions, such as collagen formation, neurotransmitter activity, and regulation of the immune system. This study explores how vitamin C can protect and potentially heal cells under oxidative stress and inflammation, using A549 cells, a human lung epithelial cell line, as the model system. The cells were exposed to varying concentrations of vitamin C dissolved in water, DMSO, and 5% DMSO. To assess the outcome, cell viability was measured with an MTT assay. In addition, the expression of key genes related to oxidative stress and …

Evaluating Spatiotemporal Vegetation Index Variation To Detect Salt Marsh Dieback On The Georgia Coast, Emmanuella Bosompemaa Obeng

College of Graduate Studies: Theses & Dissertations

Salt marshes, essential for coastal protection and carbon sequestration, are increasingly vulnerable to dieback events, threatening ecosystem resilience and vital services. Detecting these shifts early is essential for timely intervention. However, few studies have applied Early Warning Signals (EWS) to coastal marsh systems. Most EWS research has focused on lakes, forests, or climate tipping points, with limited application to salt marsh dieback in the southeastern U.S. Site-specific, long-term spatial analyses are also lacking, as prior work often examines short time frames or single disturbance events. This study investigates the spatiotemporal patterns of salt marsh dieback on the Georgia coast from …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

Design And Synthesis Of Organocatalyts For Efficient Decontamination Of Organophosphate-Based Nerve Agents And Pesticides, Emmanuel Kingsley Darkwah

Doctoral Dissertations

Exposure to organophosphate-based nerve agents and pesticides poses significant health and security threats to civilians, soldiers, and first responders. Despite extensive efforts to develop chemical detoxification agents for use in topical applications on exposed skin surfaces and for intravenous injections, there remains an unmet need for effective, non-hazardous decontaminating agents. The current state-of-the-art decontaminating agent, Dekon-139 (2,3-butanedione oxime, potassium salt), exhibits adverse effects when applied to the skin.

In this study, we designed and synthesized pharmaceutically relevant aminoguanidine-derived aldimines that are relatively non-toxic and substantially more effective at decontaminating nerve agents and pesticides compared to existing agents, and they act …

Machine Learning Models For Location Prediction, Message Routing And Path Planning For Rescue Of Underground Miners, Abhay Goyal

Doctoral Dissertations

Self-rescue during underground mine disasters is vital for miner safety. Evolving hazards and post-disaster conditions demand solutions that enable navigation under severe communication and computational constraints. Centralized systems often fail in such rugged settings, while decentralized methods—particularly Delay Tolerant Networks (DTNs), proven in battlefields and space missions—offer distinct advantages for underground applications. This research addresses five core challenges: (i) predicting miners’ next locations on low-power devices using points of interest and movement sequences; (ii) delivering timely updates on safe routes, evacuation zones, and hazardous areas; (iii) evaluating energy efficiency and comparing graph-based approaches to existing methods; (iv) enabling edge-ready frameworks, …

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

Metal-Free Photoredox Catalysis For The S-Trifluoromethylation Of Thiols And Investigation Of Ph-Dependent Decontamination Of Dimethyl Chlorophosphate, A Nerve Agent Simulant, Via Hydrolysis With Azoles, Raheemat Rafiu

Doctoral Dissertations

"Organofluorine chemistry plays a critical role in synthetic chemistry due to the unique properties of fluorine. However, the S-trifluoromethylation of thiols remains underexplored, with existing methods often relying on toxic and expensive organometallic catalysts. Given the advantages of S-trifluoromethylated compounds, including enhanced pharmacological activity and improved metabolic stability, we developed a green and cost-effective method for the synthesis of S-CF3 compounds using diacetyl as the organic catalyst and Langloi's reagent as the trifluoromethylating reagent. This reaction, which proceeds under mild conditions using visible light photoredox catalysis, works across various substrate scopes, including aromatic, heteroaromatic, and aliphatic thiols.

Chemical Warfare Agents …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Big Cells, Big Impact: Unravelling Seasonal Patterns Of Net Primary Production In Narragansett Bay, Dominique Jae Lockwood

Open Access Master's Theses

Net primary production is a measurement of available energy, or the amount of biomass that phytoplankton produce during photosynthesis that fuels marine food webs and drives carbon cycling. Despite its ecological importance, net primary production is relatively understudied in comparison to chlorophyll-a, which is often used as a proxy for biomass rather than productivity. In this study, we measured size-fractionated net primary production and chlorophyll-a concentrations in Narragansett Bay over a 14-month period from January 2022 to February 2023. Weekly surface water samples were collected and filtered to distinguish between small (> 10 μm) and large ( …

Navigating Online Perceptions Of Offshore Wind On Social Media: Facebook Discussions On Recent Whale Strandings, Giselle Hausmann

Open Access Master's Theses

The Biden-Harris administration implemented a goal to achieve 30 GW of offshore wind energy by 2030 (US Department of Interior, 2021). The rapid increase in offshore wind activity on the East Coast, combined with NOAA’s declared unusual mortality event in 2016/2017, has sparked a discussion about offshore wind being a potential cause of whale deaths. This paper aims to analyze the discourse on offshore wind and whales through a content analysis of Facebook comments. Facebook pages from Massachusetts (The New England Aquarium), Rhode Island (Rhode Island DEM), New York (The Atlantic Marine Conservation Society), New Jersey (Marine Mammal Stranding Center), …

Risk Factors For Autism Spectrum Disorder: A Statistical And Machine Learning Perspective, Supti Biswas

Open Access Master's Theses

his study explores factors associated with autism spectrum disorder (ASD) through analysis of a rich dataset comprising maternal and child characteristics. The data were carefully cleaned and preprocessed to address missing values and ensure analytical consistency. Statistical analyses and advanced visualization techniques were used to uncover patterns linking ASD status with variables such as maternal age, prenatal smoking, and education level. To further assess predictive capacity and feature importance, multiple supervised machine learning algorithms - logistic regression, decision trees, random forests, and support vector machines (SVM) - were applied. ASD appears to be influenced by a complex interplay of demographic …

The Bodhi App: A Post-Trauma Self-Regulation App For People With Intellectual And Developmental Disabilities, Liam Cannon

Open Access Master's Theses

People in the United States living with intellectual and developmental disabilities (I/DD) often experience trauma at a disproportionate rate. Due to societal and personal barriers, individuals with I/DD have problems with gaining access to therapy, thus often having to live with the negative effects of their untreated trauma. The effects of untreated trauma can cause severe impairment which affects a person’s quality of life and emotional, social, and physical development.

Post-trauma self-regulation is a way of doing activities to regulate one’s emotions to help cope with the negative effects of trauma. It has been shown as an effective method of …

From Lawns To Life: Landscaping For Biodiversity On Mason’S Island, Margit Burgess

Open Access Master's Theses

The collapse of biodiversity is an existential threat to life on Earth. Residential landscapes, dominated by open lawns maintained in turfgrass, do not support biodiversity. In the United States, turfgrass has replaced native landscapes in over 40 million acres, with 500 square miles added each year (Tallamy, 2019). Of the 78% of the U.S. land that is privately owned, approximately 135 million acres - nearly twice the size of all national parks combined - are residential (Milesi et al., 2005). With a shift in private landscape management, this land could present a largely untapped opportunity for biodiversity restoration and social …

Micro- And Nano-Plastics Preparation And Their Impact On Cobetia Marina, Lauren Lamothe

Open Access Master's Theses

Plastic pollution has become an increasing threat to marine and coastal environments. Macroplastics derived from consumer waste are broken down by chemical and physical degradation processes, resulting in micro- and nano-plastics (MPs and NPs). This thesis presents an investigation into the MP and NP pollution in the sea surface microlayer (SSML). The SSML contains surface active compounds and natural and anthropogenic matter. This study provides insight into the deeper impacts on the transport and fate of MPs and NPs and on marine bacteria. The first chapter works through the breakdown of plastic particles with the use of a top-down approach …

Investigating The Effectiveness Of Best Management Practices In Reducing Fecal Bacteria In Narrow River, Rhode Island, Katherine Thompson

Open Access Master's Theses

Fecal bacterial contamination shapes how communities manage and use estuarine waters, with consequences for ecosystem health and public access. In Rhode Island's Narrow River, elevated concentrations of fecal coliforms and enterococci have restricted shellfishing for decades despite ongoing management efforts. From 2004 to 2018, local organizations implemented Best Management Practices (BMPs) to address water quality challenges, yet their effectiveness remains incompletely understood. This study analyzes three decades of monitoring data using linear regression, generalized additive models, and Before After Control Impact methods to evaluate BMP effectiveness and identify key drivers of contamination. Linear regression revealed spatially variable trends, with bacterial …

Plastics And Their Interactions With Bacterial Biofilms Of Synechococcus Sp. Pcc 7002 And Escherichia Coli Nissle 1917, Jackson Zmich

Open Access Master's Theses

Plastic waste is a leading global pollutant with increasing concerns, as recent studies have shown that these wastes are not only concentrated on a macro scale but on a micro and nano scale. These micro- and nano- sized plastic particles, denoted as MPs and NPs, have been measured in marine and freshwater sources, air, food products, and within biosolids expelled from organisms, including humans. Due to their small size, MNPs are entering organisms through inhalation, ingestion, or dermal absorption. [1] Humans are ingesting MNPs daily, with median rates of approximately 200 ng/capita/day for children and 600 ng/capita/day for adults. [2] …

Eeg-Based Multi-Scale Perturbed Brain Cognitive Pattern Recognition Network For Gamer Level Classification, Lijun Jiang, Yanping Chen, Kexuan Liu, Xingyuan Chen, Li Dong, Weiya Ma, Diankun Gong, Dezhong Yao

General Human Environmental Sciences Faculty Publications and Presentations

Electroencephalogram (EEG)-based classification of video game experts versus amateurs reveals cognitive brain patterns underlying complex abilities, with applications in attention modulation, medical rehabilitation, and health monitoring. While deep learning has advanced EEG-based cognitive state detection, challenges remain in extracting meaningful patterns from noisy data and preventing reverse inference attacks on user privacy. Here, we propose a novel multi-scale perturbed brain cognitive pattern recognition network (MsPE). Its key contributions are: (1) a multi-scale weak encryption method with attention mechanisms that protects privacy by perturbing EEG signals in temporal and frequency domains; (2) ConvFormer modules with adaptive channel sizes (3, 5, 15) …