Digital Commons Network^™

Feasibility Of Upcycling Spent Lithium-Ion Battery To Carbon Dioxide Capture Adsorbent, Chimezie Frank Onwudinjo

Master’s Theses

This study investigates the feasibility of repurposing spent lithium-ion battery (SLIB) to lithium orthosilicate (Li₄SiO₄), a high temperature carbon dioxide sorbent. Two synthesis pathways including conventional acid-leaching method (Scenario 1) and a pyrolysis-based route (Scenario 2) were explored. Additionally, techno-economic analysis (TEA) and lifecycle assessment (LCA) of the two processes were also performed. Different analytical characterization techniques were performed to understand the material properties of Li₄SiO₄ including surface area, crystallinity, morphology and thermal stability. CO₂ capture performance of the synthesized Li₄SiO₄ was tested in a thermogravimetric analyzer (TGA) using …

Beyond The Lace Index: Benchmarking Machine Learning Architectures And Explaining 30-Day Hospital Readmission Risk With Shap Analysis, Carl E. Hughes Iii

Williams Honors College, Honors Research Projects

Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, …

Preliminary Analysis Of The Spatial Distribution Of Microplastics Along The Cuyahoga River, Northeast, Ohio, Ella Pitz

Williams Honors College, Honors Research Projects

Plastics are nearly ubiquitous throughout modern society, and when left in the natural environment, they degrade into microplastics. Microplastics are particles that range in diameter from 5 millimeters down to nanometers, and microplastics pose many potential environmental and health risks due to their small size and ease of ingestion. While the study of microplastics in the environment has become more common over the past ten years, microplastic concentrations remain unexplored in many regions throughout the country. Here I present a method for separating microplastics from organic matter and clastic sediment as well as preliminary measurements of microplastic concentrations in sediment …

Identification Of Modified Polymer End Groups For Correlations To Physical Properties, Tj Sacco

Williams Honors College, Honors Research Projects

Plastic has become the primary material for containers in both industrial and commercial fields. Plastics are the optimal choice since they have many physical properties that make them ideal candidates: durability, heat resistance, chemical stability, as well as their inexpensive production cost. Nowadays, the desire for more versatile plastics with strength, flexibility, hydrophobicity, and biodegradability has grown; however, concerns for plastic use have started to rise as well. The overuse of plastics poses a threat to the environment and people’s health: plastics are hard to break down completely which results in the formation of micro(nano)plastics (MNPs). In attempts to circumvent …

Towards Breach Hypothesis Based Predictive Autonomous Cyber Defense Ecosystem: Proactive Prevention Of Imminent Threats And Productivity Losses, Yogesh Chavarkar

Master's Theses and Doctoral Dissertations

As cyberattack tools and techniques get sophisticated and persistent, reactive cybersecurity has been unable to effectively prevent breaches and compromises. Organizations and institutions with large complex environments have wide vulnerable exposure with higher chances of a cyberattack. This also increases overall security and financial risk. Possibility of repetitive attacks from cyber threats increases, too, despite the use of standard defense measures. Security tools and system vulnerabilities often need manual patching and updates to keep the environment secure and functioning effectively. Productivity suffers from manual trade-offs in keeping the systems secure from adverse impact. Persistent high-severity cyberattacks, despite continued reactive defensive …

Comprehensive Analytical Investigation Of Tetrahydro-4h-Chromene And Dihydropyridine Derivatives, Ryan J. Burk

Chemistry & Biochemistry Dissertations

Novel calcium channel blockers (CCBs) with dihydropyridine (DHP) or tetrahydro-4H-chromene moieties are often chiral, so effective enantiomer separation methods are needed for continued drug development. The purpose of this dissertation is to outline a comprehensive method development model for the chiral separations of DHPs and tetrahydro-4H-chromene derivatives. The scope of this dissertation is to address analytical challenges during initial synthesis, chiral separations, and data analysis during post-processing for this class of chiral compounds. The first section focuses on the method development strategies for separations of DHPs in the sub/supercritical fluid (SFC) and normal phase modes with the 2-hydroxypropyl-β-cyclodextrin stationary phase. …

Novel Methods For Environmental Fluoride Measurement, Cable Warren

Chemistry & Biochemistry Dissertations

Fluoride analysis has been an important focus of analytical analysis for many years and will continue to be so going forward. Today, fluorinated compounds, specifically per/polyfluoroalkyl substances (PFAS), better known as “forever chemicals” have captured the moment and are the subject of vast research and regulation. Under this context, I have researched new methods of fluoride separation and concentration in complex media as well as a novel method of PFAS destruction and total organic fluorine analysis for screening of PFAS in aqueous samples. Through research and work on micro-scale detection methods, an understanding of the state of the art and …

The Forensic Implications Of Hydrochloric Acid (Hcl) Exposure On Dna Preservation In Human Teeth, Shaelyn Lee Zimmerman

Graduate Student Theses, Dissertations, & Professional Papers

Identity is a human right, and forensic anthropological methods are often aimed at returning identity to decedents. Three main pathways are used for the identification of human remains: fingerprints, dental records, and DNA analysis. Identification efforts may be hindered when perpetrators attempt to obscure the victim’s identity. In cases where corrosive substances, such as hydrochloric acid (HCl) are used, dental comparison and fingerprinting often fail and genetic analysis becomes the best chance of achieving personal identification.

Previous studies have shown teeth are an excellent source of DNA. Large, multi-rooted teeth, such as molars, are often preferred because they contain the …

Impact Of Water Quality And Gear Type On Eastern Oyster (Crassostrea Virginica) Growth In Narragansett Bay, Ri, Jacqueline Rosa

Open Access Master's Theses

Oyster aquaculture is expanding in Rhode Island, yet key farming regions in the lower West Passage of Narragansett Bay (WPNB) lack the in-situ, high-temporal resolution monitoring needed to evaluate emerging stressors and support production. At the same time, the industry is undergoing rapid technological development aimed at improving production while reducing labor and overall costs. In recent years, a low-maintenance, alternative surface gear was introduced in WPNB; however, its impact on oyster performance relative to traditional cultivation methods has not been quantified. This study established a 1.5-year continuous water quality time series in WPNB and paired these observations with …

Emotional Patterns In Conversational Social Media Using Graph-Based Context, Sai Sanjay Yerunkar

Master's Projects

Most social media datasets for emotion detection have not been constructed to account for conversational structure‚ so we investigate whether it carries signal for emotion prediction. Using Sentiment140 and GoEmotions Reddit threads‚ we construct their thread-based conversation graphs and compute the aggregated features of neighbors as well as the transformer and TF-IDF representations of comments. Connected comments are 4.5× more similar in emotions than expected by chance. Pairwise emotional similarity decays exponentially with geodesic distance (e.g.‚ after 2 hops). Pairs separated by a 30s timestamp difference have the highest emotional similarity. These results suggest that emotion is structured locally and …

Online Lifelong Optimal Adaptive Control Of Partially Uncertain Strict Feedback Discrete-Time Systems With Application To Quadrotor Uavs, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal adaptive tracking control of partially uncertain strict feedback discrete-time (DT) systems with application to quadrotor uncrewed aerial vehicles (UAVs). First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of the tracking error dynamics. The optimal adaptive tracking control problem is solved using an augmented system approach, where a horizon of future bounded reference trajectory points is used in the augmented state, when compared to using a single point. It is assumed that the internal dynamics of the strict feedback system are unknown, but the …

Pipeline Corrosion And Relevant Microbial Influence, Renee E. Bamberger

Williams Honors College, Honors Research Projects

This study investigated the role of microbial activity and oxygen availability in the corrosion of carbon steel pipelines with a focus on microbially influenced corrosion (MIC). Sediment collected from Old Woman Creek, Ohio, was used to simulate natural soil conditions, and carbon steel coupons were incubated under four treatments: nonsterile oxic, nonsterile anoxic, autoclaved oxic, and autoclaved anoxic over 85 days. Corrosion was assessed through mass loss measurements and the analysis of pH, sulfate, chloride, and dissolved iron concentrations. The greatest mass loss was observed in autoclaved oxic conditions, contrary to the initial hypothesis that microbial activity would greatly enhance …

Secure The Database: A Red Team, Blue Team Analysis Of Sql Injection, Andrew N. Miller

Williams Honors College, Honors Research Projects

SQL injection (SQLi) attacks are a type of cyberattack that seeks to bypass website logins and gain entry to sensitive information. These pose a significant danger to organizations holding confidential user information. Personally Identifiable Information (PII) like physical addresses, emails, phone numbers, social security numbers are at risk of theft. Login credentials like usernames, passwords, and other sensitive information like financial details and social security numbers are also exposed through SQLi attacks. SQLi attacks harm the confidentiality, integrity, and availability of people’s identity. Additionally, data breaches that reach public battention harm the reputation and trust of organizations. SQLi attacks rank …

Computer-Aided Molecular Design To Identify More Environmentally Friendly Pfas, Melvin M. Keita

Williams Honors College, Honors Research Projects

The goal of this project is to inversely design alternatives to per- and polyfluoroalkyl substances (PFAS) using Computer-Aided Molecular Design (CAMD). PFAS, also described as “forever chemicals”, have been used in industry and consumer products since the 1940s. PFAS can be found in drinking water, food, food packaging, waste sites, and other sources. Exposure to different PFAS can lead to increased risks of some cancers, immune effects, and reproductive effects. Pulling from existing data, this project will use quantitative structure-property relationships (QSPRs) to design PFAS alternatives that possess optimal properties to prevent adsorption into drinking water and other potential sources …

Using Low-Cost Sensors For Source Attribution And Health Assessment: An Air Quality Study In Brownsville, Texas, Sai Deepak Pinakana, Kabir Bahadur Shah, Daniel Jaffe, Juan L. Gonzalez, Owen Temby, Gabriel Ibarra-Mejia, Amit U. Raysoni

School of Earth, Environmental, & Marine Sciences Faculty Publications

Air quality monitoring remains a challenge in areas lacking or having sparse federal monitoring infrastructure, posing significant barriers to public health research. This study demonstrates the usage of low-cost sensors in addressing gaps in air quality monitoring, source attribution, and health risk assessment in a Brownsville, TX neighborhood impacted by emissions from a barite and celestite mineral processing unit. PM2.5 concentrations were measured using PurpleAir sensors deployed across three residential locations, with the site nearest to the processing unit recording a 24-h averaged PM2.5 concentration of 25.12 μg/m3—approximately 2.79 times higher than the nearest Texas Commission of Environmental Quality (TCEQ) …

A Comprehensive Review Of Cyber Security And Current Practices In Global Mining Critical Infrastructure, Abu Barkat Ullah, Wanli Ma, Mohiuddin Ahmed, Bazlur Rashid, Munir Ahmad Saeed, Omer Arshad, Utkarsh Raghav

Research outputs 2022 to 2026

The purpose of the study is to explore the reasons behind the low uptake of Information Security Management Standards (ISMS), Asset Management, and Business Continuity Plans despite increasing cyber threats to the mining sector. Mining companies need to modernize and automate to keep up with the ‘Fourth Industrial Revolution’, driven by disruptive technology, forcing systems and technologies to become more integrated, increasing cyber attack threats. To address this, we conducted a literature review analyzing the mining industry across various regions. The research is based on a qualitative analysis of diversified literature. The results highlighted factors behind the low uptake of …

Rapid Classification And Quality Assessment Of Citrus Essential Oils Via Machine-Learning-Assisted Raman Spectroscopy, Yong Xuan Hong, Jia Wei Tang, Jie Chen, Yun Yun Xie, Zhang Wen Ma, Qing Hua Liu, Liang Wang

Research outputs 2022 to 2026

Citrus essential oils (EOs) require accurate identification and assessment to ensure authenticity and consistency. However, conventional techniques such as gas chromatography (GC) and mass spectrometry (MS) are time-consuming and expensive, highlighting the need for novel analytical methods. This study proposes an approach for EOs detection using Raman spectroscopy (RS) combined with machine learning (ML) algorithms. Six citrus EOs underwent an evaporation experiment, with Raman spectra collected at five time points and GC-MS was used to analyze compositional changes at the starting and ending points of evaporation as a standard reference. Five ML algorithms were developed to identify differences among EOs …

A Systematic Review And Characterization Of Privacy Noncompliance In Real-World Applications, Alexander E. Charkiewicz

Graduate Studies Theses and Dissertations 2026

Software applications increasingly rely on user data to provide their functionality, but improper handling of such data can lead to serious privacy noncompliance with applicable regulations and policies. A prominent example is the Facebook–Cambridge Analytica scandal, in which a third-party application collected the personal data of approximately 87 million Facebook users without users' consent. Despite growing attention to privacy compliance, two key challenges hinder the systematic understanding and analysis of privacy noncompliance. First, unlike security vulnerabilities, which have been systematically categorized through taxonomies such as the Common Weakness Enumeration (CWE), privacy noncompliance lacks a technical taxonomy describing how it manifests …

Concept Drift Detection For Streaming Data Using One-Class Classification, Poorna Sandamini Senaratne

Graduate Studies Theses and Dissertations 2026

Modern machine learning systems are increasingly deployed in streaming environments where data arrive sequentially and the underlying data-generating process may evolve over time. This phenomenon, known as concept drift, can significantly degrade model performance if not detected and addressed in a timely manner. This dissertation proposes a principled framework for concept drift detection based on one-class classification, integrating neural network embeddings with Support Vector methodologies.

The proposed approach leverages neural networks to learn compact and informative embeddings of input data, capturing complex nonlinear structures in a lower-dimensional latent space. These embeddings are then used to construct a statistical description of …

Gender Differences In The Association Between Adverse Childhood Experiences And Drug Use: Findings From A Community-Based Survey In China, Hongyun Fu, Elizabeth Monk-Turner, Xiushi Yang

Department of Pediatrics Faculty Publications

Background

While adverse childhood experiences (ACEs) are widely recognized risk factors for behavioral health problems, including drug use, prior research has largely been conducted in Western countries, focused primarily on males, and relied on convenience samples without comparison groups of nonusers. Limited work has examined the impact of ACEs on drug use in non-Western contexts. This study examines gender differences in the relationship between ACEs and drug use in China, using data from a population-based probability sample survey.

Methods:

Cross-sectional data were collected in 2019 from one city in Yunnan Province in Southwest China and one city in Guangdong Province …

A Novel Bernstein Operational Matrix Approach For Tempered Fractional Differential Equations: Convergence And Stability Analysis, Jalal Al Hallak, Mohammed Alshbool, Ishak Hashim, Eddie Shahril Ismail, Shaher Momani

All Works

Tempered fractional differential equations (TFDEs) incorporate exponential decay into fractional operators to account for truncated memory and semi-long-range dependence in a variety of applications, including anomalous diffusion, viscoelasticity, transport phenomena, geophysical processes, and financial dynamics. In this work, a tempered fractional Bernstein method (TFBM) was proposed for the numerical solution of TFDEs involving Caputo-type derivatives. The proposed formulation combined a Bernstein polynomial approximation with an analytic representation of the Caputo–tempered fractional derivative through operational matrices. On this basis, two collocation-based variants were developed, namely, a Chebyshev-type method (TFBM-C) and a Legendre-type method (TFBM-L). For the linear setting, a convergence analysis …

Smart Health Care Application For Predicting Complications Risk In Type 2 Diabetes Management Using Personalized Digital Twins: A Focus On Early Intervention And Prevention Strategies, Haifaa Alkaabi, Ahed Abugabah

All Works

The study examined the application of Personalized Digital Twins (PDTs) to prevent complications during the management of Type 2 Diabetes, especially in early intervention and prevention plans. Based on a high-quality dataset related to the CDC Behavioral Risk Factor Surveillance System (BRFSS) data, we tested multiple predictive models such as the Random Forest, Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost). We developed a composite risk indicator from established clinical risk factors (hypertension, dyslipidemia, elevated BMI) to stratify complication risk. The Random Forest model achieved 99% accuracy (AUC: 0.98) at the population-level risk classification. The GBM model was optimized …

Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens

All Works

Objective: To develop an AI framework that combines genetic, phenotypic, and lifestyle data for profiling skin-health patterns and generating hypothesis-supporting summaries for potential decision support. Methods and procedures: A dataset of 5,254 individuals integrates six genes (FLG, AQP3, MMP-1, MMP-3, SOD2, GPX), six phenotype severities, and 20+ lifestyle factors. Mutation burden and interactions are tested by ANOVA. K-modes clustering identifies four interpretable dermatological profiles within the cohort and is embedded in leakage-free nested cross-validation (train-only selection; test labels from training centroids). Subtypes are predicted from genetics plus lifestyle using an XGBoost (XGB) classifier; explainability uses gain, permutation importance, and SHAP …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Workshop Outcomes Report: 2nd International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Daniela Morganti

Faculty Publications

The report provides an overview of the second international workshop on Seismic Resilience of Arctic Infrastructure and Social Systems. The report discusses agenda, workshop activities, interdisciplinary working groups, and results. It ends with several strategic questions and investigation plans that were developed as part of the workshop activities.

Advancing Periodic Time Series Analysis: Application, Bias Assessment, And Optimal Window Selection In The Variable Bandpass Periodic Block Bootstrap Method, Yanan Sun

Electronic Theses & Dissertations (2024 - present)

Time series analysis is essential for understanding long-term patterns, periodic behavior, and underlying correlations in complex datasets. The periodically correlated (PC) time series is a type of time series where the correlation structure repeats over fixed intervals. The Variable Bandpass Periodic Block Bootstrap (VBPBB) has recently been proposed as a resampling method that preserves PC structures through the use of periodogram, bandpass filters, and block bootstrap resampling. Although promising, VBPBB remains underutilized, and its limitations have not been fully examined. This dissertation advances both the application and methodological development of the VBPBB.

The first project applies the VBPBB to a …

Green Modification Of A Coupling Reaction Between N-Acetylated Amino Acids And P-Nitrothiophenol, Kaitlyn S. Marley, Hannah M. Werle, John P. Morgan

SURF Posters 2026

Native Chemical Ligation (NCL) is a process by which smaller peptides can be linked together to form larger peptides and proteins, but the preparation of necessary thioester starting materials often relies on hazardous reagents. We report a greener synthesis of S-(4- nitrophenyl) N-acetylphenylalaninethioate, building upon the methodology reported by Raines et al.1 This literature synthesis uses N,N-dimethylformamide (DMF) as solvent in the final thioester coupling step to accommodate the limited solubility of N-acetylphenylalanine in less polar solvents. However, DMF is a known reproductive toxin and environmental hazard. Our research demonstrates that acidic water serves as a highly effective, benign alternative …

Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail

Theses and Dissertations (Comprehensive)

Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

The Sub-Lethal Effects Of Trematode Parasites On The Migratory Behavior Of Lesser Scaup (Aythya Affinis), Scott Alan Herman

Graduate Research Theses & Dissertations

The introduction of the invasive freshwater faucet snail (Bithynia tentaculata) and its associated parasites in the Upper Mississippi River and Great Lakes Region provides the opportunity to investigate how anthropogenic stressors impact the life history of lesser scaup (Aythya affinis) using the Mississippi Flyway. To understand the life histories and interactions between lesser scaup and faucet snails we examined historical records, research, and ecology textbooks. Our literature review identified critical knowledge gaps in lesser scaup literature surrounding population dynamics, reproduction, and migration. Specifically, the sub-lethal effects of invasive trematodes on the migratory behavior of lesser scaup in the Upper Mississippi …