Digital Commons Network^™

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis

Electrical & Computer Engineering Faculty Publications

Qubit lattice algorithm (QLA) simulations are performed for a two-dimensional spatially bounded pulse propagating onto a plane interface between two dielectric slabs. QLA is an initial value scheme that consists of a sequence of unitary collision and streaming operators, with appropriate potential operators, that recover Maxwell equations in inhomogeneous dielectric media to the second order in the lattice discreteness. For the case of total internal reflection, there is transient energy transfer into the second medium due to the evanescent fields as the Poynting unit vector of the pulse is rotated from its incident to reflected direction. Because of the finite …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Fault Tolerance Of Accelerated Asynchronous Fixed-Point Iterations On Flexible Computing Infrastructure, Evan Coleman, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

Asynchronous iterative methods tolerate straggling processors by allowing workers to proceed with stale data, but at a cost: the iterates become inconsistent, potentially degrading convergence. We investigate whether convergence accelerators such as Anderson acceleration compensate for this degradation. We experimentally study three fixed-point iterations: the Jacobi method for sparse linear systems, value iteration for the Bellman equation, and the Hartree–Fock self-consistent field (SCF) iteration. The experiments are conducted using a high-performance execution framework, Ray, which abstracts the complexity of distributed systems and enables code parallelization and fault injection with minimal changes.

We establish two main results. First, straggler tolerance is …

Affordable Course Content And Open Education Resources For Undergraduate Courses Teaching Fundamentals Of Wireless Communications And Networking, Dimitrie C. Popescu, Otilia Popescu

Electrical & Computer Engineering Faculty Publications

Wireless communication systems and networks along with the services they provide have become an essential component of the modern 21st century society, fueling job growth in the wireless industry and increasing the need for engineers specialized in wireless communication systems. As a consequence, over the past two decades, undergraduate courses teaching fundamentals of wireless communication systems and networks have become common in electrical and computer engineering and technology programs. At the same time, the number of textbooks dedicated to wireless systems and networks published by mainstream publishers has also grown, with availability in various formats and offerings and a significant …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

Multivariate Quantile Autoregression-Mixed Data Sampling (Mvqar-Midas) Modeling Of Cost Of Living And Supply Chain Dynamics In Canada., Patrick Gbolonyo

Theses and Dissertations (Comprehensive)

In recent years, the rising cost of living as a result of persistent inflationary pressures, disruptions in the global supply chains, and changes in the macroeconomic landscape has become a critical topic of discussion. To address this, we move beyond a mean-based framework and employ a quantile regression approach. This allows the persistence of each series and the transmis- sion of shocks between the Consumer Price Index (CPI) (the total CPI which is a percentage change over the past 12 months), the Interest Rate (IR)(the target for the overnight rate), the New Housing Price Index (NHPI), and high-frequency supply chain …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Machine Learning Classification Of Prostate Cancer Genomic Sequences Using K-Mer And Sequence-Derived Features, Kuldeep Rawat, Hirendra Nath Banerjee, Jamie Noble, Saa Naudia Deloatch, Satyendra Banerjee, Sachin Shetty, Soumya Banerjee

VMASC Publications

Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

The Effects Of Systematic Instructional Interventions On Functional Communication, Vocational Independence, And Academic Performance For High School Students With Autism In A Self-Contained Special Education Setting, Sarah O. Shalash

University of Kentucky Master's Theses

This project evaluated the effects of systematic instruction on functional communication, vocational independence, and functional mathematics for three high school students with autism in a self-contained special education classroom using three A–B single-case designs. Interventions included a system of least prompts for AAC requesting and vocational task completion and constant time delay for a functional timer-setting task. Student performance was measured as the percentage of independently completed responses across instructional sessions. Student T demonstrated meaningful improvement in independent AAC requesting and reached 75% independence in the final session. Student B demonstrated emerging improvement in timer-setting performance and reached 60% independence …

Exploring Undergraduate Women’S Experiences Of Collaborative Learning In Engineering Education: A Phenomenological Study Of The Social World, Sandra Ireri Cruz Morena

Doctoral

The persistent underrepresentation of women in engineering education remains a concern across global and national contexts, despite rising overall enrolment of women in higher education. In Ireland, the national average of women entering Engineering, Manufacturing, and Construction (EMC) programmes has gradually increased; however, the rate at Technological University Dublin (TU Dublin) has stayed at approximately 15% since 2016. This study seeks to better understand the gendered challenges underlying such trends by exploring the lived experiences of female undergraduate engineering students at TU Dublin, particularly in relation to collaborative learning environments such as project- and problem-based learning (PBL).

Understanding Addiction As A Disease: College Students’ Perspectives On Substance Use In The Covid-19 Era, Aditi Rudrashetty

Athena: Undergraduate Research and Literary Journal

This research explores the neurological basis of addiction as a disease and how the exacerbation of external factors has contributed to negative perceptions of addiction. Specifically, it examines the COVID-19 pandemic and contemporary college student experiences as contextual factors influencing attitudes toward substance use disorders. Negative perceptions contribute to stigma, reduce help-seeking behavior, and influence policy responses, hindering effective support and recovery for individuals with substance use disorders. By examining these perceptions, this study contributes to understanding how addiction is conceptualized and potentially addressed in public health and policy contexts. Surveys and semi-structured interviews were conducted with college students to …

From Reviews To Value: Harnessing Crowdsourced Data To Capture Visitor Perceptions And Economic Benefits Of Recreation, Laura Costadone, Shan Zhang

ODU Articles

Quantifying the recreational value of protected natural areas is essential for sustainable management, conservation financing, and informed decision-making. Traditional approaches, such as on-site surveys, are often costly, spatially limited, and constrained by regulatory barriers. This study evaluates the potential of crowdsourced user-generated data to jointly assess visitor perceptions and the economic value of nature-based recreation, using Back Bay National Wildlife Refuge (Virginia, USA) as a case study. We integrated georeferenced photographs and textual content from four platforms (Flickr, TripAdvisor, Yelp, and AllTrails) with supplementary survey data to analyze visitation patterns, visitor sentiment, cultural ecosystem services, and recreational value using the …

Recent Advances In Triboelectric Nanogenerators For Biomedical And Cardiovascular Monitoring, Amit Sarode, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, …

Mechanisms Driving Disparities In Income Mobility Across The Income Distribution, Joe Larkins

Honors Theses

This study examines intergenerational income persistence across the income distribution, testing whether mechanisms driving inequality differ between families in the top and bottom halves of the income distribution. Using data from the National Education Longitudinal Study of 1988 (NELS:88), a nationally representative longitudinal survey of 8th grade students and their parents, this research estimates an interaction model comparing parental income effects for children in advantaged versus disadvantaged economic circumstances. The analysis reveals that a $1,000 increase in parental income yields eight times greater income gains for children in the bottom half of the distribution compared to those in the top …

Small Antiperfect Steiner Triple Systems, Justin Z. Schroeder, Joshua Ganschow

Research & Publications

The cycle structure of Steiner triple systems (STS) has been well studied with regard to uniform STS and cycle switching. Of particular interest among uniform STS are perfect STS, in which every cycle graph consists of a single cycle. In this paper, we initiate the study of antiperfect STS, in which every cycle graph consists of a union of at least two cycles. We prove that an antiperfect STS(n) exists for all admissible n ≥ 15 and provide a complete listing of all antiperfect STS(n) for n ≤ 19 and all antiperfect STS(21) with a non-trivial automorphism. Furthermore, it is …

Beyond The Classroom: Exploring High School Graduates' Perceptions Of How Extracurricular Activities Influence College And Career Readiness, Miranda B. Grigg

Doctor of Education Dissertations

This mixed methods study explored high school graduates’ perceptions of how participation in extracurricular activities influenced their college and career readiness. Grounded in Bandura’s (1986) Social Cognitive Theory and Deci and Ryan’s (1985) Self-Determination Theory, the study examined how experiences in athletics, clubs, organizations, and leadership roles contributed to graduates’ development of confidence, motivation, leadership skills, belonging, and real-world competencies. A sequential explanatory mixed methods design was employed. The quantitative phase consisted of a 20-item survey completed by 67 graduates of a rural South Carolina high school, with responses analyzed using descriptive statistics and chi-square goodness-of-fit tests. Reliability was further …

Education For Democratic Citizenship: Separating Purpose From Outcomes, Anne Jegede

Florida A & M University Law Review

American education is failing minority and indigent students because it disproportionately prioritizes academic achievement and economic success over creating democratic citizens and cultivating essential skills such as critical thinking, civic engagement, and cultural understanding.

Hydrogeochemical And Redox Controls On Nitrate And Arsenic Co-Occurrence In The Western Kansas High Plains Aquifer (Usa): A Composite Health Risk Assessment And The Case For Risk-Informed Private Well Governance, Jonathan Kuffour Owusu

Master's Theses or Doctor of Nursing Practice

Fifty-one private domestic wells across western Kansas were sampled to quantify nitrate and arsenic occurrence, identify geochemical controls, and evaluate carcinogenic and non-carcinogenic health risks for adult and child receptors in a region where groundwater serves as the primary drinking water source with no routine regulatory oversight. Samples were analyzed for major ions, nutrients, and trace elements by ICP-MS, ion chromatography, and UV-Vis spectrophotometry. Shapiro-Wilk testing confirmed non-normal distributions for both contaminants; inter-county comparisons were therefore conducted using Kruskal-Wallis tests with Dunn's post-hoc correction. Health risk was quantified via chronic daily intake (CDI), hazard quotient (HQ), HQ-based Water Quality Index …

News From The States, Blake Denton

The Southeastern Librarian

No abstract provided.

Impact Of Bubble-Induced Turbulence On Two-Phase Flow Dynamics At High Void Fraction In A Large Diameter Channel, Sungje Hong, Joshua P. Schlegel, Subash L. Sharma

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study represents the first investigation into the influence of bubble-induced turbulence (BIT) on interfacial area transport mechanisms in gas–liquid two-phase flows under conditions of high void fraction and high velocity in a large diameter channel. Given the unique characteristics of bubble flow in larger channels, the turbulent effects induced by bubbles differ from those observed in smaller channels. However, limited research exists regarding the impact of BIT beyond bubbly flows in large-diameter channels. To address this gap, two approaches for implementing the BIT model are explored: a direct method and an indirect method. This paper assesses both the general …

Special Issue: Innovative Numerical Approaches For Problems In Science And Engineering, Xiaoming He, Shuhao Cao, Qiao Zhuang

Mathematics and Statistics Faculty Research & Creative Works

No abstract provided.

Equilibrium Stability Under Nuclear Confrontation, Martin Bohner, A. A. Martynyuk

Mathematics and Statistics Faculty Research & Creative Works

This article proposes and analyzes mathematical models of confrontation between two and n countries, including countries with nuclear weapons. The proposed models are based on a generalization of Richardson's well-known mathematical model of the arms race. Namely, the factor of hostility is filled with expanded content, including public opinion and the armed forces of the opposing countries. Qualitative analysis of confrontation models is carried out by the method of Lyapunov functions and by applying nonlinear integral inequalities. As a result of the analysis, the conditions for the stability of the equilibrium state of the opposing countries are established, and the …

Effective Deep Learning Architectures For Structured Data Analysis And Generation, Md Atik Ahamed

Theses and Dissertations--Computer Science

The effective utilization of structured data is fundamental to modern machine learning, yet it presents distinct challenges in both predictive analysis and generative modeling. Traditional deep learning architectures, particularly Transformers, often suffer from quadratic computational complexity when processing long sequences. This dissertation addresses these limitations by introducing novel architectures based on State-Space Models (SSMs) and Diffusion Models. In the area of predictive analysis, we focus on overcoming the computational bottlenecks of attention mechanisms for tabular and time-series data. First, we introduce MambaTab, a selective state-space architecture designed for efficient tabular classification. By leveraging the linear complexity of SSMs, MambaTab significantly …