Digital Commons Network^™

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

Optimizing Superconducting Nb Film Cavities By Mitigating Medium-Field Q-Slope Through Annealing, B. Abdisatarov, G. Eremeev, H. E. Elsayed-Ali, D. Bafia, A. Murthy, Z. Sung, A. Netepenko, A. Romanenko, C. P.A. Carlos, G. J. Rosaz, S. Calatroni, S. Leith, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Niobium films are of interest in applications in various superconducting devices, such as superconducting radiofrequency cavities for particle accelerators and superconducting qubits for quantum computing. In this study, we address the persistent medium-field Q-slope issue in Nb film cavities, which, despite their high-quality factor at low RF fields, exhibit a significant Q-slope at medium RF fields compared to bulk Nb cavities. Traditional heat treatments, effective in reducing surface resistance and mitigating the Q-slope in bulk Nb cavities, are challenging for Nb-coated copper cavities. To overcome this challenge, we employed DC bias high-power impulse magnetron sputtering to deposit …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Time-Marching Quantum Algorithm For Simulation Of Nonlinear Lorenz Dynamics, Efstratios Koukoutsis, George Vahala, Min Soe, Kyriakos Hizanidis, Linda Vahala, Abhay K. Ram

Electrical & Computer Engineering Faculty Publications

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state …

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

The Effect Of Electrode Geometry On Excited Species Production In Atmospheric Pressure Air-Hydrogen Streamer Discharge, Shirshak Kumar Dhali, Stuart Reyes

Electrical & Computer Engineering Faculty Publications

When a gas is overvolted at or near atmospheric pressure, it results in a streamer discharge formation. Electrode geometries exert significant impact on the electrical breakdown of gases by altering the spatial profile of the electric field. In many applications the efficient generation of radicals is critical and is determined by the characteristics of the streamer discharge. We examine the effect of electrode geometry on the streamer characteristics and the production of radicals. This is performed for three different electrode geometries: plane–plane, pin–plane, and pin–pin. A two-dimensional rotationally symmetric fluid model is used for the streamer discharge simulation in the …

The Effect Of Electrode Geometry On Excited Species Production In Atmospheric Pressure Air–Hydrogen Streamer Discharge, Shirshak Kumar Dhali, Stuart Reyes

Electrical & Computer Engineering Faculty Publications

When a gas is overvolted at or near atmospheric pressure, it results in a streamer discharge formation. Electrode geometries exert significant impact on the electrical breakdown of gases by altering the spatial profile of the electric field. In many applications the efficient generation of radicals is critical and is determined by the characteristics of the streamer discharge. We examine the effect of electrode geometry on the streamer characteristics and the production of radicals. This is performed for three different electrode geometries: plane–plane, pin–plane, and pin–pin. A two-dimensional rotationally symmetric fluid model is used for the streamer discharge simulation in the …

Drone-Based Medication Delivery For Rural, Flood-Prone Coastal Cities, Yin-Hsuen Chen, Amro M. El-Adle, Kevin J. O'Brien, Taylor Wentworth, Heather G. Richter

Center for Geospatial Science, Education & Analytics Faculty Publications

Access to healthcare remains a critical challenge for rural populations, particularly in flood-prone coastal communities where transportation barriers limit access to essential medical services. This study evaluates the effectiveness of drone-based medication delivery in improving healthcare accessibility for vulnerable populations on Virginia’s Eastern Shore. Compared to traditional personal vehicle travel, drone delivery reduced trip times from up to 50 minutes to under 10 minutes for more than 80% of the population, including elderly patients. Using publicly available datasets, we developed two transportation vulnerability indices that incorporate age, travel time, and flood risk to prioritize patients for drone-based pharmaceutical delivery. These …

A Spine-Specific Lexicon For The Sentiment Analysis Of Interviews With Adult Spinal Deformity Patients Correlate With Sf-36, Sf-36, And Odi Scores: A Pilot Study Of 25 Patients, Ross Gore, Michael M. Safaee, Christopher J. Lynch, Christopher P. Ames

VMASC Publications

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis …

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability, Tan Le, Vanessa Le, Sachin Shetty

VMASC Publications

Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers—anomaly detection, intrusion confirmation, and multiattack classification—each aligned with O-RAN’s telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic …

A Framework For Task Offloading In Heterogeneous Computing Applications Within The Fog Ran Architecture, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

VMASC Publications

Fog Radio Access Network (Fog RAN) has recently emerged as a promising architecture for supporting low-latency applications by bringing fog nodes and cloud resources closer to end users. However, existing research on computational offloading in Fog RAN lacks a comprehensive framework that addresses three key aspects: where to offload tasks, which processing nodes to utilize, and how to allocate resources for tasks with varying latency requirements. To address this gap, we propose TOFRA (Task Offloading for Fog RAN), a novel latency-aware task offloading framework. TOFRA is a centralized system that determines the optimal offloading strategy, whether to execute tasks locally, …

Evaluating The Long-Term Effectiveness Of Marsh Terracing For Conservation With Integrated Geospatial And Wetland Simulation Modeling, Nick Carpenter, Laura Costadone, Thomas R. Allen

ODU Articles

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea Level Affecting Marshes Model (SLAMM) to assess the potential of marsh terraces to mitigate future losses, while also examining the model’s limitations, including its assumptions and capacity to reflect complex marsh processes. A geospatial approach was used to generate 3D representations of terraces through morphostatic modeling within digital elevation models (DEMs). Under a no-restoration scenario, SLAMM projections …

Advances In Bacterial Cellulose-Based Scaffolds For Tissue Engineering: Review, Rewati Raman Ujjwal, Gymama Slaughter

Center for Bioelectronics Publications

Bacterial cellulose (BC) has emerged as a highly versatile and promising biomaterial in tissue engineering, with potential applications across skin, bone, cartilage, and vascular regeneration. Its exceptional properties like high mechanical strength, superior biocompatibility, excellent moisture retention, and inherent ability to support cell adhesion and proliferation, make BC particularly effective for wound healing and skin regeneration. These attributes accelerate tissue repair and foster new tissue formation, highlighting its value in skin-related applications. Additionally, BC's capacity to support osteogenic differentiation, combined with its mechanical robustness, positions it as a strong candidate for bone tissue engineering, facilitating regeneration and repair. Recent advancements …

Fabrication Of Thylakoid Membrane-Based Photo-Bioelectrochemical Bioanode For Self-Powered Light-Driven Electronics, Amit Sarode, Gymama Slaughter

Center for Bioelectronics Publications

The transition toward sustainable and decentralized energy solutions necessitates the development of innovative bioelectronic systems capable of harvesting and converting renewable energy. Here, we present a novel photo-bioelectrochemical fuel cell architecture based on a biohybrid anode integrating laser-induced graphene (LIG), poly(3,4-ethylenedioxythiophene) (PEDOT), and isolated thylakoid membranes. LIG provided a porous, conductive scaffold, while PEDOT enhanced electrode compatibility, electrical conductivity, and operational stability. Compared to MXene-based systems that involve complex, multi-step synthesis, PEDOT offers a cost-effective and scalable alternative for bioelectrode fabrication. Thylakoid membranes were immobilized onto the PEDOT-modified LIG surface to enable light-driven electron generation. Electrochemical characterization revealed enhanced redox …

Electrochemical And Nanomaterial Based Strategies For Nonenzymatic Glucose Detection: A Review, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Electrochemical glucose sensing technologies have undergone significant evolution, with continual advancements aimed at improving sensitivity, selectivity, and user convenience. This review systematically explores the development of emerging nonenzymatic glucose sensor designs. Nonenzymatic sensors are critically evaluated for their ability to overcome enzymatic limitations, leveraging novel materials and catalytic mechanisms. Additionally, the emergence of smartphone-integrated glucose monitoring systems is highlighted as the fifth generation, representing a paradigm shift toward personalized, real-time healthcare management. Emphasis is placed on the strategies employed to reduce the working electrode potential and enhance analytical performance. Key analytical metrics and real-sample applicability are evaluated, and persistent challenges …

The First Iucn Red List Of Cold-Water Corals Highlights Global Declines, Julia D. Sigwart, A. Louise Allcock, Renata Carolina Mikosz Arantes, Kelsey Archer Barnhill, Narissa Bax, Julia S. Beneti, Saskia Brix, Gudmundur Gudmundsson, Catherine S. Mcfadden, Severin A. Korfhage, Christi Linardich, Declan Morrissey, Pedro De Oliveira Nascimento, Bárbara De Moura Neves, Steinunn H. Ólafsdóttir, Stefán Ragnarsson, Kaveh Samimi-Namin, Íris Sampaio, Mark E. De Wilt

Biological Sciences Faculty Publications

The most well-known species-based conservation tool is the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. The current coverage of species in the Red List is known to under-represent benthic marine species. Cold-water corals (CWCs) are increasingly recognised as key to deep-water biodiversity and integral to protected vulnerable marine ecosystems (VMEs), but no deep-sea coral species were previously included in the Red List. We selected 22 cold-water coral species in the Northeast Atlantic, including 4 reef-forming stony corals and 18 octocorals including sea pens and gorgonians, and completed the first IUCN Red List global assessments …

Integrated Valuation Of The Ecological, Social And Economic Benefits Provided By A Multifunctional Nature-Based Solution, Laura Costadone, Shan Zhang

ODU Articles

Nature-based Solutions (NbS) offer multifunctional approaches to address climate change and environmental challenges, providing a sustainable alternative to traditional gray infrastructure. Despite their promise, widespread adoption remains limited, in part due to an incomplete understanding of their full costs and benefits relative to conventional infrastructure. Traditional benefit–cost analyses often overlook non-monetized benefits and the interconnected ecosystem services provided by NbS. This study introduces a methodological approach to quantify both the physical and monetary value of ecosystem services and co-benefits delivered by an NbS project. We applied an integrated valuation framework to a case study in Virginia Beach, VA, USA, where …

Match Accuracy Of Burned Teeth: A Pilot Study Of Allied Dental Professionals, Brenda T. Bradshaw, Marsha A. Voelker, Samantha C. Vest, Sinjini Sikdar

Dental Hygiene Faculty Publications

Purpose: The purpose of this pilot study was to assess allied dental professionals' match accuracy of burned teeth; a skill required by disaster victim identification (DVI) team members.

Methods: This cross-sectional study used a convenience sample of registered dental hygienists (RDH) (n=15) and dental assistants (DA) (n=15) to assess their match accuracy of burned teeth with simulated antemortem (AM) and postmortem (PM) images. Fifteen human teeth were heated at 400°C for 15 minutes. Prior to and following heat alteration, each tooth was photographed and radiographed. Images were presented to participants in randomized order, and they were instructed to correctly match …

Improving Dental Hygiene Students' Knowledge, Attitudes, And Confidence Toward Prenatal Oral Health Through Experiential Learning, Denise C. Mckinney, Sinjini Sikdar, Shillpa Naavaal

Dental Hygiene Faculty Publications

Purpose Maintaining optimal oral health is essential for maternal health as well as the health of their child. The purpose of this pilot study was to examine dental hygiene (DH) students' knowledge of prenatal oral health before and after receiving an asynchronous prenatal oral health care training (POHCT), and their attitudes and self-perceived confidence before and after receiving an experiential learning opportunity with a pregnant and/or post-partum client at a Women's Infants and Children nutrition program (WIC) clinic.

Methods A one-group pre/post-test design was used. This IRB-approved study (#2027439-4) utilized a convenience sample of DH student participants (n=37) in their …

Optimization Of Serum And Salivary Cortisol Interpolation For Time-Dependent Modeling Frameworks In Healthy Adult Males, Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

Rehabilitation Sciences Faculty Publications

Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches that can handle differing biological sampling frequencies to maximize these data in more detailed and time-dependent analyses. Methods: Healthy adult males [N = 8; 26.1 (±3.1) years; 176.4 (±8.6) cm; 73.1 (±12.0) kg)] completed two 24 h admissions: one at rest and one including a high-intensity exercise session on the cycle ergometer. Serum and salivary cortisol were sampled every 60 and 120 min, respectively. Six alternative sampling profiles were defined by downsampling …

Clinician Perspectives On Virtual Reality Use In Physical Therapy Practice In The United States, Danielle T. Felsberg, Jared T. Mcguirt, Scott E. Ross, Louisa D. Raisbeck, Charlend K. Howard, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

The primary goal of physical rehabilitation is to assess movement impairments and restore function to improve overall quality of life. Virtual reality (VR) may provide the optimal environment to promote these goals due to its motivating and modifiable nature which can be difficult to accomplish through traditional real-world therapeutic methods. Current research of VR for rehabilitation has demonstrated that VR interventions can produce clinically meaningful change in motor outcomes. Despite this, adoption and usage of VR by physical therapy professionals is unclear due to the limited research in this area. Thus, the purpose of this study was to identify the …

Salary Negotiation Dynamics For Athletic Trainers: Insights From Employers On Expectations And Organizational Influences, Julie M. Cavallario, Forrest Andrews, Kendallyn Johnson, Katherine Schalk, Cailee E. Welch Bacon

Rehabilitation Sciences Faculty Publications

Context

While researchers have previously identified that most athletic trainers (ATs) do not negotiate their salary, little is known about employers' expectations and behaviors relative to establishing and negotiating salaries for the ATs they hire.

Objective

To examine employers' expectations and behaviors regarding AT salary negotiation.

Design

Cross-sectional.

Setting

Web-based survey.

Patients or other participants

413 employers who are in a role that requires them to negotiate salary offers with prospective AT employees and who have minimally hired one AT into the organization they work for accessed the survey from a random sample of 7,000 ATs (6% access rate); 324 …

An 11-Year (2012-2022) Review Of Journal Of Athletic Training Publication Study Designs And Sample Sizes, Zachary K. Winkelmann, Samantha E. Scarneo-Miller, Emily C. Smith, Ryan M. Argetsinger, Lindsey E. Eberman

Rehabilitation Sciences Faculty Publications

Background

Research findings must be representative by creating a sample of individuals, ensuring the results can be generalized and applicable to a larger population, which has historically been guided by a power analysis. However, the varied research design methods require a unique approach to sampling and a formula for recruitment and size. Therefore, the purpose of this study was to analyze historical data from published manuscripts in the Journal of Athletic Training (JAT) relative to study design and sample sizes. A secondary purpose was to further explore metrics for survey-based research.

Methods

This descriptive analysis explored 1267 publications in each …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

High-Resolution Modeling Of Extreme Heat Events With Socioeconomic Consideration: A Real-Case Wrf-Les Approach, Maryam Golbazi, Frank Liu, Yin-Hsuen Chen, Timothy W. Juliano, Heather Richter

ODU Articles

The overarching goals of this work is to explore best practices for micro-scale modeling of a real case, identify relevant phenomena by using high-resolution modeling, and to explore their implications for public health, and climate resilience strategies in Hampton Roads, VA, USA. This project employs the Weather Research and Forecasting (WRF) model to conduct a comprehensive study of Hampton Roads, utilizing a coupled mesoscale to microscale modeling capable of resolving boundary layer turbulence. This study has three primary objectives: (1) to establish the optimal mesoscale to Large-Eddy Simulation (LES) configurations for complex geographical regions such as the Hampton Roads (HR) …

Engagement In Practice: Partnering With Communities To Address Nuisance Flooding Challenges, Carol L. Considine, Mujde Erten-Unal, Dalya Ismael, Luka Alandra Hamal-Serenity, Farzaneh Soflaei

Engineering Technology Faculty Publications

Many communities are already experiencing the impacts of climate change that disrupt their daily lives. In Coastal Virginia, these impacts take the form of nuisance and stormwater flooding caused by sea level rise and changes in precipitation. Coastal Virginia has one of the highest relative sea level rise rates on the Atlantic Coast and the regional planning district commission recommends that Atlas 14 rainfall intensity, duration, and frequency curves be increased by 20% to account for changes in rainfall. The Coastal Community Design Collaborative (CCDC), a partnership between Hampton University Architecture and Old Dominion University Engineering & Technology, has had …