Digital Commons Network^™

Rapid Prediction Of Coastal Flooding With Deep Neural Networks, Ali Shahabi, Navid Tahvildari

Graduate Student Government Association Research Conference

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from extreme storm tides and recurrent nuisance flooding. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, enormous efforts have been made to utilize machine learning (ML) based data-driven models for the emulation and prediction of storm tides. However, flood advisory systems still rely on running computationally demanding real-time hydrodynamic models. because developing highly reliable ML-based models suitable for real-time forecasting and capable of capturing any surge levels is challenging. …

Zombie-Auras: Genai And Hybrid Text Production, Joshua Nieubuurt

English Faculty Publications

This paper explores the iterative evolutions of textual production and their impact on the “aura” of texts, as conceptualized by Walter Benjamin. The study identifies three key phases of textual production: the natural, the mechanized, and the digitized, each progressively displacing the “cult value” of texts. This cult value is lost through increased ease of creation, reproduction, dissemination, and dislocation of creators and audiences in time and space. The advent of Generative AI (GenAI) marks the latest evolution, transforming the “aura” into a memetic “zombie” form—familiar yet opaque, evoking both the sublime and fear. By examining Benjamin’s notion of “aura” …

The Elizabeth River Marine Debris Stakeholder Roundtable, Wie Yusuf, Joseph Barnes, Robin Dunbar, Jennifer L. Whytlaw, Tom Allen

School of Public Service Faculty Publications

Convened by Virginia Sea Grant, Old Dominion University, Clean Virginia Waterways (CVW), Virginia Coastal Zone Management Program (CZM), and the Virgina Marine Resources Commission. Supported by Sea Grant Marine Debris Community Action Coalition funding (for project titled From Learning to Willing to Doing: A Collaborative Approach to implementing the Virginia Marine Debris Reduction Plan.

Bridging The Gap: Understanding And Mitigating Csrf Threats In Service Worker Environments, Sivakanesan Dhanushkanda, Mustafa A. Ibrahim

Graduate Student Government Association Research Conference

Progressive Web Applications (PWAs) are gaining popularity due to their rich features. Service Workers (SWs), one of its integral components, make this possible by providing users with offline functionality, improved performance, and effective caching techniques. SWs act as proxies positioned between the client browser and the web server, capable of intercepting requests and responses. Recent research has revealed that, despite being designed with security in mind, there are ways to circumvent these security precautions and launch various attacks.

Sensitive functions in JavaScript are functions that can introduce security vulnerabilities if not properly coded or validated. These functions can manipulate the …

Perspectives On A Recently Merged Family Medicine Residency Program: A Post-Match Survey Of Ranked, Unmatched Applicants, John E. Snellings, Karo G. Ohanian, Ismail El Moudden

Eastern Virginia Medical Journal

Introduction: Residency program mergers are a topic of much consternation and relatively little scholarly investigation in graduate medical education. After undergoing a residency merger in July 2020, the newly formed Eastern Virginia Medical School (EVMS) Family Medicine Residency Program investigated the effects of the merger on the perspectives of the applicant pool for the subsequent 2020–2021 recruiting season. Methods: We conducted an anonymous survey of EVMS Family Medicine applicants during the 2020– 2021 recruiting season. Students who interviewed with the residency and were subsequently placed on the program’s National Residency Matching Program (NRMP) rank order list (ROL) but did not …

Successfully Navigating The Disruption Ai Will Bring To Survey Research, David M. Rothschild, Trent D. Buskirk, Stephanie Eckman, D. Sunshine Hillygus, Frauke Kreuter, David Lazer

Information Technology & Decision Sciences Faculty Publications

Surveys are a core methodological tool in government, industry, and academia, providing essential data for theory development and evidence-based decision-making. As artificial intelligence continues its rapid advancement, it stands to fundamentally transform the entire survey lifecycle - from design and administration to analytics and reporting. Previous transitions to new technologies, such as telephone, internet, and non-probability surveys, led to divisions within the survey research community with real consequences for both the trajectory of research and trust in the industry. We believe the survey community should take proactive steps now to avoid similar challenges with AI integration. Specifically, our paper examines …

Empirical Vulnerability Function Development Based On The Damage Caused By The 2014 Chiang Rai Earthquake, Thailand, Patcharavadee Hong, Masashi Matsuoka

Institute for Innovation & Entrepreneurship Publications

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand's building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving …

Restored Wetlands Show Rapid Vegetation Recovery And Substantial Surface-Water Expansion, Thilina D. Surasinghe, Yin-Hsuen Chen, Kunwar K. Singh

Center for Geospatial Science, Education & Analytics Faculty Publications

Ecological restoration is essential for improving the ecological integrity of degraded ecosystems to enhance ecosystem services and biodiversity. In this study, we assessed the effectiveness of wetland restoration on retired cranberry farms by analyzing vegetation recovery and surface-water dynamics using the enhanced vegetation index (EVI) and normalized difference water index (NDWI) derived from Sentinel-2 satellite imagery. To quantify temporal dynamics of both vegetation recovery and surface-water cover, we identified the spectral distinctions among restored wetland plant communities. Our results indicated the emergence of distinct plant communities upon restoration. Restored wetlands in general showed significant and progressive vegetation recovery and expanding …

Landslide At River's Edge: Alum Bluff, Apalachicola River, Florida, Joann Mossa, Yin-Hsuen Chen

Center for Geospatial Science, Education & Analytics Faculty Publications

When rivers impinge on the steep bluffs of valley walls, dynamic changes stem from a combination of fluvial and mass wasting processes. This study identifies the geomorphic changes, drivers, and timing of a landslide adjacent to the Apalachicola River at Alum Bluff, the tallest natural geological exposure in Florida at similar to 40 m, comprising horizontal sediments of mixed lithology. We used hydrographic surveys from 1960 and 2010, two sets of LiDAR from 2007 and 2018, historical aerial, drone, and ground photography, and satellite imagery to interpret changes at this bluff and river bottom. Evidence of slope failure includes a …

Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

Golden Spirals Everywhere? Solutions For Fermi Questions, January 2025, John Adam

Mathematics & Statistics Faculty Publications

The article discusses different types of spirals, including Archimedean, hyperbolic, and logarithmic spirals, with a focus on the golden ratio and golden spirals. It addresses the misconception that golden rectangles and spirals can be found in various natural and man-made structures, emphasizing the importance of understanding the properties of logarithmic spirals. The article provides mathematical explanations and solutions for questions related to pitch angles and self-similarity in logarithmic spirals, using examples like the nautilus shell and an ammonite-like stone. It concludes by referencing additional sources for further exploration of the golden ratio and golden spiral myths.

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

A Time-Domain Boundary Integral Equation For Moving Acoustic Sources In Uniform Flow And Its Solution By An Advanced Time Propagation Approach, Fang Q. Hu, Douglas M. Nark

Mathematics & Statistics Faculty Publications

This paper presents a time-domain boundary integral equation (TDBIE) formulation for predicting acoustic scattering from moving sources in a uniform mean flow. This work is motivated by the increasing need for accurate aeroacoustic modeling of modern aircraft configurations, including VTOL and eVTOL systems with rotating components. A key challenge in time-domain scattering simulations with moving sources is the determination of retarded time for a given observer time, which involves solving an implicit equation at each time step. This can be computationally costly, particularly for numerical solution of the TDBIE where every surface element on the scattering body acts as an …

Covariate Selection For Rna-Seq Differential Expression Analysis With Hidden Factor Adjustment, Farzana Noorzahan, Hyeongseon Jeon, Yet Nguyen

Mathematics & Statistics Faculty Publications

In RNA-seq data analysis, a primary objective is the identification of differentially expressed genes, which are genes that exhibit varying expression levels across different conditions of interest. It is widely known that hidden factors, such as batch effects, can substantially influence the differential expression analysis. Furthermore, apart from the primary factor of interest and unforeseen artifacts, an RNA-seq experiment typically contains multiple measured covariates, some of which may significantly affect gene expression levels, while others may not. Existing methods either address the covariate selection or the unknown artifacts separately. In this study, we investigate two integrated strategies, FSR_sva and SVAall_FSR, …

The Integration Of Big Data In Fintech: Review Of Enhancing Financial Services Through Advanced Technologies, Soudeh Pazouki, Mohammad B. Jamshidi, Mirarmia Jalali, Arya Tafreshi

Management Faculty Publications

Big data analytics is revolutionizing the FinTech industry, offering new opportunities for real-time decision-making, personalized financial services, and improved risk management. By leveraging advanced technologies like machine learning and artificial intelligence, financial institutions can efficiently detect fraud, predict market trends, and create innovative solutions tailored to customer needs. Big data also plays a critical role in promoting financial inclusion through alternative credit scoring models, providing access to credit for underserved populations and fostering broader participation in the financial system.

However, the integration of big data into FinTech is not without its challenges. Issues such as data privacy concerns, regulatory complexities, …

Analysis Of Errors In The Management Of Cutaneous Disorders, Robert J. Pariser, Sarah Alnaif

Department Dermatology Faculty Publications

In this study, we prospectively and retrospectively evaluated the occurrence of errors in the management of cutaneous disorders from patient visits and medical records in a single dermatology practice in southeast Virginia over a 3-year period (June 2020-July 2023). Providers should be able to improve diagnostic accuracy by utilizing established rapid bedside diagnostic techniques.

Teaming With Technology: Adaptive Automation In Joint Cognitive Systems For Industry 5.0, Jessica Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Adaptive automation enables dynamic reallocation of functions between people and autonomous agents to improve performance in complex work. This paper presents a meta-analysis of experimental and quasi-experimental studies (2000-2025) on joint cognitive systems in industrially relevant contexts, quantifying effects on task performance, safety/failure management, workload, trust, and learning. Across studies, adaptive automation reliably reduces operator workload and shows moderate gains in task performance and safety, with healthier trust dynamics when adaptations are triggered by human-state or event cues, made transparent to the user, and remain rapidly overridable. Risks emerge when performance-triggered switching is opaque or poorly timed, which can erode …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Extending A Pretrained Language Model (Bert) Using An Ontological Perspective To Classify Students' Scientific Expertise Level From Written Responses, Heqiao Wang, Kevin C. Haudek, Amanda D. Manzanares, Chelsie L. Romulo, Emily A. Royse, Caterina B. Azzarello

Human Movement Studies & Special Education Faculty Publications

The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students' contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the current teaching and learning of science. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students' responses from a cognitive perspective. …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Information Technology & Decision Sciences Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Determining The Transient Electric Field And The Effect Of Pulse Repetition Frequency On The Field In A Repetitive 10-Ns Pulsed Discharge In A Quiescent Ch₄-Air Mixture, Chunqi Jiang

Bioelectrics Publications

The goal of this research is to understand the underlying physics enabled by nanosecond pulsed power in a discharge initiation and the following physicochemical processes that favor lean-fuel plasma ignition for combustion. The hypothesis of this project is that pulsed power waveforms such as the pulse repetition frequency (PRF), voltage amplitude, and dielectric surface alter the reduced electric field during the initiation of the discharge, resulting in different plasma properties, which will vary the physicochemical processes for efficient and selective radical productions. This is especially important for lean-burn combustion and reducing emission.

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Psychology Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Fault Tree Analysis For Robust Design, Jonathan Degroff, Gene Jean-Win Hou

Mechanical & Aerospace Engineering Faculty Publications

The objective of this research is to incorporate system failure into a robust design formation and solution process. The system failure referred to here will be built using fault tree analysis (FTA), which will take all lower-level failure events into consideration. Two examples are investigated here. One will directly treat the probabilities of the basis events as design variables, The other will be formulated in five different models: deterministic design optimization, the reliability index-based, the “and” gate-based, the “or” gate-based and the “inhibit” gate-based robust design. Their corresponding optimization solutions will be compared with each other. The post-optimality analysis of …

Polarized Positrons At Ce⁺Baf, A. Ushakov, J. Benesch, M. Bruker, L. Cardman, J. Conway, S. Covrig Dusa, P. Degtiarenko, Y. Enomoto, S. Gessner, P. Ghoshal, S. Gopinath, J. Grames, C. Gulliford, S. Habet, G. Hays, C. Hernández-García, A. Hofler, R. Kazimi, M. Kostin, V. O. Kostroun, F. Lin, V. Lizárraga-Rubio, K. Mahler, Y. Morikawa, S. Nagaitsev, S. Ogur, G. Palacios-Serrano, N. Raut, B. Rimmer, Y. Roblin, A. Seryi, K. Smolenski, M. Stutzman, R. Suleiman, A. Sy, N. Taylor, D. Turner, A. Ushakov, C. Valerio-Lizarraga, E. Voutier, S. Wang, Y. Zhang

Mechanical & Aerospace Engineering Faculty Publications

A baseline concept for a continuous wave (CW) polarized positron injector was developed for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab [1]. This concept is based on positron beam generation by a high current polarized electron beam (1 mA, 120 MeV, >85% polarization) irradiating a rotating water-cooled tungsten target or liquid metal target. The update on the development of the Ce⁺BAF injector concept including the polarized electron source, the development of high power targets, simulations of positron capture, the design of the transport line from the positron injector to the CEBAF North Linac and the planned experiment …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Mass-Adaptive Admittance Control For Robotic Manipulators, Hossein Gholampour, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in realtime. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

Line List For The A²Π−X²Σ⁺ And The B²Σ⁺−X²Σ⁺ Band Systems Of Caf, Léo Lavy, Peter F. Bernath, Adam Pastorek

Chemistry & Biochemistry Faculty Publications

The spectral analysis of molecule-rich asymptotic giant branch (AGB) stars is challenging. Although other calcium and fluorine bearing molecules are observed in the microwave and in the visible spectrum of AGB stars, CaF has never been detected, despite favorable chemical equilibrium predictions. Yet, measuring the CaF abundance could give more insight into the fluorine budget of AGB stars and allow better simulation of stellar spectra. In this work, we present an analysis of the visible spectrum of CaF obtained with a Fourier transform spectrometer. CaF A²Π−X²Σ⁺ and B²Σ⁺−X²Σ⁺ band systems were excited with a hollow cathode discharge. Using previous fluorescence …

Collision-Induced Absorption Spectra Of N₂ And Ch₄, Ryan M. Johnson, Peter F. Bernath, Brant Billinghurst, Jianbao Zhao

Chemistry & Biochemistry Faculty Publications

Collision-induced spectra are essential for radiative transfer modeling of Titan's atmosphere. We present experimental spectra of nitrogen–methane mixtures with an accompanying fit of N₂-CH₄ collision-induced absorption in the 30–400 cm⁻¹ region at about 130 K. We found a peak absorption of 2.30 × 10⁻⁵ cm⁻¹ amagat⁻² at 75 cm⁻¹ and a secondary peak of 1.35 × 10⁻⁵ c⁻¹ amagat⁻² at 206.5 cm⁻¹, which agrees with previous studies. Our work does not support the suggestion that N₂-CH₄ CIA in the far-infrared should be increased by about 50%, as suggested by spectroscopic modeling of Titan using data from A. Borysow & C. …