Digital Commons Network^™

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

A Review Of Emerging Sensor Technologies For Tank Inspection: A Focus On Lidar And Hyperspectral Imaging And Their Automation And Deployment, Sergio Pallas Enguita, Chung-Hao Chen, Samuel Kovacic

Electrical & Computer Engineering Faculty Publications

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

Predictive Modeling Of Healthcare Traffic Using Machine Learning: A Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md. Rafid Hassan, Nondon Lal Dey, Md. Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Effective healthcare traffic management is critical for ensuring prompt medical services, particularly in emergencies where delays can have life-threatening consequences. This study conducts a comparative analysis of three popular machine learning models—Linear Regression, Decision Trees, and Random Forests—for predicting healthcare-related traffic volumes. Utilizing a comprehensive dataset from a metropolitan interstate traffic system, the models were evaluated based on key performance metrics, including Mean Squared Error (MSE), R² Score, and execution time. The findings demonstrate that the Random Forest model outperforms the others, offering superior predictive accuracy and efficiency. These insights are valuable for optimizing traffic management in healthcare, ultimately contributing …

Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in …

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

Embedding Software Engineering In Mixed Methods: Computationally Enhanced Risk Communication, Ann Marie Reinhold, Madison H. Munro, Elizabeth A. Shanahan, Ross J. Gore, Barry C. Ezell, Clemente I. Izurieta

VMASC Publications

Mixed methods research ameliorates many convergent research challenges within the contemporary sociotechnical landscape. We suggest the integration of software engineering in mixed methods studies is a critical step to address some of the remaining and persistent challenges. One such research challenge where software engineering is particularly well suited is in hazard preparedness—in particular, the creation of risk communication messages to mitigate or prevent harm. Computationally enhanced risk communication is convergent research that integrates software engineering and social science research for the benefit of protecting humans and infrastructure. To this end, we developed a mixed methods framework for the efficient construction …

Cyclegan-Gradient Penalty For Enhancing Android Adversarial Malware Detection In Gray Box Setting, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures, Aymar Le Père Tchimwa Bouom, Jean-Pierre Lienou, Wilson Ejuh Geh, Frederica Nelson, Sachin Shetty, Charles Kamhoua

VMASC Publications

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node …

Classification Of Instructional Activities In Classroom Videos Using Neural Networks, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos have become an integral part of classroom observation and instructional quality studies. Researchers encounter substantial financial and time-consuming costs gathering trained personnel to catalogue and analyze a large quantity (i.e., hundreds of hours) of classroom videos. In this chapter, we argue that deep-learning neural networks have potential to help with these tasks by detecting features of instruction in videos. We evaluated the performance of three neural networks in detecting instructional activities in classroom videos. The video dataset included a total of forty-six hours, which was evenly split between elementary mathematics and English language arts lesson recordings. Results showed …

Targeting Ocean Conservation Outcomes Through Threat Reduction, Joseph A. Turner, Malcolm Starkey, Nicholas K. Dulvy, Frank Hawkins, Louise Mair, Adeline Serckx, Thomas Brooks, Beth Polidoro, Stuart H. M. Butchart, Kent Carpenter, Minna Epps, Rima W. Jabado, Nicholas B. W. Macfarlane, Leon Bennun

Biological Sciences Faculty Publications

Nations have committed to reductions in the global rate of species extinctions through the Sustainable Development Goals 14 and 15, for ocean and terrestrial species, respectively. Biodiversity loss is worsening despite rapid growth in the number and extent of protected areas, both at sea and on land. Resolving this requires targeting the locations and actions that will deliver positive conservation outcomes for biodiversity. The Species Threat Abatement and Restoration (STAR) metric, developed by a consortium of experts, quantifies the contributions that abating threats and restoring habitats in specific places offer towards reducing extinction risk based on the IUCN Red List …

When And Where Can Coastal Wetland Restoration Increase Carbon Sequestration As A Natural Climate Solution, Scott F. Jones, Ariane Arias-Ortiz, Dennis Baldocchi, Meagan Eagle, Daniel A. Friess, Catrina Gore, Greg Noe, Stefanie Nolte, Patty Oikawa, Adina Paytan, Jacqueline L. Raw, Brian J. Roberts, Kerrylee Rogers, Charles Schutte, Camille L. Stagg, Karen M. Thorne, Eric J. Ward, Lisamarie Windham-Myers, Erik S. Yando

Biological Sciences Faculty Publications

Coastal wetlands are hotspots of carbon sequestration, and their conservation and restoration can help to mitigate climate change. However, there remains uncertainty on when and where coastal wetland restoration can most effectively act as natural climate solutions (NCS). Here, we synthesize current understanding to illustrate the requirements for coastal wetland restoration to benefit climate, and discuss potential paths forward that address key uncertainties impeding implementation. To be effective as NCS, coastal wetland restoration projects will accrue climate cooling benefits that would not occur without management action (additionality), will be implementable (feasibility) and will persist over management-relevant timeframes (permanence). Several issues …

Response Of Helenium Amarum To Environmental Stressors In The Presence Of Mycorrhizal Fungi And Its Role As A Pollinator Resource, Austin Jameson, Lisa Horth

Biological Sciences Faculty Publications

Globally, climate change is causing shifts in precipitation patterns. Shifting precipitation patterns can be a stressor for many species. One species that is experiencing such stress is Helenium amarum Raf (H. Rock). H. amarum, thrives in dry upland pastures and low-lying grassy and sandy habitat in the mid-Atlantic (USA), where it persists despite storm-driven standing water. Helenium amarum is a species that is toxic to livestock and has been found outside its native range. Temperature and light impacts on germination are already known, but the role of water availability and potential ameliorating effects of mycorrhizae during water stress have …

Meeting The Needs Of Diverse Esl Classrooms: A Team Approach To The Professional Development Of Educators, Alla Zareva, Silvana Watson

English Faculty Publications

The focus of this chapter is threefold: 1) To report on the effectiveness of a professional development program offered to elementary school educators to work effectively with diverse ELs; 2) to present the results from a pre-professional development survey which helped identify specific aspects of working with diverse ELs in immediate need of professional development; and 3) to discuss the wider implications of our findings and recommendations for teacher preparation programs. The chapter reports on three main areas of a year-long professional development training provided to teams of in-service elementary school teachers, school administrators, and other specialists (N=60) in the …

Enhancing Rural Science Education Through School District-University Partnership, Venicia A. Ferrell, Alexis S. Tharpe

Center for Educational Partnerships Publications

This instrumental case study describes the steps taken to establish and implement a university–school partnership to improve rural science teachers’ content knowledge and teaching practices and students’ achievement in elementary science and high school biology courses. Our research questions explored the impact of professional development and place-based learning on student outcomes, with the study’s methodology involving needs assessments, teacher training, and continuous support through modeling and coaching. The findings include gains in achievement and substantial gains in science education. The findings have implications for the design, implementation, and evaluation of university–school partnerships designed to build teachers’ capacity to deliver high-quality …

Allied Dental Students' Perceived Knowledge, Confidence, And Attitudes Regarding Disaster Victim Identification Topics, Samantha C. Vest, Brenda T. Bradshaw, Marsha A. Voelker, Ann M. Bruhn, Tara L. Newcomb, Sinjini Sikdar

Dental Hygiene Faculty Publications

Purpose Disaster victim identification (DVI) service requires knowledge, confidence, and an attitude (KCA) of readiness. The purpose of this study was to assess allied dental students’ perceived KCA regarding DVI skills and topics.

Methods A convenience sample of senior dental hygiene students (n=27) and senior dental assistant students (n=14) were recruited by email then presented mismatched simulated antemortem (AM) and postmortem (PM) bitewing radiographs and asked to indicate correct matches. Collectively, participants made 205 radiographic matches and indicated 205 degrees of certainty binarily as “positive” or “possible” (one per match). Participants also completed a researcher designed pretest/posttest electronic survey with …

Assessing Technology Usage In Relation To The Quality Of Life In Autistic Children, Cristina Costescu, Ioana Tufar, Laura Chezan, Mălina Șogor, Alexandra Confederat

Human Movement Studies & Special Education Faculty Publications

Objective

The aim of this study was to investigate the frequency and duration of technology use by autistic children, their primary activities when engaging with technology, and the association between technology use and quality of life. We assumed that technology serves as a means of communication with peers, and it is associated with an improved quality of life.

Methods

The study sample consisted of 61 parents of autistic children aged 5-10 years old. The Quality of Life for Children with Autism Spectrum Disorder Scale was used to measure children's quality of life based on parent report, and the Technology Use …

A Model For Community-Driven Development Of Best Practices: The Ocean Observatories Initiative Biogeochemical Sensors Data Best Practices And User Guide, Hilary I. Palevsky, Sophie Clayton, Heather Benway, Mairead Maheigan, Dariia Atamanchuk, Roman Battisi, Jennifer Batryn, Annie Bourbonnais, Ellen M. Briggs, Filipa Carvalho, Alison P. Chase, Rachel Eveleth, Rob Fatland, Kristen E. Fogaren, Jonathan Peter Fram, Susan E. Hartman, Isabela Le Bras, Cara C. M. Manning, Joseph A. Needoba, Merrie Beth Neely, Hilde Oliver, Andrew C. Reed, Jennie E. Rheuban, Christina Schallenberg, Ian Walsh, Christopher Wingard, Kohen Bauer, Baoshen Chen, Jose Cuevas, Susana Flecha, Micah Horwith, Melissa Melendez, Tyler Menz, Sara Rivero-Calle, Nicholas P. Roden, Tobias Steinhoff, Paulo Nicolás Trucco-Pignata, Michael F. Vardaro, Meg Yoder

OES Faculty Publications

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment of in-situ platforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for …

Interpreting An Archaean Paleoenvironment Through 3d Imagery Of Microbialites, Cecilia M. Howard, Nathan D. Sheldon, Selena Y. Smith, Nora Noffke

OES Faculty Publications

While stromatolites, and to a lesser extent thrombolites, have been extensively studied in order to unravel Precambrian (>539 Ma) biological evolution, studies of clastic-dominated microbially induced sedimentary structures (MISS) are relatively scarce. The lack of a consolidated record of clastic microbialites creates questions about how much (and what) information on depositional and taphonomic settings can be gleaned from these fossils. We used μCT scanning, a non-destructive X-ray-based 3D imaging method, to reconstruct morphologies of ancient MISS and mat textures in two previously described coastal Archaean samples from the ~3.48 Ga Dresser Formation, Pilbara, Western Australia. The aim of this …

Retention And Transfer Of Fractal Gait Training, Logan J. Frame, Nikita A. Kuznetsov, Louisa D. Raisbeck, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Background/Purpose: Fractal gait patterns have been shown to be modifiable, but the extent to which they are retained and transferred to new contexts is relatively unknown. This study aimed to close those gaps by enrolling participants (N = 23) in a seven-day fractal gait training program. Methods: Building on related work, the fractal gait training occurred on a treadmill over a 10-min period. Before and after the treadmill training, each participant walked for 10 min overground without the fractal stimulus used during training. The daily post-test was used to examine immediate retention and transfer of the fractal gait patterns from …

Project Control System (Pcs) Implementation In Engineering And Construction Projects: An Empirical Study In Saudi's Petroleum And Chemical Industry, Sahar Jawad, Ann Ledwith, Rashid Khan

School of Cybersecurity Faculty Publications

Purpose

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

Design/methodology/approach

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Findings

Data from a …

Optimal Network Analysis Through Vertex Order Coloring Of Intuitionistic Fuzzy Graph Operations, A. Meenakshi, S. Dhanushiya, Hong Qin, Maniyandy Elangovan

Data Science Faculty Publications

Intuitionistic fuzzy graphs IFGs are a powerful tool for modeling uncertainty and complex relationships. They offer versatile frameworks for addressing real-world challenges. In this research, we have introduced intuitionistic fuzzy vertex order coloring IFVOC and analyzed the alpha-strong (alpha str), beta-strong (beta str), and gamma-strong (gamma str) vertices through their degree. We explored important theorems based on the types of strong vertices, broadening the scope of our study. We analyzed multiple IFG products to determine the most optimal network based on some important metrics, including the weight and total number of alpha str vertices, the chromatic number, and the weight …

Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik

Data Science Faculty Publications

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. …

Interpreting Neural Operators: How Nonlinear Waves Propagate In Non-Reciprocal Solids, Jonathan Colen, Alexis Poncet, Denis Bartolo, Vincenzo Vitelli

Data Science Faculty Publications

We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. We exemplify our method with data from microfluidic experiments where crystals of streaming droplets support the propagation of nonlinear waves absent in passive crystals. By combining physics-inspired neural networks, known as neural operators, with symbolic regression tools, we generate the solution, as well as the mathematical form, of a nonlinear dynamical system that accurately models the experimental data. Finally, we interpret this continuum model from fundamental physics principles. Informed by …

Large Offshore Wind Farms Have Minimal Direct Impacts On Air Quality, Maryam Golbazi, Cristina L. Archer

Data Science Faculty Publications

Wind power has rapidly grown over the past decade because it is clean, renewable, and abundant. However, wind farms can affect local weather and possibly alter the transport, diffusion, and concentration of air pollutants. Given the unprecedented expansion of offshore wind farms planned along the U.S. East Coast by the Bureau of Ocean Energy Management (BOEM), this study aims to investigate if and how those future offshore wind farms might directly affect air pollution along the densely populated East Coast, in particular, the concentrations of ozone (O₃), fine particulate matter (PM_2.5), sulfur dioxide (SO₂), …

Anonymous Attribute-Based Broadcast Encryption With Hidden Multiple Access Structures, Tran Viet Xuan Phuong

School of Cybersecurity Faculty Publications

Due to the high demands of data communication, the broadcasting system streams the data daily. This service not only sends out the message to the correct participant but also respects the security of the identity user. In addition, when delivered, all the information must be protected for the party who employs the broadcasting service. Currently, Attribute-Based Broadcast Encryption (ABBE) is useful to apply for the broadcasting service. (ABBE) is a combination of Attribute-Based Encryption (ABE) and Broadcast Encryption (BE), which allows a broadcaster (or encrypter) to broadcast an encrypted message, including a predefined user set and specified access policy to …

Deep Transfer Learning-Based Bird Species Classification Using Mel Spectrogram Images, Mrinal Kanti Baowaly, Bisnu Chandra Sarkar, Md.Abul Ala Walid, Md. Martuza Ahamad, Bikash Chandra Singh, Eduardo Silva Alvarado, Imran Ashraf, Md. Abdus Samad

School of Cybersecurity Faculty Publications

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet …

A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi

School of Cybersecurity Faculty Publications

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …