Digital Commons Network^™

Forcing A Molecule To Switch: Quantifying Mechanical Control At The Atomic Scale, A. M. Shashika D. Wijerathna, Markus Zirnheld, Michael L. Hildebrand, Myles Perry, Marjorie Cenese, Yuan Zhang

Physics Faculty Publications

Mechanically induced conformational switching at the single-molecule level represents a fundamental mechanism for molecular functionality, yet quantitative characterization of the underlying force and energy landscape remains limited. Here, we study individual TBrPP-Co(II) molecules on Au(111) using qPlus atomic force microscopy. By reconstructing interaction potentials from 3D Δf(x,y,z) data, we determine a threshold force of ∼96 ± 8 pN and a tip-induced switching interaction energy of ∼38 ± 4 meV associate with the conformational transition. The isolated tip-molecule force follows a power law (exponent ∼6), indicating dominance of long-range van der Waals interactions. …

Reaching The Intrinsic Performance Limits Of Superconducting Nanowire Single-Photon Detectors Up To 0.1 Mm Wide, Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martina J. Stevens, Alex Gurevich, Adam N. Mccaughan

Physics Faculty Publications

Superconducting nanowire single-photon detectors combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting “rails” placed on either side of the detector that redistribute current across its width to achieve peak performance. This technique reduces …

High-Tcr Multivalence Vanadium Oxide Thin-Films From Deposition Parameter Control To Microbolometer Applications, Latika Susheel M. Chaudhary

Electronic Theses & Dissertations (2024 - present)

This thesis details the development of multivalence-nanostructured vanadium oxide (VOₓ) thin films for uncooled microbolometer applications, with a systematic optimization of magnetron-sputtering parameters. The temperature coefficient of resistance (TCR), resistivity, and optical response of VOₓ thin-film sensing layers are controlled by valence composition, grain growth, and surface morphology. The primary goal was to achieve a high TCR with low resistivity to improve thermal detector performance.

Multivalent VOₓ thin films were deposited on silicon, SiO₂, and glass substrates using DC magnetron sputtering. Three key parameters were systematically varied: Ar:O₂ ratio (18:2 to 15:5), deposition time (60–120 minutes), and DC power (300W …

Load-Settlement Behaviour Of Tailings Dam Foundation Strengthened Using The Wraparound Geosynthetic Reinforcement Technique, Francis Otieno, Sanjay Kumar Shukla

Research outputs 2022 to 2026

This paper presents a numerical investigation of the load–settlement behaviour of a geosynthetic-reinforced tailings foundation comprised of gold tailings, subjected to the weight of a tailings dam embankment. The study considered varying conditions, including the horizontal and vertical components of the wraparound reinforcement, reinforcement burial depth, and shear strength parameters of the foundation material. A particular focus of this study is to investigate the effect of reinforcement wraparound ends on the settlement behaviour of the foundation using commercially available finite element (FE) software. The reinforcement is a single-layer biaxial woven geotextile, with and without wraparound ends, at varying depths from …

Stem-Fit And Soil-Fit: Integrated Plant And Soil Nitrogen-Hormone Sensing With Machine Learning-Based Forecasting For Next-Generation Precision Agriculture, Nafize I. Hossain, Mohammad Solaiman, A.K.M. Ahsanul Habib, Md Al Mahmud Hossain Al Hadi, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient and stress dynamics. This research aims to develop and validate a multiplexed sensing platform for real-time, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, …

Development Of Feature Tokenizer Deep Learning Model For Fault Diagnosis In Marine Propulsion System, Pratik Anand Deshpande, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

Low-Energy Co2 Sequestration Via Palm Waste Biochar Activated With Carbide Lime For Mineral Carbonation Of Industrial Wastes, Maisa El Gamal, Ameera F. Mohammad, Basim Abu-Jdayil, Imen Ben Salem, Suhaib Hameedi

All Works

A sustainable method for enhancing CO₂ capture and sequestration was developed by accelerating the carbonation of alkaline industrial wastes using carbide lime-activated biochar (BC(CLW)), an alkaline by-product of acetylene production. The biochar was produced from palm tree waste through slow pyrolysis at 400 °C, followed by activation with carbide lime slurry to enhance its alkalinity, surface basic sites, and reactivity toward CO₂. Three industrial residues, namely ladle furnace slag (LF), baghouse dust (BH), and cyclone silo dust (CD), were blended with 0, 5, 7, and 10 wt. % BC(CLW) and carbonated under ambient conditions. Characterization by thermogravimetric analysis (TGA), Fourier-transform …

Improving Evacuation Effectiveness On Cruise And Large Passenger Ships : An Analysis Of Human Factors, Safety Culture, And Behavioral Dynamics, Antonios Andreadakis

World Maritime University Ph.D. Dissertations

Maritime trade has long constituted a central pillar of economic development, supporting global commerce and cultural exchange. Nevertheless, the maritime environment is inherently hazardous and therefore the protection of human life has consistently constituted and will continue to remain a central priority (Casson, 1995). An instrumental turning point in maritime safety occurred after the sinking of Titanic in 1912, which prompted in 1914 an International Conference dedicated to the protection of human life at sea; which ultimately led to the adoption of the International Convention on Safety of Life at Sea (SOLAS) (Dalaklis, 2017). This Convention introduced internationally imposed safety …

Microstructure And Properties Of Oxide Dispersion-Strengthened Alloys, Ertugrul Demir, Seung Min Ha, Anish Ranjan, Xingshuo Zhang, Aaron Penders, Mukesh Bachhav, Xiaochun Li, Lin Shao, Alexander Demblon, Haiming Wen, Enrique Lavernia

Materials Science and Engineering Faculty Research & Creative Works

Oxide dispersion-strengthened (ODS) alloys are a critical class of structural materials for extreme environments, owing to their unique combination of high-temperature strength, thermal stability, and radiation tolerance, enabled by a very high density of nanoscale oxide dispersoids. These features make ODS alloys attractive for advanced nuclear systems, aerospace applications, and other harsh-service conditions where conventional alloys rapidly degrade. Despite decades of development, key challenges remain in understanding how nanoscale oxides interact with matrix microstructures, alloy chemistry, and irradiation-induced defects to control macroscopic performance. This review provides a focused, mechanism-based synthesis of the microstructural features that govern the properties of ODS …

High Temperature Mold Gap Measurement Using Optical Fiber Interferometry, Jie Huang, Ronald O'Malley, Dinesh Reddy Alla, Deva Prasaad Neelakandan, Muhammad Roman

Patents

A temperature insensitive fiber optic Fabry-Perot interferometer based mold gap measurement system that can be employed in foundry and continuous casting facilities for both non-ferrous and ferrous production applications. For steel continuous casting, the sensor is also capable of detecting crystallite formation in the mold flux layer within the mold gap by detecting reflections from both the flux layer and the steel shell to facilitate direct mold lubrication monitoring for continuous casting. These interferometers can also be easily multiplexed to perform interface shape measurements or monitor gap variations at various positions within a complex mold design. The ability to measure …

Fluorescent Security Glass, Rachel Leigh Grodsky, Richard Brow, Melissa Malone Teague

Patents

A tamper-evident glass and methods of using the tamperevident glass are provided herein. The tamper-evident glass comprising at least a first plurality of tamper-evident elements, which may be unique or near unique to each manufacturing of tamper-evident glass. The initial state of tamper-evident glass and positioning of the plurality of tamper-evident elements may be mapped through digital scanning to create a digital file of the current state of the tamper-evident glass. The tamper-evident glass may be scanned again to create a new digital file for comparison and to determine if a tampering event has occurred. The tamper-evident elements may include …

Transverse Magnetic Mode Split Post Dielectric Resonator, Donghyun Kim, Xiaoning Ye, Chaofeng Li

Patents

A device for measuring a property of a substrate comprises an enclosure,two resonators,and two probes.The substrate has a top surface, and the enclosure defines a cavity for placing the substrate. The resonators extend into the cavity of the enclosure. The probes extend into the cavity, and each of the probes comprises a loop that extends about an axis that is substantially parallel to the top surface of the substrate so that the probes are operable to excite a transverse magnetic mode signal in the cavity.

Five Tensions Of Artificial Intelligence Adoption For Organ Allocation: Applying The Technology–Organization–Environment Framework, Amaneh Babaee, Daniel Burton Shank, Casey I. Canfield, Joely Grace Hall, Krista L. Lentine, Henry Randall, Mark Schnitzler

Psychological Science Faculty Research & Creative Works

Background: The US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern. Objective: We explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation. Methods: Ten structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework. Results: Overall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, …

The Fox Versus The Hedgehog: Firm Survival During Unprecedented Crises, Sheng Fang, Shaomin Li, Liu Wang

Management Faculty Publications

This paper examines how sales channel diversification influences firm resilience during the COVID crisis. Drawing on a series of logit regressions analyzing 11,662 firms across 31 developing countries, we find that firms embodying an open mind-set and a diverse array of sales channels, akin to foxes, are more likely to survive, as compared to their hedgehog counterparts, which focus on one particular task. Our mediation analysis suggests that this is mainly due to their faster growth in sales and exports. Moreover, we find that the impact of fox-like traits on firm survival is moderated by female leadership and corporate innovation.

The Operation And Control Strategy Of Microgrids: A Brief Review, Mohammad Alam, Salsabila Zaman

Electrical Engineering Student Publications

A microgrid is an effective approach for integrating various distributed energy resources to meet local energy demand. It possesses the capability to operate independently or in conjunction with the main utility grid. Typically, a microgrid is a small-scale power system (usually several megawatts or less) comprising three primary components: the ability to function in both grid-connected and islanded modes, distributed power generation sources, and autonomous control centers. Microgrids enhance system resilience by ensuring uninterrupted operation during grid disturbances. Additionally, they are environmentally sustainable and contribute to improved power quality. As a crucial element of modern power systems, microgrids can effectively …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

A Real-Time Autonomous Mobile Robotic System For Crack Detection And 3d Laser Profiling In Construction Using Two-Stage Deep Learning, Emmanuella Ogun

College of Graduate Studies: Theses & Dissertations

This thesis presents an autonomous mobile robotic system that integrates real-time visual crack detection with laser-based three-dimensional profiling for building inspection. The system coordinates SLAM-based navigation, deep learning perception, and manipulator-guided measurement within a unified ROS 2 framework, enabling scan-on-demand workflows where laser profiling occurs selectively at visually detected defect locations. The detection pipeline employs a two-stage architecture combining U-Net semantic segmentation with Pix2Pix residual refinement, achieving 73.9% mean intersection-over-union and 76.4% F1-score on the CrackSeg9k benchmark while maintaining 16 frames-per-second real-time throughput. Real-world validation demonstrated 95% detection success on 80 campus images under challenging conditions. The profiling subsystem utilizes …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

Exploring Drone Technology For The Survey And Documentation Of Aerospace Archaeology Sites, David G. Morgan, Thomas R. Allen

Political Science & Geography Faculty Publications

This research examines the implementation of Unmanned Aerial Systems (UAS) during a pilot mission in Nike Park to support its aerospace preservation. Located in Carrollton, Virginia, it was a Cold War missile installation that formed part of the area’s air-defense network. This project aims to use a drone to capture high-resolution video and imagery of Nike Park’s Administrative Office Supply and PX building and the Nike Ajax missile to create virtual 3D models for the Isle of Wight County Museum’s exhibit. For this mission, the DJI Mini 4 Pro drone manually flew a circular flight pattern around the missile and …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Temporal Patterns Of Storm Surges: Event-Identification, Clustering, And Count Modeling, Ariadna Martin Oliva

Graduate Studies Theses and Dissertations 2026

Coastal regions face compounding risks when extreme events strike in rapid succession, yet most statistical frameworks treat such events as independent, a simplification that can critically underestimate hazard. This thesis addresses that gap through three interconnected contributions focused on the temporal clustering of storm surges at the global scale. A new automated method is developed to extract independent storm surge events from continuous tide gauge records. By leveraging event correlations and a soft-margin approach that accounts for local variability, the method identifies characteristic event durations across 1,485 global tide gauge stations, revealing the influence of both local site conditions and …