Physical Sciences and Mathematics Commons^™

Intrinsic Motivation, Future Orientation, And Financial Stress: A Student-Centered Model Of Metaverse Classroom Adoption In Low-Income Contexts, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

All Works

The rapid rise of immersive technologies has placed metaverse-based classrooms at the center of higher education innovation. Yet, little is known about how students in low-income contexts perceive and adopt these platforms, particularly when motivation, career goals, and financial pressures intersect. This study develops and tests a student-focused model that integrates intrinsic motivation, future time perspective, career relevance, and financial stress to explain behavioral intention toward metaverse adoption. A survey of 292 university students in Jordan—a lower-income national setting—was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results showed all hypothesized paths were significant. Future time perspective and career …

Deep Learning Models Based On Cnn, Rnn, And Lstm For Rainfall Forecasting: Jordan As A Case Study, La'aly A. Al-Samrraie, Ayman M. Abdalla, Khalideh Al Bkoor Alrawashdeh, Abeer Al Bsoul, Mohammad Abu Awad, Kamel Alzboon, Ahmed A. Al-Taani

All Works

This study is the first to compare deep learning models for rainfall prediction across several Jordanian cities representing diverse climates using 11 years of recorded climate data, something that previous studies have not addressed in the Jordanian context. The climate records for four Jordanian cities (Amman, Irbid, Karak, and Ajloun) were recorded hourly. The data was divided into training sets (80%) and test sets (20%), with and without the application of correlation analysis, feature selection, and data standardization steps applied. Three neural network models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) were …

Functionalized 2d Hexaazatriphenylene As Hydrogen Storage Platform: A Dft-Guided Design Approach, Islam Gomaa

Nanotechnology Research Centre

No abstract provided.

Hydrogen-Enriched Lng As A Mid-Term Solution To Mitigate Greenhouse Gas Emissions From Shipping, Kang-Ki Lee

World Maritime University Ph.D. Dissertations

In light of the International Maritime Organization’s (IMO) ambitious Initial GHG Strategy, the rise in international shipping’s carbon dioxide emissions by 2 percent in 2022 compared to 2019 poses a formidable challenge. This increase underscores the pressing need to address the limited availability of green hydrogen, prompting the exploration of mid-term solutions to bridge this critical gap. The IMO’s 2023 Strategy on Reduction of GHG Emissions from Ships attempted to tackle this challenge head-on, recognizing the urgency of reducing the industry’s substantial carbon footprint. While hydrogen offers a simple solution as a zero-carbon fuel, LNG remains the most widespread alternative …

Charting A Sustainable Course Beyond Carbon : A Critical Carbon-Water-Energy Assessment Of Net-Zero Shipping Scenarios With Synthetic Fuels And Ghc Offsetting, Alexandre Bellot

World Maritime University Ph.D. Dissertations

This thesis extends the scope of IMO maritime decarbonization studies by incorporating cradle-to-grave approaches. This study advocates for an approach incorporating cradle-to-grave resource efficiency and carbon neutrality, assessing cumulative energy demand and net freshwater withdrawal as key sustainability metrics. The thesis specifically addresses the question of the sustainability of decarbonization strategies centred on substituting fossil fuels with synthetic alternatives and offsetting the remaining carbon footprint. Advanced ship energy modelling: The thesis first introduces a ship energy model capable of evaluating ships’ fuel consumption with various propulsion and energy generation options. The model simulates energy transfers across a concept ship’s systems, …

Safety And Sustainability In The Domestic Ferry Sector : A Pci Framework For Esg-Aligned Maritime Governance, Mirza Zeeshan Baig

World Maritime University Ph.D. Dissertations

The domestic ferry sector is a significant component of maritime transportation. It strengthens social ties, supports economic growth and connects communities. Despites its importance, this industry faces ongoing challenges. These include fragmented governance, operational adequacies, safety risks and environmental concerns. The study deals with these issues by combining a systems-based approach with governance and change management theories. It examines the interplay between human, technical and organizational aspects. The study integrates the rule-based and rights-based maritime governance along with the Lewin’s three stage change management model (Unfreeze, Change, and Refreeze).

At the core of the study is the development of a …

Incremental Cluster Validity Index-Based Offline Clustering For Machine Learning, Leonardo Enzo Brito Da Silva, Donald C. Wunsch Ii, Nagasharath Rayapati

Patents

A neural network model replaces the supervised labeling component of a supervised learning system with an incremental cluster validity index-based unsupervised labeling component. An implementation is presented combining fuzzy adaptive resonance theory predictive mapping (ARTMAP) and incremental cluster validity indices (iCVI) for unsupervised machine learning purposes, namely the iCVIARTMAP. An iCVI module replaces the adaptive resonance theory (ART) module B of a fuzzy ARTMAP neural network model and provides assigmnents of input samples to clusters (i.e., labels) at each learning iteration in accordance to any of several possible iCVI methods described. A map field incrementally builds a many-to-one mapping of …

Nontoxic High Temperature Resistant Hydrogels, Thomas P. Schuman, Baujun Bai, Buddhabhushan Pundlik Salunkhe

Patents

Compositions of swellable non-toxic hydro gels and their use in conformance control are described herein. More specifically, the present invention generally relates to the hydro gels compositions that can be used to treat the oil reservoirs having high temperature and high salinity conditions. The compositions generally comprise a plurality of swellable particles having one or more crosslinkers interspersed within a polymer matrix. The compositions can also be used in biomedical, agricultural, fracking and similar applications wherein thermally-stable, nontoxic hydrogels are required.

Substrate Preference And Foraging Behavior Of Two Declining Warblers In Appalachian Shrublands, Samantha Fishman

Theses and Dissertations

Abstract Shrubland and early successional habitats (ESH) support a diversity of wildlife, including many declining songbirds. However, nonnative shrubs increasingly dominate these habitats, potentially altering habitat quality by influencing prey availability. Habitat quality can be assessed by observing foraging behaviors of songbirds and identifying preferred and avoided substrates as well as testing the predictions of optimal foraging theory; specifically that birds will maximize their energy gain relative to the costs. We examined foraging behaviors of Golden-winged Warblers (Vermivora chrysoptera; GWWA) and Chestnut-sided Warblers (Setophaga pensylvanica; CSWA) in ESH on private lands in Virginia with varying amounts …

Clea Labs For Planetary Astronomy Bottled For Silicon Macintosh, Yuktha Shetty, Hansika Reddy Bandela, Jack C. Straton

Physics Faculty Publications and Presentations

Project CLEA -- CONTEMPORARY LABORATORY EXPERIENCES IN ASTRONOMY -- develops laboratory exercises that illustrate modern astronomical techniques using digital data and color images.

These Labs were developed for Windows, but the authors have bottled them for Silicon Macintosh computers.

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

Effective Approaches To The Dynamical Properties Of Two Distinguishable Bose Polarons, F. Theel, S. I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We explore effective approaches for describing the dynamics of induced interactions among two noninteracting distinguishable impurities (Bose polarons) when their couplings to the host species are switched on. First, we evaluate the time-dependent characteristics of each polaron through reduced single-particle models. Their validity is ultimately judged by comparing them to observables obtained within a many-body variational approach. We argue that utilizing a time-dependent optimization process with the effective mass and frequency being fitting parameters leads to an accurate matching on the level of one-body observables with the prediction of the many-body method. Next, we construct two-body effective models, which, in …

Two-Component Droplet Phases And Their Spectral Stability In One Dimension, E. G. Charalampidis, S. I. Mistakidis

Physics Faculty Research & Creative Works

We unravel the existence and stability properties of one-dimensional droplets arising in genuine two-component particle-imbalanced bosonic mixtures under the influence of a weak harmonic confinement. A plethora of miscible droplet phases is found with the majority component atoms in the vicinity of the minority ones assembling in a droplet configuration, while the remaining excess atoms are in the gas state. Our computations identify a transition from a bound to a trapped gas many-body state for increasing total atom number and fixed interactions, with the bound character being prolonged for stronger intercomponent attractions. The spectral stability of these two-component droplets is …

Momentum Imaging Of Electrons And Recoil Ions From Anion-Neutral Interactions In A Cryogenic Ion Storage Ring, F. Herrmann, W. Zhang, M. Schulz, D. V. Chicharro, A. Dorn, M. Grieser, F. Grussie, H. Kreckel, O. Novotny, F. Trost, A. Wolf, T. Pfeifer, C. D. Schröter

Physics Faculty Research & Creative Works

We have momentum-analyzed electrons and recoil ions in triple coincidence with projectiles neutralized in 300-keV Si-+Ar collisions. The experiment was performed at a cryogenic ion storage ring with an in-ring reaction microscope. Differential momentum distributions for detachment with simultaneous target ionization were obtained. At more than 15% of the detachment-only rate the ionization rate is surprisingly large. A correlated two-electron channel is found to be dominant. This is also unexpected because the projectile energy is well below the threshold for the correlated process.

Elucidating The Role Of Hydrogen At C-In2o3/A-In2o3−X Interface, Julia E. Medvedeva, Mariana I. Bertoni

Physics Faculty Research & Creative Works

Ab initio molecular dynamics simulations and hybrid functional electronic structure calculations are employed to determine the formation, the structural and electronic properties, and the dynamics of covalent (In-OH) and ionic (In-H-In) hydrogen defects at crystalline-In2O3/amorphous-In2O3−x interface. This comprehensive computational study considers (i) various interstitial and substitutional hydrogen site locations within the crystalline, amorphous, and interfacial regions; (ii) several oxygen-to-hydrogen ratios; and (iii) possible defect charged states. The results reveal hydrogen's inability to fully passivate the undercoordinated under-shared in atoms in amorphous highly substoichiometric oxide, giving rise to the formation of deep electron traps even in net-charge neutral structures. These trap …

Amplitude Mode At The Superfluid-Insulator Transition On A Random Lattice, Pulloor Kuttanikkad Vishnu, Rajesh Narayanan, Thomas Vojta

Physics Faculty Research & Creative Works

We study the superfluid-insulator quantum phase transition of interacting bosons by means of large-scale Monte Carlo simulations in the presence of both topological and generic quenched disorders. Recent work has demonstrated that the amplitude mode at this transition broadens and localizes in the presence of dilution disorder, whereas it remains a well-defined delocalized excitation for topological (connectivity) disorder. Here, we analyze the crossover between systems with purely topological disorder and systems with additional bond randomness to disentangle the roles that disorder and interactions play for amplitude mode localization. Specifically, we analyze the scalar susceptibility and its spectral density on both …

The Organization Of Serotonergic Fibers In The Pacific Angelshark Brain: Neuroanatomical And Supercomputing Analyses, Skirmantas Janušonis, Ralf Metzler, Thomas Vojta

Physics Faculty Research & Creative Works

Serotonergic axons (fibers) are a universal feature of all vertebrate brains. They form meshworks, typically quantified with regional density measurements, and appear to support neuroplasticity. The self-organization of this system remains poorly understood, partly because of the strong stochasticity of individual fiber trajectories. In an extension to our previous analyses of the mouse brain, serotonergic fibers were investigated in the brain of the Pacific angel shark (Squatina californica), a representative of a unique (ray-like) lineage of the squalomorph sharks. First, the fundamental cytoarchitecture of the angel shark brain was examined, including the expression of ionized calcium-binding adapter molecule 1 (Iba1, …

The Kinetics Of Shj Degradation: Time And Stressor Dependent Chemical Analysis And Simulation, Nicholas Moser-Mancewicz, Jorge Ochoa, Michael Martinez-Szewczyk, Thomas Bantle, Dana Kern, Dirk Jordan, Steve Johnston, Julia E. Medvedeva, Mariana Bertoni

Physics Faculty Research & Creative Works

Despite record efficiencies, the wide adoption of silicon heterojunction is hampered by higher costs and uncertainties in long-term performance. Although recent publications report performance loss rates comparable to other field-deployed cSi technologies, the limited availability of field data and the dispersion in performance degradation results - particularly in open-circuit voltage - remain significant concerns. Various factors, including bulk and interfacial defects, charge carrier recombination dynamics, and environmental stressors such as temperature and humidity, contribute to this degradation. Understanding the mechanisms behind the open circuit voltage degradation as well as the kinetics involved is essential for developing mitigation strategies that enhance …

Students’ Perceptions Of Self And Peers Predict Self-Reports Of Cheating, Amber M. Henslee, Luke Settles, Sara E. Johnson, Gayla R. Olbricht

Psychological Science Faculty Research & Creative Works

Academic dishonesty and how to address it are common concerns across higher education disciplines, but engineering students admit to higher rates of academic dishonesty than other students. However, first-year students may be particularly receptive to prevention efforts. Considering self-perception, social norming, and behavioral choice theories, we hypothesized that 1.) Students who perceived themself as ethical and more knowledgeable of the consequences for misconduct would be less likely to self-report cheating and 2.) Students who perceived cheating and plagiarism to be common would be more likely to self-report cheating. For this study, freshmen engineering students (N=703) reported their self-perception, perception of …

Improving The Performance Of Multi-Stakeholder Partnerships For Sustainable Development In Coastal Areas : Sweden (Hanö Bay) As A Case Study, Jennie Larsson

World Maritime University Ph.D. Dissertations

Coastal areas are vital for both ecosystems and human societies. Comprising diverse terrestrial, freshwater, and marine ecosystems, coastal areas provide us with essential resources and services. However, these areas are under threat from human activities and climate change, necessitating new governance structures to ensure their sustainable management and conservation. This research investigates how to improve the performance of local multi-stakeholder partnerships (M-SPs) in coastal areas, promoted as key mechanisms for achieving sustainable development goals. By drawing on stakeholder theory and using Pattberg & Widerberg’s (2014) analytical framework with nine building blocks for successful M-SPs as a foundation, the study examined …

Nexus: Network Exploration For Exploiting Unsafe Sequences In Multi-Turn Llm Jailbreaks, Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Large Language Models (LLMs) have revolutionized natural language processing, yet remain vulnerable to jailbreak attacks—particularly multi-turn jailbreaks that distribute malicious intent across benign exchanges, thereby bypassing alignment mechanisms. Existing approaches often suffer from limited exploration of the adversarial space, rely on hand-crafted heuristics, or lack systematic query refinement. We propose NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks. NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains; (2) a feedback-driven Simulator that iteratively refines and prunes these chains …

Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim

School of Cybersecurity Faculty Publications

In response to the demand for Artificial Intelligence (AI) experts, this study introduced a curriculum development initiative. The aim was to design and implement a Private AI curriculum to understand the computer science (CS) students’ evaluations of the curricular activities and their levels of interest and motivation. Twenty-five students, a mix of undergraduates and graduates, were recruited and a scaled-down version of the curriculum was implemented. A parallel mixed-methods approach was employed. The results reinforced the significance of problem-centered curricula in CS context. Students rated the curricular activities highly and demonstrated strong motivation; however, graduates expressed more favorable view of …

Privacy-Preserved And Incentivized Knowledge Sharing For Reinforced-Learning Based Iot Platform Security, Weichao Wang, Md Morshed Alam, Yu Wang

School of Cybersecurity Faculty Publications

With the fast development and deep penetration of IoT devices and smart environments, using localized machine learning models to detect malicious activities has also been developed and deployed. However, these isolated learning models and results cannot be effectively federated together because of privacy concerns and lack of incentivization. This paper proposed several mechanisms to solve the problem. A verification method was designed for phased learning results to protect user privacy and prevent individual parties from manipulating the verification selection. The paper also presented an incentive method based on delay of distribution of the latest federated learning results. Extensive simulations were …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

The Effectiveness Of Tech Support Fraud In Damaging Older Individual’S Financial Security, Vanessa Perera

Theses : Honours

This study discovers the tactics employed to create detrimental effects upon older people impacted from fraudulent tech-support incidents. It examines social engineering, and financial confusion of older people. This is significant considering adaptations towards digital banking and payment management. This research looked at online and active over 65s. Using largely qualitative approaches over 65s were interviewed and responses validated against cyber-professionals’ responses. This identified three key findings: older adults feel confused and misunderstand tech support scams; threat actors build trust by pretending to offer technical help but use this to deceive their victims; and older adults face serious social and …

Swatting Flies: Biting Insects As Non-Invasive Samplers For Mammalian Population Genomics, Anubhab Khan, Ryan Carter, Chengetai D. Mpamhanga, Daniel Masiga, Manun Channumsin, Marc Ciosi, Oliva Manangwa, Furaha Mramba, Umer Zeeshan Ijaz, Harriet Auty, Barbara K. Mable

All Peer-Reviewed Publications

Advances in next-generation sequencing have allowed the use of DNA obtained from unusual sources for wildlife studies. However, these samples have been used predominantly to sequence mitochondrial DNA for species identification while population genetics analyses have been rare. Since next-generation sequencing allows indiscriminate detection of all DNA fragments in a sample, technically it should be possible to sequence whole genomes of animals from environmental samples. Here we used a blood-feeding insect, tsetse fly, to target whole genome sequences of wild animals. Using pools of flies, we compared the ability to recover genomic data from hosts using the short-read sequencing (Illumina) …