Physical Sciences and Mathematics Commons^™

Aoi-Aware Agentic Federated Mixture-Of-Digital-Twin Experts For 6g Vehicular Edge Intelligence, Asadullah Tariq, Mohamed Adel Serhani, Ikbal Taleb, Shayma Alkobaisi, Tariq Qayyum, Irfan Ud Din

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Digital twin-enabled vehicular edge intelligence is expected to become a fundamental service paradigm for sixth-generation (6G) intelligent transportation systems. However, the performance of such systems depends not only on model accuracy, but also on the freshness of digital twin states, timeliness of inference, privacy-preserving model training, and efficient use of heterogeneous edge resources. Existing DT-assisted federated learning and edge mixture-of-experts solutions optimize digital twin synchronization, distributed learning, and sparse inference largely independently, without allowing digital twin states to actively govern expert specialization, expert refreshing, and distributed orchestration. Nevertheless, the joint problem of how digital twins should guide federated expert specialization, …

Mapping Llm Misuse In Computing Education: A Survey-Based Risk Analysis Of Faculty And Student Contexts, Noura Alzaabi, Mohamed El-Attar, Sarah Kohail, Mahmood Niazi

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Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the …

Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati

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Misuse case diagrams are a widely adopted technique in security requirements engineering, enabling analysts to model adversarial threats and derive countermeasures early in the software development lifecycle. However, manual construction of these diagrams is prone to incompleteness and subjectivity, requiring significant security expertise. Large language models (LLMs) such as ChatGPT present a promising opportunity to automate this process, yet their effectiveness for generating structured security modeling artifacts remains largely unexplored. This paper presents an exploratory study evaluating ChatGPT-5's ability to generate misuse case diagrams directly from textual security requirements, using 12 case studies of varying complexity spanning small, medium, and …

From Image To Insight: Evaluating Llm Accuracy In Understanding Uml Use Case Diagrams With Claude, Mohamed El-Attar, Yasser Khan, Mahmood Niazi, Sajjad Mahmood, Mohammad Alshayeb

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UML use case diagrams are a prominent artefact of requirements engineering, capturing the functional scope of a software system in terms of actors, use cases, and their stereotyped relationships. The emergence of multimodal large language models with image understanding capabilities raises the question of whether such models can reliably extract structured construct-level information from use case diagram images. This paper reports an empirical evaluation of Claude on the task of counting 14 notational construct types from a corpus of 78 computer-generated UML use case diagrams, assessed against manually verified ground truth annotations. Results reveal a strongly differentiated accuracy profile: Claude …

Cliffinsight: An Educational Web Application That Visualizes The Calculation Of Effect-Sizes Using Cliff's Delta, Mohamed El-Attar, Ahmed Shuhaiber, Rima Grati, Sarah Kohail

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The purpose of calculating effect sizes in statistics is to quantify the practical significance of observed differences beyond mere statistical significance. While standardized mean difference measures such as Cohen’s d are widely used, they require normally distributed data, an assumption frequently violated in educational and social science research. Non-parametric alternatives such as Cliff’s delta (δ) are more robust under these conditions yet remain underused due to perceived computational complexity and limited accessible resources. Existing web-based tools for Cliff’s delta function primarily as numerical calculators and do not expose the underlying dominance structure that gives the statistic its meaning. This paper …

A Preliminary Exploratory Assessment Of Chatgpt To Generating Stride Data Flow Diagrams, Hassan Alsayegh, Mohamed El-Attar

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Threat modeling is a core activity in security-by-design practices, enabling early identification of architectural weaknesses before system implementation. The drawings used during STRIDE analysis are typically Data Flow Diagrams (DFDs), referred to as “STRIDE diagrams” in this paper. STRIDE diagrams provide a visual approach for categorizing security threats; however, constructing accurate STRIDE diagrams require experience and is often time-consuming. Recent advances in Large Language Models (LLMs), such as ChatGPT, raise important questions about their suitability for supporting structured security modeling tasks. This study presents a preliminary exploratory assessment of ChatGPT’s ability to generate, analyse, and iteratively refine STRIDE diagrams from …

Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan

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The increasing digitization of urban infrastructure has introduced advanced efficiency and connectivity in smart cities while exposing them to sophisticated cybersecurity threats. This study explores how Quantum Storage Mechanisms (QSM) can be integrated with digital forensic readiness systems to enhance smart city security and incident response. Through a simulated environment, the research evaluates the effectiveness of QSM against three critical cyberattack scenarios: Distributed Denial of Service (DDoS), sensor spoofing, and supply chain firmware attacks. The findings reveal that QSM-enabled systems outperform traditional cybersecurity tools by ensuring tamper-proof evidence collection, real-time threat detection, and secure long-term data retention. The study also …

Drone Authentication System Using Radio Frequency Fingerprinting, Jamila Muhsen Alnuaimi, Shamma Ghaleb Almansoori, Noura Ahmed Alrumeithi, Richard Ikuesan

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The widespread integration of unmanned aerial vehicles (UAVs) across domains such as logistics, surveillance, and emergency response has introduced critical security challenges, particularly unauthorized access, identity spoofing, and drone cloning. Traditional software-based authentication methods, including GPS tracking and encryption, have proven inadequate against advanced cyber-physical threats. This paper proposes a secure and automated drone authentication framework based on Radio Frequency (RF) fingerprinting, leveraging intrinsic hardware-level signal imperfections to generate unique and unclonable drone identities. Using Random Forest classifiers, the system captures, preprocesses, and analyses RF features to distinguish between authorized and unauthorized UAVs. Validation with real-world RF datasets demonstrates high …

Towards A Context-Aware Driving Assistance System (Ca-Das): Advancing Intelligent Vehicular Safety Through Multimodal Context Integration, Fatma Outay, Siham Farrag, Anjum Zameer, Ansar Yassar

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Driving-related behavioural factors are responsible for 90% of traffic collisions. The rapid growth of urbanization and the complexity of the traffic conditions demand a smart, efficient, and flexible transportation system. The advancement of transportation through technologies such as the Internet of Things (IoT) and AI have reshaped the way that drivers interact with their vehicles and the surrounding environment. In this paper, we propose a comprehensive Context-Aware Driving Assistance System (CA-DAS) that employs sensor fusion, semantic context modelling, along with a machine-learning-based approach to provide personalised and proactive driving assistance across dynamic scenarios. The proposed CA-ADS was developed using a …

Supporting Data – T-3 Stream Stage And Streambed Well Data, Mclean County, Il, April 12, 2025 To November 1, 2025, Eric Wade Peterson, Eric Brunner

Faculty Publications - Geography, Geology, and the Environment

Between April 12, 2025, and November 1, 2025, observation wells at the T-3 site in central Illinois were monitored at an upstream and downstream site along the stream. A conductivity, depth, and pressure (CTD) sensor inside each well collected continuous in-situ measurements for the water column height (mm), the water temperature (°C), and the water conductance (mS/cm). The available dataset provides in-situ readings every 15-minutes from seven (7) locations: upstream stream, streambed, and shallow aquifer, downstream stream, streambed, and shallow aquifer, and Well 12, and provides the air temperature.

Blockchain-Based Nostrification: A Privacy-Preserving Framework For Documents Verification, Mohamad Badra, Rouba Borghol

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Many employers and institutions will not complete a hire until they verify a candidate's foreign qualifications. This nostrification step exists for a simple reason: they need to know that certificates, medical records, financial papers, and other official documents are real and not forged. For years, signatures and stamps were enough. But the shift to online applications changed the game. Today, anyone can upload a polished PDF, and with basic editing tools, fake documents can be created in minutes. The old system no longer protects anyone. On the other hand, the blockchain offers a stronger and more practical solution. Instead of …

Adaptive Multi-Agent Learning For Infrastructure-Aware Its: The Imer Data-Processing Approach, Mayssa Hamdani, Nafaa Jabeur, Ansar Yasar, Fatma Outay, Li Li

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The performance of Intelligent Transportation Systems (ITS) critically depends on accurate and efficient road-condition monitoring. This paper presents IMER (Inspect–Map–Eliminate–Reduce), a novel AI-driven data-processing framework that extends the traditional Map-Reduce paradigm for infrastructure maintenance. IMER integrates confidence-based validation, redundancy elimination, and severity prioritization to enhance data quality and decision efficiency. Implemented within a multi-agent architecture, IMER enables autonomous agents to inspect, classify, and fuse multi-source road data in real time, supporting predictive and adaptive maintenance planning. Simulation results using augmented pothole datasets demonstrate a 39.9 % reduction in redundant reports and 39.8 % fewer false positives. These findings highlight IMER’s …

Human Factors In Visual Attention: Gender Differences In Engagement With Male-Oriented Ads, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar, Mohammed Hussain

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In today's competitive market, it is increasingly important to understand how visual design shapes customer behaviour. This study examines the decision drivers influencing female customers' purchase choices when buying male-oriented products as gifts, identifying the visual components of advertisements that attract them by analysing the relationship between purchase intention and visual attention. Results show that participants with higher purchase intent focused more on product imagery and branding, indicating that visual appeal, perceived quality, and brand familiarity significantly guide their decisions, with brand awareness speeding up decision-making by reducing the need for repeated visual checks. Conversely, those with low purchase intent …

Exploring The Potential Of Renewable Energy For Sustainable Mobility: A Simulation- Based Study Of Hydrogen Vehicle Penetration In Oman’S Road Network, Siham Farrag, Tarek R. Sheltami, Fatma Outay, Ansar Ul Haque Yasar

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Hydrogen fuel is gaining attention as a promising zero-emission energy source, aligning with global sustainability goals and supporting the transition to zero carbon emissions. This study examines the potential of using hydrogen as an alternative fuel for sustainable mobility in Muscat, Oman. We developed an integrated modelling framework that combines microscopic traffic simulation, energy demand modeling, refueling infrastructure station’ estimation, and well-to-wheel (WTW) emissions evaluation. A microscopic simulation software (SUMO) was applied to evaluate the penetration rate of hydrogen-powered vehicles (0%, 20%, 40%, 60%) with different hydrogen production pathways. Results indicate that with a 60% penetration of green hydrogen, total …

Smart Health Care Application For Predicting Complications Risk In Type 2 Diabetes Management Using Personalized Digital Twins: A Focus On Early Intervention And Prevention Strategies, Haifaa Alkaabi, Ahed Abugabah

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The study examined the application of Personalized Digital Twins (PDTs) to prevent complications during the management of Type 2 Diabetes, especially in early intervention and prevention plans. Based on a high-quality dataset related to the CDC Behavioral Risk Factor Surveillance System (BRFSS) data, we tested multiple predictive models such as the Random Forest, Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost). We developed a composite risk indicator from established clinical risk factors (hypertension, dyslipidemia, elevated BMI) to stratify complication risk. The Random Forest model achieved 99% accuracy (AUC: 0.98) at the population-level risk classification. The GBM model was optimized …

Modelling Route-Level Interzonal Travel Time Using Gps Trajectories, Muhammad Faiq Ahmed, Tom Bellemans, Fatma Outay, Muhammad Ahmed, Afzal Ahmed, Feng Liu, Muhammad Adnan

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Activity-based models (ABMs) require accurate travel-time estimates for accessibility calculations, yet many implementations rely on static routing outputs that fail to capture temporal congestion dynamics due to limited high-resolution data. This paper develops route-level travel-speed prediction models using GPS trajectory data from 48 vehicles in Flanders, Belgium. GPS trajectories are integrated with OpenStreetMap and land-use data through destination-based segmentation, in which trips from fixed origins are cumulatively segmented at zone crossings. To capture behavioural differences by trip length, separate Gamma regression models are estimated for short (≤5 km) and long (>5 km) trips using temporal, network, and spatial variables. …

The Deepfake Litmus Test: A Multimedia Authenticity Mechanism, Amna Alzaabi, Hessa Alqubaisi, Fatima Alzaabi, Richard Ikuesan

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Deepfake technologies have made it increasingly difficult to distinguish authentic video content from manipulated media. This paper presents a forensic detection framework, referred to as the Litmus Test, which focuses on structural analysis of MP4 container files to detect signs of tampering. Unlike conventional AI-based approaches that operate as black boxes, this method examines the atomic composition of video containers to identify anomalies. The proposed method performs atom-level inspection of MP4 file hierarchies and structural markers to uncover anomalies indicative of synthetic manipulation. Evaluations using datasets such as CelebDF, UADFV, and DeeperForensics reveal that the framework can identify inconsistencies common …

Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens

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Objective: To develop an AI framework that combines genetic, phenotypic, and lifestyle data for profiling skin-health patterns and generating hypothesis-supporting summaries for potential decision support. Methods and procedures: A dataset of 5,254 individuals integrates six genes (FLG, AQP3, MMP-1, MMP-3, SOD2, GPX), six phenotype severities, and 20+ lifestyle factors. Mutation burden and interactions are tested by ANOVA. K-modes clustering identifies four interpretable dermatological profiles within the cohort and is embedded in leakage-free nested cross-validation (train-only selection; test labels from training centroids). Subtypes are predicted from genetics plus lifestyle using an XGBoost (XGB) classifier; explainability uses gain, permutation importance, and SHAP …

A Novel Bernstein Operational Matrix Approach For Tempered Fractional Differential Equations: Convergence And Stability Analysis, Jalal Al Hallak, Mohammed Alshbool, Ishak Hashim, Eddie Shahril Ismail, Shaher Momani

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Tempered fractional differential equations (TFDEs) incorporate exponential decay into fractional operators to account for truncated memory and semi-long-range dependence in a variety of applications, including anomalous diffusion, viscoelasticity, transport phenomena, geophysical processes, and financial dynamics. In this work, a tempered fractional Bernstein method (TFBM) was proposed for the numerical solution of TFDEs involving Caputo-type derivatives. The proposed formulation combined a Bernstein polynomial approximation with an analytic representation of the Caputo–tempered fractional derivative through operational matrices. On this basis, two collocation-based variants were developed, namely, a Chebyshev-type method (TFBM-C) and a Legendre-type method (TFBM-L). For the linear setting, a convergence analysis …

Empirically Evaluating The Accessibility Of A Pon-Enable Feature Diagrams Notation By The Red-Green Colorblind Community, Mohamed El-Attar, Sarah Kohail, Rima Grati

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In 2016, an enhanced version of a feature diagram notation developed using the Physics of Notations (PoN) framework was introduced. Empirical evidence demonstrated that this revised notation was more cognitively effective than the original. However, the new notation relies on color, specifically red, which poses accessibility challenges for individuals with red–green color vision deficiency, as they cannot perceive the notation as originally intended. Consequently, the cognitive effectiveness of a red–green–deficient (RGD) version of the new notation relative to the original notation remained unknown. Although the PoN framework specifies several principles that may be satisfied with or without the use of …

Rumooz Aljareemah: An Intelligent Search System For Uae Criminal Law With Case Correlation Framework For Forensic Investigators, Rahaf Alnuaimi, Maryam Almarzooqi

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Digital forensic investigations in the UAE encounter dual challenges: effectively correlating data across cases and complying with local legal frameworks. Conventional methods create information silos that hide connections between instances and increase the probability of procedural errors. This paper presents RUMOOZ ALJAREEMAH, a prototype platform for case correlation designed for cybersecurity experts and forensic investigators in the UAE. The approach integrates a correlation engine with a UAE-specific legal compliance framework, using authentication protocols, bilingual assistance, and text-based search algorithms. Our theoretical framework suggests enhancements in investigative efficiency, including reduced case resolution durations, improved identification of cross-case relationships, and a decrease …

Slm With Swarm Intelligence For Efficient Representation Of Medical Claims, Mohamed Ahmed Abo El-Enen, Ravi S. Sharma, Mustafa Abdulrazek, Amril Nazir, Reem Muhammad, Ahmed Talat Sahlol

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Healthcare industry faces significant challenges due to fraudulent medical insurance claims, which result in substantial financial losses. We propose an automated system using domain-specific Small Language Models (SLMs) with a narrower scope and smaller parameter count than general-purpose Large Language Models (LLMs), combined with optimization algorithms to improve fraud detection. Our approach integrates numerical features, such as age and claim amount, with textual descriptions, including diagnoses and procedures, into a unified textual representation for each medical activity. This representation captures complex patterns, enhancing the model’s predictive ability. SLMs fine-tuned on medical corpora transform these textual inputs into fixed-dimensional numerical embeddings, …

Optimized Hybrid Beamforming For Ris-Assisted Multi-User Mimo In 6g Mmwave Networks: A Low-Complexity Approach To Spectral Efficiency And Interference Mitigation, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Thippa Reddy Gadekallu, Hana Mujlid, Hashim Elshafie, Rashid A. Saeed

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The joint optimization of hybrid beamforming and reconfigurable intelligent surface (RIS) phase shifts in multi-user millimeter-wave (mmWave) MIMO systems is a challenging problem, mainly due to high computational complexity and the lack of adaptive interference management. Existing approaches typically rely on fixed Zero-Forcing (ZF) or Maximum-Ratio Transmission (MRT) designs or require iterative optimization with high overhead, limiting their practical use in dense 6G environments. To overcome these challenges, this research proposes a RIS-Aided Adaptive Zero-Forcing and Maximum-Ratio Transmission Hybrid Precoding (RA-ZMHP) framework for 6G mmWave multi-user MIMO systems. The main novelty of the method lies in an adaptive ZF–MRT mixing …

Data Repository For The Distribution Of Small Impact Craters Along The Msl Curiosity Rover Traverse In Gale Crater, Mars, Megan E. Hoffman

Earth and Planetary Sciences Faculty and Staff Publications

The theoretical limit of the smallest impact crater diameter that can form under current martian atmospheric conditions is approximately 0.25 m, which is near the projected pixel size of the best orbital imagers around Mars. As a result, there have been limited studies into how the retention of the smallest impact craters on Mars varies with surface properties, since orbital images alone cannot confidently resolve them. Rovers and landers are needed to confidently differentiate and measure these smallest craters. Here we catalog small impact craters seen along the first 25 km, or 3120 sols of the Mars Curiosity rover traverse …

Data Repository For: Dorsey Et Al., 'Rapid Transient Uplift Driven By Active Slab Tear, Southern Calabria, Italy', Rebecca J. Dorsey, Nathan D. Brown, Sergio G. Longhitano, Marco Meschis, Domenico Chiarella, Charles P. Ogle

Earth & Environmental Sciences Datasets

Southern Italy preserves a well-studied record of deformation and uplift related to migrating oblique collision, trench retreat, and tearing of subducted ocean slabs. However, the surface expression of these processes is incompletely understood due to a lack of reliable ages for marine terraces >200 m above sea level (masl). Here we use luminescence methods to date shallow marine sands from terraces ~ 100 to 1,000 masl in southern Calabria and interpolate the results with regional surface mapping. Burial ages overlap across the full range of sampled elevations with a weighted mean of 118.8 ± 8.7 thousand years before present (ka). …

Data For "Large Projected Increases In Area Burned And Wildfire Frequency By 2050 In Utah, Usa", Joseph D. Birch, Yoshimitsu Chikamoto, James A. Lutz

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Changes in wildfire regimes may disrupt ecosystem processes as wildfires burn larger areas or burn more frequently than the recent natural range of variability. The climatic drivers of wildfire behavior may change in strength but these effects are not likely to be uniform across space and between different vegetation types. Increased understanding of how weather and climate influence patterns of burn area and frequency across vegetation types may assist in better predicting and managing future wildfire regimes. We examined a dataset of all 1469 wildfires ≥40 ha from 1984 – 2021 in Utah, USA and used antecedent daily weather data …

Predicting Skin Concern Severity From Genetic And Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework, Yassine Benachour, Lina Maloukh, Sadok Bouamama, Barbara Geusens

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Personalized dermatology increasingly leverages both genetic predispositions and lifestyle behaviors to model individual skin health outcomes. This study proposes a multi-output machine learning framework to predict the severity of six dermatological phenotypes—acne, redness, dryness, sensitivity, scarring, and pigmentation—using a multimodal dataset of 5,254 individuals. Input features include mutation profiles for six skin-related genes (FLG, MMP1, MMP3, AQP3, SOD2, GPX) and 22 lifestyle variables such as sun exposure, stress, and hydration. We train and evaluate LightGBM models under independent, multi-output, and chained configurations. Performance is assessed using Mean Absolute Error (MAE) and average Quadratic Weighted Kappa (QWK). The proposed ordinal-aware independent …

Enhancing Breast Cancer Detection In Mammographic Imaging Using Explainable Clinical Decision Support System And Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

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Breast cancer remains one of the leading causes of mortality among women worldwide, where early and precise detection plays a vital role in improving survival rates and treatment outcomes. However, conventional deep learning approaches often encounter challenges in handling dense mammographic tissues and lack transparency in decision-making, limiting their clinical reliability. To address these limitations, this study introduces TransYOLO-GJO, an explainable and optimized detection framework that integrates transformer-based attention mechanisms into the YOLOv9 architecture and leverages the Golden Jackal Optimization (GJO) algorithm for hyperparameter tuning. The transformer encoder enhances contextual feature extraction, particularly in dense breast regions, while GJO dynamically …

Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua

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Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) applications. Software-Defined Networking (SDN) is an emerging networking paradigm that extends the lifetime of WSNs by transferring the resource-intensive routing task from sensor nodes to a centralized controller. However, many SDN-based routing schemes for WSNs employ inefficient algorithms at the controller. Traditional shortest-path methods often create traffic imbalances across neighboring nodes, while Reinforcement Learning (RL)-based approaches typically generate excessive control traffic. Both issues accelerate energy depletion and reduce network lifetime. Moreover, existing algorithms frequently overlook critical factors, such as buffer occupancy, when selecting relay nodes, which can lead …

Gets: Greenhouse Environment Time Series, Scott Grimshaw, Natalie J. Blades, Grant R. Mcqueen

ScholarsArchive Data

This dataset contains high-frequency environmental measurements from a single greenhouse used to study multivariate statistical process control under strong autocorrelation. The data consist of a continuous Phase 1 monitoring period of approximately four weeks, during which environmental sensors recorded conditions inside the greenhouse once per minute.

The primary variables included in the archived dataset are:

• date – Date-time stamp at one-minute resolution (local greenhouse time).

• co2_ppm – Carbon dioxide concentration in parts per million (ppm).

• humidity_pct – Relative humidity (%).

• soil_temp_F – Soil temperature in degrees Fahrenheit.