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The Ywca Goes To The Countryside: The Rural Work Of The Chinese Young Women’S Christian Association, 1926-1947, Elizabeth Littell-Lamb Phd Jan 2026

The Ywca Goes To The Countryside: The Rural Work Of The Chinese Young Women’S Christian Association, 1926-1947, Elizabeth Littell-Lamb Phd

Asia Pacific Perspectives

This is the first study of the Chinese YWCA’s rural work. The decision to “go to the countryside” in part grew out of a critique by Chinese YWCA women of its Westerninfluenced agenda. That critique led the YWCA national committee to establish rural work as a first step toward a China-centered agenda. Key aspects of that agenda were the empowerment of rural secretaries in the field and the inclusion of rural women as members. The empowerment of rural secretaries contributed to the democratization of professional relationships within the association. The inclusion of rural women as members allowed the YWCA to …

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Out-Group And In-Group Relational Trust In Group Coaching With First-Grade Teachers: A Single Case Study, Jentry S. Barrett, Rachel E. Schachter, Jungwon Eum, Gwen Nugent, Kathleen M. Rudasill Jan 2026

Out-Group And In-Group Relational Trust In Group Coaching With First-Grade Teachers: A Single Case Study, Jentry S. Barrett, Rachel E. Schachter, Jungwon Eum, Gwen Nugent, Kathleen M. Rudasill

Department of Child, Youth, and Family Studies: Faculty Publications

Coaching has become a prominent professional learning tool for helping early childhood teachers (birth–8) implement curriculum and research-based practices in their classrooms. One coaching strategy that supports positive changes in teacher practice and child outcomes is building trust between the coach and teacher(s). However, there is limited understanding of the nature of trust-building in coaching, particularly in group coaching configurations. There are two types of trust to consider in the context of group coaching: out-group trust between an outside coach and the teachers, and in-group trust between the teachers experiencing coaching who already have a relationship. This case study aimed …

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Towards Developing A Career Technology Fit Framework And Analyzing Its Influence On Work-Related Outcomes Among It Professionals, Gunjan Tomer Jan 2026

Towards Developing A Career Technology Fit Framework And Analyzing Its Influence On Work-Related Outcomes Among It Professionals, Gunjan Tomer

Journal of International Technology and Information Management

With growing attrition rate and significant demand for skilled IT professionals, the importance of studying their behaviour has become important for both academia and industry. Despite ample amount of research, there is still a gap between theory and practice. Based on our qualitative study conducted on Indian IT professionals we propose that technology allocation might contribute in understanding the behaviour of IT professionals. We found that IT professionals evaluate the technology allocated to them based on their individual career motives. This evaluation, either positive or negative, influences their job outcomes. Further, we explored the factors that make a technology preferable …

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High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian Jan 2026

High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian

Journal of International Technology and Information Management

While India has made vast strides in information technology in the last few decades, its success is mainly attributed to its software, rather than its hardware sector. In fact, India’s attempts at developing computer hardware that can match international standards have largely been unsuccessful. A notable exception is its development of a series of supercomputers that match and exceed many international standards. This paper looks at an interesting period in India’s computing history – namely the 1980s and 1990s – focusing on its development of an indigenous supercomputer. During that period, supercomputers were thought to be the sole privy of …

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Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad Jan 2026

Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad

Journal of International Technology and Information Management

This study examines whether employee-perception survey data can support responsible people-analytics decisions about remote-work productivity. Using the public New South Wales (NSW) Remote Working Survey 2021 (N=1,512), the study benchmarks statistical and machine-learning classifiers for self-reported perceived productivity classes (same, less, or more productive when working remotely relative to onsite work), not objective output, under default, class-weighted, and resampling protocols. Main evidence comes from 5×5 repeated stratified cross-validation using macro F1 and balanced accuracy with fixed model specifications. Class-balanced separability is modest. Random Forest, CatBoost, and LightGBM form a leading cluster with overlapping confidence intervals (macro F1 ≈0.51–0.52). Affective/well-being items, …

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The Value Of Personal Data Ecosystems: A Flemish Media Sector Case Study, Maarten De Mildt, Melanie Verstraete, Sofie Verbrugge, Didier Colle Jan 2026

The Value Of Personal Data Ecosystems: A Flemish Media Sector Case Study, Maarten De Mildt, Melanie Verstraete, Sofie Verbrugge, Didier Colle

Journal of International Technology and Information Management

Personal Data Stores (PDSs) have been proposed as a privacy-preserving approach to data sharing that increases individual control over personal data while enabling new forms of cross-organizational collaboration. This collaboration leads to the emergence of Personal Data Ecosystems (PDEs). Despite growing interest in PDEs, limited research has examined how the organizational and economic barriers identified in prior studies manifest in practice. This paper investigates these challenges through a case study of the Flemish media sector within the Solid4Media project, which explores the use of PDSs to support data sharing and personalization across media organizations. Using a qualitative research design, data …

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Knowledge Regarding Cervical Cancer Among Adults Studying In A Selected Institution In Mandya District, Karnataka: A Descriptive Study, Megha Maria Joy, Nikitha R, Anitha Victoria Noronha Jan 2026

Knowledge Regarding Cervical Cancer Among Adults Studying In A Selected Institution In Mandya District, Karnataka: A Descriptive Study, Megha Maria Joy, Nikitha R, Anitha Victoria Noronha

Manipal Journal of Nursing and Health Sciences

Introduction: Cervical cancer remains a major public health concern, particularly in low- and middle-income countries. Adequate knowledge regarding cervical cancer, its etiology, prevention, and screening is essential for early detection and reduction of disease burden. Objectives: This study aimed to assess the knowledge regarding cervical cancer among adults studying in a selected institution in Mandya District, Karnataka, and to determine the association between knowledge levels and selected demographic variables. Methods: A descriptive cross-sectional design was adopted among 60 adults selected through simple random sampling. Data were collected using a validated structured knowledge questionnaire administered through Google Forms. …

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Sustainable Paper Production From Date Palm And Reed Leaves Through The Valorization Of Agricultural Waste Products, Imane Belyamani, Alreem Alameri, Jacqueline Soghman Jan 2026

Sustainable Paper Production From Date Palm And Reed Leaves Through The Valorization Of Agricultural Waste Products, Imane Belyamani, Alreem Alameri, Jacqueline Soghman

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The environmental consequences of wood-based paper production, including greenhouse gas emissions, have accelerated the search for sustainable alternatives. This study investigates the use of reed and date palm fibers as eco-friendly raw materials for paper production, focusing on starch's influence on their thermal, structural, and mechanical properties. Reed fibers exhibited a higher pulp yield (58.2 %) and lower lignin content (7.8 %) compared to date palm fibers (55.9 % yield, 14.1 % lignin), contributing to papers with smoother textures and greater flexibility. The incorporation of starch into both fiber types resulted in notable performance improvements, though the effects were more …

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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 Jan 2026

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, …

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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 Jan 2026

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 …

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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 Jan 2026

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 …

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Empirically Evaluating The Accessibility Of A Pon-Enable Feature Diagrams Notation By The Red-Green Colorblind Community, Mohamed El-Attar, Sarah Kohail, Rima Grati Jan 2026

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 …

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Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan Jan 2026

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 …

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The Deepfake Litmus Test: A Multimedia Authenticity Mechanism, Amna Alzaabi, Hessa Alqubaisi, Fatima Alzaabi, Richard Ikuesan Jan 2026

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 …

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Modelling Route-Level Interzonal Travel Time Using Gps Trajectories, Muhammad Faiq Ahmed, Tom Bellemans, Fatma Outay, Muhammad Ahmed, Afzal Ahmed, Feng Liu, Muhammad Adnan Jan 2026

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. …

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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 Jan 2026

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 …

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Adaptive Multi-Agent Learning For Infrastructure-Aware Its: The Imer Data-Processing Approach, Mayssa Hamdani, Nafaa Jabeur, Ansar Yasar, Fatma Outay, Li Li Jan 2026

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 …

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Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid Jan 2026

Energy-Efficient Mixed-Criticality Multicore Systems, Fayyaz Ali, Saud Wasly, Amjad Ali, Shahid Iqbal, Asad Masood Khattak, Bashir Hayat, Shah Khalid

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Balancing energy efficiency with stringent timing guarantees in real-time mixed-criticality systems (MCS) is a key challenge, especially in multicore architectures. This paper introduces a novel energy-aware scheduling framework that integrates dynamic voltage and frequency scaling (DVFS) with a Decreasing-Criticality-Decreasing-Utilization (DCDU) allocation approach. The optimal operating frequencies are obtained at each criticality level; high-criticality tasks are assigned to cores at full operating frequency to maintain timing guarantees, while low-criticality tasks are allocated using worst-case execution times scaled to their optimal frequency. A fixed-priority response-time analysis is used for schedulability in low mode, high mode, and during mode changes. The extensive simulations …

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Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin Jan 2026

Edge-Aware Ris-Assisted Dynamic Channel Allocation With Lightweight Llm Decision Agent For Interference Mitigation In Low-Altitude Remote Sensing Networks, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Muhammad Attique Khan, Rashid A. Saeed, Hashim Elshafie, Bishwajeet Kumar Pandey, Khairul Akram Zainol Ariffin

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Low-altitude remote sensing networks are increasingly important for applications, such as environmental monitoring, disaster response, infrastructure inspection, and real-time sensing services. However, when many sensing nodes share limited spectrum resources, severe cochannel interference can degrade communication reliability and delay sensing-data delivery. This challenge becomes more critical in edge-enabled deployments, where control decisions must be made under strict latency, memory, and computational constraints. To address this issue, this article proposes a large language model (LLM)-enhanced edge-aware lightweight reconfigurable intelligent surface (RIS)-assisted dynamic channel allocation (EL-RIS-DCA) framework for interference mitigation in dense low-altitude remote sensing networks. The novelty of the proposed framework …

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Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed Jan 2026

Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed

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Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification …

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A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee Jan 2026

A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

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Pre-Visit preparation plays a critical role in shaping visitors’ learning and engagement in cultural heritage sites; however, existing approaches largely rely on static and passive materials that fail to foster meaningful understanding before the physical visit. Extended Reality (XR) technologies offer new opportunities to address this gap by enabling immersive, narrative-driven pre-visit learning experiences. This paper proposes a conceptual architecture for XR-based pre-visit cultural heritage learning applications, grounded in Design Science Research (DSR). Drawing on museum pedagogy, experiential learning, and XR interaction design, the study identifies key educational and technical requirements and translates them into a layered, modular system architecture. …

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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 Jan 2026

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 …

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Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati Jan 2026

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 …

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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 Jan 2026

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, …

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Kidney Dysfunction, Biochemical Changes, Dna Alteration, And Mapks Regulation Following Chronic Exposure To Regular And Occasional Hookah Smoke In Mice, Naserddine Hamadi, Anas Nemmar, Sumaya Beegam, Nur Elena Zaaba, Ozaz Elzaki, Abderrahim Nemmar Jan 2026

Kidney Dysfunction, Biochemical Changes, Dna Alteration, And Mapks Regulation Following Chronic Exposure To Regular And Occasional Hookah Smoke In Mice, Naserddine Hamadi, Anas Nemmar, Sumaya Beegam, Nur Elena Zaaba, Ozaz Elzaki, Abderrahim Nemmar

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Regular hookah smoking (Reg-HS) has become a major global public health issue, linked to significant health risks, including kidney damage. A less frequent pattern of use, known as occasional hookah smoking (Occ-HS), is also common; however, there has been little progress in understanding the direct impact of Occ-HS on kidneys. To investigate how varying frequencies of HS inhalation affect the kidney, we exposed mice to nose-only HS under two regimens, occasional (30 min once weekly) and regular (30 min five times per week) for a duration of 6 months. This study explored the impact on renal damage, inflammatory responses, oxidative …

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Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens Jan 2026

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 …

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Cyber Behaviors And Well-Being In Emerging Adults: Patterns, Determinants, And Gender Differences, Areej Elsayary, Juan Calmaestra, Mercedes Gómez-López Jan 2026

Cyber Behaviors And Well-Being In Emerging Adults: Patterns, Determinants, And Gender Differences, Areej Elsayary, Juan Calmaestra, Mercedes Gómez-López

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Technological advances have reshaped the ways in which young people interact, giving rise to a complex digital landscape with implications for their well-being. Despite the growing body of research on cyber behaviors, their potential for both positive and negative well-being outcomes is not yet fully understood, nor is the role of gender in shaping these relationships. The present study aimed to explore the relationship between online moral disengagement, problematic internet use, online disinhibition, and cyberbullying dimensions and emotional, social, and psychological well-being among 671 emerging adults, as well as to examine the potential moderator role of gender. The results revealed …

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Rumooz Aljareemah: An Intelligent Search System For Uae Criminal Law With Case Correlation Framework For Forensic Investigators, Rahaf Alnuaimi, Maryam Almarzooqi Jan 2026

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 …

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Drone Authentication System Using Radio Frequency Fingerprinting, Jamila Muhsen Alnuaimi, Shamma Ghaleb Almansoori, Noura Ahmed Alrumeithi, Richard Ikuesan Jan 2026

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 …

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Blockchain-Based Nostrification: A Privacy-Preserving Framework For Documents Verification, Mohamad Badra, Rouba Borghol Jan 2026

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 …

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