Search Results (694)

The Internet of Things (IoT) is a growing area of interest with an increasing number of applications, including cyber–physical systems (CPS). Emerging threats in the IoT context make software integrity verification a key solution for checking that IoT platforms have not been tampered with so that they behave as expected. Trusted Computing techniques, in particular Remote Attestation (RA), can address this critical need. RA techniques allow a trusted third party (Verifier) to verify the software integrity of a remote platform (Attester). RA techniques rely on the presence of a secure element on the device that acts as a Root of Trust (RoT). Several specifications have been proposed to build RoTs, such as the Trusted Platform Module (TPM), the Device Identifier Composition Engine (DICE), and the Measurement and Attestation RootS (MARS). IoT contexts are often characterized by a highly dynamic scenario where platforms are constantly joining and leaving networks. This condition can be challenging for RA techniques as they need to be aware of the nodes that make up the network. This paper presents the EMBedded Remote Attestation and Verification framEwork (EMBRAVE). It is a TPM-based RA framework designed to provide a dynamic and scalable solution for RA in IoT networks. To support dynamic networks, we designed and developed Join and Leave Protocols, permitting attestation of devices that are not directly under the control of the network owner. This paper discusses the design and open-source implementation of EMBRAVE and presents experimental results demonstrating its effectiveness. Full article

This study examines how educational attainment and creative-class identity influence public library use among Black homeowners in Ward 8, Washington, D.C., a historically disinvested, yet resilient, Black community. Using an adapted theoretical framework (Chatman’s Small World Theory, Florida’s creative class theory, and Crenshaw’s intersectionality), the research investigates how symbolic capital informs institutional engagement in a racially homogeneous but economically stratified setting. A survey of 56 Black homeowners examined the relationships among education, income, creative-class identity, and library use. Logistic regression analysis revealed that higher educational attainment was a significant predictor of identification with the Black Creative Class^TM. However, neither income nor creative-class identity significantly predicted public library use. These findings challenge the assumption that middle-class status or creative-class affiliation ensures participation in educational or cultural institutions. Instead, they suggest that deeper dynamics, such as cultural relevance, perceived alignment, and trust, may shape engagement with public libraries. The study advances knowledge in library and information science (LIS) and urban studies by demonstrating how spatial context and class distinctions within Black communities shape library engagement. The results underscore the need for culturally responsive library strategies that recognize class-based variation within racial groups, moving beyond monolithic models of community outreach. Full article

In recent years, China has vigorously promoted the EPC mode in the construction industry. However, under the weak trust environment of China’s construction industry, both owners and general contractors are involved in the design stage of EPC projects. Owing to conflicting interests in the design stage, there is a two-stage design interest paradox between the owners and general contractors of Chinese EPC projects, and this causes significant difficulties and challenges for project implementation. To resolve this paradox, this study proposes the “DART-PDCA” design management model by integrating value co-creation theory with the PDCA cycle. Applied to the Yuzhou High-speed Rail Station Square and Related Infrastructure PPP Project and the extended case, the model demonstrates how it resolves the paradox by (1) establishing structured dialogue platforms for aligning evolving design intentions, (2) enhancing information access and transparency through agreed protocols, and (3) facilitating dynamic risk assessment and allocation mechanisms. The results confirm that (1) the two-stage design interest paradox negatively impacts design management quality in China’s low-trust environment; and (2) the “DART-PDCA” design management model effectively resolves this paradox, leading to demonstrable improvements in design management quality, efficiency, and stakeholder alignment. This research forges novel interdisciplinary linkages among owner–general contractor relationships, design management, and EPC projects, providing critical insights into managing multi-organizational dynamics in complex EPC project environments. Full article

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ($\lambda$), which extends the classical Q($\lambda$) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q($\lambda$), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ($\lambda$) consistently outperforms traditional Q-learning, Q($\lambda$), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments. Full article

Municipal solid waste (MSW) incineration projects often trigger “Not In My Backyard” (NIMBY) conflicts, which pose persistent crises to social development and sustainable governance. This study introduces a novel “reputation–interest” space model grounded in scenario–response theory to reframe NIMBY conflicts as processes of crisis transformation. We construct a multi-stakeholder indicator system and propose a crisis resilience degree model to capture both the risks and opportunities embedded in conflict dynamics. The application object is a waste incineration project in Hangzhou, China. The analysis reveals how NIMBY conflict can evolve from strong resistance to a neighbor–benefit effect. Empirical results show that the crisis resilience degree of the project evolved from 37.26% to 89.26%, from the initial strong resistance of the residents to the successful in situ landing, which improved the crisis resilience, recovering resilience from the crisis. The results provide actionable insights for policymakers to turn NIMBY conflicts into drivers of social trust and sustainable urban transformation. Full article

Empirical evidence highlights the critical role of effective school leadership in driving school improvement, enhancing teacher performance, and improving student outcomes. In Omani schools, where adaptive strategies are increasingly essential, the collaborative roles of principals and teachers are pivotal in achieving meaningful educational change. This qualitative study explores how principal–teacher interactions influence teacher leadership development and contribute to overall school improvement. Using a multiple-case study design across eight schools in the Muscat Governorate, in-depth interviews with eight principals and focus group discussions with twelve teachers were conducted to capture diverse perspectives across these settings. The data were analyzed using thematic analysis to identify key leadership dynamics and their implications for teacher leadership and school improvement. Findings indicate that principals who act as change agents, offer targeted support, and cultivate a collaborative culture empower teachers to take on leadership roles. Key themes include fostering professional growth, building trust, and addressing systemic challenges. These interactions enhance school culture, classroom practices, and student outcomes, ultimately contributing to sustainable school improvement. The study underscores the importance of collaborative leadership practices and calls for strategies that optimize these dynamics to advance educational outcomes. Future research should explore the broader applicability of these findings across diverse educational contexts to inform policy and practice. Full article

As digital infrastructures become increasingly dynamic and complex, traditional static access control mechanisms are no longer sufficient to counter advanced and persistent cyber threats. In response, Zero Trust Architecture ($ZTA$) emphasizes continuous verification and context-aware access decisions. To realize these principles in practice, this study introduces a Trust Score ($TS$)-based access control model as a systematic alternative to legacy, rule-driven approaches that lack adaptability in real-time environments. The proposed $TS$ model quantifies the trustworthiness of users or devices based on four core factors—User Behavior (B), Network Environment (N), Device Status (D), and Threat History (T)—each derived from measurable operational attributes. These factors were carefully structured to reflect real-world Zero Trust environments, and a total of 20 detailed sub-metrics were developed to support their evaluation. This design enables accurate and granular trust assessment using live operational data, allowing for fine-tuned access control decisions aligned with Zero Trust principles. A comprehensive sensitivity analysis was conducted to evaluate the relative impact of each factor under different weight configurations and operational conditions. The results revealed that B and N are most influential in real-time evaluation scenarios, while B and T play a decisive role in triggering adaptive policy responses. This analysis provides a practical basis for designing and optimizing context-aware access control strategies. Empirical evaluations using the UNSW-NB15 dataset confirmed the $TS$ model’s computational efficiency and scalability. Compared to legacy access control approaches, the $TS$ model achieved significantly lower latency and higher throughput with minimal memory usage, validating its suitability for deployment in real-time, resource-constrained Zero Trust environments. Full article

Background: Artificial Intelligence (AI)-driven Decision Support Systems (DSSs) promise improvements in diagnostic accuracy and clinical workflow efficiency, but their adoption is hindered by inadequate confidence calibration, limited transparency, and poor alignment with real-world decision processes, which limit clinician trust and lead to high override rates. Methods: We developed and validated a dynamic scoring framework to enhance trust in AI-generated diagnoses by integrating AI confidence scores, semantic similarity measures, and transparency weighting into the override decision process using 6689 cardiovascular cases from the MIMIC-III dataset. Override thresholds were calibrated and validated across varying transparency and confidence levels, with override rate as the primary acceptance measure. Results: The implementation of this framework reduced the override rate to 33.29%, with high-confidence predictions (90–99%) overridden at a rate of only 1.7%, and low-confidence predictions (70–79%) at a rate of 99.3%. Minimal transparency diagnoses had a 73.9% override rate compared to 49.3% for moderate transparency. Statistical analyses confirmed significant associations between confidence, transparency, and override rates (p < 0.001). Conclusions: These findings suggest that enhanced transparency and confidence calibration can substantially reduce override rates and promote clinician acceptance of AI diagnostics. Future work should focus on clinical validation to optimize patient safety, diagnostic accuracy, and efficiency. Full article

Background/Objectives: For runners competing abroad in sports events, the hours before a race are marked by heightened psychological tension, where even food choices can feel crucial to success. While pre-race nutrition is often addressed in terms of physiological needs, little is known about the inner psychological processes that accompany food decisions in unfamiliar cultural and environmental contexts. This study explores the inner dialogues, anxieties, and coping mechanisms of international runners facing the question of whether and what to eat before competition. Methods: A qualitative study was conducted with twelve international participants (from the United Kingdom, Germany, and Ukraine) of the Poznan Half Marathon 2025. Data were collected through semi-structured in-depth interviews. Participants possessed a minimum of two years’ experience competing in international events. Results: Three thematic areas were identified: (1) anticipatory anxiety and fear of making nutritional mistakes before the race, (2) internal negotiation between prior nutritional knowledge and situational trust, and (3) ritualization and individualized norms as fundamental mechanisms of psychological regulation. These themes influenced how runners experienced pre-race nutrition, shaping their emotional states, decision-making processes, and coping strategies in the context of international competition. Conclusions: Pre-race nutrition decisions are deeply embedded in emotional and cognitive landscapes shaped by stress, cultural context, and individual history. Recognizing these inner dynamics can help coaches, sports nutritionists, and event organizers better support the psychological well-being of traveling athletes. Full article

This study examines the evolution of willingness to accept the monkeypox (Mpox) vaccine in Romania between 2022 and 2025. It explores key sociodemographic and behavioral predictors of vaccine acceptance and investigates how public perceptions—particularly concerning disease severity and conspiracy beliefs—have shifted across two independent cross-sectional samples. Two nationally distributed surveys were conducted in July 2022 (n = 820) and January–February 2025 (n = 1029), targeting Romanian residents aged 18 and above. Data were analyzed using descriptive statistics, Chi-square tests, Kolmogorov–Smirnov tests, and a Random Forest classification model to assess the relative importance of predictors of vaccine acceptance. Between 2022 and 2025, vaccine acceptance increased modestly, particularly among individuals aged 36–65 and those with prior experience of voluntary or COVID-19 vaccination. Random Forest analysis identified behavioral factors as the strongest predictors of acceptance in both years, while the influence of education and gender varied over time. Belief in conspiracy theories slightly declined and lost predictive relevance by 2025. Perceptions of pandemic potential and fear of infection also decreased, suggesting reduced risk salience and possible pandemic fatigue. Despite a slight upward trend, overall Mpox vaccine acceptance in Romania remains among the lowest in Europe. These findings highlight the need for targeted public health communication, particularly toward skeptical or demographically vulnerable groups. Prior vaccination behavior emerged as a key driver of acceptance, indicating that trust-building strategies should capitalize on existing pro-vaccination habits. Future research should adopt qualitative and longitudinal approaches to better capture the evolving psychosocial dynamics of vaccine hesitancy. Full article

The integration of blockchain technology into healthcare offers a robust solution to challenges in secure data sharing, privacy protection, and operational efficiency. Effective exchange of sensitive patient information among hospitals, clinics, insurers, and researchers is essential for better outcomes and medical advancements. Traditional centralized systems often suffer from data breaches, inefficiency, and poor interoperability. This paper presents a blockchain-based secure data-sharing framework tailored for healthcare, addressing these limitations. The framework employs a hybrid blockchain model, combining private and public blockchains: the private chain ensures fast transactions and controlled access, while the public chain fosters transparency and trust. Advanced cryptographic methods—such as asymmetric encryption, hashing, and digital signatures—safeguard patient data and maintain integrity throughout the datalifecycle. Smart contracts automate processes like consent management, access control, and auditing, ensuring dynamic permission enforcement without intermediaries. Role-based access control (RBAC) further limits access to authorized entities, enhancing privacy. To tackle interoperability, standardized data formats and protocols enable smooth communication across diverse healthcare systems. Large files, such as medical images, are stored off-chain, with only essential metadata and logs on the blockchain. This approach optimizes performance, scalability, and suitability for large-scale healthcare deployments. Full article

This paper proposes a collaborative framework to support task offloading in connected vehicular environments. The approach relies on the dynamic formation of temporary teams of connected vehicles in a mobile edge computing scenario. A novel trust model is introduced, which integrates both quality of service and quality of results into a unified reliability score, and combines this score with distributed reputation to build a comprehensive trust metric. This trust metric is then exploited to guide a decentralized team formation algorithm, ensuring lightweight, interpretable, and scalable decision-making processes. Simulation results demonstrate that the proposed framework improves task execution quality and fairness, especially for low-performing vehicles. These contributions highlight the novelty and strengths of our collaborative model, positioning it as a promising solution for enhancing cooperation in vehicular edge systems. Full article

The proliferation of fake profiles on social media presents critical cybersecurity and misinformation challenges, necessitating robust and scalable detection mechanisms. Such profiles weaken consumer trust, reduce user engagement, and ultimately harm brand reputation and platform credibility. As adversarial tactics and synthetic identity generation evolve, traditional rule-based and machine learning approaches struggle to detect evolving and deceptive behavioral patterns embedded in dynamic user-generated content. This study aims to develop an AI-driven, multi-modal deep learning-based detection system for identifying fake profiles that fuses textual, visual, and social network features to enhance detection accuracy. It also seeks to ensure scalability, adversarial robustness, and real-time threat detection capabilities suitable for practical deployment in industrial cybersecurity environments. To achieve these objectives, the current study proposes an integrated AI system that combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) for deep semantic textual analysis, ConvNeXt for high-resolution profile image verification, and Heterogeneous Graph Attention Networks (Hetero-GAT) for modeling complex social interactions. The extracted features from all three modalities are fused through an attention-based late fusion strategy, enhancing interpretability, robustness, and cross-modal learning. Experimental evaluations on large-scale social media datasets demonstrate that the proposed RoBERTa-ConvNeXt-HeteroGAT model significantly outperforms baseline models, including Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). Performance achieves 98.9% accuracy, 98.4% precision, and a 98.6% F1-score, with a per-profile speed of 15.7 milliseconds, enabling real-time applicability. Moreover, the model proves to be resilient against various types of attacks on text, images, and network activity. This study advances the application of AI in cybersecurity by introducing a highly interpretable, multi-modal detection system that strengthens digital trust, supports identity verification, and enhances the security of social media platforms. This alignment of technical robustness with brand trust highlights the system’s value not only in cybersecurity but also in sustaining platform credibility and consumer confidence. This system provides practical value to a wide range of stakeholders, including platform providers, AI researchers, cybersecurity professionals, and public sector regulators, by enabling real-time detection, improving operational efficiency, and safeguarding online ecosystems. Full article

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

Generative artificial intelligence (GenAI) is advancing rapidly and is increasingly integrated into educational settings. How to effectively leverage GenAI to support instructional design has thus become a critical issue in teacher education. While existing studies have validated the technical potential and functional value of GenAI in instructional design, there remains a notable gap in qualitative investigations into pre-service teachers’ subjective willingness to adopt GenAI and its underlying influencing factors. To address this gap, this present study employed grounded theory to explore the factors that shape pre-service teachers’ intention to use GenAI for instructional design. Semi-structured interviews were conducted with 23 pre-service teachers from Shaanxi Normal University, and the data were analyzed through open coding, axial coding, and selective coding. A theoretical model comprising four major dimensions was developed as follows: (1) technical factors (relative advantage and ease of use), (2) environmental factors (social impact, opinion leader, and facilitating conditions), (3) usage characteristics (purpose of use and method of use), and (4) psychological factors (trust, perceived risk, and a professional self-concept). The findings reveal that pre-service teachers’ intention to use GenAI is not shaped by a single factor but is instead the result of dynamic and interrelated interactions among the four dimensions. This study extends current technology acceptance theories and offers practical insights for the effective integration and promotion of GenAI in instructional design. Full article