Search Results (2,303)

Mobile crowdsensing (MCS) is an emerging paradigm that enables cost-effective, large-scale, and participatory data collection through mobile devices. However, the open nature of MCS raises significant privacy and trust challenges. Existing reputation models have made progress in assessing the quality of contributions, but they still struggle to manage prolonged inactivity, which can lead to outdated scores that no longer reflect current engagement. To address these issues, this paper presents RBCrowd, a dynamic reputation management system based on a dual blockchain architecture. It consists of the Sensing Chain (SC), a public blockchain recording sensing tasks and results, and the Reputation Chain (RC), a consortium blockchain managing user reputation scores. To guarantee privacy, the framework limits identity verification to the RC, ensuring that data on the SC is stored without direct links to the worker. We paired this privacy mechanism with a reputation model that rewards consistent, high-quality contributions. The system updates reputation scores by first validating the specific task and then adjusting for historical engagement, specifically penalizing prolonged inactivity. We evaluate RBCrowd through simulations in realistic MCS scenarios, and the results show that our framework provides more effective dynamic trust management than existing models. It also achieves increased reliability and fairness while managing prolonged inactivity through adaptive penalties. Full article

Background/Objectives: Medical device-related pressure injury (MDRPI) is a significant patient safety issue associated with increased morbidity, prolonged hospitalization, and healthcare costs. Although evidence-based guidelines for MDRPI prevention exist, nurses’ prevention performance remains suboptimal, and the mechanisms linking workload to preventive practice remain insufficiently elucidated. Within a patient safety and quality improvement framework, this study aimed to examine whether occupational coping self-efficacy (OCSE) is statistically consistent with an indirect association linking nurses’ workload and MDRPI prevention performance across the nursing practice continuum. Methods: This descriptive correlational study used a mediation model with data from 181 registered nurses working in intensive care units, general wards, and integrated nursing care wards in South Korea. Workload, OCSE, and MDRPI prevention performance were measured using validated instruments. Mediation was tested using hierarchical regression and bootstrapped analysis (PROCESS macro Model 4, 5000 resamples), controlling for demographic and work-related variables. Results: Higher workload was associated with lower OCSE, while higher OCSE was associated with better MDRPI prevention performance. When OCSE was included in the model, the direct association between workload and prevention performance was no longer significant. Bootstrapping confirmed a significant indirect association through OCSE, consistent with a full mediation pattern. Conclusions: Nurses’ workload appears to be indirectly associated with MDRPI prevention performance through OCSE. These findings suggest that strengthening nurses’ coping self-efficacy, alongside organizational strategies, may be essential for sustainable MDRPI prevention and patient safety improvement. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

The connectivity demands of high-performance computing (HPC), artificial intelligence (AI) and data centers are driving the development of a new generation of multimode optical components. This paper discusses the vertical cavity surface emitting laser (VCSEL) bandwidth and noise performance needed to support 106 Gbd line rates with PAM4 modulation for 200 Gbps per lane multimode optical links. A −3 dB bandwidth greater than 35 GHz and a RIN of less than −152 dB/Hz are demonstrated. No uncorrectable errors were observed over 50 m of OM4 fiber, demonstrating good link stability. VCSEL device performance and the associated wear-out life are presented. Leveraging good device reliability and low power consumption of VCSEL-based links, a novel VCSEL near-packaged optics (NPO) concept is proposed for optical interconnects in AI scale-up network applications. Optical interconnects allow for longer reaches, compared to copper interconnects, which facilitate larger AI clusters with network disaggregation. The proposed VCSEL NPO can achieve an energy efficiency of ~1 pJ/bit, which is the highest among optical interconnects. Full article

Effective disaster management is critical for safeguarding lives, infrastructure and economies in an era of escalating natural hazards like floods and landslides. Despite advanced early-warning systems and coordination frameworks, a persistent “last-mile” challenge undermines response effectiveness: transforming fragmented and unstructured multimodal data into timely and accountable field actions. This paper introduces ResQConnect, a human-centered, AI-powered multimodal multi-agent platform that bridges this gap by directly linking incident intake to coordinated disaster response operations in hazard-prone regions. ResQConnect integrates three key components. It uses an agentic Retrieval-Augmented Generation (RAG) workflow in which specialized language-model agents extract metadata, refine queries, check contextual adequacy and generate actionable task plans using a curated, hazard-specific knowledge base. The contribution lies in structuring the RAG for correctness, safety and procedural grounding in high-risk settings. The platform introduces an Adaptive Event-Triggered (AET) multi-commodity routing algorithm that decides when to re-optimize routes, balancing responsiveness, computational cost and route stability under dynamic disaster conditions. Finally, ResQConnect deploys a compressed, domain-specific language model on mobile devices to provide policy-aligned guidance when cloud connectivity is limited or unavailable. Across realistic flood and landslide scenarios, ResQConnect improved overall task-quality scores from 61.4 to 82.9 (+21.5 points) over a standard RAG baseline, reduced solver calls by up to 85% compared to continuous re-optimization while remaining within 7–12% of optimal response time, and delivered fully offline mobile guidance with sub-500 ms response latency and 54 tokens/s throughput on commodity smartphones. Overall, ResQConnect demonstrates a practical and resilient approach to AI-augmented disaster response. From a sustainability perspective, the proposed system contributes to Sustainable Development Goal (SDG) 11 by improving the speed and coordination of disaster response. It also supports SDG 13 by strengthening adaptation and readiness for climate-driven hazards. ResQConnect is validated using real-world flood and landslide disaster datasets, ensuring realistic incidents, constraints and operational conditions. Full article

To ensure the coordinated interaction between the beam and track of large-span bridges and achieve smooth rail transition at beam joints, rail expansion regulators and beam-end expansion devices are essential at bridge ends. However, these devices are structurally fragile, making them a weak link in the seamless track system. This study selected a long-span railway bridge and its expansion devices as research objects, summarized typical in-service diseases of beam-end expansion devices (e.g., adjustable sleeper offset, sleeper skewing, and expansion device jamming), and constructed a train–track–bridge coupled model incorporating these devices. The model was used to analyze the structural performance and train operation safety under defective conditions. Based on the analysis findings, a maintenance evaluation method for the beam-end region was proposed. Criteria include adjustable sleeper offset, lateral displacement difference between adjacent beam-ends, horizontal rotation angle of adjacent beams, vertical rotation angle of beam-ends, and longitudinal expansion amount of beam-end expansion devices in order to address the corresponding issues and achieve sustainable maintenance and operation of bridge structures. Full article

The digital transformation of electric power systems into smart grids has significantly expanded the cybersecurity risk landscape of the energy sector. While advanced sensing, communication, automation, and data-driven control improve efficiency, flexibility, and renewable energy integration, they also introduce complex cyber–physical interdependencies and new vulnerabilities across interconnected technical and organisational domains. This study adopts a scoping review methodology in accordance with PRISMA-ScR to systematically analyse smart grid cybersecurity from an architecture-aware and resilience-oriented perspective. Peer-reviewed scientific literature and authoritative institutional sources are synthesised to examine modern smart grid architectures, key security challenges, major cyberthreats, and documented real-world cyber incidents affecting energy infrastructure up to 2025. The review systematically links architectural characteristics such as field devices, communication networks, software platforms, data pipelines, and externally operated services to specific threat mechanisms and observed attack patterns, illustrating how cyber risk propagates across interconnected grid components. The findings show that cybersecurity challenges in smart grids arise not only from technical vulnerabilities but also from architectural dependencies, software supply chains, operational constraints, and cross-sector coupling. Based on the analysis of historical incidents and emerging research, the study identifies key defensive strategies, including zero-trust architectures, advanced monitoring and anomaly detection, secure software lifecycle management, digital twins for cyber–physical testing, and cyber-resilient grid design. The review concludes that cybersecurity in smart grids should be treated as a systemic and persistent condition, requiring resilience-oriented approaches that prioritise detection, containment, recovery, and safe operation under adverse conditions. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

Waste electrical and electronic equipment (WEEE) is one of the fastest-growing waste streams globally. Disposable e-cigarettes are among the products that have gained popularity in recent years. Their complex construction and embedded lithium-ion batteries (LIBs) present environmental, safety, and resource recovery challenges. Despite growing research interest, integrated analyses linking material composition with user disposal behavior remain limited. This study is the first to incorporate device-level mass balance, material contamination assessment, battery residual charge measurements, and user behavior to evaluate the waste management challenges of disposable e-cigarettes. A mass balance of twelve types of devices on the Polish market was performed. Plastics dominated in five devices, while non-ferrous metals prevailed in the others, depending on casing design. Materials contaminated with e-liquid residues accounted for 4.4–10.7% of device mass. Battery voltage measurements revealed that 25.6% of recovered LIBs retained a residual charge (greater than 2.5 V), posing a direct fire hazard during waste handling and treatment. Moreover, it was estimated that 7 to 12 tons of lithium are introduced annually into the Polish market via disposable e-cigarettes, highlighting substantial resource potential. Survey results showed that 46% of users disposed of devices in mixed municipal waste, revealing a knowledge–practice gap largely independent of gender or education. Integrating technical and social findings demonstrates that improper handling is a systemic issue. The findings support the relevance of eco-design requirements, such as modular casings for battery removal, alongside the enforcement of Extended Producer Responsibility (EPR) schemes. Current product fees (0.01–0.03 EUR/unit) remain insufficient to establish an effective collection infrastructure, highlighting a key systemic barrier. Full article

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional Chebyshev designs reported in the literature, this study proposes an integrated methodology that combines theoretical synthesis, full-wave electromagnetic modeling, and multivariable optimization, while accounting for manufacturing constraints. The developed method encompasses the electromagnetic characterization of individual resonators, the extraction of cavity–cavity coupling parameters, and the complete implementation of the filter using full-wave electromagnetic simulations. A distinctive aspect of the proposed approach is the explicit incorporation of practical manufacturing effects, such as rounded corners induced by machining processes, alongside analytical and numerical geometric compensation procedures that preserve the device’s electrical response. Furthermore, a combination of gradient-based optimization algorithms and evolutionary strategies is employed to align the electromagnetic response with the target theoretical behavior. The results obtained through electromagnetic simulation indicate that the designed filter achieves return losses exceeding 20 dB and a fractional bandwidth of 4.95%, consistent with the reference Chebyshev response. Finally, the potential extension of the proposed approach to higher frequency bands is discussed conceptually, laying the groundwork for future work that includes experimental validation. Full article

Background: A sedentary lifestyle contributes to chronic disease risk in adults and may predict unfavourable body composition in adolescents. Declining physical activity and rising sedentary behaviour are linked to increasing global obesity rates. Given the scarcity of longitudinal studies examining how participation in organized sports and screen device use relate to body composition in early adolescence, this study aims to address this gap by analyzing temporal trends in both sexes. Methods: A sample of 158 Italian students, 38% of whom were female, was followed longitudinally from ages 11 to 13. Annual anthropometric assessments were conducted, and self-reported data on screen time and organised sports participation were collected. Fat mass (FM), fat-free mass (FFM), fat mass index (FMI), fat-free mass index (FFMI), body mass index (BMI), and waist-to-height ratio (WHtR) were subsequently calculated, along with annual increments. Repeated-measures ANOVA assessed age and sex effects, while multiple regression models evaluated associations between behavioural variables or sex and body composition indices. Results: Significant differences in %F, FM, FFM and its increment, WHtR and its increment, FMI, and FFMI (all p < 0.01) were observed by age and sex interaction. At age 13, weekly sports participation was negatively associated with annual increments in %F (β = −0.204, p = 0.04) and FMI (β = −0.227, p = 0.03). Female sex was associated with greater increments in %F (β = 0.188, p < 0.05) and WHtR (β = 0.323, p < 0.01), and with smaller increments in FFM (β = −0.421, p < 0.01). No significant associations were found for screen time (p > 0.05). Conclusions: Sporting during early adolescence seems to have positive effects on body composition changes, while sex-specific patterns warrant further attention. A deeper understanding of how early adolescent lifestyle factors, such as physical activity and sedentary behaviour, shape body composition is essential for promoting long-term health. Full article

Background/Objectives: Recent studies have reported the effectiveness of cold plasma technology in treating skin inflammation and wounds. We investigated the effect of an air-based no-ozone cold plasma device (Air NCP) on the inflammatory response in human keratinocytes (HaCaT). Methods: The cytotoxicity of Air NCP was assessed using the sulforhodamine B assay, and its ozone concentration and operating temperature were measured to evaluate safety. To determine its anti-inflammatory effect, inflammation was induced with tumor necrosis factor-alpha (TNF-α), and changes in inflammation-related gene expression were analyzed using reverse transcription-polymerase chain reaction and Western blot analysis. The level of prostaglandin E2 (PGE2), an indicator of inflammation, was measured using an enzyme-linked immunosorbent assay. Results: Air NCP showed no cytotoxicity in HaCaT cells. Moreover, the expression of TNF-α, interleukin-6, and interleukin-1β significantly decreased following treatment (p < 0.001). The levels of phosphorylated nuclear factor kappa B and phosphorylated signal transducer and activator of transcription-3 were also reduced (p < 0.001). Western blot analysis further confirmed that inflammation-activated mitogen-activated protein kinase factors were reduced by Air NCP, while cyclooxygenase-2 and PGE2 levels similarly decreased. Conclusions: These results indicate that Air NCP treatment suppresses the expression of inflammatory mediators in skin inflammation, demonstrating a clear anti-inflammatory effect. Full article

Quantum Key Distribution (QKD) is a process to establish a symmetric key between two parties using the principles of quantum mechanics. Currently, commercial QKD systems are still expensive, they require specific infrastructure, and they are impractical for deployment in portable or resource-constrained devices. In this article, we introduce the Butterfly Protocol (and its extended version) that enables QKD to be offered as a service to non-QKD-capable (portable or IoT) devices. Our key contributions include (1) resilience to the compromise of any single classical link, (2) protection against malicious QKD users, (3) implicit mutual authentication between users without relying on large post-quantum certificates, and (4) the Double Butterfly extension, which secures communication even when the underlying QKD network cannot be fully trusted. We also demonstrate how to integrate the Butterfly Protocol into TLS 1.3 and provide its initial security analysis. We present preliminary performance results and discuss the main bottlenecks in the Butterfly Protocol implementation. We believe that our solution represents a practical step toward integrating QKD into classical networks and extending its use to portable devices. Full article

Digital identity operates as the connective infrastructure of the digital age, linking individuals, organisations, and devices into networks through which services, rights, and responsibilities are transacted. Despite this centrality, the field remains fragmented, with technical solutions, disciplinary perspectives, and regulatory approaches often developing in parallel without interoperability. This paper presents a systematic survey of digital identity research, drawing on a Scopus-indexed baseline corpus of 2551 publications spanning full years 2005–2024, complemented by a recent stratum of 1241 publications (2023–2025) used to surface contemporary thematic structure and inform the ontology-oriented synthesis. The survey contributes in three ways. First, it provides an integrated overview of the digital identity landscape, tracing influential and widely cited works, historical developments, and recent scholarship across technical, legal, organisational, and cultural domains. Second, it applies natural language processing and subject metadata to identify thematic patterns, disciplinary emphases, and influential authors, exposing trends and cross-field connections difficult to capture through manual review. Third, it consolidates recurring concepts and relationships into ontological fragments (illustrative concept maps and subgraphs) that surface candidate entities, processes, and contexts as signals for future formalisation and alignment of fragmented approaches. By clarifying how digital identity has been conceptualised and where gaps remain, the study provides a foundation for progress toward a universal digital identity that is coherent, interoperable, and socially inclusive. Full article