Search Results (545)

This study investigates the impact of assimilating Advanced Scatterometer (ASCAT) Level-2 surface soil moisture retrievals into the Application of Research to Operations at Mesoscale (AROME) model, the operational numerical weather prediction system of the Hungarian Meteorological Service. The Level-2 retrievals are geophysical soil moisture estimates derived from satellite radar backscatter observations and represent the uppermost soil layer (approximately 0–5 cm). Data assimilation is performed using a Simplified Extended Kalman Filter (SEKF) within the SURFEX surface modeling platform. In the reference configuration (REF), the same SEKF framework is applied, as used operationally for the assimilation of 2 m temperature and relative humidity observations. A second experiment (ASCAT) extends this configuration by additionally assimilating ASCAT surface soil moisture retrievals. The experimental period covers May–October 2023. The objective of the study is to quantify the added value of ASCAT soil moisture assimilation relative to the REF experiment, which does not assimilate ASCAT retrievals. Results indicate a systematic improvement in root-zone soil moisture and soil temperature, suggesting that the assimilation of surface soil moisture observations propagates beneficially to deeper soil layers. Verification against in situ and model-derived diagnostics shows a positive impact on near-surface atmospheric variables, particularly for 2 m temperature and humidity during nighttime conditions. Furthermore, precipitation verification reveals a measurable improvement, suggesting a beneficial influence of improved land–atmosphere coupling on short-range forecasts. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

South Korea has achieved internationally recognized digital government capacity and platform-based service integration, yet recent privacy and cybersecurity incidents reveal growing risks to digital trust. This article examines how national governance models shape digital trust risk under conditions of platform dependency, large-scale data breaches, and artificial intelligence (AI)-enabled cybersecurity threats. Drawing on a comparative socio-technical systems perspective, the study analyzes three digitally advanced but institutionally distinct cases: South Korea, Estonia, and Taiwan. South Korea is examined as a reactive platform-accountability model, Estonia as an architecture-based data-auditability model, and Taiwan as a civic-resilience and joint-defense model. Rather than applying a formal quantum-probability model, the article uses the QP-Gov framework as a bounded analytical lens for interpreting context sensitivity, latent trust, accountability visibility, and abrupt trust-risk shifts. Methodologically, the study adopts a most-different systems design and combines structured profile analysis, case tracing, and an evidence matrix. The comparison focuses on five dimensions: digital density, data concentration, accountability visibility, incident responsiveness, and AI-era readiness. The findings show that digital trust depends not simply on technological sophistication, but on whether citizens can observe how data are accessed, how breaches are handled, how responsibility is allocated, and whether institutions learn from incidents before trust damage becomes systemic. The analysis suggests that Korea’s reactive model demonstrates strong post-incident investigative and regulatory capacity but remains vulnerable when accountability becomes visible only after major breaches. By contrast, Estonia and Taiwan illustrate alternative mechanisms of preventive auditability and resilience-based preparedness. The article concludes by proposing a sequenced policy pathway for South Korea, including citizen-facing data-access logs, systemic platform duties, breach-consequence dashboards, AI-agent audit trails, zero-trust infrastructure, and independent digital trust oversight. Full article

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

Digital financial inclusion (DFI) has become a central topic in financial inclusion research because digital payments, mobile money, internet banking, and platform-based finance can reduce access barriers and expand formal financial participation. Prior studies have documented the development relevance of financial inclusion and have constructed multidimensional financial inclusion and DFI indices, often using PCA and related composite-indicator methods. A remaining measurement gap concerns the equal observability of different digital-finance architectures within a common cross-country indicator set. This study addresses that gap by analyzing an existing PCA-based DFI score for 40 Organisation of Islamic Cooperation (OIC) countries over 2015–2024 through a channel-observability framework. The objective is to examine whether the observed DFI ranking is captured more directly through mobile-money indicators than through the available infrastructure-based representation of bank-led digital finance. The analysis decomposes the six available indicators into a bank-led visibility proxy, based on internet penetration and ATM density, and a mobile-money visibility proxy, based on mobile agents, mobile accounts, mobile transaction volume, and transaction value relative to GDP. The OIC-wide mean DFI score increased from 11.31 in 2015 to 31.24 in 2024, while dispersion widened and the 2015 and 2024 top-ten country groups had zero overlap. The channel diagnostics show that the highest observed DFI scores are concentrated among countries whose digital-finance activity is directly recorded through mobile-money indicators, whereas several financially advanced economies are visible mainly through the bank-led infrastructure proxy. Zero-coded mobile-money observations are interpreted as indicator-visibility signals for the standalone mobile-money channel and considered separately from broader digital-finance activity. The Random Forest analysis functions as a bounded internal sensitivity audit of the existing six-indicator score and shows that mobile-money transaction variables carry the largest within-score explanatory weight. The theoretical contribution is to frame DFI measurement as an architecture-dependent observability problem rather than only as a weighting problem. The practical implication is that cross-country DFI rankings should be interpreted together with channel diagnostics, especially when bank-led digital services such as mobile banking, card payments, POS transactions, QR payments, and instant-payment systems are outside the balanced indicator set. Full article

Traditional celebrity endorser models conceptualize expertise as a static trait focused on the possession of product knowledge. However, the synchronous and highly interactive nature of live commerce challenges these static models in explaining consumer engagement. Grounded in Social Presence Theory and a contextual competence perspective, the present study reconceptualizes live-streamer professionalism as a set of dynamic competencies enacted through real-time interaction. Using a sequential mixed-methods design, Study 1 employs a grounded theory analysis of qualitative data collected from consumers, streamers, and platform practitioners to identify core dimensions of streamer professionalism. Study 2 develops and validates a multidimensional measurement scale. Study 3 leverages a comprehensive engagement model to benchmark the proposed framework against competing, expertise-based explanations. The results reveal five distinct dimensions of streamer professionalism (Business Knowledge Reserve, Expressive Ability, Professional Quality, Interactive Ability, External Visible Traits) and a dual-pathway mechanism: Business Knowledge Reserve primarily enhances cognitive product involvement, whereas Interactive Ability strengthens context attachment by amplifying perceived social presence. The proposed model demonstrates greater explanatory power than traditional expertise frameworks, particularly in explaining affective attachment. Collectively, these findings shift the analytical focus from who the source is to how professionalism is performed within interactive service encounters, thereby advancing a performance-oriented view of professionalism in live commerce and informing platform governance and influencer management. Full article

The power density of modern high-power integrated processors keeps rising rapidly. Among them, chiplet-based high-power AI accelerators exhibit local peak heat flux exceeding 1 kW/cm², which leads to concentrated hotspots, severe internal temperature gradients, device performance degradation and reliability deterioration. Conventional heat dissipation approaches are limited by the bottleneck of series interfacial thermal resistance and fail to meet the cooling demands of complex integrated architectures. Chip-level thermal management serves as a core method to suppress hotspots near heat sources and reduce overall system thermal resistance, which guarantees long-term stable operation of high-power integrated processors and plays a vital role in improving the energy efficiency and service life of computing platforms. This paper systematically reviews mainstream chip-level thermal management technologies for high-power integrated processors, covering heterogeneous integration of high-thermal-conductivity substrates, embedded microchannel liquid cooling, solid-state active heat pumps, multi-physics co-design and advanced packaging manufacturing processes. The basic working principles and state-of-the-art research progress of each cooling technology are elaborated in detail. The common engineering bottlenecks, including ultra-high heat flux endurance, packaging process compatibility, fluid leakage risks and multi-layer interfacial thermal resistance, are summarized, and the future development trends of this field are clarified. This review can provide comprehensive theoretical guidance for structural design and large-scale engineering implementation of near-junction thermal management solutions for various high-power integrated processors, especially high-computing-power AI accelerators. Full article

Diabetes mellitus is a major chronic disease that requires continuous monitoring and timely risk assessment to reduce the likelihood of severe long−term complications. Recent advances in mobile health technologies and machine learning (ML) provide new opportunities for developing intelligent systems that support diabetes self−management and early risk screening. This paper presents an end−to−end mobile health platform that integrates diabetes monitoring functionalities with an ML−based prediction service within a modular client−server architecture. The proposed system enables users to record glucose measurements, insulin injections, physical activity, and other health−related information while providing historical data visualization, automated reminder notifications, and real−time diabetes risk prediction. The prediction module was trained using the publicly available PIMA Indians Diabetes Dataset and evaluated using Decision Tree (DT), Random Forest (RF), and XGBoost classifiers. Model performance was assessed using accuracy, precision, recall, specificity, F1−score, calibration analysis, and the area under the receiver operating characteristic curve (AUC). Experimental results showed that the RF classifier achieved the highest AUC (0.925), demonstrating superior discrimination capability among the evaluated models and making it the most suitable candidate for deployment within the proposed platform. Although the current prediction model was trained on a benchmark public dataset, the proposed framework provides a practical foundation for integrating ML-driven decision support into mobile health applications and can be further extended through external clinical validation and personalized prediction models. Full article

Considering that smart meter roll-out has already been completed in several European countries for some years now, this review assesses the current state and future opportunities for the direct integration of commercial wallboxes and smart meters in Europe. Despite successful smart meter roll-outs, direct integration remains challenging: while commercial wallboxes are sold on international markets and follow recognized standards, installed smart meters and related cloud platforms are mostly national or regional products, and grid operators have developed proprietary technologies to support their own Advanced Metering Infrastructures (AMI). Here, we first advocate the case for direct integration, noting that it is particularly well suited for local load management when EVs are the only flexible loads and for the provision of novel flexibility services based on real-time grid signals. We then review smart meters data exchange protocols and communication interfaces and identify the common issues hindering effective smart meters exploitation. We eventually propose a set of recommendations to tackle current smart metering infrastructures limitations and unlock their identified potential. Full article

Vehicular Ad Hoc Networks (VANETs) are mobile networks that offer new services and communication between moving vehicles, roadside infrastructure, and a trusted authority. With the development of autonomous and connected vehicles, the issue of authentication in VANETs has become increasingly prominent due to the lack of mutual trust among network entities. However, standard authentication models for VANETs must account for total computational and communication overhead, regardless of the timing of authentication message generation. To address this limitation, this work proposes an advanced authentication paradigm for VANETs called the online/offline VANET framework, and formalizes this novel framework to realize lightweight authentication by shifting heavy computational overhead to the offline phase. The proposed model is divided into an offline phase and an online phase. In the offline phase of the free time before the message becomes available, it allows more powerful trusted authority to pre-compute, and in the online phase, resource-constrained devices only execute a small set of residual operations. Based on this model and a new identity-based signature, we give an efficient instantiation and use a mobile platform to evaluate it. The experimental results demonstrate that our construction achieves low online computational and communication overhead. Full article

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

Dental caries remains one of the most prevalent yet preventable non-communicable diseases worldwide, disproportionately affecting populations with limited access to dental care and persistent socioeconomic inequalities. Early-stage lesions frequently remain undetected because of their asymptomatic nature, inadequate screening infrastructure, and the absence of continuous monitoring technologies, resulting in preventable complications and increased healthcare costs. To address these challenges, this study proposes an Internet of Things (IoT)-enabled intraoral wearable sensing device (I-OWSD) for continuous, quantitative, real-time monitoring of biomarkers associated with caries progression. The proposed framework integrates intraoral wearable sensing, cloud-based telemedicine services, and artificial intelligence (AI)-assisted analytics to support preventive oral healthcare and remote clinical decision-making. Two primary contributions are presented. First, a fractional-order delay-type model (FODM) based on the Caputo–Fabrizio derivative is proposed to capture the memory-dependent and nonlocal dynamics of caries progression. Mathematical analysis establishes the model’s non-negativity, boundedness, existence, uniqueness, and stability properties. Second, a biocompatible intraoral sensor interface is designed to enable continuous data acquisition and secure wireless communication with digital health platforms. Simulation results based on the proposed FODM suggest that, under an estimated adoption rate of 67.49%, the I-OWSD framework could reduce caries prevalence by approximately 15% while improving opportunities for early intervention and preventive care. The findings demonstrate the potential of combining fractional-order modelling, wearable sensing, and AI-driven teledentistry to advance continuous oral health monitoring and preventive dental care. Full article

In large-scale seismic scenarios, the operational continuity of evacuation shelters is frequently compromised by limited governmental capacity, necessitating the strategic integration of Disaster Relief Volunteers (DRVs). Traditional assessments often rely on coarse regional aggregates, overlooking the critical spatial mismatch between volunteer availability and localized demand. In this study, a high-resolution, spatially explicit framework was developed to evaluate urban operational resilience in New Taipei City. Utilizing the TERIA platform, a nocturnal magnitude 6.8 Shanjiao Fault rupture was simulated, identifying that approximately 460,000 individuals would be affected, with shelter demand reaching 300,000—double the current capacity. Service areas were delineated using ArcGIS Network Analyst (Dijkstra’s algorithm) based on an 800 m walking threshold, further refined by a secondary assignment rule to redistribute “shadow demand” from peripheral populations. Quantitative analysis of the newly introduced “DRV shortfall” metric reveals that 74% of shelters (148/200) face concurrent spatial and manpower saturation. Notably, 42 analysis units lack resident volunteers entirely, with the most severe shortfall magnitude reaching 361 DRVs at a single “high-risk convergence node”. These results uncover a profound deficiency in urban disaster resilience driven by significant spatial mismatch. This research contributes a three-fold advancement: (i) the high-resolution coupling of volunteer residential data with dynamic demand patterns; (ii) the formalization of the DRV shortfall as a standardized metric for resource adequacy; and (iii) the formulation of a strategic policy framework for multi-shelter activation sequencing, “Support Hub” designation, and resource synchronization in hyper-dense urban environments. Full article

Advance salary payment services are increasingly used to improve employee financial flexibility; however, their implementation is often constrained by fragmented workflows, disconnected HR–payroll coordination, inconsistent accounting synchronization, and limited transaction traceability. This study proposes and evaluates a Traceability-Oriented Enterprise Architecture (TOEA) for advance salary payment management in enterprise payroll environments. The proposed architecture integrates employee self-service interaction, workflow automation, payroll synchronization, payment processing, audit-oriented monitoring, and enterprise interoperability within a unified digital platform. The study employs a Design Science Research approach and introduces a layered architectural framework together with a unified enterprise data model supporting end-to-end lifecycle traceability through globally unique identifiers. The architecture was implemented as a functional prototype and evaluated through a pilot deployment in a university environment employing more than 1000 staff members. The evaluation included process-based comparison, expert validation involving 16 participants from HR, payroll accounting, and management, scalability assessment under increasing workload scenarios, and qualitative stakeholder feedback analysis. The results indicate improvements in workflow transparency, auditability, payroll synchronization, and organizational coordination, while reducing process fragmentation and manual coordination activities. Expert assessments demonstrated positive perceptions of workflow transparency, traceability, and operational monitoring. The findings suggest that traceability-oriented architectural principles provide an effective foundation for interoperable and auditable salary advance management systems and offer a scalable basis for future integration of intelligent analytics and decision-support services. Full article