Search Results (152)

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. Full article

Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

Background: Blockchain is increasingly proposed to strengthen pharmaceutical traceability, anti-counterfeiting, and chain of custody in multi-actor supply chains, but the evidence base remains heterogeneous in technical rigor and operational clarity. Methods: We conducted a mapping review of Scopus and Web of Science to map publication patterns, identify dominant thematic configurations, and compare citation-salient studies across recurring solution profiles and operational design dimensions. The final corpus comprised 103 records. Results: The literature expanded rapidly from 2019 to 2025, with notable geographic concentration and dissemination mainly through technically focused outlets. Keyword analysis identified a core traceability theme, an implementation stream centered on smart contracts, Ethereum, and security, and additional streams involving vaccines and regulatory or credentialing concerns. Citation-salient studies clustered into implemented systems and prototypes, architecture or framework proposals, and contextual maturity or decision-layer evidence. Across these profiles, transferability depended less on platform choice than on governance and access-control assumptions, modular smart contract roles, and verifiable on-chain/off-chain data placement. Conclusions: Chain-of-custody semantics and evaluation methods remain inconsistently formalized, limiting cross-study comparability and the interpretability of operational claims. Benchmark-oriented assessments and minimal reporting standards specifying governance parameters, logistics scope and checkpoints, workload, measurement conditions, and concrete evidence artifacts are needed. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

Food security is being experienced particularly deeply in vulnerable regions that are impacted by climate change. Therefore, this study aims to examine the impact of climate change and gross national income on food security in the Gulf Cooperation Council (GCC) countries. The study utilized cross-country panel data for GCC countries from 2000 to 2024, with food access acting as the dependent variable for food security. The annual meteorological temperature, energy-related carbon emissions, and gross national income are involved as independent variables representing the factors of climate change and economic growth, respectively. The Pedroni and Johansen–Fisher panel cointegration tests were implemented. Furthermore, the study employs Bayesian random-effects (BRE) and Bayesian mixed-effects (BME) models, estimated through Markov Chain Monte Carlo (MCMC) methods, for achieving posterior distributions of the model’s parameters. The results confirm the existence of a long-term cointegrating relationship among the selected variables. Gross national income has a positive impact on food security, whereas carbon emissions exert a negative effect. The findings reveal that food security is shaped by interconnected economic and climate factors, with notable differences between countries. These results underline the importance of regional cooperation and country-specific policies that focus on enhancing income, mitigating emissions, and investing in food systems. Full article

The accelerating frequency of emerging infectious diseases (EIDs) in livestock poses a significant threat to global food security, as well as to animal and public health. While wastewater-based surveillance (WBS) has advanced significantly for human health surveillance, its application to livestock production systems remains fragmented and lacks standardization. This review synthesizes current evidence on livestock wastewater-based surveillance (L-WBS) as an early-warning sentinel for emerging viral pathogens, evaluating their dynamics, economic impacts, biosecurity measures, and One Health implications. Existing studies demonstrate that L-WBS effectively detects emerging viral pathogens in agricultural effluent, swine manure, and municipal wastewater systems serving livestock regions, frequently preceding clinical outbreak recognition. We further conceptualized a multifactorial framework linking environmental drivers such as climate and ecological disruption and agricultural intensification to pathogen emergence dynamics. Economic assessments show substantial direct losses (approximately US$ 950 per H5N1-infected dairy cow and US$ 25.9 billion in African swine fever virus (ASFV)-related damages across China) alongside indirect costs from biosecurity implementation, workforce disruption, and supply-chain instability. We recommend prioritizing methodological standardization through unified sampling and extraction protocols, integration of next-generation sequencing for genomic surveillance, and cross-sectoral policy frameworks to operationalize L-WBS as a global early-warning infrastructure for mitigating zoonotic spillover and livestock-dependent community resilience. Full article

In an era of rising geopolitical tensions, repeated global crises, and growing uncertainty in trade and finance, economic resilience has become a key priority for policymakers. This study presents an understanding by distinguishing regional resilience from global resilience, offering hardnosed explanations of both concepts and outlining mensurable indicators for each. Regional resilience is the capacity of an economy to endure and recuperate from shocks by way of strong, cost-effective connections in its region. These could be seen in terms of intra-bloc trade power, trade concentration, intra-regional investment flows and constant capital flows, which indicate the deep economical integration and interdependence. On the contrary, global resilience is concerned with the extent to which an economy is guarded by larger global diversification. It is quantified by the distribution of exports and investments geographically, the extent and diversity of trade partners, membership on global value chains, and the stability of the cross-border capital flows. Understanding the difference between these two forms of resilience has become increasingly important for policy design, especially in a period marked by repeated crises, geopolitical tension, and shifting trade and financial conditions. Countries must decide not only how open their economies should be, but also whether openness should be integrated regionally, diversified globally, or stable through a hybrid approach. Further, it argues that regional integration is peculiarly invaluable during region-wide disruptions such as pandemics, financial crises, or supply shortages, where integrated policies can reduce adjustment costs and protect demand and supply chains. However, global diversification becomes significant in areas such as energy and commodity security, where dependence on limited suppliers can magnify risks. Ultimately, most economies benefit from combining both approaches (a hybrid approach), adapting their strategy to the development stage, institutional strengths, and exposure to external shocks. Full article

Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data often forms “data silos” due to privacy regulations and a lack of trust between collaborating entities. Existing integrated schemes combining “Federated Learning + Blockchain” have achieved a certain degree of process traceability and automated payments, but risks of gradient-level privacy leakage persist, and inflexible and delayed incentive mechanisms result in low participation quality. To systematically address these bottlenecks, this paper proposes the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture, whose core innovation lies in the native integration of cryptographic security and mechanism design theory. It includes the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol, which integrates partial homomorphic encryption and zero-knowledge proofs to provide verifiable privacy guarantees for gradient contributions while enabling efficient secure aggregation, defending against inversion attacks at the source; alongside this, it includes the Economy-on-Chain incentive (EconChain) mechanism, which designs an on-chain economic system based on blockchain, achieving precise measurement and sustainable incentivization of training process contributions through fine-grained instant micro-rewards and a dynamic reputation model. Experiments show that, compared to baseline schemes, FLARE can effectively enhance node participation enthusiasm and contribution quality without compromising model accuracy, providing a new paradigm with both strong security and high vitality for the trusted and efficient circulation of data. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

Java deserialization vulnerabilities remain a critical security threat, with existing detection tools facing significant challenges in generating functional exploit payloads due to semantic blindness and constraint complexity. This paper presents LLM-JDFuzz, the framework that leverages Large Language Models for automated Java deserialization payload generation. By reframing payload generation as a constraint-aware code synthesis problem, LLM-JDFuzz introduces three key innovations: (1) an Autoprompting engine that transforms Gadget Chain specifications into structured natural language instructions, (2) an adaptive multi-strategy fuzzing loop that dynamically selects between generation, mutation, and semantic exploration based on execution feedback, and (3) a fine-grained feedback mechanism that performs error classification and root cause attribution to guide iterative refinement. We evaluate LLM-JDFuzz on 34 Gadget Chains from the ysoserial benchmark. Results show that LLM-JDFuzz achieves a 70.6% success rate. While maintaining comparable effectiveness to the state-of-the-art static tool JDD, LLM-JDFuzz demonstrates superior performance in processing cross-language scripting engine chains and achieves a 4× improvement in generation efficiency. Our work demonstrates that LLMs possess inherent semantic understanding capabilities for security-critical code generation, opening new research directions for AI-assisted vulnerability exploitation. Full article

The rapid integration of Internet of Things (IoT) technologies is transforming electrical power systems into intelligent, interconnected, and data-driven infrastructures, enabling advanced monitoring, control, and optimization across the entire energy value chain. IoT-based smart electrical systems enable advanced monitoring, control, and optimization of energy generation, distribution, and consumption, while also introducing new challenges related to interoperability, security, scalability, and data management. Despite the growing body of literature, existing surveys typically address these challenges in isolation, focusing on individual technological or operational aspects and thus failing to capture their strong cross-dependencies in real-world deployments. This paper delivers a comprehensive survey that systematically analyzes and interrelates nine key dimensions that prior literature largely examines in separate silos: architectural models, communication protocols, reference standards, cybersecurity and privacy mechanisms, data processing paradigms (edge, fog, and cloud), interoperability solutions, energy management strategies, application scenarios, and future research directions. Unlike conventional reviews confined to single-layer or domain-specific perspectives, this survey adopts a holistic, cross-layer approach, explicitly linking architectural choices, protocol stacks, interoperability frameworks, and security mechanisms with application and energy management requirements. Full article

It is a hot topic to enhance the stability, security, and sustainability of industrial chains, against the backdrop of adjustments and rising uncertainty in global value chains. Using Chinese A-share listed firms from 2012 to 2022 as the research sample, this study treats the establishment of Cross-Border E-Commerce Comprehensive Pilot Zones (CBECCPZs) as a quasi-natural experiment and employs a difference-in-differences approach to empirically examine the impact of digital trade (DT) on industrial chain resilience (ICR) and its underlying mechanisms. The findings demonstrate that DT exerts a significantly positive effect on ICR, providing strong support for the long-term sustainability of the economic system. This conclusion remains robust after a series of robustness checks, including the incorporation of high-dimensional fixed effects, exclusion of confounding policy effects, adjustments to the sample, dimension-specific tests, consideration of lagged effects, and propensity score matching. Mechanism analysis reveals that DT strengthens ICR primarily by promoting firms’ digital transformation and improving human capital levels. The heterogeneity results suggest that the contribution of digital trade to resilience differs markedly across structural dimensions: the effect is more significant among firms located in eastern regions, state-owned enterprises, firms operating in regions with higher levels of digitalization, manufacturing firms, firms in more competitive industries, and firms with stronger internal control systems. From the perspective of ICR, this study elucidates the intrinsic mechanisms through which DT fosters high-quality development and sustainable economic growth. The findings provide robust empirical evidence for understanding the strategic role of DT in enhancing the security, stability, and sustainable operation of industrial chains and in building a modern industrial system that is autonomous, controllable, secure, and efficient. Moreover, the study offers important policy implications for governments seeking to advance DT institutional innovation and promote coordinated regional development, as well as for firms aiming to leverage DT to enhance long-term competitiveness and achieve sustainable development goals. Full article