Search Results (783)

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems. Full article

Modern military operations place significant physiological and cognitive demands on soldiers, necessitating innovative strategies to monitor and optimize health and performance. This narrative review examines the role of continuous physiological state monitoring and precision health strategies to enhance soldier resilience and operational readiness. Advanced wearable biosensors were analyzed for their ability to measure vital physiological parameters—such as heart-rate variability, core temperature, hydration status, and biochemical markers—in real-time operational scenarios. Emerging technological solutions, including AI-driven analytics and edge computing, facilitate rapid data interpretation and predictive health assessments. Results indicate that real-time physiological feedback significantly enhances early detection and prevention of conditions like exertional heat illness and musculoskeletal injuries, reducing medical attrition and improving combat effectiveness. However, ethical challenges related to data privacy, informed consent, and secure data management highlight the necessity for robust governance frameworks and stringent security protocols. Personalized training regimens and rehabilitation programs informed by monitoring data demonstrate potential for substantial performance optimization and sustained force readiness. In conclusion, integrating precision health strategies into military operations offers clear advantages in soldier health and operational effectiveness, contingent upon careful management of ethical considerations and data security. Full article

Against the backdrop of China’s large-scale collective forest tenure reform, examining the actual effects of land policies at the household level is crucial for advancing sustainable forestry. This study aims to comprehensively analyze the impacts of tenure formalization (forest tenure certificates) and market-based incentives (bamboo forest certification) on household production inputs and harvesting behavior by disentangling the objective implementation of policies from households’ subjective perceptions. Based on survey data from 1090 households in Fujian Province, China, and employing double-hurdle and Tobit models, this study reveals a central finding: households’ management decisions are driven more strongly by their subjective perceptions than by objectively held policy instruments. Specifically, perceived tenure security serves as a key incentive for increasing production inputs and adopting long-term harvesting plans, whereas the mere possession of forest tenure certificates exhibits limited direct effects. Similarly, households’ positive expectations about the market value enhancement from bamboo forest certification significantly promote investments and sustainable harvesting practices—an effect substantially greater than that of mere participation in certification. Consequently, this study argues that the successful implementation of land governance policies depends not only on the rollout of instruments but, more critically, on fostering households’ trust and positive perceptions of policies’ long-term value. Full article

Agricultural drought, increasingly intensified by climate change, poses a significant threat to food security and water resources in semi-arid regions, including Türkiye’s Konya Closed Basin. This study evaluates six satellite-derived indices—Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), Evapotranspiration Condition Index (ETCI), and Soil Moisture Condition Index (SMCI)—to monitor agricultural drought (2001–2024) and proposes a drought vulnerability map using a novel Drought Vulnerability Index (DVI). Integrating Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Center InfraRed Precipitation with Station (CHIRPS), and Land Data Assimilation System (FLDAS) datasets, the DVI combines these indices with weighted contributions (VHI: 0.27, ETCI: 0.25, SMCI: 0.22, PCI: 0.26) to spatially classify vulnerability. The results highlight severe drought episodes in 2001, 2007, 2008, 2014, 2016, and 2020, with extreme vulnerability concentrated in the southern and central basin, driven by prolonged vegetation stress and soil moisture deficits. The DVI reveals that 38% of the agricultural area in the basin is classified as moderately vulnerable, while 29% is critically vulnerable—comprising 22% under high vulnerability and 7% under extreme vulnerability. The proposed drought vulnerability map offers an actionable framework to support targeted water management strategies and policy interventions in drought-prone agricultural systems. Full article

The Source Region of the Yangtze and Yellow Rivers (SRYY), situated on the Qinghai-Tibet Plateau, serves as a vital ecological barrier and a critical component of the global carbon cycle. However, this region faces severe ecosystem degradation driven by climate change and human activities. This study establishes an integrated ecological security assessment framework that couples ecological risk, ecosystem health, and ecosystem services to evaluate ecological dynamics in the SRYY from 2000 to 2020. Leveraging multi-source data (vegetation, hydrological, meteorological) and advanced modeling techniques (spatial statistics, geographically weighted regression), we demonstrate that: (1) The Ecological Security Index (ESI) exhibited an initial increase followed by a significant decline after 2010, falling below its 2000 level by 2020. (2) The rising Ecological Risk Index (ERI) directly weakened both the ESI and Ecosystem Service Index (ESsI), with this negative effect intensifying markedly post-2010. (3) A distinct spatial gradient pattern emerged, shifting from high-security core areas in the east to low-security zones in the west, closely aligned with terrain and elevation; conversely, areas exhibiting abrupt ESI changes showed little correlation with permafrost degradation zones. (4) Vegetation coverage emerged as the key driver of ESI spatial heterogeneity, acting as the central hub in the synergistic regulation of ecological security by climate and topographic factors. Full article

In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to enhance efficiency and resilience. Experimental validation on three E-government datasets (95,000–230,000 records) demonstrates that the proposed model improves processing speed by up to 40% and enhances adversarial robustness by maintaining accuracy reductions below 2.5% under attack scenarios. Compared with baseline neural networks, the architecture achieves higher accuracy (up to 95.1%), security (up to 98% attack prevention), and efficiency (processing up to 1600 records/sec). These results confirm the model’s applicability for large-scale, real-time E-government systems, providing a practical path for sustainable and secure digital public administration. Full article

Post-occupancy evaluation is a critical mechanism for ensuring the sustained success and continuous improvement of industrial heritage revitalization initiatives. The quality of the visitor experience plays a key role in determining a project’s long-term vitality. This study focuses on assessing user satisfaction with a revitalized industrial heritage site by employing web crawling and data mining techniques to systematically collect and analyze user-generated reviews from major online platforms. Using the 1933 Old Millfun in Shanghai, China, as an example, this research identifies six core evaluation dimensions derived from extensive user commentary: project accessibility, cultural legibility, aesthetic distinctiveness, commercial appeal, facility completeness, and sense of security. These dimensions are integrated into a comprehensive analytical framework, with the Fuzzy Comprehensive Evaluation (FCE) method applied to quantitatively assess the site’s performance across each category. By combining qualitative sentiment data with quantitative evaluation techniques, the data-driven presentation provides nuanced insights into the evolving user experience. The research results contribute to the development of a replicable and scalable paradigm for measuring user experience in industrial heritage revitalization and highlights the potential of digital platforms as valuable tools for heritage site management and continuous optimization. Full article

Lithium-ion batteries (LIBs) are critical components in safety-critical systems such as electric vehicles, aerospace, and grid-scale energy storage. Their degradation over time can lead to catastrophic failures, including thermal runaway and uncontrolled combustion, posing severe threats to human safety and infrastructure. Developing a robust AI framework for degradation prognostics in safety-critical systems is essential to mitigate these risks and ensure operational safety. However, sensor noise, dynamic operating conditions, and the multi-scale nature of degradation processes complicate this task. Traditional denoising and modeling approaches often fail to preserve informative temporal features or capture both abrupt fluctuations and long-term trends simultaneously. To address these limitations, this paper proposes a hybrid data-driven framework that combines Sample Entropy-guided Variational Mode Decomposition (SE-VMD) with K-means clustering for adaptive signal preprocessing. The SE-VMD algorithm automatically determines the optimal number of decomposition modes, while K-means separates high- and low-frequency components, enabling robust feature extraction. A dual-branch architecture is designed, where Gated Recurrent Units (GRUs) extract short-term dynamics from high-frequency signals, and Transformers model long-term trends from low-frequency signals. This dual-branch approach ensures comprehensive multi-scale degradation feature learning. Additionally, experiments with varying sliding window sizes are conducted to optimize temporal modeling and enhance the framework’s robustness and generalization. Benchmark dataset evaluations demonstrate that the proposed method outperforms traditional approaches in prediction accuracy and stability under diverse conditions. The framework directly contributes to Artificial Intelligence for Security by providing a reliable solution for battery health monitoring in safety-critical applications, enabling early risk mitigation and ensuring operational safety in real-world scenarios. Full article

The surface quality of industrial gaskets directly impacts sealing performance, operational reliability, and market competitiveness. Inadequate or unreliable defect detection in silicone gaskets can lead to frequent maintenance, undetected faults, and security risks in downstream systems. This paper presents VST-YOLOv8, a trustworthy and secure defect detection framework built upon an enhanced YOLOv8 architecture. To address the limitations of C2F feature extraction in the traditional YOLOv8 backbone, we integrate the lightweight Mobile Vision Transformer v2 (ViT v2) to improve global feature representation while maintaining interpretability. For real-time industrial deployment, we incorporate the Gating-Structured Convolution (GSConv) module, which adaptively adjusts convolution kernels to emphasize features of different shapes, ensuring stable detection under varying production conditions. A Slim-neck structure reduces parameter count and computational complexity without sacrificing accuracy, contributing to robustness against performance degradation. Additionally, the Triplet Attention mechanism combines channel, spatial, and fine-grained attention to enhance feature discrimination, improving reliability in challenging visual environments. Experimental results show that VST-YOLOv8 achieves higher accuracy and recall compared to the baseline YOLOv8, while maintaining low latency suitable for edge deployment. When integrated with secure industrial control systems, the proposed framework supports authenticated, tamper-resistant detection pipelines, ensuring both operational efficiency and data integrity in real-world production. These contributions strengthen trust in AI-driven quality inspection, making the system suitable for safety-critical manufacturing processes. Full article

The integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility. Full article

The evolution of industrial automation toward Industry 3.0 and 4.0 has driven the emergence of Industrial Edge-Cloud Platforms, which increasingly depend on web interfaces for managing and monitoring critical operational technology. This convergence introduces significant security risks, particularly from Broken Access Control (BAC)—a vulnerability consistently ranked as the top web application risk by the Open Web Application Security Project (OWASP). BAC flaws in industrial contexts can lead not only to data breaches but also to disruptions of physical processes. To address this urgent need for robust web-layer defense, this paper presents VeriFlow, a static verification framework for access control in web applications. VeriFlow reformulates access control verification as a consistency problem between two core artifacts: (1) a Formal Access Control Policy (P), which declaratively defines intended permissions, and (2) a Navigational Graph, which models all user-driven UI state transitions. By annotating the graph with policy P, VeriFlow verifies a novel Path-Permission Safety property, ensuring that no sequence of legitimate UI interactions can lead a user from an authorized state to an unauthorized one. A key technical contribution is a static analysis method capable of extracting navigational graphs directly from the JavaScript bundles of Single-Page Applications (SPAs), circumventing the limitations of traditional dynamic crawlers. In empirical evaluations, VeriFlow outperformed baseline tools in vulnerability detection, demonstrating its potential to deliver strong security guarantees that are provable within its abstracted navigational model. By formally checking policy-graph consistency, it systematically addresses a class of vulnerabilities often missed by dynamic tools, though its effectiveness is subject to the model-reality gap inherent in static analysis. Full article

Industrial control systems (ICS) are increasingly vulnerable to evolving cyber threats due to the convergence of operational and information technologies. This research presents a robust cybersecurity framework that integrates machine learning-based anomaly detection with advanced cryptographic techniques to protect ICS communication networks. Using the ICS-Flow dataset, we evaluate several ensemble models, with XGBoost achieving 99.92% accuracy in binary classification and Decision Tree attaining 99.81% accuracy in multi-class classification. Additionally, we implement an LSTM autoencoder for temporal anomaly detection and employ the ADWIN technique for real-time drift detection. To ensure data security, we apply AES-CBC with HMAC and AES-GCM with RSA encryption, which demonstrates resilience against brute-force, tampering, and cryptanalytic attacks. Security assessments, including entropy analysis and adversarial evaluations (IND-CPA and IND-CCA), confirm the robustness of the encryption schemes against passive and active threats. A hardware implementation on a PYNQ Zynq board shows the feasibility of real-time deployment, with a runtime of 0.11 s. The results demonstrate that the proposed framework enhances ICS security by combining AI-driven anomaly detection with RSA-based cryptography, offering a viable solution for protecting ICS networks from emerging cyber threats. Full article

The rapid expansion of mobile financial services (MFSs) has brought about benefits in terms of financial inclusion in developing countries; however, threats have also emerged on the sides of cybersecurity and privacy. Traditional fraud-detection strategies are usually not responsive in time or adaptive to changing threat scenarios. This study investigates how artificial intelligence (AI) can be employed to strengthen fraud detection and methods to address user privacy concerns within MFS platforms in emerging markets. A mixed-method approach was adopted, i.e., a quantitative survey (n = 151) and a qualitative analysis of open-ended response. A reliability analysis showed internal consistency (Cronbach’s alpha > 0.70 across constructs). The descriptive results demonstrate that 95.4% of those questioned raised privacy concerns, whereas 78.2% recognized the benefits of AI-driven fraud detection. Regression analysis showed that AI significantly improved perceived security (β = 0.63, p < 0.01), although transparency and explainability were critical determinants of trust. The findings indicate that users consider AI a capable real-time fraud detection tool; however, doubts remain regarding data transparency, sharing with third parties, and lack of user-opted control, resulting in the erosion of user trust. The study also indicates that the socio-cultural factors and weak regulatory contexts weigh heavily on users’ acceptance of these AI-powered systems. This study proposes the promotion of Explainable AI (XAI) systems along with privacy-by-design user controls and localized communication approaches to foster trust and further adoption. The study contained within are thus a critical guide for policymakers, fintech developers, and providers, who seek to innovate with user protection within digital fintech. Full article