Search Results (434)

Modern passport systems face significant challenges in secure data sharing, real-time verification, and user-controlled authorization, particularly in cross-border scenarios. Existing digital passport solutions, often built on permissioned blockchains, suffer from limited transparency, scalability, and high operational costs. This paper proposes a decentralized passport management system based on an Ethereum Layer 2 architecture that combines global governance with high-throughput and cost-efficient passport operations. The system adopts a hybrid design in which a Global Passport Registry smart contract is deployed on the Ethereum mainnet for cross-country coordination, while passport issuance, access control, and identity management are handled on Layer 2 networks through country-operated Passport Managers and user-specific Personal Passport smart contracts. Extensive performance evaluations show that Ethereum Layer 1 throughput saturates at approximately 40–50 transactions per second (TPS), whereas the proposed Layer 2 deployment consistently exceeds 150 TPS and reaches up to 300 TPS under higher-performance environments, significantly surpassing the estimated system requirement of 70 TPS. These improvements result in faster response times, reduced congestion, and substantially lower transaction costs, demonstrating that public Ethereum Layer 2 infrastructures can effectively support a scalable, self-sovereign, privacy-preserving, and globally verifiable digital passport system suitable for real-world deployment. Full article

In many unmanned aerial vehicle (UAV) applications, achieving stable flight despite actuator failures is crucial. Among the many existing fault-tolerant control (FTC) methods, adaptive control is a practical approach. In this article, we present experimental verification of a backstepping-based adaptive fault-tolerant controller previously proposed by the authors. As the first step of the experimental verification, we focus on the attitude-loop control of the quadrotor. We construct a quadrotor testbed integrating a self-developed flight controller. After parameter identification, we implement the adaptive fault-tolerant controller on the quadrotor. Finally, real-time experiments on attitude stabilization following actuator faults are conducted. As a result, we confirmed that the controller can be implemented and can stabilize the attitude even in the presence of multi-actuator faults. Full article

Ensuring the reliable, auditable, and privacy-oriented distribution of donations in disaster logistics constitutes a critical challenge due to multi-stakeholder coordination difficulties and the risk of misuse. This study presents a modular architecture, named SecureRelief, operating on a permissioned Hyperledger Fabric platform. The architecture integrates authentication based on Self-Sovereign Identity (SSI), Decentralized Identifiers (DID), and WebAuthn, together with Attribute-Based Access Control (ABAC), and enables the verification of delivery evidence through privacy-preserving validation using zero-knowledge proofs (ZKP). Documents are stored off-chain on the InterPlanetary File System (IPFS), while only cryptographic summary (hash) values sufficient for integrity verification are maintained on-chain. In scenario-based laboratory experiments, the blockchain layer demonstrated low latency (p95 < 16 ms) and stable transaction throughput, confirming its scalability. While the API layer handled high burst request loads with a 0% error rate, the additional computational overhead introduced by the integrated privacy-preserving (ZKP) mechanisms kept the end-to-end transaction latency within acceptable limits for disaster management applications (3.5–4.5 s). Full article

Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability. Full article

Steering angle control in self-driving cars is usually organized in layers combining trajectory planning, path tracking, and low-level actuator control. The steering controller converts the planned path into a desired steering angle and then ensures accurate tracking by the electric power steering (EPS). However, automotive electric power steering (AEPS) systems face many problems caused by model uncertainties, disturbances, and unknown system dynamics. In this paper, a robust finite-time control strategy based on an adaptive backstepping scheme is proposed to handle these problems. First, radial basis function neural networks (NNs) are designed to approximate the unknown system dynamics. Then, an adaptive sliding-mode disturbance observer (ASMDO) is introduced to address the impacts of the lumped disturbance. Enhanced control performance for the AEPS system is implemented using a combination of the above technologies. Numerical simulations and a hardware-in-the-loop (HIL) experimental verification are performed to demonstrate the significant improvement in performance achieved using the proposed strategy. Full article

Sustainable tourism has become a priority as environmental pressures on the hospitality sector intensify. Despite increasing promotion of green hotels, a persistent gap remains between pro-environmental intentions and actual booking behavior. Prior applications of the Theory of Planned Behavior (TPB) largely focus on developed economies and offer limited insight into how digital platforms, organizational credibility, and information-seeking behavior shape green hotel decisions in emerging tourism markets. To address this gap, this study extends TPB by integrating social media marketing, environmental knowledge, organizational green practices awareness, self-image in environmental protection, and consumer information-seeking behavior. Survey data from 538 foreign tourists staying in hotels in Turkey were analyzed using Partial Least Squares Structural Equation Modeling. The findings indicate that awareness of organizational green practices is the strongest predictor of consumer attitude, followed by self-image, social media marketing, and environmental knowledge. Consumer attitude, subjective norms, and perceived behavioral control shape purchase intention, while purchase behavior is driven by intention, perceived behavioral control, and information-seeking behavior. Notably, information-seeking behavior exerts a direct and mediating effect on purchase behavior but does not moderate the intention–behavior relationship, indicating a post-intentional verification role. Full article

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

Existing coal spontaneous combustion liability assessments suffer from incomplete temperature range coverage, poor cross-rank comparability, and weak correlations between microscopic essence and macroscopic criteria—issues that undermine reliability and risk coal mine safety. This study aims to establish a structure-driven intrinsic identification system to address these gaps. Using 10 cross-rank coal samples (lignite, bituminous coal, and anthracite), we conducted systematic research via experiments, model building, and theoretical verification. We integrated three stage-specific parameters (each matching a combustion phase): saturated oxygen uptake (VO₂, 30 °C chromatographic adsorption), average heating rate R70 (40–70 °C adiabatic oxidation), and Fuel Combustion Characteristic index (FCC, 110–230 °C crossing point method). With Information Entropy weighting (VO₂: 0.296; R70: 0.292; and FCC: 0.412), we constructed the Multi-Factor Comprehensive Spontaneous Combustion Index (MF-CSCI). We also screened functional groups via FTIR, built a microstructure-driven model (MD-CSEI, linking groups to MF-CSCI), and verified mechanisms via DFT. Results show MF-CSCI covers the full “adsorption-heat accumulation-self-heating” process: HG lignite (MF-CSCI = 1.0) had high liability and YCW anthracite (MF-CSCI = 7.98) had low liability, solving cross-rank issues. Pearson analysis found –OH positively correlated with MF-CSCI (r ≈ −0.997), C=C negatively (r ≈ −0.951); MD-CSEI achieved R² = 0.863 (p = 0.042). This study improves cross-rank assessment accuracy, enables rapid micro-to-macro risk prediction, and provides a theoretical basis for on-site coal safety management. Full article

This article analyses the professional routines of health journalists in Spain, and their role in tackling disinformation in health reporting targeted at people over the age of 74. It is based on the premise that this age group, being highly exposed to health issues and particularly vulnerable to health-related misinformation, requires content that is tailored, reliable and easy to understand. The research adopts an exploratory-descriptive approach through a self-administered questionnaire addressed to health journalists belonging to professional associations and working in both general and specialist media outlets. As this is an ongoing study, the preliminary results indicate that these professionals report applying rigorous verification mechanisms, which suggests a trend within the surveyed group towards the consolidation of practices against disinformation. The findings also reveal a preference for informative styles that avoid sensationalism and prioritise clarity, although there remains a tendency towards high-impact topics and those linked to media figures. In contrast, attention to the informational needs of older adults is limited and addressed only occasionally. The study concludes that, although the interviewed professionals consider that health journalism in Spain maintains high standards of rigor, it still faces the challenge of systematically adapting its communicative practices to the needs of vulnerable audiences. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

As a cornerstone of China’s energy infrastructure, the coal mining industry relies heavily on the stability of its underground roadways, where the support of soft rock formations presents a critical and persistent technological challenge. This challenge arises primarily from the high content of expansive clay minerals and well-developed micro-fractures within soft rock, which collectively undermine the effectiveness of conventional support methods. To address the soft rock control problem in China’s Longdong Mining Area, an integrated material–structure control approach is developed and validated in this study. Based on the engineering context of the 3205 material gateway in Xin’an Coal Mine, the research employs a combined methodology of micro-mesoscopic characterization (SEM, XRD), theoretical analysis, and field testing. The results identify the intrinsic instability mechanism, which stems from micron-scale fractures (0.89–20.41 μm) and a high clay mineral content (kaolinite and illite totaling 58.1%) that promote water infiltration, swelling, and strength degradation. In response, a novel synergistic technology was developed, featuring a high-performance grouting material modified with redispersible latex powder and a tiered thick anchoring system. This technology achieves microscale fracture sealing and self-stress cementation while constructing a continuous macroscopic load-bearing structure. Field verification confirms its superior performance: roof subsidence and rib convergence in the test section were reduced to approximately 10 mm and 52 mm, respectively, with grouting effectively sealing fractures to depths of 1.71–3.92 m, as validated by multi-parameter monitoring. By integrating microscale material modification with macroscale structural optimization, this study provides a systematic and replicable solution for enhancing the stability of soft rock roadways under demanding geo-environmental conditions. Soft rock roadways, due to their characteristics of being rich in expansive clay minerals and having well-developed microfractures, make traditional support difficult to ensure roadway stability, so there is an urgent need to develop new active control technologies. This paper takes the 3205 Material Drift in Xin’an Coal Mine as the engineering background and adopts an integrated method combining micro-mesoscopic experiments, theoretical analysis, and field tests. The soft rock instability mechanism is revealed through micro-mesoscopic experiments; a high-performance grouting material added with redispersible latex powder is developed, and a “material–structure” synergistic tiered thick anchoring reinforced load-bearing technology is proposed; the technical effectiveness is verified through roadway surface displacement monitoring, anchor cable axial force monitoring, and borehole televiewer. The study found that micron-scale fractures of 0.89–20.41 μm develop inside the soft rock, and the total content of kaolinite and illite reaches 58.1%, which is the intrinsic root cause of macroscopic instability. In the test area of the new support scheme, the roof subsidence is about 10 mm and the rib convergence is about 52 mm, which are significantly reduced compared with traditional support; grouting effectively seals rock mass fractures in the range of 1.71–3.92 m. This synergistic control technology achieves systematic control from micro-mesoscopic improvement to macroscopic stability by actively modifying the surrounding rock and optimizing the support structure, significantly improving the stability of soft rock roadways. Full article

Nowadays, biowaste valorization is a key point in the circular economy. Digestate and compost from organic waste treatment can be used as nutrient-rich fertilizers. In Europe, the use of biowaste-derived fertilizers is promoted by the European Fertilizer Regulation (EU) 2019/1009, which requires verification of their biological stability through regulated indices; however, it is not clear whether the proposed indices and threshold values indicate the same level of stability and what correlations there are between them. This study compared four biological stability indices, namely Oxygen Uptake Rate (OUR), Self-Heating (SH), Residual Biogas Potential (RBP), and Dynamic Respirometric Index (DRI), which were tested on 50 samples of compost and digestate. Overall, the results revealed that most of the compost and digestate samples were quite far from European standards. On the contrary, the RBP test seemed to be less stringent than the other indices, since a much larger number of samples was closer to or in compliance with the established threshold. Data analysis using Pearson’s coefficients showed a strong linear correlation between the indices. Nevertheless, the linear regression predictive model based on experimental data demonstrated that the indices could not represent the same level of stability, providing poor consistency and variability in the predicted values and established threshold. In particular, the DRI test appeared to be more severe than the other aerobic indices. This work could provide valuable support in improving evaluation criteria and promoting a sustainable use of compost and digestate as organic fertilizers from a circular economy perspective. Full article

This paper proposes a process-aware, tag-based digital identity framework that enhances interoperability while enabling identity unlinkability and selective disclosure across multi-party workflows involving sensitive data. We realize this framework within the self-sovereign identity (SSI) paradigm, employing zk-SNARK–based zero-knowledge proofs to enable verifiable identity authentication without plaintext disclosure. The framework introduces a protocol-tagging mechanism to support multiple proof systems within a unified architecture, thereby enhancing SSI scalability and interoperability. Its core innovation lies in combining identity unlinkability and process-driven data disclosure: derived sub-identities mitigate identity-linkage attacks, while layered encryption enables selective, stepwise decryption of sensitive information (e.g., delivery addresses), ensuring participants access only the minimal information necessary for their tasks. In addition, zero-knowledge proof-based verification guarantees that the validation of derived sub-identities can be performed without sharing any plaintext attributes or identifying factors. We applied the framework to logistics, where sub-identities anonymize participants and layered encryption allows for delivery addresses to be decrypted progressively along the logistics chain, with only the final courier authorized to access complete information. During the parcel receipt process, users can complete verification using derived sub-identities and zero-knowledge proofs alone, without disclosing any real personal information or attributes that could be linked back to their identity. Trusted Execution Environments (TEEs) ensure the authenticity of decryption requests, while blockchain provides immutable audit trails. A demonstration system was implemented, formally verified using Scyther, and performance-tested across multiple platforms, including resource-constrained environments, showing high efficiency and strong practical potential. The core paradigms of identity unlinkability and process-driven data disclosure are generalizable and applicable to multi-party scenarios involving sensitive data flows. Full article

Automatically generated chains-of-thought (gCoTs) have become common as large language models adopt deliberative behaviors. Prior work emphasizes fidelity to internal processes, leaving explanatory properties underexplored. Our central hypothesis is that these traces, produced by highly capable reasoning models, are not arbitrary by-products of decoding but exhibit stable and practically valuable textual properties beyond answer fidelity. We apply a multidimensional text-evaluation framework that quantifies four axes—structural coherence, logical–factual consistency, linguistic clarity, and coverage/informativeness—that are standard dimensions for assessing textual quality, and use it to evaluate five reasoning models on the GSM8K arithmetic word-problem benchmark (~1.3 k–1.4 k items) with reproducible, normalized metrics. Logical verification shows near-ceiling self-consistency, measured by the Aggregate Consistency Score (ACS ≈ 0.95–1.00), and high final-answer entailment, measured by Final Answer Soundness (FAS0 ≈ 0.85–1.00); when sound, justifications are compact, with Justification Set Size (JSS ≈ 0.51–0.57) and moderate redundancy, measured by the Redundant Constraint Ratio (RCR ≈ 0.62–0.70). Results also show consistent coherence and clarity; from gCoT to answer implication is stricter than from question to gCoT support, indicating chains anchored to the prompt. We find no systematic trade-off between clarity and informativeness (within-model slopes ≈ 0). In addition to these automatic and logic-based metrics, we include an exploratory expert rating of a subset (four raters; 50 items × five models) to contextualize model differences; these human judgments are not intended to support dataset-wide generalization. Overall, gCoTs display explanatory value beyond fidelity, primarily supported by the automated and logic-based analyses, motivating hybrid evaluation (automatic + exploratory human) to map convergence/divergence zones for user-facing applications. Full article

As key driving and supporting components of spacecraft, pod-type space self-deployable structures have terminal pointing accuracy that directly affects overall spacecraft performance. To clarify the influence of the structure’s machining deformation on its pointing accuracy, this study focuses on two key processes, namely laser welding and hot forming. Based on the bionic symmetric structural characteristics of pod-type structures, a laser welding finite element model with a surface Gaussian heat source and a hot forming constitutive model coupled with creep aging were established. An orthogonal experimental design was adopted: for laser welding, three parameters, namely laser power, spot diameter, and welding speed, each with three levels, were selected, and an L₉(3³) orthogonal table was constructed to conduct nine groups of simulations; for hot forming, two parameters, namely processing temperature and holding time, each with three levels, were chosen, and nine groups of simulations were designed based on the first two columns of the L₉(3⁴) orthogonal table. The combined method of residual analysis and analysis of variance was used to quantitatively identify the influence of each process parameter on pointing accuracy. The results show that in laser welding, welding speed has the most significant impact on deformation, followed by laser power, and spot diameter has the least; in hot forming, processing temperature and holding time have similar effects on deformation. Physical machining verification was performed, and the actually measured deformations are 0.164 mm and 0.034 mm, which are close to the simulation results of 0.176 mm and 0.047 mm, meeting the index requirement that the terminal pointing deformation of a single pod structure is less than 0.2 mm. The results can provide a theoretical basis and engineering reference for the actual machining of such structures. Full article