Search Results (143)

The integration of blockchain into parked vehicle edge computing (PVEC) has emerged as a promising approach to mitigate the inherent trust challenges in distributed and untrusted computing environments. However, during task offloading and consensus, vehicles are vulnerable to location information disclosure, leading to privacy leakage. To address this problem, we propose a location differential privacy-enabled blockchain PVEC (DBPVEC) framework to protect location information during offloading and consensus. Specifically, we design a location differential privacy mechanism based on the Laplace mechanism and theoretically prove that it satisfies ε-differential privacy. This mechanism perturbs vehicles’ locations, and a privacy-preserving offloading strategy is designed to enhance the Hotstuff consensus and protect location privacy in edge computing. Subsequently, we formulate a joint optimization problem, considering system energy consumption, latency, and privacy strength. To solve it, we design a two-layer deep reinforcement learning (DRL) algorithm, with a Deep Q-Network (DQN) as the upper layer and a Deep Deterministic Policy Gradient (DDPG) as the lower layer, to determine the optimal offloading strategy. The experimental results demonstrate that our scheme achieves significant reductions compared to the two baseline methods: the total cost decreases by 68.31% and 63.25%, energy consumption by 9.96% and 16.27%, and delay by 31.46% and 18.07%, respectively. Moreover, it effectively preserves vehicle location privacy during task offloading and consensus while maintaining favorable performance in energy consumption and latency. Full article

The coupling of water and nitrogen (N) availability critically constrains alpine plant growth and ecosystem productivity, yet the mechanistic links between plant functional traits and resource use efficiencies (rain use efficiency, RUE; nitrogen use efficiency, NUE) along precipitation gradients remain unclear. This study aimed to test whether coordinated shifts in plant functional traits are associated with spatial variation in RUE and NUE across a precipitation gradient on the Changtang Plateau. Here, combining transect surveys with N-addition experiments on the Changtang Plateau, we measured biomass and leaf/root functional traits on four typical grasslands and analyzed the spatial variations in RUE, NUE, and fertilizer use efficiency (FUE). Our results demonstrated contrasting spatial patterns: with increasing precipitation, soil resource availability, community species richness, and biomass significantly improved, and vegetation shifted from a water-conservative strategy in arid regions to a nutrient-efficient strategy in humid regions. FUE increased with precipitation (p < 0.05), with low-dose nitrogen addition exerting more pronounced effects in humid regions, indicating greater responsiveness to fertilization. This transition in resource use patterns is underpinned by a coordinated divergence in functional traits: as water limitation eases, communities exhibited decreasing specific root length (high specific root length, SRL) coupled with increasing specific leaf area (high specific leaf area, SLA) along the gradient. Our findings demonstrate that functional trait variation is associated with the optimization of resource acquisition across environmental gradients. These results provide a mechanistic basis for adaptive management in climate-sensitive alpine biomes, where differentiated grassland management schemes may enhance ecosystem productivity—water conservation and reduced disturbance in arid regions, with moderate low-dose nitrogen fertilization and species diversity protection in humid regions. Long-term ecosystem responses to such management approaches require further investigation. Full article

To address pressure-drop-induced safety risks in high-drop gravity-fed irrigation pipelines, this study investigates coordinated prevention and control strategies that integrate air release and vacuum valve groups with flow-adaptive valve closure rules. A large-scale self-pressurized irrigation network (1.33 × 10⁸ m²) in Karamay, Xinjiang, China, is selected as a representative case study. Based on one-dimensional transient flow modeling, pressure drop and negative-pressure characteristics induced by inlet valve closure in the main pipeline are analyzed using wave speed theory, governing differential equations, and the finite difference method. A coordinated protection framework is proposed that explicitly links valve operating patterns with the spatial configuration of protective devices. Unlike conventional schemes that rely on empirical layouts and fixed closure rules, this study introduces a critical-flow-velocity-based valve grouping method combined with flow-dependent valve closure strategies. Simulation results demonstrate that a strategically optimized configuration of air release and vacuum valves along the main pipeline is sufficient to eliminate negative pressure under all operating conditions. For flow rates below 6 m³/s, linear valve closure ensures safe operation, whereas a two-stage closure is required for higher flow rates (6–10 m³/s). As flow increases, reducing the fast-closure ratio and extending the total closure time effectively suppress pressure-drop-dominated transient effects at vulnerable inlet sections. By effectively mitigating transient pressure surges, the proposed coordinated “valve closure-protection device” strategy improves system adaptability to flow variability and provides practical engineering guidance for the safe operation of gravity irrigation systems, particularly high-gradient self-pressurized networks. Full article

The rapid advancement of smart grids, while enhancing the efficiency of power systems, has also raised serious concerns regarding the privacy and security of end-users’ electricity consumption data. Traditional privacy protection methods struggle to meet users’ individualized privacy requirements and often lead to a significant decline in data aggregation accuracy. To address the core contradiction between personalized privacy protection and high-precision grid analytics, this paper proposes an efficient data aggregation scheme based on personalized local differential privacy (EDAS-PLDP) tailored for smart grids. The proposed scheme enables smart terminal users to autonomously select their privacy protection levels based on individual needs, thereby breaking the limitations of the traditional “one-size-fits-all” approach. To mitigate the accuracy loss caused by personalized perturbations, a mean square error-based weighted aggregation strategy is introduced at the gateway side. This strategy evaluates the data quality from groups with different privacy preferences and adjusts aggregation weights to optimize the estimation accuracy of the global mean electricity consumption. Extensive experimental results demonstrate that, compared to existing mainstream schemes, EDAS-PLDP achieves higher estimation accuracy under various distributions of privacy preferences, user scales, and data granularities, while exhibiting lower time consumption, making it suitable for resource-constrained smart grid environments. Furthermore, the scheme shows excellent robustness against false data injection attacks. In summary, EDAS-PLDP provides a balanced and efficient solution for reconciling personalized privacy protection with high-precision data utility in smart grids. Full article

The proliferation of Location-Based Services (LBSs) has generated vast trajectory datasets that offer immense analytical value but pose critical privacy risks. Achieving an optimal balance between data utility and privacy preservation remains a challenge, a difficulty compounded by the limitations of existing methods in modeling complex, long-term spatiotemporal dependencies. To address this, this paper proposes a trajectory data publishing scheme combining a Transformer decoder with differential privacy. Unlike traditional single-layer approaches, the proposed method establishes a systematic generation–generalization framework. First, a Transformer decoder is integrated into a Generative Adversarial Network (GAN). This architecture mitigates the gradient vanishing issues common in RNN-based models, generating high-fidelity synthetic trajectories that capture long-range correlations while decoupling them from sensitive source data. Second, to provide rigorous privacy guarantees, a clustering-based generalization strategy is implemented, utilizing Exponential and Laplace mechanisms to ensure $ϵ$-differential privacy. Experiments on the Geolife and Foursquare NYC datasets demonstrate that the scheme significantly outperforms leading baselines, achieving a superior trade-off between privacy protection and data utility. Full article

This study focuses on the surface materials of rammed-earth walls of Fujian Tulou in Xiaoshu Village, exploring the microscopic characteristics of rammed earth in different orientations and the microclimate adaptation mechanism and degradation law of the walls. Specimens were collected from the inner and outer surface soil layers of the four directional walls of a representative Tulou. SEM, XRD, and XRF analyses were performed to characterize the materials’ microstructure, mineral composition, and elemental distribution, with the test results correlated to the microclimatic conditions of each wall orientation. The conclusion is as follows: (1) The microscopic particle size of rammed earth exhibits significant directional differences at dual scales of 300 nm and 2 μm. Solar radiation duration and wind speed are positively correlated with the coefficient of variation in particle size. (2) The southeast and north walls were the most severely damaged (soil loss, quartz enrichment: 79.9%), the west wall had minor cracks, the north wall showed slight salt crystallization (Halite = 0.3%), and the east wall exhibited moisture-related moss growth. (3) Traditional organic additives (bamboo strips, rice husks) mitigate deterioration and enhance structural integrity. (4) The diversity of soil color (related to hematite and iron oxide) can serve as a simple indicator of deterioration. This study has proposed differentiated protection schemes for the “microclimate-compounds” on walls facing different directions on the rammed-earth surface of the Tulou. The findings provide a theoretical basis for orientation-specific conservation of Tulou heritage and offer scientific references for the modification of modern rammed-earth materials. Full article

In practice, village planning often suffers from an “emphasis on plan preparation but neglect of implementation”, a challenge that is especially evident in Sichuan Province, China, where highly diverse landforms and uneven development foundations make one-size-fits-all evaluation approaches difficult to apply. This study aims to develop a locally adaptable and operational method to quantify village planning implementation effectiveness control, enabling cross-type comparison and bottleneck diagnosis. We construct a three-level indicator system spanning eight domains—baseline control, land-use layout and construction, ecological protection and restoration, industrial development, infrastructure, public service facilities, living environment, and disaster prevention and mitigation—and determine indicator weights using the Analytic Hierarchy Process (AHP). To capture both compliance and progress, a dual-path scoring strategy is employed: constraint-based indicators are assessed using a threshold method by comparing current values (T1) with planning standards/thresholds (T2), while expectation-based indicators adopt a progress-ratio method incorporating baseline values before plan preparation (T0), current status (T1), and targets (T2). Three representative villages—Gaohuai (peri-urban integration), Sanlongchang (agglomeration and upgrading), and Lianmeng (characteristic protection)—are examined. Results show medium-to-high comprehensive scores (81–85) with pronounced type differences: Gaohuai ranks highest (85.37), whereas Sanlongchang is lowest (81.40), and Lianmeng is intermediate (83.71). Comparative diagnosis reveals shared bottlenecks driven by the superposition of “quota–space–ecological constraints”, alongside type-specific weaknesses requiring differentiated control strategies. The proposed framework offers a replicable, multi-source-data-oriented tool for implementation monitoring and adaptive policy adjustment. The novelty lies in reframing village plan implementation evaluation as implementation control effectiveness under a baseline-constrained planning system, while operationalizing a dual-path, unified-scale scoring scheme with a type-screenable indicator library for cross-type comparison and checklist-oriented diagnosis. Full article

China possesses 400 million hectares of grasslands that provide regulating ecosystem services (ESs), including wind erosion control, water conservation, and carbon sequestration. The central government implemented the Grassland Ecological Protection Subsidy and Reward Policy (GERCP) in 2011, allocating 150 billion yuan (approximately $23 billion) through 2020, while national vegetation coverage increased from 51.0% in 2011 to 56.1% in 2020. Existing valuation studies emphasize total economic value but rarely quantify the concentration of ES values across space or their alignment with fiscal allocation. We compiled 734 grassland ES valuation observations from 186 studies published between 2000 and 2024, and estimated a multi-level mixed-effects meta-regression model for benefit transfer. We projected standardized county-level ES values, decomposed spatial inequality using the Gini coefficient and Theil index, and assessed the mismatch between value-informed allocation weights and observed GERCP transfers. Predicted values exhibit high concentration (Gini coefficient = 0.58), and between-zone differences explain 52% of total Theil inequality. The mismatch analysis identifies 94 high-value and low-compensation counties concentrated in southern Qinghai and northern Tibet, where per-hectare values are 180 to 240% above national medians, and compensation is 35 to 55% below the median. The results support value-informed targeting and redistribution of fiscal weights across regions, while payment levels require pricing benchmarks based on opportunity cost or conservation cost rather than total economic value. We propose calibrating compensation rates through a tiered schedule based on ESV quantiles or standardized ecosystem-service bundles, and implementing county-level differentiated payments with periodic updating tied to monitoring and evaluation. As a minimum viable step, we recommend piloting this scheme in counties with high ESV yet low current compensation, and integrating it into existing ecological compensation funding channels to reduce administrative frictions. Full article

Farmland quantity continues to decline, land abandonment is a serious concern, and local quality degradation remains unresolved. This situation, in which large-scale farmland abandonment continues, is likely to induce a series of food security and ecological protection problems. However, strengthening the protection and development of abandoned farmland (AF) is very difficult. In response to this issue, this paper provides a comprehensive review and synthesis of domestic and international research on AF. The results show that the prior research has largely focused on information acquisition and the analysis of driving factors, while relatively limited attention has been given to pathways for the reuse and management of AF, with few relevant studies and practical examples available. In addition, no clear theoretical framework has been developed to evaluate and manage the multiple elements of and the overall process leading to AF. Building on an examination of the feasibility of applying resilience theory to the management of AF, this paper defines the conceptual scope and core meaning of AF resilience management and constructs a resilience management implementation path based on the steps of objective determination, problem profiling, evaluation feedback, and scheme formulation. This framework helps reveal the structure–process–function evolutionary characteristics of AF across different development stages and provides analytical support for the design of differentiated and adaptable management strategies. Full article

The heavy use of digital images across network systems has become a major concern regarding data confidentiality and unauthorized access. Conventional image encryption techniques hardly achieve high security levels efficiently, especially in real-time and resource-constrained environments. These challenges motivate the development of more robust and efficient encryption mechanisms. In this paper, a dual-chaotic image encryption framework is developed where two complementary chaotic systems are combined to effectively realize confusion and diffusion. The proposed method uses a chaotic permutation mechanism to find the pixel positions and enhanced chaotic diffusion to change the pixel values for eliminating the statistical correlations. An extended family of piecewise affine chaotic maps is designed to enhance the dynamic range and complexity of the diffusion process for strengthening the resistance capability against cryptographic attacks. Intensive experimental validations confirm that the proposed scheme well obscures the visual information and strongly reduces the pixel correlations in the encrypted images. High entropy values, uniform histogram distributions, high resistance to differential attacks, and improved robustness are further evidenced by statistical and security analyses compared to some conventional image encryption techniques. The results also show extremely low computational overheads, hence allowing for efficient implementation. The proposed encryption framework provides more security for digital image transmission and storage, and the performances are still practical. Given its robustness, efficiency, and scalability, it is equally adequate for real-time multi-media applications and secure communication systems, hence promising to offer a reliable solution for modern image protection requirements. Full article

The rapid rise of inverter-based resources (IBRs) such as solar, wind, and battery energy storage is transforming power grids and creating new challenges for protection. Unlike synchronous generators, many IBRs are interfaced through grid-following (GFL) inverters that operate as controlled current sources and rely on an external voltage reference, resulting in fault responses that are current-limited and controller-shaped. These characteristics reduce fault current magnitude and can undermine conventional protection schemes. In contrast, emerging grid-forming (GFM) inverters behave as voltage sources that establish local voltage and frequency, offering improved disturbance support but still transitioning to current-limited operation under severe faults. This review summarizes GFL versus GFM operating principles and deployments, compares their behavior under balanced and unbalanced faults, and evaluates protection impacts using a protection-relevant taxonomy supported by illustrative electromagnetic transient (EMT) case studies. Key challenges, including underreach/overreach of impedance-based elements, reduced overcurrent sensitivity, and directional misoperation, are identified. Mitigation options are discussed, spanning adaptive/supervised relaying, communication-assisted and differential protection, and inverter-side fault current shaping and GFM integration. The implications of IEEE 1547-2018 and IEEE 2800-2022 are reviewed to clarify ride-through and support requirements that constrain protection design in high-IBR systems. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

Severe surface deformation induced by super-large mining height longwall extraction poses a significant threat to the safe operation of high-voltage transmission towers. In this study, a 330 kV straight-line transmission tower located above the 122104 working face of the Caojiatan Coal Mine was selected as a case study to investigate tower stability under mining-induced surface deformation and to develop corresponding protection technologies. An integrated monitoring system combining instantaneous and long-term measurements was established to characterize surface movement throughout the mining process. The results indicate that the maximum surface subsidence reached 7300 mm, while the maximum inclination and curvature attained 50 mm/m and 0.62 mm/m², respectively, reflecting intense deformation of the overlying ground. Numerical simulations based on ANSYS 2021R1 were conducted to systematically evaluate the effects of surface inclination, compressive deformation, and tensile deformation on the structural response of the transmission tower. The critical deformation thresholds leading to structural failure were identified as 30 mm/m for inclination, −7.2 mm/m for horizontal compression, and 7.7 mm/m for horizontal tension. Based on these findings, a comprehensive protection system was proposed, integrating tower body reinforcement, combined foundation reconstruction, surface subsidence monitoring, dynamic jacking-based rectification, and foundation grouting reinforcement. The proposed scheme was successfully implemented in field practice. Monitoring results demonstrate that, after reinforcement and rectification, differential settlement of the tower foundation was controlled within 20 mm, and tower inclination remained below 1‰. This ensured uninterrupted underground mining operations and continuous power transmission within the Caojiatan Coal Mine corridor. The outcomes of this study provide a practical reference for the protection of high-voltage transmission towers under similar mining conditions. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

The development of high-performance DC circuit breakers (DCCBs) and rapid fault detection schemes is a crucial and challenging part of advancing Modular Multilevel Converter (MMC) HVDC grids. This paper introduces a new current-limiting DCCB that uses the differential discharge times of shunt capacitors to generate artificial current zero-crossings, thus facilitating arc quenching. This mechanism significantly reduces the effect of fault currents on the MMC. The shunt capacitors and arresters in the proposed breaker also offer voltage support during faults, effectively stopping transient traveling waves from spreading to nearby non-fault lines. This feature creates an effective line protection boundary in multi-terminal HVDC systems. Additionally, a fast fault detection scheme with primary and backup protection is proposed. A four-terminal MMC-HVDC (±500 kV) simulation model is built in PSCAD/EMTDC to validate the scheme. The results demonstrate the excellent fault detection performance of the proposed method. The voltage and current behavior during the interruption process of the new DCCB is also analyzed and compared with that of a hybrid DCCB. Full article