Search Results (23,040)

Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article

Traditional sliding mode control (SMC) lacks an active mechanism for redistributing energy among state channels during transient convergence, resulting in a rigid trade-off between response speed, overshoot suppression, and energy efficiency. This paper proposes a virtual state coupled SMC method that introduces a dynamic virtual state with bilinear product coupling $x_1{x}_2$ into the sliding surface. Unlike conventional virtual states that serve as static linear combinations or observer-based estimates, the proposed virtual state evolves dynamically and establishes an active energy exchange channel between the real and virtual state dynamics. Linearization and Lyapunov-based analyses prove local asymptotic stability of the closed-loop system. The coupling strength $\gamma$ is shown to be decoupled from the linearized local eigenvalues and thus governs the energy–performance trade-off independently, while the condition $c>\gamma /4$ guarantees a non-vanishing domain of attraction. Simulations demonstrate that the proposed method achieves up to 53.2% control energy reduction under disturbance-free conditions compared with conventional SMC. Under persistent high-frequency disturbances, increasing $\gamma$ reduces oscillations by 54.2% at a controllable energy cost of 45.7%. Systematic parameter selection guidelines are provided, and Monte Carlo simulations (500 trials, $\pm 30\%$ parameter perturbations) confirm 100% convergence. The proposed method offers an independently adjustable energy–performance trade-off mechanism suitable for sensor-based motion systems with stringent transient and energy requirements. Full article

The wheel–rail impact effect is prominent in the low vibration track (LVT) in the curved sections of heavy-haul railways, where fastener failure and the support block hanging void are prone to occurring. To investigate the impact of these issues on the dynamic characteristics of the vehicle–track coupled system, this study establishes a coupled dynamics model of a heavy-haul train and LVT, taking into account the topological relationships of vehicle components, multipoint wheel–rail contact, and track irregularities. Comparative analyses are conducted to evaluate the effects of the location, quantity, and failure degree of fastener failure and support block hanging voids on running safety and stability. The results show that (1) compared to the normal condition, fastener failure and support block hanging voids lead to varying degrees of increases in response indicators, thereby intensifying the wheel–rail impact; (2) bilateral failure exhibits more pronounced dynamic responses than unilateral failure, and when the number of failed fasteners or hanging voids exceeds one, the maximum wheel load reduction rate increases significantly; (3) as the gap of the hanging void increases, the dynamic response also increases, and when the gap reaches approximately 3 mm, the support block can be considered fully suspended; and (4) comprehensive analysis indicates that fastener failure poses a greater threat to running safety than support block hanging voids and thus warrants greater attention in practical engineering applications. This study provides theoretical support for the maintenance and repair of heavy-haul railways. Full article

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort. Full article

Load forecasting is no longer only a point-prediction problem for aggregate demand. In renewable-rich and electric-vehicle-intensive power systems, forecasts must support net-load balancing, charging-demand management, uncertainty-aware operation, and spatially coupled decision-making. This review presents a quantitative evidence map based on a curated DOI-linked corpus of 116 papers published between 1960 and 2026. Each paper is coded by dominant model family, application theme, forecast horizon, and frontier feature tags. Publication era and dominant model family are strongly associated (${\chi }^2\left(21\right)=93.69$, $p=3.70\times {10}^{-11}$, Cramérś $V=0.519$). Post-2020 studies are sharply enriched in transformer/graph-neural-network/foundation-model content (13/43 versus 0/73; Haldane-corrected odds ratio 65.07; Fisher $p=6.65\times {10}^{-7}$), electric-vehicle or charging themes (7/43 versus 0/73; odds ratio 30.21; $p=6.91\times {10}^{-4}$), and deep-learning content (14/43 versus 7/73; odds ratio 4.36; $p=2.76\times {10}^{-3}$). To address category coarseness, the frontier family is further decomposed into transformer-only, graph-neural-network-only, hybrid spatiotemporal, and foundation-model subfamilies. The central conclusion is that the most important forecasting topic for current electrical power systems is not generic short-term load forecasting, but the integrated forecasting stack required by electrified, renewable-rich, and spatially coupled grids. Full article

Underground duct cables are widely used in urban power distribution systems, but their enclosed installation environment makes defect inspection difficult, labor-intensive, and potentially hazardous. Infrared thermography can capture abnormal temperature distributions caused by insulation degradation, conductor damage, sheath failure, or severe structural defects, while robot-based inspection provides a promising solution for confined duct environments. However, thermographic fault detection for underground small-diameter duct cables remains insufficiently studied, and practical deployment requires lightweight models suitable for embedded edge devices. In this study, an improved YOLO26-based thermographic fault detection framework is proposed for underground duct cable inspection. A Cable-Thermo dataset is constructed using an ANSYS 2025 R2-based thermoelectric coupling simulation, covering four defect categories: hollow-type damage, conductor burnout, sheath damage, and severe damage. To balance detection accuracy and deployment efficiency, two model variants are developed. YOLO26-Thermo-E retains the original detection scales and integrates CDA and SimSPPF modules for accuracy-prioritized diagnosis. YOLO26-Thermo-H further removes the small-scale detection branch as a deployment-oriented design choice, based on the scale distribution observed in the simulation dataset, where most fault-induced thermal anomalies appear as spatially continuous medium- or large-scale regions. This design assumption still requires further validation using real duct thermographic data. Experiments show that YOLO26-Thermo-E achieves the highest mAP50 of 99.20%. YOLO26-Thermo-H maintains a mAP50 of 99.00% while reducing GFLOPs by 34.3% and parameters by 16.2% compared with YOLO26. On an NVIDIA Jetson Orin NX, YOLO26-Thermo-H reaches 34 FPS under FP16 inference and 45 FPS under INT8 inference. These results demonstrate the feasibility of the proposed framework under controlled simulation conditions and its potential for edge deployment. The limitations of the simulation-based dataset are also discussed, and future work will focus on real-scene data collection and simulation-to-real generalization. Full article

Gravity energy storage systems (GESS) have emerged as a promising long-duration energy storage technology capable of supporting large-scale renewable integration and enhancing grid resilience. However, the modeling framework for the hoisting electromechanical subsystem in wire-rope-based GESS remains underdeveloped, thereby limiting the accurate characterization of its transient grid-connected behavior, dynamic operating response, and cross-domain coupling effects. Existing studies commonly simplify wire ropes and related transmission components as rigid bodies or low-dimensional mechanical elements, failing to adequately account for their flexibility and the resulting high-dimensional nonlinear dynamics. Although related studies in mine hoisting and elevator systems have addressed mechanical vibration phenomena, they primarily focus on mechanical-side effects, such as shock loading and guide-structure response, whereas the mechanism by which flexible mechanical vibrations propagate through electromechanical coupling and influence electrical dynamic performance remains inadequately understood. To address this gap, this study establishes a distributed-parameter model for the wire-rope hoisting mechanism based on Hamilton’s principle and solves the corresponding vibration governing equations using the Galerkin method to capture nonlinear multi-modal dynamics. An electromechanical coupling model is then developed to elucidate how rope-vibration-induced tension fluctuations propagate through the drive chain, resulting in torque ripple, electrical interharmonics, and low-frequency grid-side oscillations. A Bessel-function-based analytical representation is further introduced to explain the formation of interharmonic clusters and beat-frequency phenomena under converter modulation. An experimental prototype is constructed to validate the proposed modeling framework. The measured vibration spectra, beat-frequency characteristics, and torque ripple align closely with analytical predictions, confirming the model’s capability to capture key propagation paths from rope vibration to electromechanical oscillation and grid-side dynamic response. The results provide a solid theoretical foundation for vibration mitigation, dynamic analysis, and control design of hoisting electromechanical subsystems in gravity energy storage applications. Full article

This study presents an integrated experimental–numerical approach for evaluating the wear behavior of three non-standardized hypereutectic aluminum–silicon (Al–Si) piston alloys based on the AlSi25CuCr system, namely AlSi25Cu4Cr (M1), AlSi25Cu5Cr (M3), and AlSi25Cu5Cr (M5). The wear coefficient was determined experimentally under boundary-lubrication conditions, while the contact conditions in the piston–cylinder system were evaluated using Finite Element Analysis (FEA) and implemented within the Archard wear model. The results reveal a pronounced inconsistency between hardness and wear resistance. Although hardness increases from 1363 MPa (M1) to 1677 MPa (M5), the corresponding wear depth increases from 13.94 nm to 27.61 nm per engine cycle. This behavior is attributed to differences in microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, which significantly influence the tribological performance of hypereutectic Al–Si alloys. The experimentally determined wear coefficient K also shows a significant increase, rising from 12.14 × 10⁻⁵ (M1) to 29.59 × 10⁻⁵ (M5). The lowest wear is observed for alloy M1, whereas M5 exhibits the poorest tribological performance. These findings demonstrate that microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, have a dominant influence over hardness in governing wear behavior. The main scientific contribution lies in the direct coupling of experimentally determined material properties with realistically simulated contact conditions, enabling a quantitative and physically consistent comparison of piston alloys under identical operating regimes. The proposed methodology provides a reliable framework for material selection and optimization of piston alloys with enhanced wear resistance. Full article

Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose severe communication constraints on ground vehicles, necessitating the introduction of aerial vehicles as relays to maintain reliable connectivity. The resulting cooperative trajectory planning problem is challenging for three reasons. First, the kinematic and communication constraints are tightly coupled. Second, the optimization landscape is highly non-convex and non-differentiable. Third, the planner must balance topological exploration with real-time efficiency. To address these challenges, we propose a hierarchical cooperative trajectory planning framework for an air–ground robotic system. Specifically, in the upper layer, a heuristic-search-guided reinforcement learning mechanism is employed to narrow the search space and circumvent the sparse reward problem, rapidly generating an initial solution. Subsequently, the lower-layer planner utilizes an optimization-based solver, together with a corridor-based constraint formulation method, to refine the initial solution into a kinematically feasible cooperative trajectory. Ultimately, this strategy improves real-time efficiency while improving the quality of feasible cooperative trajectories. Extensive ablation studies and comparative experiments with representative baselines demonstrate that the proposed framework improves collision avoidance, communication reliability, trajectory smoothness, and computational efficiency in the tested urban canyon scenarios. Full article

REST APIs have become a dominant interface for modern web applications and cloud services, and a growing body of work has studied automated testing and reproducible error discovery for such systems. Prior approaches have explored dependency inference, cross-request value reuse, and, more recently, learning- or LLM-based test generation. However, deep stateful multi-step reproducible error discovery remains difficult in practice because sequence construction is still often performed directly in the endpoint space, reusable runtime artifacts are not always tightly coupled with sequence expansion, and online LLM-driven generation may introduce cost and instability. We present Spec2SeqFuzz, a stateful multi-step fuzzing framework for REST API systems. The central idea is to guide online exploration in a compact category space rather than directly in the full endpoint space. Spec2SeqFuzz uses LLMs only in an offline pre-processing stage to normalize public multi-step PoCs, classify OpenAPI endpoints into a transferable category taxonomy, and construct training data for next-category prediction. During online fuzzing, the framework predicts the next likely API category from the executed prefix and observed response feedback, maps the predicted categories back to concrete endpoints, and combines this guidance with black-box endpoint fuzzing, proxy-based payload collection, and snapshot-assisted state restoration. We implemented a prototype and evaluated it on GitLab and WordPress, using MINER as the primary reproduced baseline in our current study. The results show that Spec2SeqFuzz is promising for both multi-step and single-endpoint error discovery on these two targets. Following the terminology used in MINER, we report reproducible errors rather than treating every triggered failure as a confirmed security vulnerability. Across the two targets, Spec2SeqFuzz discovers more reproducible multi-step errors than MINER, while the ablation results further suggest that category guidance, payload reuse, and depth-first stateful exploration are important to the final error-discovery performance. Full article

To balance ultra-early strength development and workable time in cement-based grouting materials for rapid repair applications, an ultra-early high-strength grout system was developed by regulating the dosage of an accelerating agent (CF), retarder content, and water-to-binder ratio (w/b). The effects of these parameters on setting behavior, workability, mechanical properties, volumetric stability, and durability were systematically investigated. X-ray diffraction (XRD) and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS) were further conducted to qualitatively evaluate the hydration characteristics and microstructural evolution of the optimized system. The results showed that CF accelerated early hydration and promoted the rapid formation of ettringite (AFt), which contributed to the development of ultra-early strength. The incorporation of a retarder effectively prolonged the workable time and improved slurry workability. Increasing the w/b ratio enhanced flowability and toughness, although excessive w/b reduced compressive strength. The optimal mixture contained 30% CF, 0.02% retarder, and a w/b ratio of 0.19. Under this condition, the grout exhibited a flowability of 312 mm and compressive strengths of 81.4 MPa at 1 h and 121.3 MPa at 28 d. In addition, low air shrinkage (0.027% at 28 d) and excellent chloride penetration resistance (12 C at 28 d) were achieved. Microstructural observations suggested that the dense structure formed by AFt and C–S–H gel contributed to the improved macroscopic performance. This study provides an engineering-oriented reference for the mix design and performance optimization of ultra-early high-strength cement-based grouting materials for rapid repair applications. Full article

Background: Reports on subchondral bone mechano-structure in individuals with various stages of knee osteoarthritis (KOA) are limited and often conflicting in contemporary literature. Our study aimed to assess differences in subchondral trabecular bone mechano-structure across late KOA stages in a homogenous group of patients with varus malalignment (confirmed by negative hip-knee-ankle-angle values). Methods: This retrospective cross-sectional study included micro-computed tomography scanning and Vickers micro-hardness testing of 90 bone samples (30 femoral and 60 tibial) collected from 15 adult patients with primary KOA undergoing total knee arthroplasty (TKA). The Kellgren–Lawrence grading system was used to assess the severity of KOA lesions in the included individuals, and bone samples were divided into the following groups: moderate KOA (42 samples from seven patients, age: 70 ± 7 years, females: 3/7) and end-stage KOA (48 samples from eight patients, age: 70 ± 6 years, females: 5/8). Results: Our data revealed site-specific sclerotic alterations in subchondral trabecular bone mechano-structure (thicker trabeculae, coupled with higher bone mineral content and increased bone micro-hardness) in individuals with end-stage KOA compared to moderate KOA, supporting its role in KOA pathogenesis beyond the exclusive cartilage degeneration effect. Our data also revealed that most heterogeneous subchondral trabecular mechano-structure was present in bone samples obtained from the medial part of the tibial and femoral condyle, revealing the substantial effect of mechanical loading during varus knee malalignment. Conclusions: Observed site-specific alterations in subchondral bone mechano-structure in individuals with end-stage KOA supported the role of subchondral sclerosis in primary KOA pathogenesis beyond its exclusive effect on cartilage degeneration. Full article

To ensure a reliable electrical isolation point in power systems, the seismic performance assessment of disconnect switches is of critical importance for maintaining operational continuity under earthquake excitations. In this study, a hybrid method combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed for the seismic intelligent classification and response prediction of disconnect switches. Unlike conventional approaches that rely on finite element simulations or shake table tests with high computational costs, the proposed method learns directly from raw ground motion records. The CNN component is designed to capture local frequency characteristics of input ground motions, enabling automatic classification into low-, medium-, or high-frequency categories. Subsequently, category-specific LSTM models are established to map the ground motion time series to multi-dimensional performance indicators of the disconnect switch. These indicators include top absolute accelerations, bottom shear forces, and relative deformations of porcelain posts. A training set comprising 102 ground motion records is constructed based on numerical simulations of a validated simplified model, while another testing set comparing 21 ground motion records are employed to validate the performance of predicted models. Training and validation results demonstrate that the CNN achieves a great classification accuracy. The LSTM predictions show good agreement with the computed time-history responses, with errors of root-mean-square responses generally within 10%. The proposed method provides a rapid, data-driven alternative to traditional seismic analysis, significantly reducing computational time while preserving prediction fidelity. It also enables the parallel prediction of multiple coupled performance indicators, which is not readily achievable by existing single-output surrogate models. Full article

Fiber Bragg Grating (FBG) sensors are widely used for strain and temperature monitoring due to their high sensitivity, compact size, electromagnetic immunity, and multiplexing capability. While conventional FBG interrogators remain bulky and costly, Photonic Integrated Circuit (PIC) platforms provide a promising route toward compact, scalable, and low-power FBG interrogation. However, the choice of architecture strongly determines the achievable resolution, bandwidth, multiplexing capacity, and robustness. This review compares on-chip demodulation architectures, evaluating their performance in resolution, bandwidth, and interrogation speed. We show that the optimal architecture depends strongly on the application: AWG-based schemes excel in compact, multi-FBG readout; ring-resonator systems are highly effective for tunable filtering; and interferometric phase-domain schemes offer the highest sensitivity for dynamic strain sensing. Despite these architectural advances, practical deployment remains constrained by system-level bottlenecks. These challenges primarily include source/detector integration, fiber–chip coupling, packaging robustness, and thermal drift. Overcoming these barriers requires a shift in future development from isolated photonic-device optimization toward comprehensive, system-level co-design. Full article

Accurate dynamic state reconstruction in complex multi-node coupled systems is critical for ensuring operational stability and reliability. However, this task is highly challenging due to spatially sparse measurement sensors, strong dynamic coupling among nodes, and the intractability of explicitly modeling the underlying physical mechanisms. Conventional data-driven methods exhibit limited generalization under sparse labels or out-of-distribution conditions, whereas strict physics-driven solvers often fail to converge in complex environments with unmodeled dynamics. To address these limitations, this paper proposes a physics-informed neural network (PINN)-inspired topology-aware learning framework for multi-node state reconstruction. Rather than acting as a strict physical equation solver, the proposed method innovatively injects physical priors into data-driven temporal modeling. By incorporating physical consistency constraints, latent dynamic regularization, topology-aware priors, and an observer-style multi-branch hybrid fusion strategy, the framework effectively overcomes the drawbacks of single-paradigm models to enhance estimation accuracy and robustness. Extensive experiments on real-world coupled system data demonstrate that the proposed framework outperforms state-of-the-art linear, tree-based, and pure sequential models. Specifically, the proposed topology-aware hybrid observer achieves a Root Mean Square Error (RMSE) ≈ 0.02604 and an $R^2\approx$ 0.79060 on the multi-node harmonic reconstruction task, demonstrating superior accuracy and dynamic tracking capability compared to the other baselines. Furthermore, cross-node virtual sensing and ablation experiments verify that the constructed physics-guided observer achieves stable cross-node reconstruction under limited physical observations. The results indicate that integrating PINN-inspired learning with topology-aware modeling provides a highly robust and feasible paradigm for ubiquitous sensing and state estimation in complex networks under restricted measurement conditions. Full article