Search Results (670)

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Safety signs in innovative manufacturing environments fail to match dynamic risks due to the separation of perception, semantics, and decision-making. Existing methods lack closed-loop integration of IoT sensor streams, knowledge graph reasoning, and adaptive signage control. This paper proposes a framework that fuses dynamic graph attention networks with hierarchical temporal knowledge graphs and reinforcement learning optimization. The framework extracts spatiotemporal dependencies from multi-source sensors, traces risk propagation paths on an industrial knowledge graph, and generates adaptive signage actions. Experimental results demonstrate that the proposed method achieves 96.7% risk identification accuracy, a 91.3% risk propagation F1 score, a 94.2 semantic matching score, and 43.65 milliseconds response latency. Real-world validation on an aerospace workshop confirms the method’s effectiveness. This work provides a closed-loop solution from physical perception to adaptive semantic expression for intelligent manufacturing safety. Full article

Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical semantic path inference problem, where prediction is performed over a constrained path space rather than independent label spaces. We propose HiCoPro, a graph-conditioned structured inference framework for modeling multi-level dialogue semantics. The framework consists of the following: (i) a Graph-Conditioned Label Space (GCLS) that encodes hierarchical dependencies into label embeddings via graph propagation; (ii) a compatibility-based logit fusion mechanism that jointly scores semantic relevance and structural consistency; and (iii) a constraint-aware decoding strategy that enforces hard parent–child dependencies during inference. By integrating semantic representations with graph-conditioned label structures via a bilinear compatibility function and learnable logit-level fusion, the model jointly captures semantic relevance and structural consistency. To support this task, we construct PrefDial, a general-domain hierarchical dialogue dataset with systematic three-level annotations, serving as a benchmark for structured dialogue understanding. Experimental results demonstrate that HiCoPro achieves superior Macro F1, Exact Match, and Hierarchical Consistency on PrefDial, while remaining competitive on multiple public benchmarks. Further analysis highlights the effectiveness of graph-conditioned modeling in balancing semantic discrimination, hierarchical consistency, and robustness. Full article

Traditional white canes offer a limited perception range, whereas end-to-end visual models face challenges in real-time deployment on edge devices. To address these limitations, this paper proposes a lightweight real-time assistive system that integrates geometric topology reconstruction with state-adaptive warning logic. The system utilizes YOLOv9 to extract discrete semantic primitives of tactile paving. It constructs a dual-branch perception framework based on Median Absolute Deviation and the Minimum Spanning Tree algorithm to analyze the topological structure of tactile paving. For complex intersections characterized by warning indicators, a one-dimensional connectivity clustering algorithm based on longitudinal topology is proposed. It generates accurate macroscopic feasible directional prompts under field-of-view boundary constraints. Additionally, a hierarchical scheduling framework dynamically orchestrates scenario-specific finite state machines to enable continuous dynamic interaction across typical high-risk scenarios. Evaluated on a custom real-world dataset, the system achieves a 95.21% frame-level comprehensive accuracy for straight-path deviation correction and intersection directional prompting. Dynamic temporal stress tests confirm the temporal stability and logical coherence of state transitions. Furthermore, latency evaluations demonstrate the logic layer’s minimal computational overhead, proving its theoretical feasibility for real-time edge deployment. This approach provides an effective, low-latency solution for delivering directional prompts and hazard warnings to visually impaired users. Full article

The reliability of the steering system directly impacts the safety of autonomous driving. Addressing the issue of trajectory deviation easily caused by motor failure in redundant steer-by-wire (SBW) systems, this paper aims to improve vehicle tracking accuracy under fault conditions. A hierarchical fault-tolerant control strategy based on a zeroing neural network (ZNN) is proposed: the upper layer uses the Stanley algorithm for path planning, while the lower layer designs a ZNN controller with preset performance constraints, and instantaneous power reconfiguration is achieved through Jacobi pseudo-inverse. Simulation results show that under high-speed lane changes and sinusoidal conditions, this strategy can achieve millisecond-level task reassignment, and compared to PID control, the maximum absolute error of lateral tracking under fault conditions is reduced by over 50%, and the root mean square error is reduced by over 30%. This method effectively improves driving safety and trajectory fidelity when actuators fail. Full article

The microstructure of as-cast Al-xSi (x = 4, 7, 10) alloys solidified under various cooling rates was characterized using scanning electron microscopy (SEM). To overcome the multicollinearity among eutectic silicon parameters, Principal Component Regression (PCR) analysis was employed to quantitatively evaluate the effects of silicon morphology, scale, and content on the electrical conductivity of the alloys. The results demonstrate that rapid solidification significantly refines the plate-like eutectic silicon and reduces its volume fraction, leading to improved electrical conductivity. The PCR model shows that a hierarchical mechanism: volume fraction (PC1) acts as the principal determinant, increasing baseline resistance primarily by truncating the electron mean free path (MFP); meanwhile, within identical alloy systems, morphological parameters (PC2) play a dominant regulatory role. A semi-quantitative electron drift path model was established, confirming that the morphological deviation of eutectic silicon from a spherical shape (i.e., increased aspect ratio) causes a non-linear increase in the amplitude of electron detours. This geometric elongation significantly degrades electrical conductivity, providing theoretical guidance for the microstructural design of high-conductivity Al-Si alloys, which can be practically applied to the manufacturing and optimization of lightweight, heat-dissipating enclosures for new energy vehicle (NEV) motors and power distribution systems. Full article

A novel hybrid ISSA-GWO (Improved Sparrow Search Algorithm–Grey Wolf Optimizer) is proposed for the path planning of Unmanned Wing-in-Ground-Effect Craft (UWIGC), integrating ground-effect constraints and island-reef environments into a unified optimization framework. Leveraging its exceptional ultra-low-altitude flight capability and high economic efficiency, the UWIGC offers unique advantages in maritime missions such as island patrol and rapid replenishment. However, its path planning faces the dual challenge of precise obstacle avoidance and ultra-low-altitude maintenance, due to the obstacle distribution in island regions and the altitude window constraints inherent to ground-effect flight. To address this, the proposed method integrates the swarm intelligence of the Sparrow Search Algorithm and employs a self-destruction mechanism to escape local optima. Furthermore, it combines the hierarchical guidance of the Grey Wolf Optimizer to enhance convergence accuracy. The algorithm incorporates ground-effect maintenance constraints and an island-reef threat model, and it smooths the final path using cubic B-spline curves. Simulation results demonstrate that the proposed algorithm outperforms the standard Sparrow Search Algorithm, Grey Wolf Optimizer, and Particle Swarm Optimization in terms of convergence speed, optimization accuracy, and obstacle avoidance success rate. It is capable of generating a feasible, safe, and smooth path, thereby supporting the autonomous navigation of UWIGC in island reef waters. Full article

Improving the cultivated land use eco-efficiency (CLUE) is crucial to achieving sustainable land use and the green transformation of agriculture. This study is based on the data from 353 prefecture-level cities in China from 2013 to 2021. The slacks-based measurement (SBM)-undesirable model, the social network analysis (SNA), and the fuzzy set qualitative comparative analysis (fsQCA) are adopted to measure and analyze the spatial patterns, network characteristics, and multiple driving pathways of inefficiency in the cultivated land use eco-efficiency in prefecture-level administrative units. Results show the following: (1) From 2013 to 2021, CLUE in the study areas shows spatial heterogeneity, with most efficiency values at a moderate level and showing a fluctuating downward trend over time. (2) The nine major agricultural regions have formed a complex association network, with the overall network connectivity being weak but efficiency relatively high. The hierarchical structure is gradually flattening, and inter-regional cooperation is increasing. (3) There are significant differences in influence, control, and accessibility within individual networks, and the collaborative network is developing into a “multi-core-hierarchical” structure. (4) The formation of inefficiency involves multiple concurrent mechanisms. Four typical inefficiency paths were identified, with significant heterogeneity across different agricultural regions. In the future, differentiated land use and ecological protection policies should be implemented based on the spatial network characteristics and inefficiency driving pathways of each agricultural region to promote the coordinated improvement of CLUE. Full article

To reveal the influencing mechanism of safety resilience in prefabricated building systems (PBS), identify key risk nodes, and support targeted resilience enhancement, this study develops an improved ISM–BN analytical model. Based on 136 domestic safety accident cases involving prefabricated buildings (PB) from 2016 to 2025, and combined with bibliometric analysis, 13 causal factors were identified and an indicator system was established. Grey Relational Analysis (GRA) was introduced to improve the traditional Interpretive Structural Modeling (ISM) method, through which the causal factors were divided into four hierarchical levels and the hierarchical relationships among the factors and levels were clarified. Subsequently, the hierarchical structure derived from the improved ISM was mapped into a Bayesian Network (BN), and parameter learning was conducted using accident data. Through backward diagnosis and sensitivity analysis, five key risk nodes and two critical transmission paths were identified, based on which targeted improvement strategies were proposed. The results can provide methodological support and decision-making references for key risk control and resilience enhancement in PBS. Full article

The present study provides a multi-faceted validation and refinement of a distributed system architecture designed to improve emergency response in resource-constrained urban areas. The architecture integrates IoT sensors, edge computing, field-programmable gate arrays and distributed shortest-path algorithms to enhance resilience and operational efficiency. As a primary validation strategy, a survey of 78 Cameroonian experts in software engineering, distributed systems, urban planning and emergency technologies was conducted. The survey yielded quantitative and qualitative data across multiple analytical dimensions, including subgroup analysis and a transferability assessment covering Nigeria, Senegal, and Kenya. The statistical analysis confirmed that the architecture is technically feasible, adaptable to local constraints, and has the potential to reduce response times. As a secondary validation strategy, a simulation-based study was conducted using iFogSim on smart-city models ranging from 25 to 100 nodes, encompassing five experiments: result consistency, geographic sensitivity, concurrent incident management, path-caching efficiency, and scalability analysis. The simulation results quantitatively corroborate the expert assessments, demonstrating low end-to-end latency and sustained throughput with realistic urban load conditions. Key challenges identified include interoperability, urban data structuring, financial sustainability and inter-institutional coordination. Experts have proposed a hierarchical structure of priority actions and concrete recommendations for engineers, researchers and policymakers. The combined findings validate the architecture and establish a replicable expert-simulation evaluation framework applicable to analogous distributed emergency-response systems in comparable resource-constrained contexts. The empirical results further constitute a reference baseline for the design and implementation of similar architectures. Full article

The complexity of a graph, defined as its number of spanning trees, serves as a key measure of network reliability. Stacked graphs constitute a significant and versatile class of graphs, formed by superimposing multiple copies of a base graph upon a shared central vertex set. Their inherent layered symmetry and structural regularity make them compelling models for a wide range of real-world networks, including multi-tier communication systems, hierarchical data networks, and resilient distributed architectures. Moreover, their systematic construction from well-known graph families renders the study of their complexity both mathematically rich and algorithmically meaningful. In this paper, we derive closed-form formulas for the complexity of several stacked graph families based on path- and cycle-based structures with a central vertex, including stacked fan and wheel graphs, stacked double fan and double wheel graphs, and stacked path flower, cycle flower, and gear graphs. The derivations are based on techniques from linear algebra, matrix theory, and Chebyshev polynomials. Full article

Autonomous path planning for unmanned aerial vehicles (UAVs) in complex three-dimensional environments requires a balance among search efficiency, obstacle avoidance safety, and trajectory smoothness. However, conventional A* algorithms often suffer from redundant node expansion, insufficient safety awareness, and poor turning performance. To overcome these limitations, this study proposes a hierarchical hybrid planning framework that integrates an improved A* algorithm, particle swarm optimization (PSO), and B-spline trajectory generation. In the global planning stage, a composite cost function is designed by considering path length, safety margin, and turning penalty. Meanwhile, a directional dynamic window and Top-$K$ candidate selection strategy are introduced to reduce invalid expansions and improve search efficiency. In the local refinement stage, key turning regions along the coarse path are identified and optimized using an improved PSO method with adaptive inertia attenuation, reflective boundary handling, and stagnation-triggered reseeding. Finally, B-spline fitting is applied to generate a continuous and executable UAV trajectory. Simulation results show that all compared methods achieved a 100% success rate in the randomized environments. The proposed framework achieved a mean runtime of 20.664 s, compared with 47.108 s for standard A* and 134.666 s for composite-cost A*. Meanwhile, it maintained a comparable path length, indicating robust feasible-path generation, preserved path quality, and acceptable computational feasibility under the tested randomized environments. Full article

Speech-based suicide risk prediction is vital for psychological support hotlines but remains challenging because existing methods often insufficiently incorporate clinically relevant prior cues and have difficulty identifying sparse high-risk signals in long-form speech. We propose the Affective & Cognitive Distortions-assisted Speech Suicide Risk Prediction Network (ACD-SSRNet) to address these challenges. First, we construct a multi-view feature system that integrates general acoustic-textual features with affective and cognitive-distortion cues motivated by clinical knowledge. Second, a hierarchical cascaded decoupling module is developed to reduce heterogeneous feature redundancy while preserving task-critical information. Finally, we design a prior-guided multi-path graph attention structure to locate sparse high-risk segments and capture long-range temporal dependencies. Experiments on a real-world hotline dataset show that ACD-SSRNet outperforms state-of-the-art baselines, achieving a 2.79% improvement in F1-score and a 2.57% improvement in accuracy. We further conducted an expert evaluation on five representative de-identified hotline cases, showing that the model can capture key affective and cognitive-distortion segments associated with suicide risk. Full article

To address the large deformation and instability of gob-side entry roofs in soft, thick coal seams induced by residual cavities left by hydraulic flushing, the 1609 working face of Jiulishan Coal Mine was selected as the engineering background. Field investigation, numerical simulation, and industrial field testing were combined to investigate the deformation and failure characteristics of surrounding rock and the corresponding control technology for gob-side entries with cavity-bearing roofs. The results indicate that residual cavities created by hydraulic flushing disrupt the stress transfer path within the roof, causing stress field distortion and expansion of tensile stress zones, thereby significantly weakening the roof load-bearing capacity. As the cavity size increases, the surrounding rock deformation and plastic zone continuously expand. When the cavity size exceeds 1.0 m, roof subsidence exhibits a nonlinear increase, and the fractured zone around the cavity connects with the roof plastic zone, forming a continuous failure band that serves as the key factor leading to surrounding rock instability. Based on the deformation characteristics of the cavity-bearing roof, namely shallow fragmentation, deep-seated separation, and structural instability, a collaborative control technology consisting of multi-level cable bolts, steel-beam reinforcement, and grouting through injection pipes was proposed. By establishing a shallow–intermediate–deep hierarchical load-bearing structure and reinforcing the fractured cavity zone through grouting, the technology reconstructs the surrounding rock load-bearing system and optimizes the stress environment. Field application results show that, for a roof containing a 1.5 m cavity, the maximum roof subsidence and separation were controlled within 102 mm and 55 mm, respectively, and the roadway maintained a stable condition throughout the monitoring period. The findings provide both a theoretical basis and engineering guidance for surrounding rock control of gob-side entries with cavity-bearing roofs in soft, thick coal seams. Full article

Digital transformation is reshaping technical and vocational education and training (TVET), yet the behavioral processes through which students’ digital learning competence is associated with learning performance remain underexplored. Drawing on Biggs’ presage–process–product (3P) model, this cross-sectional study examined a dual-path moderated mediation model in which active and rule-based learning participation served as differentiated process pathways, while teacher–student interaction and curriculum practicality were specified as contextual moderators. Survey data were collected from 3693 students in Chinese higher vocational colleges. Hierarchical regression and bootstrapped moderated mediation analyses indicated that digital learning competence was positively associated with learning performance. Active learning participation mediated this association, whereas rule-based learning participation did not function as a stable positive mediator. At higher levels of teacher–student interaction and curriculum practicality, digital learning competence showed stronger associations with active learning participation and stronger indirect associations with learning performance. The rule-based pathway appeared more conditional and reflected an externally regulated, prescribed-task-oriented form of behavioral participation, rather than a stable process pathway associated with deep learning. These findings extend the 3P model to digital learning in higher vocational education, differentiate behavioral forms of participation, and highlight the importance of interactive and practice-oriented instructional contexts. Full article