Search Results (10,582)

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

In the context of highly interconnected global markets and the rapid dissemination of multilingual information, traditional risk prediction methods that rely on single numerical sequences or monolingual text are insufficient for achieving early perception of cross-market risks. To address this issue, a cross-market risk early warning framework based on multilingual large language models and multimodal sensing fusion is proposed. The proposed approach is centered on a unified risk semantic space, where cross-lingual semantic alignment is employed to reduce semantic discrepancies across languages. Furthermore, a semantic–volatility coupling attention mechanism is introduced to capture the dynamic relationship between textual semantic evolution and market fluctuations. In addition, cross-market knowledge transfer and low-resource enhancement strategies are incorporated to improve the model’s generalization capability across multilingual and multi-market environments, thereby establishing an intelligent perception and early warning system for complex sensing scenarios. Experimental results demonstrate that the proposed method significantly outperforms multiple baseline models in multilingual cross-market risk prediction tasks. In the main experiment, the model achieves a root mean squared error (RMSE) of $0.1127$, an mean absolute error (MAE) of $0.0846$, and an area under the curve (AUC) of $0.8879$, while the early warning gain is improved to $5.2$ days, which is substantially better than the Transformer model (RMSE $0.1365$, AUC $0.8042$) and the multilingual BERT-based fusion model (AUC $0.8395$). In terms of classification performance, higher accuracy, precision, and recall are consistently achieved, with overall accuracy exceeding $0.88$, and both precision and recall are maintained above $0.85$, indicating strong discriminative capability in risk identification tasks. Cross-lingual generalization experiments further verify the robustness of the proposed framework. When trained solely on the English market, the model achieves AUC values of $0.8624$ and $0.8471$ on the Chinese and European markets, respectively, with RMSE reduced to $0.1185$, significantly outperforming competing methods. Overall, the proposed approach achieves substantial improvements in prediction accuracy, cross-lingual generalization, and early warning performance, providing an effective solution for artificial intelligence-driven sensing and risk early warning. Full article

This study introduces two compact wideband substrate-integrated waveguide (SIW) filters fabricated using thin-film technology. The wideband bandpass response is achieved by incorporating interdigital capacitor (IDC) structures into a half-mode SIW (HMSIW) transmission line. An equivalent LC circuit model is formulated to analyze the influence of IDC parameters on the generation of transmission zeros. For the first filter (BPF 1), a third-order IDC coupling configuration is employed, resulting in a 1 dB passband spanning 11 GHz to 18 GHz, a minimum insertion loss of 0.66 dB, three transmission zeros that enhance stopband performance, and a compact core dimension of $0.49{\lambda }_g\times 0.29{\lambda }_g$. For further miniaturization, a modified HMSIW transmission line incorporating a metal-insulator-metal (MIM) capacitor at the equivalent magnetic wall is proposed. This design effectively reduces the transverse dimension of the waveguide while maintaining the original cutoff frequency. Utilizing this configuration, the second bandpass filter (BPF 2) was designed and fabricated employing double-layer ceramic thin-film technology. The resulting filter exhibits a 1 dB passband spanning 10 GHz to 18 GHz, a compact footprint measuring $0.44{\lambda }_g\times 0.23{\lambda }_g$, a minimum insertion loss of 0.58 dB, and features three transmission zeros. The fabricated and measured results of both filters show good agreement with simulations. Compared with previously reported wideband SIW filters, the proposed designs demonstrate comprehensive advantages in fractional bandwidth, insertion loss, out-of-band suppression, and circuit size, providing effective filtering solutions for high-density integration of microwave and millimeter-wave RF systems. Full article

This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

This study numerically investigates the influence of different fluid temperatures on the cavitation characteristics of a space-use micropump under microgravity conditions. A homogeneous multiphase model coupled with a thermal modified Zwart–Gerber–Belamri cavitation model is employed, and the SST turbulence model is applied to resolve the cavitating flow under rated and off-design flow rates. Results indicate that cavitation behavior is strongly dependent on both temperature and flow rate. At low temperatures, cavitation intensity increases, leading to reductions in head and efficiency and a slight increase in shaft power. In contrast, elevated temperatures suppress cavitation development, resulting in milder performance degradation and, in some cases, slight improvements in head and shaft power. Internal flow analysis reveals that lower temperatures promote more extensive vapor fraction distributions and greater flow distortion, while entropy production analysis shows that cavitation contributes limited additional loss overall, though entropy generation rises markedly under combined low temperature and high flow rate conditions. The findings highlight that cavitation effects are more pronounced at low temperatures and are further amplified at higher flow rates, providing insights for the design and reliable operation of space micropumps in on-orbit thermal management systems. Full article

Extreme natural disasters may simultaneously disrupt power distribution infrastructures and their supporting communication systems, significantly degrading post-disaster recovery performance. To enhance coordinated restoration under such coupled failure conditions, this study proposes a unified optimization framework for pre-disaster deployment and post-disaster repair and service restoration in interdependent distribution–communication networks. First, an interdependency model is developed to characterize the physical and operational couplings between the distribution and communication networks. The impacts of communication outages on remotely controlled switches and repair crew dispatching are quantitatively analyzed, revealing how communication failures influence the restoration process. Based on this interdependency representation, a coordinated optimization model is established to jointly determine repair crew routing, mobile power allocation, and critical load restoration sequencing. The objective is to minimize cumulative outage losses over the recovery horizon, thereby achieving coordinated allocation and routing of multiple types of emergency repair resources. Furthermore, by jointly considering pre-disaster deployment planning and post-disaster restoration strategies, a two-stage emergency recovery framework is designed to integrate pre-event preparedness with post-event response for distribution networks. Case studies on a modified IEEE 33-bus cyber–physical distribution system demonstrate that the proposed coordinated restoration strategy restores approximately 50% of critical loads within the first 3 h, which is of direct significance for maintaining essential services such as hospitals and emergency shelters during the acute phase of a disaster. The proposed approach reduces the total load loss by 49.5% and shortens the restoration time by 120 min. In terms of pre-disaster deployment, the proposed strategy reduces average load shedding by 33.4% and 46.5% relative to the heuristic and random deployment strategies, respectively, demonstrating the effectiveness of proposed method for grid resilience enhancement. Full article

Research on hub-and-spoke logistics networks can effectively advance the construction of the Northeast China Sea–Land Grand Corridor. In the context of regional synergistic development, this study investigates the optimization of the logistics network for the Northeast China Land–Sea Grand Corridor. Focusing on 43 prefecture-level cities across Liaoning, Jilin, Heilongjiang, and Inner Mongolia, a hub-and-spoke logistics network optimization model is developed. The model aims to minimize total network costs while satisfying specific network resilience thresholds. It integrates multi-modal transport and incorporates considerations such as economies of scale, node heterogeneity in resilience evaluation, and route redundancy. Based on this, the study employs the entropy weight method to establish a comprehensive evaluation system for regional logistics and economic development levels and applies an improved coupling coordination degree model to assess the synergistic relationship between these two systems. A modified gravity model, with the coupling coordination degree as a moderating coefficient, is constructed to quantify the strength of logistics–economic linkages between cities. Furthermore, social network analysis and a logistics affiliation model are used to identify key hub cities. The results demonstrate that the optimized network significantly enhances transport efficiency, achieves substantial economies of scale and strikes a balance between cost efficiency and system resilience. This research provides a quantitative foundation and practical reference for node layout planning and multi-modal transport organization along the Northeast China Sea–Land Grand Corridor, and its methodological framework can inform logistics network planning in similar regions. Full article

Over six decades, satellite remote sensing of water resources has evolved from manual interpretation of weather photographs to AI systems that learn hydrologic predictions directly from satellite imagery. This review traces that evolution through the observation-to-inference arc—a framework for the progressively tightening coupling between what satellites observe and what hydrologists infer. Using illustrative applications in precipitation, evapotranspiration, soil moisture, snow, surface water, and groundwater, we show how early observations (1960–1985) remained disconnected from operational hydrology; how calibrated retrieval algorithms (1985–2000) established a one-way pipeline from satellites to models; how operational infrastructure (2000–2015), anchored by MODIS, GRACE, GPM, and Sentinel, achieved assimilative coupling through computational feedback between models and observations; and how deep learning (2015–present) is beginning to collapse this pipeline. Multi-source data fusion has been a recurring enabler at each stage. We articulate a four-level AI vision and research trajectory, from AI-assisted interpretation through AI-native retrieval and AI-driven inference to autonomous Earth observation intelligence. Persistent challenges in mission continuity, calibration, equity of access, and translating satellite-derived information into operational water management decisions provide essential context for evaluating both the promise and limits of this trajectory. Full article

To address the prominent fatigue failure risk of planetary gears in power-split hybrid buses and the lack of quantitative damage analysis across various operating modes in existing studies, this paper focuses on the front planetary gear set of a power-split hybrid bus. Based on a full-vehicle co-simulation model, loads under full operating conditions are decomposed into 11 operating modes, mode-switching loads are analyzed and extracted, and mode-decomposed and mode-switching fatigue loading spectra are compiled. Fatigue simulation is then conducted using Miner’s linear damage accumulation rule. Results show that the sun gear directly coupled to motor is the system’s most fatigue-susceptible component, exhibiting significant asymmetric unilateral tooth flank damage. The hybrid electric vehicle (HEV) mode contributes approximately 88% of total damage to the sun gear’s right flank, dominating system fatigue damage. Transient mode-switching conditions account for approximately 60% of total damage to the sun gear’s left flank, serving as the core damage source. Compared with the traditional full-condition merging method, the proposed mode-decomposed method improves the conservatism of life prediction. This work provides methodological support for refined strength design and targeted optimization of power-split hybrid transmission systems. Full article

New quality productive forces are increasingly recognized as important drivers of coordinated regional development, with urban agglomerations acting as key vehicles for their spatial implementation. Based on the theory of new quality productive forces, this study takes the 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration as its research subjects, spanning the period from 2005 to 2023, and constructs a four-dimensional evaluation index system for new quality productive forces covering economic, social, ecological, and technological dimensions. It employs the entropy method to determine indicator weights and calculate development indices for each dimension and utilizes a coupling coordination model to measure the overall and subsystem-level coordination by analyzing their spatiotemporal evolution characteristics. The results indicate a steady upward trend in the overall coordination level, progressing from a low level to an intermediate level, with the state of coordination continuously improving; spatial differentiation is significant, forming a gradient development pattern centered on Beijing, with marked disparities in coordination levels among cities. Subsystem analysis reveals an imbalanced synergy structure: while economic and ecological synergy levels are relatively high, the coupling and synergy between science and technology and the economy and society remain prominent weaknesses. Most cities in Hebei Province lack sufficient scientific and technological innovation capabilities, resulting in a weak supportive role for economic and social development. Based on these findings, this study proposes policy recommendations such as establishing a regional innovation community, promoting the integration of factor markets, and strengthening collaborative governance of the ecological environment, with the aim of leveraging new quality productive forces to drive a qualitative leap in the coordinated development of the BTH urban agglomeration. Full article

As a primary energy consumer and carbon emitter, the construction industry (CI) faces a growing conflict between traditional energy-intensive growth models and global sustainable development goals. To promote the sustainable development of the CI, this study establishes a sequential analytical framework following the logic of “coupling evaluation–driving force identification–causal inference” across 30 developed economies (DE) from 2000 to 2022. Initially, the coupling coordination degree (CCD) between the economic and environmental systems of the CI was evaluated, utilizing the Environmental Kuznets Curve (EKC) to characterize the transition from relative to absolute decoupling. The results show that the economy and the environment in the construction industry (CEECI) for DE is generally high (0.70–0.90). Subsequently, based on Green Innovation Growth (GIG) theory, Panel Data Analysis (PDA) is employed to identify the key drivers of the coupling between the economy and CEECI. The results show that for every 1% increase in per capita GDP, CEECI increases by approximately 0.035; for every 1% increase in science and technology investment (ST Inv), CEECI increases by 0.045; and for every 1 unit increase in building energy use (BEU), CEECI decreases by 0.008. Furthermore, Granger causality analysis (GCA) was used to examine the bidirectional predictive relationship. Furthermore, there is a two-way correlation between GDP and CEECI, and a one-way correlation between CEECI and ST Inv. Overall, our results show that further decoupling requires innovation, not just economic growth; therefore, the CI should optimize its industrial structure, prioritize technological innovation, strengthen lifecycle energy management, and promote coordinated global CI improvement. Full article

Wide-body dump trucks in open-pit mines frequently operate under high loads and severe road conditions, demanding superior dynamic performance from their suspension systems. Existing studies tend to focus only on the influence of individual parameters on the dynamic characteristics of hydro-pneumatic suspensions, lacking systematic analysis of parameter coupling effects and optimal parameter combinations. Taking the two-stage pressure hydro-pneumatic suspension of a wide-body dump truck as the research object, this paper theoretically analyzes its working characteristics and establishes an AMESim model under multiple excitation conditions to reveal how parameter interactions affect the dynamic performance of the suspension. With peak liquid pressure, maximum liquid pressure fluctuation, and maximum vehicle body vertical acceleration as optimization objectives, a multi-objective optimization algorithm is employed to determine the optimal suspension parameters. The results indicate that the interactive responses of damping orifice diameter and check valve diameter with respect to peak pressure and body vertical acceleration exhibit strong nonlinearity. Compared with the original parameter scheme, the optimized design reduces peak liquid pressure, maximum pressure fluctuation, and peak body vertical acceleration by 8.76%, 29.1%, and 11.7%, respectively, significantly improving vehicle ride comfort and mitigating pressure oscillations in the hydro-pneumatic suspension. The research results can provide theoretical support and engineering reference for intelligent operation and maintenance of mine heavy equipment, optimization design of suspension systems and efficient and reliable operation. Full article

In battery systems, external mechanical compression is commonly applied to pouch/prismatic cells to improve their electrical performance and mechanical integrity. However, cell clamping can hinder system heat rejection by introducing an additional thermal insulation layer. A novel battery clamping scheme was designed with reduced contact area to explore the system thermal behaviour under different cooling regimes. Experimental data obtained from battery characterisation and performance tests is analysed with a thermal-coupled equivalent circuit model to quantify changes in cell impedance and system thermal properties. By reducing the clamping area by 70%, the temperature rise of the cell was decreased by 0.5 °C in comparison to the reference condition of a cell with no clamping during a 1C discharge under natural convection. Under immersion cooling using BOT2100 dielectric liquid, the thermal benefit was amplified, resulting in temperature reductions of 0.9 °C at 1C and 4 °C at 3C. The principal conclusion of this work is that reshaping the clamping plate has the potential to reduce ohmic heating by lowering battery internal resistance, which outweighs the additional thermal resistance introduced by partial surface coverage. This novel experimental approach demonstrates the potential to improve battery thermal management through geometry-optimised cell clamping, particularly for high-power applications, and further directs the community towards cell clamping solution designed to optimise both thermal and mechanical cell performance. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article