Search Results (491)

Flexible hoses connecting seabed mining vehicles to relay stations must remain suspended, limit vehicle loads, satisfy bending constraints, and maintain stable slurry transport. This study combines a lumped mass hose model, time domain vehicle motion analysis, and sequential structural-to-flow coupling with CFD–DEM to determine the safe operating domain of a 220 m single-arch hose. Thirteen buoyancy layouts were screened using seabed clearance, effective tension, curvature, and vehicle loads. Among the tested layouts, a buoyancy section extending from the vehicle end to 0.6L provided the best compromise. During outward travel, turning-induced peaks governed structural safety; after path optimization, the peak longitudinal and lateral hose loads were 17.78 and 22.38 kN, respectively, and the minimum bending radius remained above 2 m. At a reference slurry velocity of 5 m/s and solid volume fraction of 10%, a 40 m vehicle–relay spacing produced strong particle slip and concentration rebound near the lower bend, whereas 197 m promoted particle accumulation and a thicker moving bed. Integrating structural and conveying constraints yielded a recommended horizontal spacing of 80–160 m. Scaled pool tests and a published vertical pipe benchmark supported the numerical approach. The resulting domain provides a practical basis for path boundary design under the assumptions adopted here. Full article

Addressing the opportunistic behavior of retailers operating within the unique spaces of transportation hub ecosystems, which exploit strong temporal and spatial constraints and geographic monopolies, this paper constructs a three-party evolutionary game model involving transportation hub authorities, micro-merchant enterprises, and transient consumers. Using China’s high-speed rail hubs as the research context, this study develops a theoretical evolutionary game model with numerical simulations and endogenously embeds the backlash from informal digital public opinion into the regulatory decision-making framework. The study thoroughly deconstructs the mechanisms of behavioral evolution and control pathways within highly constrained micro-spaces. The study reveals that: (1) Static, constant regulation, lacking negative feedback linked to the system’s state, inevitably causes the system to fall into a non-convergent “regulation-speculation” cyclical oscillation; (2) A state-dependent dynamic regulatory mechanism can effectively serve as an adaptive damper to quell strategic oscillations; however, because it fails to eliminate speculative premiums, the system becomes locked in a mediocrity trap characterized by low compliance rates; (3) Endogenous digital public opinion leverage can reconfigure the phase space structure and trigger topological bifurcations in the system, breaking the attractor region of the mediocrity equilibrium and driving the game system to undergo a structural transition, with asymptotic convergence to the pure-strategy ideal point. Furthermore, digital empowerment drives consumers to exhibit an inverted U-shaped transient behavioral trajectory of “forced silence—awakening to rights advocacy—rational self-governance,” thereby scientifically deconstructing the public’s dual silence paradox in the digital age. This study not only provides a mathematical basis for the public sector to achieve a paradigm shift from rigid regulation to flexible adaptive governance and structural safe retreat at the micro level, but also offers strategic insights for intertemporal compliance management by micro-merchants. Full article

Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-dimensional target–horizon problem covering three generation families and five forecast horizons: H1, H3, H6, H12, and H24. A leakage-safe data construction protocol generates causal predictors from meteorological observations, generation history, calendar cycles, lagged and rolling statistics, ramp descriptors, and physics-informed transformations. PI-BHTF partitions the 422-dimensional predictor space into solar, wind, temporal-calendar, and cross-context components, encodes them through parallel nonlinear branches, and combines the representations using cross-energy gated fusion. Its neural core uses a heteroscedastic predictive head and Monte Carlo dropout to distinguish input-dependent aleatoric uncertainty from epistemic variability, whereas the final hybrid forecasts are calibrated using residual quantiles computed from a chronologically held-out validation segment. Across three prespecified random seeds, the full PI-BHTF achieved a mean MAE of 0.0797 ± 0.0007 on the internal chronological test and 0.0686 ± 0.0006 on locked external SCADA validation. Performance and calibration varied substantially across target–horizon tasks, including marked short-horizon external undercoverage for wind generation. Component-wise ablation supported semantic feature partitioning and the heteroscedastic head, whereas the physics-informed features, cross-energy gate, and physics-consistency loss did not independently reduce aggregate MAE. PI-BHTF should therefore be interpreted as a reproducibly competitive framework that balances multi-horizon accuracy, structured representation, uncertainty estimation, and external transferability rather than as a universally dominant model. Full article

Reliable operation of hydro turbine-generator units (HTGUs) is central to safe, flexible, and efficient hydropower generation. State-of-the-art approaches to condition monitoring, fault diagnosis, and early fault detection increasingly rely on artificial intelligence (AI)-driven methods. However, labeled fault data remain scarce and industrial validation of these AI methods remains limited. This paper presents a systematic, algorithm-focused review of predictive maintenance (PdM) for HTGUs. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided literature search, we map data-driven and AI-enabled methods from classical machine learning through deep Autoencoders and hybrids to Transformers and state-space models (SSMs), and we compare them with respect to their interpretability, data requirements, and industrial deployability. Relative to plant-wide sensing surveys and generator-centric AI reviews, the contribution is an HTGU-wide assessment of which method families are validated on operational plants versus rotating-machinery benchmarks. Across the reviewed studies, several general patterns emerge: hydropower-specific work is dominated by unsupervised anomaly detection, classical machine learning approaches remain a strong, plant-validated baseline, and high accuracies of recent Transformer/SSM architectures should be interpreted as architectural potential on related assets, not as demonstrated HTGU deployability. We translate the identified gaps into a short-/medium-/long-term roadmap toward transferable, explainable, and industrially validated PdM. Full article

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of $0.01075 \mathrm{V}$ for HDP and $0.03534 \mathrm{V}$ for UDDS. Unseen-data validation further yields RMSE and MAE values of $0.0082$ and $0.0061 \mathrm{V}$, respectively, for HDP, and $0.0416$ and $0.0299 \mathrm{V}$, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification. Full article

Against the backdrop of rapid development in marine engineering, the construction industry faces practical challenges, such as water scarcity, limited availability of natural river sand, and high raw material transportation costs. This has led to an increasing demand for resource-efficient concrete production technologies and improved construction economic efficiency. Seawater–sea-sand concrete (SWSSC) offers a locally sourced solution that effectively reduces the construction sector’s overreliance on freshwater and river sand, lowers material transportation costs for coastal infrastructure projects, and supports marine engineering and infrastructure development along the Belt and Road Initiative. However, existing research lacks systematic organization and visualized quantitative analysis. Utilizing the CiteSpace 7.0.R0 knowledge graph analysis software, this study selects 982 relevant papers published in the Web of Science (WOS) Core Collection between 2016 and 2025 as the sample. By employing analytical methods—including annual publication volume statistics, collaboration networks among researchers, keyword co-occurrence patterns, and temporal evolution charts—we systematically delineate the overall research landscape, distribution of key research institutions, trends in research hotspots, and future frontier directions in this field. The analysis results indicate that: (1) The total number of publications in the global seawater–sand concrete field has been increasing year by year. From 2016 to 2018, it was the basic exploration period, with an average annual publication volume of less than 10. From 2019 to 2021, it was the deepening and expansion period, with research expanding from the performance of a single material to material modification and structural application. From 2022 to 2025, it was the rapid prosperity period, with the publication volume reaching its peak in 2024–2025 (208 articles and 203 articles), and the publication volume continued to rise. (2) China ranks first globally with 798 publications, but its centrality in international cooperation networks is only 0.24, reflecting low overall collaboration density and loose partnerships between institutions and authors, without the formation of cross-institutional core research teams with global leadership. (3) Research hotspots in this field primarily focus on material properties, durability characteristics, and mechanical strength, among which FRP reinforcement systems serve as a bridge for interdisciplinary research bridging material fundamentals and engineering applications, representing a key research branch. (4) From the perspective of evolutionary trends, the field exhibits three major developmental shifts from macroscopic mechanical performance characterization to in-depth investigation of microscopic damage mechanisms, from single-material studies to composite structural systems, and from short-term laboratory accelerated testing to full life-cycle performance evaluation, with the low-carbon potential of seawater–sand concrete increasingly becoming a prominent research focus. Therefore, this paper advocates strengthening international and inter-institutional academic collaboration, fostering multidisciplinary innovation, and prioritizing breakthroughs in key areas, such as large-scale intelligent performance prediction, long-term performance database development, and digital-twin-based operation and maintenance management, to facilitate the transition of seawater–sand concrete technology toward efficient, low-carbon, safe, and intelligent engineering applications. Full article

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations ($N=50$) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of $3.60\pm 0.18 \mathrm{s}$ and a path success rate of $95.8\pm 1.2\%$, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections. Full article

Current distribution in serial–parallel battery packs is commonly assumed to be uniform in the absence of cell-to-cell variations. However, in practical systems, the electrical topology and finite resistance of current-collecting busbars can introduce significant inhomogeneities even when all cells are identical. In this work, a matrix-based modeling framework is developed to analyze the current and voltage distribution in large battery packs with arbitrary serial–parallel configurations. The results reveal that the resistance of current-supplying busbars plays a dominant role in shaping current distribution, leading to pronounced current imbalance that increases with both resistance and operating C-rate. To quantify this effect, a current non-uniformity factor is introduced and used to define an illustrative criterion for acceptable operation. Based on this metric, together with a maximum-cell-voltage constraint, design maps are constructed to identify operating regions that are acceptable or critical with respect to current overload and localized overvoltage as a function of busbar resistance and charging rate. The analysis further demonstrates that topology-induced current inhomogeneity can lead to cell-level voltage divergence and localized overcharge under high-current operation. Such local effects may remain hidden when only the pack voltage or the voltage of a series-connected cell group is monitored, because conventional battery management systems (BMSs) typically do not resolve individual cell currents or local voltage drops within parallel-connected cell groups. The proposed approach enables the derivation of design-oriented constraints linking electrical performance to physical parameters such as busbar resistance and cell spacing. The resulting design maps provide a practical tool for battery pack engineering, enabling the determination of the maximum allowable busbar resistance or operating current to ensure safe, homogeneous pack operation. Full article

To address challenges associated with high-dimensional coordination and strict physical constraints in fixed-base space manipulator tool transfer under zero gravity, this paper proposes a preference-conditioned safe reinforcement learning algorithm, which is called $\alpha$PPO-LAG ($\alpha$-Preference Proximal Policy Optimization with Lagrangian). The algorithm introduces a preference factor $\alpha$ to regulate the trade-off between task performance and physical safety through constraint-aware policy optimization. Meanwhile, an adaptive Lagrangian regulation mechanism based on constraint estimation is developed to improve safety satisfaction during training. Experimental results demonstrate that with $\alpha =0.5$, the proposed method achieves an average reward of 990, outperforming PPO-LAG with 881 and CPO with 915. Furthermore, $\alpha$PPO-LAG obtains a safety score of 0.935 while reducing the contact-force violation rate to 1.0% and maintaining a low end-effector velocity violation rate of 10.0%. The empirical safety-performance analysis reveals effective operating points under different safety preferences, providing a practical solution for safe and compliant fixed-base tool transfer in zero-gravity environments. Full article

Modern power systems increasingly depend on energy storage devices to manage fluctuations in renewable generation and load demand. Coordinating multiple heterogeneous storage units in a grid-level system while enforcing individual state-of-charge (SoC) limits constitutes a complex, high-dimensional control problem that cannot be resolved by conventional proportional-sharing schemes. This work formulates the Distributed Optimal Energy Management (DOEM) problem for a grid comprising n parallel storage units with power-dependent efficiency and heterogeneous capacities. Optimality conditions are derived using Pontryagin’s Minimum Principle (PMP) and a smooth penalty function is introduced to handle hard SoC constraints without state-space discretisation. For the practically important class of lossless storage devices, an explicit closed-form control law is obtained, in which each unit is dispatched proportionally to its storage capacity. Numerical validation is performed on the Israeli power grid, modelling three pumped-hydro systems with a combined capacity of 8.0 GWh, using MATLAB/Simulink R2018b. Across the base net-load scenario and four additional load profiles, the cost achieved by the proposed method matches the dynamic programming (DP) benchmark within 1.1%, while the maximum state-of-charge violation is limited to 0.64% of total capacity at the default penalty setting. Computationally, the proposed update requires only 2.21 s for nine storage units compared to 59.30 s for DP, a 26.8-fold speedup, and scales with $O\left(n\right)$ arithmetic operations per time step. The results confirm a clear pathway to optimal, safe, and scalable real-time control of large-scale heterogeneous energy storage ensembles. Full article

In increasingly multicultural societies, preparing teachers to engage effectively with human diversity and to foster inclusive, equitable, and democratic learning environments has become a central priority for teacher education. This study examines (1) how a human diversity perspective contributes to the conceptualisation and development of intercultural and democratic competences in teacher education, and (2) how non-formal, participatory, dialogical, experiential, and heritage-based pedagogies support the development of these competences among pre-service and in-service teachers through participation in the IMAGINE programme. Moving beyond approaches that primarily frame diversity in terms of cultural difference, minority status, or racialised identities, the study conceptualises human diversity as an inherent condition of contemporary democratic societies and explores its potential as both a conceptual framework and a pedagogical resource for competence development. Drawing on established international frameworks, particularly Deardorff’s model of intercultural competence and the Council of Europe’s Reference Framework of Competences for Democratic Culture (RFCDC), the study adopts a qualitative longitudinal case study design to examine IMAGINE, an annual residential teacher education seminar implemented since 2021. The analysis explores participants’ understandings of human diversity and examines how participatory, dialogical, experiential, and heritage-based pedagogies create the conditions that support intercultural and democratic competence development across successive editions of the programme. The findings highlight the relevance of a human diversity perspective for teacher education and identify a set of interconnected pedagogical conditions that support competence development. These include engagement with minority narratives and lived experiences, dialogical and collaborative learning, emotional and experiential engagement, critical reflection, the creation of safe spaces for dialogue, and heritage-based educational experiences. Rather than operating independently, these conditions appear to reinforce one another by enabling participants to encounter different perspectives, critically examine assumptions, engage emotionally with experiences of diversity, and negotiate meaning collaboratively. The study contributes to research on intercultural and democratic teacher education in two main respects. First, it provides empirical insights into the potential of human diversity as a broader conceptual lens through which diversity may be understood as constitutive of democratic societies, thereby opening discussion on the possibility of moving beyond predominantly culture-centred understandings of diversity towards a more encompassing human diversity perspective. Second, it provides empirical insights into the pedagogical conditions through which intercultural and democratic competences can be fostered, demonstrating the potential of dialogical, experiential, collaborative, and heritage-based learning environments for the professional development of both pre-service and in-service teachers. Full article

Background: Hospital-wide centralized bed allocation, known in China as the “one-bed-for-the-whole-hospital” model, aims to improve inpatient access by pooling beds across specialties. Existing studies have mainly examined operational outcomes, process risks, or staff competence. Less is known about how bed redistribution interacts with clinical responsibility, professional roles, functional support, and shared governance. Objectives: This study explored healthcare professionals’ perspectives on the barriers, facilitators, and strategies for sustaining the hospital-wide “One Bed” model. It also examined how bed integration and care integration aligned or diverged across the dimensions of the Rainbow Model of Integrated Care. Methods: A single-center descriptive qualitative study was conducted in a Grade A tertiary hospital in Shanghai, China, where the model had operated across 11 pilot wards for approximately 48 months. Between December 2024 and March 2025, 25 healthcare professionals, including 14 clinical nurses, eight head nurses, and three physicians, completed face-to-face semi-structured interviews. Data were analyzed using inductive qualitative content analysis. After themes and subthemes were developed from participants’ accounts, the Rainbow Model of Integrated Care was used as an interpretive framework to map the findings across clinical, professional, organizational, system, functional, and normative integration. Results: Five themes were generated. Participants perceived centralized bed allocation as shortening waiting time and improving bed use, but also as intensifying ward workload and making single efficiency indicators insufficient. Patients could move to available wards before medical response, responsibility, and physician visibility were fully aligned. Cross-specialty case mixes exceeded what nurses could manage through temporary learning alone. Information, logistics, space, equipment, and supplies did not always move with patients, leaving nurses to maintain workflow through manual and often invisible coordination. Sustained bed sharing also depended on clearer boundaries for patient selection, specialty fit, severity, nursing workload, leadership authority, resources, and incentives. Together, these themes showed that bed resources were integrated faster than care processes, professional capability, functional support, and shared governance. Conclusions: Hospital-wide centralized bed allocation should be understood as an uneven process of integration rather than only as a bed-management strategy. These findings primarily reflect the perceptions and experiences of nurses and nursing managers, supplemented by limited physician input. The distinctive finding of this study is that beds may be pooled, and patients may move rapidly, while medical response, nursing competence, functional support, workload recognition, and governance arrangements do not always move at the same pace. Safe and sustainable implementation therefore requires bounded flexibility: bed allocation should be guided not only by bed vacancy but also by clinical suitability, specialty fit, nursing workload, timely medical response, functional systems that move with patients, and shared accountability. Full article

Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems. We propose a Hybrid Quantum Recurrent Neural Network (HQRNN) for jet-engine RUL forecasting on the NASA C-MAPSS FD001 benchmark. The HQRNN stacks Quantum Long Short-Term Memory (QLSTM) layers, replacing each LSTM gate’s linear transformation with a Quantum Depth-Infused (QDI) circuit; this is followed by classical dense layers. Quantum and hybrid quantum–classical methods for turbofan RUL prediction are still at an early stage. Our study is therefore among the first to evaluate a gate-based QLSTM hybrid at matched parameter counts, comparing it against classical and joint state-of-the-art models on this benchmark and complementing that comparison with a circuit-level analysis of the quantum layer. Encoding the gate signals in a quantum feature space is intended to help the network represent high-frequency degradation patterns with fewer trainable parameters than a matched classical counterpart. The HQRNN improves mean RMSE and mean MAE by about $5\%$ over matched-parameter stacked-LSTM RNNs across 10 random seeds, and attains a test RMSE of $15.46$, outperforming Random Forest, CNN, and MLP baselines. ZX calculus, Fisher information, and Fourier analyses indicate that the QDI circuit is compact, trainable, and expressive. Advanced joint deep-learning models still outperform the stand-alone HQRNN, indicating that quantum-enhanced recurrent modules are best deployed as components within composite prognostics pipelines rather than stand-alone predictors. Full article

The massive integration of “hydro–wind–solar hybrid” generation transmitted via AC/DC hybrid grids with High-Voltage Direct Current introduces unprecedented challenges to power system transient stability. Traditional Dynamic Security Assessment heavily relies on time-domain simulation and high-density Monte Carlo sampling, which suffer from massive computational burdens and are strictly prohibitive for intra-day operational dispatch. To address this long-standing bottleneck, this paper proposes a novel data-driven comprehensive security region assessment framework based on an Active Learning query strategy and a Support Vector Machine surrogate model. By seamlessly coupling Python with the DIgSILENT PowerFactory simulator, the proposed AL algorithm actively queries and evaluates only the critical operating points near the stability margin, effectively avoiding redundant simulations in obviously safe or unsafe zones. Extensive simulations under a severe N-1-1 contingency demonstrate that the proposed framework can accurately map the non-linear boundaries of both transient rotor angle and short-term voltage stability constraints. Furthermore, the framework’s scalability is rigorously validated in complex 3D high-dimensional operational spaces. By integrating an ϵ-greedy exploration strategy with a Label Flip Rate (LFR) early stopping criterion, the proposed algorithm successfully overcomes the curse of dimensionality. Quantitatively, the proposed method achieves nearly identical boundary resolution using merely 65 physical simulations, as opposed to the 40,000 evaluations required by conventional high-density grid scanning. The total computational time is drastically reduced from 28.6 h to approximately 2.8 min, yielding a remarkable acceleration of over 600 times, making it highly suitable for near-online dynamic security monitoring. Full article

Aiming at the problems of intelligent ship inductive power transfer (IPT) systems under complex marine operating conditions, such as susceptibility to parameter perturbations and power device faults, which result in low modeling accuracy, slow dynamic response and poor post-fault stability, this paper investigates an integrated full-system fault diagnosis and hierarchical fault-tolerant control strategy. Based on the complex Fourier series and generalized state-space averaging (GSSA) method, a complete nonlinear time-domain model of the IPT system is established. The high-order switching-coupled system is accurately reduced to a first-order dominant model, and the inherent over-damping characteristics of the system as well as the influence rules of relevant parameters are clarified. A PI closed-loop regulation strategy is designed, and the trade-off mechanism of proportional integral parameters regarding steady-state accuracy, response speed and fault robustness is revealed. Comparative theoretical analysis and simulation results verify that the established model is highly consistent with the dynamic characteristics of the practical system, with the steady-state error controlled within 2%. Under the open-circuit fault of power switches, the system can still maintain stable output current without instability or sharp current drop, demonstrating excellent fault tolerance. The research findings provide a theoretical basis and technical support for high-precision modeling, parameter tuning and the safe and reliable operation of wireless charging systems for intelligent ships. Full article