Search Results (2,172)

Multi-UAV cooperative search with static unknown targets requires both efficient regional allocation and responsive local maneuvering. However, single-level learning methods often suffer from redundant coverage, unclear division of labor, and unstable training. This paper proposes a high- and low-level decoupled reinforcement learning method for multi-UAV cooperative search. The high level periodically generates UAV-specific regional goals from visitation maps, target-existence belief maps, and UAV positions, while a spatial self-attention module enhances the representation of unvisited regions, high-belief target areas, and UAV distributions. The low level performs discrete steering actions based on local observations and high-level contexts, supported by a structured reward that encourages coverage, target discovery, goal-oriented progress, repeated-visit suppression, and boundary-safe motion. Simulation experiments are conducted in a two-dimensional grid environment with static targets and ideal sensing. Under this simplified simulation setting, the proposed method achieves higher training return and coverage rate than representative baseline algorithms while maintaining a high final target discovery rate and reaching the discovery threshold earlier. Ablation and visualization results further demonstrate the effectiveness and interpretability of the proposed hierarchical guidance mechanism within the considered simulation scenario. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

An automated CFD-based workflow for the design, evaluation, and comparative optimization of 2D tidal-stream turbine blade sections is presented for early-stage design exploration. The workflow is intended to efficiently derive an improved section using a consistent and higher fidelity evaluation approach, which is particularly relevant for floating tidal concepts where the effective angle of attack can vary. HEEDS is used to manage a SHERPA optimization loop, while candidate geometries are regenerated in Rhino Grasshopper through a control point parameterization with thickness bounds and smooth interpolation. STAR-CCM+ simulations are executed in an automated manner and the resulting lift and drag responses are returned to HEEDS to evaluate performance over four representative angles of attack, 0, 3, 6, and 9 deg. A total of 1000 design evaluations are conducted for a baseline NACA 63–815 section at Reynolds number 1 × 10⁷, using a two metric formulation that targets high mean lift to drag ratio while limiting the maximum drag coefficient within the same angle set. The optimization history shows rapid early improvement followed by a plateau and identifies a final best design at Design 746. Compared with the original section, the optimized section increases lift and improves the lift-to-drag ratio across the operating range, while keeping the peak drag constrained. Cavitation inception characteristics also improve, with the optimized section delaying inception at the same lift criterion and sustaining a cavitation free state at higher lift for the same cavitation number. Pressure coefficient distributions indicate that these changes are primarily associated with altered suction side loading in the front to mid chord region and modified pressure recovery behavior. A preliminary full 3D RANS CFD rotor comparison under a prescribed rotor geometry further shows that the optimized section can improve rotor power performance in the main operating TSR range, although the benefit becomes limited at high TSR. Full article

The blast furnace–basic oxygen furnace long process is the dominant steel production route in China. Increasing the scrap ratio is an effective way to reduce cost and carbon emissions, and scrap preheating is a key technology to achieve a high scrap ratio. To improve the low thermal efficiency and poor deep-bed melting performance of converter gas-based scrap preheating, an innovative process using oxygen-enriched combustion in a hot metal ladle is proposed. Numerical simulation is essential for capturing the complex multiphysics phenomena, as real-time monitoring of melting inside the packed scrap bed is extremely difficult. In this study, a novel multiphysics approach based on a User-Defined Function (UDF) is developed to dynamically track the progressive melting of the scrap skeleton, overcoming the key limitation of conventional enthalpy–porosity models that cannot capture the feedback between phase change and porous medium property evolution. A three-dimensional transient model was established, integrating turbulent combustion, gas–solid convective heat transfer in porous media, and solid–liquid phase change. The effects of impact pit depth, scrap porosity, and converter gas flow rate on temperature distribution, melting behavior, and thermal efficiency were systematically investigated. Results showed that porosity had the strongest influence; thermal efficiency increased from 33.92% to 65.59% as porosity rose from 0.6 to 0.8, due to a transition from conduction-dominated to coupled convection–conduction heat transfer. Converter gas flow rate exhibited a non-monotonic effect, peaking at 3688.14 m³·h⁻¹, highlighting a trade-off between energy input and gas residence time, while impact pit depth showed a limited effect with diminishing returns. A 600 s full-process simulation revealed stage-dependent melting, and the initial phase was crucial for process optimization. The optimal condition, with a pit depth of 64 cm, porosity of 0.8, and converter gas flow rate of 3688.14 m³·h⁻¹, achieved a 1.23% melting fraction and 65.59% thermal efficiency within 120 s. These findings clarify the combined roles of geometric confinement, permeability, and energy-residence time interactions, providing guidance for industrial scrap preheating design. Full article

Decision-making is a critical module for intelligent vehicles to achieve safe and efficient autonomous driving. However, most existing reinforcement learning-based decision-making methods optimize policies by maximizing the expected return, which may inadequately account for low-probability but high-cost safety risks in complex traffic interactions. To address this issue, this paper proposes a Risk-Sensitive Distributional Proximal Policy Optimization (PPO) method, termed Risk-Sensitive Distributional Proximal Policy Optimization (RSDPPO), for highway lane-changing decision-making. Within the PPO framework, a distributional state-value function is introduced to model the return distribution under the current policy, and a Wang distortion-based risk measure is further incorporated to construct a risk-sensitive advantage function. In this way, risk information contained in the return distribution can be propagated into the policy gradient update, guiding the learned policy to avoid high-risk driving behaviors while maintaining training stability. Simulation experiments are conducted in a highway lane-changing scenario with heterogeneous surrounding vehicles. The results show that, under medium-density traffic, the proposed method outperforms representative baseline algorithms in cumulative reward, success rate, and safety reward. Further evaluation under higher-density traffic demonstrates that RSDPPO maintains better overall performance, indicating stronger adaptability to denser traffic conditions. Ablation studies further show that risk-averse distortion improves the balance between safety and efficiency by increasing safety margins during car-following and lane-changing maneuvers. These results indicate that RSDPPO provides an effective risk-sensitive policy optimization framework for safety-oriented highway lane-changing decision-making. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

Background: Population ageing increases the burden of chronic diseases, multimorbidity, and functional limitations, making nutrition and lifestyle important modifiable determinants of healthy ageing. Universities of the Third Age (U3A) provide an educational and social environment for older adults, but multidimensional relationships between nutrition knowledge, diet quality, lifestyle, and health status in this population remain insufficiently characterized. This study aimed to assess these associations among older adults attending U3A in Poland. Methodology: A cross-sectional online survey was conducted between January and April 2026 among community-dwelling older adults participating in U3A programs. Of 700 distributed invitations and 520 returned questionnaires, 450 complete and eligible responses were included. The questionnaire was based on the KomPAN^® framework and expanded with items on health, lifestyle, psychosocial resources, barriers to healthy eating, and sources of health information. Diet quality was assessed using the pro-Healthy Diet Index, non-Healthy Diet Index, and overall Diet Quality Index (DQI). Nutrition knowledge was measured using a 24-item scale. Analyses included distributional diagnostics, non-parametric group comparisons, FDR-corrected Spearman correlations, psychometric assessment, principal component analysis, multivariable regression with model diagnostics, and profile segmentation. Results: The mean age was 73.63 ± 5.73 years, and most participants were women. The median DQI was 15.59 [3.93–24.86], with a predominance of neutral diet quality. Nutrition knowledge was moderate, with a median score of 12.00 [9.00–15.00], and the scale showed very good internal consistency. PCA identified three dietary patterns: convenience/ultra-processed, prudent/health-promoting, and traditional meat-and-fat. Higher DQI was associated with better nutrition knowledge, greater physical activity, a more favorable sleep profile, regular meal timing, and lower disease burden. Participants with multimorbidity had significantly lower DQI. Segmentation distinguished a health-engaged/higher-resource profile and a lower-resource/nutritionally vulnerable profile. Conclusions: U3A participants in Poland are educationally and socially active but nutritionally heterogeneous. The predominance of neutral diet quality, moderate nutrition knowledge, and identifiable knowledge gaps indicates the need for targeted, practical, and behavior-oriented nutrition education supporting healthy ageing. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Numerous construction enterprises have insufficient efficiency in resource utilization for construction and demolition waste (CDW), restricting global circular economic development. How to improve resource utilization has become an urgent problem. While existing studies have extensively explored operational decisions in CDW resource supply chains, insufficient attention has been given to construction enterprises’ information misreporting and its interaction with on-site conversion efficiency. This paper aims to elucidate the mechanism of action of misreporting and systematically analyzes its effects on the pricing decisions of the CDW supply chain. Drawing on information misreporting theory, this study constructs a Stackelberg game model involving construction firms and recycled building materials manufacturers, and compares supply chain decision-making behaviors under two scenarios: information misreporting and honest disclosure. The main conclusions are as follows: (1) misreporting alters recycled building material pricing and profit distribution by affecting manufacturers’ supply capacity expectations; (2) higher on-site conversion efficiency enhances CDW treatment ability and affects stakeholders’ profits; and (3) misreporting is related to on-site conversion efficiency and onsite conversion costs—enterprises prefer misreporting for short-term gains under low on-site conversion efficiency or high costs, while higher on-site conversion efficiency makes truthful disclosure conducive to long-term stable returns. This paper reveals the CDW supply chain decision-making mechanism from enterprises’ perspective, providing a new theoretical basis and practical value for CDW utilization and supply chain optimization. Full article

This paper analyzes a single-server queueing system with infinite capacity, where arrivals follow a Markovian arrival process and service and repair times are modeled by phase-type distributions. The service mechanism is two-tier: every customer undergoes a mandatory primary service, after which an optional secondary service is available upon request. When the system is empty, the server initiates a closedown process before taking successive multiple vacations; upon return, the server goes through a setup process before beginning service again. Service can be interrupted by random breakdowns in either mode, triggering a phase-type repair. Matrix-analytic methods are used for the steady-state analysis, yielding the stability condition, stationary probability vectors, busy period analysis and key performance measures. A cost analysis framework is also developed. Numerical experiments validate the analytical results and illustrate the practical applicability of the model. Full article

This study presents the preliminary development of a clustered hybrid propulsion module, and its experimental validation from static motor characterization to dynamic 1-D vertical drop tests to assess the feasibility of a hybrid propulsion system for soft-landing applications. The research progresses from preliminary design of core components (such as fuel, oxidizer supply system, engine configuration), to the performance verification of the clustering module. First, the trade-off between high regression rates and mechanical integrity was evaluated for paraffin-based fuels. However, high-density polyethylene (HDPE) was utilized as the baseline to ensure predictable combustion behavior. Second, cold flow tests of the designed multi-port manifold demonstrated a highly uniform oxidizer distribution, validating the geometric design with a maximum spatial pressure deviation of 2.44% across the four engines. Third, static fire tests confirmed robust dynamic control capabilities, successfully throttling the average chamber pressure from 100% (7.00 bar) down to 43% (3.01 bar) and back to 100% (7.01 bar) with a transient response time of approximately 0.6 s. Finally, the 1-D vertical drop test validated the operational readiness of the system; the open-loop thrust modulation successfully counteracted the module’s dynamic weight, achieving a terminal descent velocity of 1.46 m/s, which strictly satisfies planetary soft-landing safety criteria. These results demonstrate the feasibility and performance of clustered hybrid propulsion systems for planetary exploration, extending to surface launch technology for sample return missions from the Moon and Mars, and precision booster recovery for small launch vehicles. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

Financial instability is traditionally measured using indicators such as volatility levels, financial stress indices, or forecast errors, limiting the ability to capture the state-conditional and distributional properties of market dynamics. In this study, financial instability is reformulated as deviations from the conditional return distribution under the prevailing macro-financial state. To operationalize this formulation, a latent macro-financial state is estimated using a Dynamic Factor Model and integrated with KOSPI returns through an AI-based conditional density modeling framework consisting of a Conditional Time Variational Autoencoder combined with a state-conditional spline-flow density. Financial instability is then measured as the negative log-likelihood of the observed return under the estimated conditional density. The resulting index aligns with established benchmarks such as the CBOE Volatility Index and the South Korea Financial Instability Index, while capturing state-dependent distributional abnormalities that are not fully reflected in conventional volatility-based measures. It exhibits heightened sensitivity to periods of acute financial stress and identifies state-dependent anomalies that remain largely undetected by existing indicators. The proposed framework establishes a probabilistic and distribution-aware interpretation of financial instability, providing an interpretable foundation for sustainable financial risk management and long-term financial resilience beyond traditional volatility-based approaches. Full article