Search Results (353)

Wind forcing is a primary driver of lake circulation, yet in shallow basins it is strongly constrained by morphometry, limited depth, and aquatic vegetation. We quantified the velocity and direction of horizontal wind-driven currents in Lake Wolsztyńskie (western Poland) and assessed their spatial and vertical variability in relation to depth, wind speed, and effective fetch. Monthly field measurements (June 2019–May 2020) at eight sites showed a consistent, monotonic decline in current speed with depth across the lake. Mean circulation speed increased with wind, but the relationship was weak, indicating that local controls and non-linear response dominate over simple wind–current scaling. In macrophyte-covered littoral zones, currents were substantially attenuated relative to unvegetated sites of comparable depth. Directional analysis revealed that surface flow aligns with wind-driven transport in fewer than half of observations, while compensating (return) currents with opposing directions near the bottom are frequent. Clockwise veering of current direction with depth—expected under a classical Ekman spiral—was only intermittent, consistent with truncation of Ekman dynamics in a shallow water column and a prevailing two-layer circulation pattern. Full article

Surface-deployed wireless sensor networks (WSNs) provide a flexible platform for ocean monitoring, but ocean-current-dominant marine forcing causes persistent topology evolution, backbone distortion, and route breakage. This paper proposes an event-driven self-healing routing and topology-maintenance mechanism for drift-prone surface WSNs. The design combines dual-threshold cluster-head handover, CH-HELP backbone repair, Node-HELP member reattachment, loop-free upstream reselection, and conditional global reclustering as a low-frequency corrective layer for long-term topology degradation. Unlike fixed-round reorganization, the proposed framework prioritizes local repair and triggers global refresh only when backbone quality persistently deteriorates. Simulations driven by Taiwan Strait current-dominant flow–wind data show that the full Proposed-Hybrid method reduces the CH-disconnection rate from 8.15% in DARCR to 5.15%, whereas the local-only configuration without conditional global reclustering yields 9.13%. Conditional global reclustering further suppresses late-stage topology degradation, reducing the final-third mean CH-disconnection rate from 16.32% to 8.51% and the late-stage 95th-percentile peak from 34.43% to 17.21%. DARCR remains competitive in some late-stage metrics because of its fixed-period global reorganization. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

Soil erosion is a critical environmental issue restricting ecological security and agricultural sustainable development in China. Traditional studies have predominantly focused on physical driving factors such as hydraulic and wind erosion, while the regulatory effects of hydrochemistry on soil erosion have long been neglected. To clarify the mechanisms and regulatory processes of hydrochemistry-driven soil erosion in China, this study collected 795 relevant publications from the Web of Science Core Collection spanning from 2000 to 2025. Based on bibliometric methods, visualization software including VOSviewer 1.6.20 and CiteSpace 6.4.R1 were adopted to analyze publication trends, author distributions, research institutions, and keyword co-occurrence characteristics. The results indicated that the number of publications concerning hydrochemistry-driven soil erosion in China has increased year by year since 2000. China ranks first in total publication output, showing a dominant research position in this field. The Chinese Academy of Sciences contributed the largest number of publications among all research institutions. Keyword co-occurrence analysis over the past 25 years demonstrated that soil erosion, runoff, and water erosion serve as the core research hotspots. Further analysis revealed the regulatory mechanisms of key hydrochemical parameters (e.g., pH value, ionic strength, and dissolved organic carbon) throughout erosion processes. In-depth keyword analysis confirmed that current research on hydrochemistry-driven soil erosion in China remains at the preliminary stage, lacking comprehensive exploration of microcosmic mechanisms and systematic regulation strategies. Therefore, intensified research efforts and optimized regulatory frameworks are urgently required in future studies. This study can provide theoretical foundations and technical references for improving the understanding of erosion driving mechanisms and enhancing soil erosion management efficiency across diverse regions of China. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

Eastern Boundary Upwelling Systems (EBUSs) are among the most productive marine biomes globally, renowned for their substantial pelagic fisheries. While the role of wind-driven upwelling in stimulating primary production is well-documented, the integral contributions of the marine benthos in maintaining ecosystem productivity and fishery yields are often underrepresented. This article analyzes evidence from the Humboldt, California, Benguela, and Canary Current systems to delineate the critical functions of the seabed and its resident communities. Three primary pathways through which the benthos supports fisheries are described: (1) by facilitating the efficient regeneration of nutrients from sedimenting organic matter, thereby replenishing the inorganic nutrient pool for subsequent primary production; (2) by providing essential habitat structure that supports the life history of a myriad of species, including demersal and coastal fish species, serving as nursery and feeding grounds; and (3) by forming the foundational trophic base for benthic-feeding fishes and invertebrates of commercial importance. By comparing system-specific characteristics, such as the influence of oxygen minimum zones on benthic community structure, the integrity of the benthic subsystem as a fundamental determinant of the productivity and sustainability of EBUS fisheries is demonstrated. A holistic management approach that includes benthic habitat conservation is therefore paramount. Full article

Understanding the respective roles of thermal and athermal effects during electric current treatment is critical for advancing current-assisted processing of metallic materials. In this study, strain hardening in cold-rolled A6061 was effectively relieved using high-density pulsed electric current. By conducting comparative experiments under room-temperature and liquid-nitrogen conditions, the thermal and athermal contributions were quantitatively evaluated. The results indicate that thermal effects dominate over athermal effects in dislocation density reduction and strain-hardening relief. Nevertheless, the athermal effect, driven by electron wind force, is capable of promoting dislocation motion and annihilation. This work provides a practical framework for evaluating thermal and athermal contributions and offers new insights into microstructure control via electric current, with implications for the design of advanced structural materials. Full article

High-temperature weather increases the failure rates of distribution lines and distribution transformers, thereby affecting the safe operation of urban distribution networks. To address this issue, a hybrid risk situation prediction framework combining model-driven and data-driven approaches is established based on analyses from both distribution lines and distribution transformers. First, from the model-driven perspective, the region is partitioned into grid units, and surface temperature is estimated based on ambient air temperature. The failure mechanisms of distribution equipment under high-temperature conditions are then analyzed. By integrating transformer winding temperature and line current-carrying capacity, a fault model for distribution network equipment is constructed. Second, the failure probabilities obtained by the Weibull proportional hazards model are introduced into the CNN–BiGRU–Attention network as prior risk features. Meanwhile, multiple environmental and geographic feature variables are collected from a data-driven perspective, which effectively integrates physical mechanism analysis and data-driven risk temporal prediction to complete risk situation forecasting. Finally, a case study is conducted by applying the IEEE 33-bus system to a specific region, verifying the prediction accuracy of the proposed method. The results demonstrate that the CNN–BiGRU–Attention model is capable of effectively performing risk situation prediction and can provide valuable support for operators in preventing potential fault risks. Full article

Marine renewable energy systems, including offshore wind, tidal, and wave technologies, are central to global decarbonisation strategies but remain constrained by reliability-driven costs and uncertainty in long-term structural performance. Existing qualification approaches are largely based on empirical methodologies and deterministic safety factors that inadequately capture coupled degradation mechanisms operating in harsh offshore environments. This review presents a mechanism-based perspective on structural integrity in marine renewable energy systems by linking microstructure-sensitive deformation and damage processes with engineering-scale reliability assessment. Key degradation mechanisms, including corrosion–fatigue, hydrogen embrittlement, wear, and manufacturing-induced variability, are critically examined together with their interactions across multiple length scales. The review synthesises recent advances in multiscale modelling frameworks spanning crystal plasticity, damage mechanics, fracture mechanics, probabilistic reliability methods, and digital twin technologies. Particular emphasis is placed on the role of manufacturing variability, inspection-informed updating, and hybrid physics–data approaches in improving predictive capability and reducing uncertainty. The review identifies major limitations in current offshore qualification practice, including uncoupled degradation assumptions, insufficient representation of manufacturing effects, and limited integration of monitoring data within lifecycle assessment. Building on these findings, an integrated framework is proposed that combines multiscale modelling, manufacturing-aware qualification, adaptive inspection, and digital twin-enabled updating to support predictive and reliability-informed structural integrity assessment for next-generation marine renewable energy systems. Full article

Grain-oriented (GO) silicon steel cores in low-voltage current transformers suffer magnetic degradation from residual stress and increased dislocation density during slitting and winding. This study addresses the gap in systematic optimization of secondary annealing on assembled toroidal cores using a 3² full-factorial design varying temperature (650, 850, 1050 °C) and holding time (60, 90, 120 min) on M4 grade cores. Results showed temperature is the dominant factor, while holding time exhibits a synergistic non-linear effect. The optimal condition (850 °C, 90 min) reduced specific losses from 0.85 W/kg to 0.43 W/kg (49% reduction). Mechanistic analysis confirmed this improvement is driven by complete primary recrystallization (equiaxed grains ~50–60 µm), dislocation annihilation (~10 HV hardness reduction), and reinforcement of the Goss texture ({110} <001>). SEM, EDS, and ICP-OES demonstrated that the Carlite coating remained dimensionally (1.67–1.83 µm) and chemically stable, with beneficial decarburization. Temperatures above 850 °C caused magnetic deterioration due to excessive grain growth. These results provide a validated, industrial framework for recovering magnetic efficiency in wound toroidal cores without compromising coating integrity. Full article

Accurate spatiotemporal forecasting of sea surface temperature (SST) makes a great difference to offshore wind power development, since SST is a crucial factor influencing wind field patterns. In this work, a remote sensing-driven, multi-parameter field-coupled co-forecasting approach is proposed to utilize the cross-field interaction mechanisms among different physical fields to enhance forecasting performance. With this approach, more than one physical field can be simultaneously forecasted, thus improving forecasting efficiency. Compared with pure SST forecasting cases, the advanced enhancement of SST forecasting performance based on this approach is achieved by coupling SST with geostrophic current (GC) in data-driven forecasting. Also, both the spatiotemporal SST and GC fields are demonstrated to be accurately forecasted simultaneously. In addition, the causal effects between SST and GC are demonstrated as a reliable factor for evaluating the coupling scheme. To further improve co-forecasting performance, an exponential cross-entropy loss function is proposed for multi-physical field co-forecasting scenes, and shows more satisfying performance than a classical cross-entropy loss function. The results demonstrate that the data-driven multi-physical field-coupled co-forecasting approach is an advanced, highly efficient method that can accurately forecast more than one physical field at the same time. Full article

The stability and functionality of offshore wind energy systems depend critically on how offshore platforms interact with the geotechnical features of the seabed. This review describes developments in five areas: (i) offshore geotechnical site investigation and strength assessment; (ii) seabed geohazard causes and deep-water mooring challenges; (iii) frameworks for seabed modeling; (iv) sediment behavior influencing anchor and mooring performance; and (v) selection of anchors based on their interactions with various soils. The review emphasizes developments in seabed assessment and modeling using field, lab, and numerical methods. It discusses how the new advances in analytical and simulation frameworks have enhanced our knowledge of anchor–mooring responses, cyclic loading behaviors, and soil–structure interactions under changing seabed conditions. The key findings reveal that: (1) cyclic loadings considerably change anchor holding capacity and evolution of seabed trenching, yet most existing design methods still use quasi-static loads; (2) site-specific data from integrated geophysical–geotechnical surveys are vital to reduce uncertainty in anchor penetration and the frictional resistance of chains; (3) geohazards, such as shallow gas, marine landslides, and seabed erosion, pose under-recognized risks to long-term anchor reliability. The lack of knowledge on the coupled, long-term evolution of the seabed–anchor–mooring line system is identified as another gap in the literature. Major gaps exist in validating the life cycle of anchor performance under real-scale storm–wave sequences for offshore geotechnical risk management in layered soils. At the end of the discussion, the current study also highlights the need for flexible, data-driven frameworks that integrate geotechnical, hydrodynamic, and structural analyses in a coupled framework to improve reliability in next-generation offshore wind energy systems. Full article

Shallow lakes in the Yangtze River Delta are characterized by fragile ecosystems, strong sediment–water interactions, and poor resistance to pollution shocks; they are prone to shift from macrophyte-dominated clear-water states to phytoplankton-dominated turbid states under intensive human disturbance. To improve the efficacy of aquatic ecological restoration, this study takes a typical shallow urban lake—Kuilei Lake in Kunshan—as the research object, and establishes a two-dimensional hydrodynamic and water quality model to simulate the temporal and spatial variations in flow fields, flow circulations, and water quality indicators (TP, NH₃-N, COD_Mn) throughout the year. The results are as follows: (1) The hydrodynamic regime of Kuilei Lake is dominated by wind-driven currents, with seasonal flow circulations regulating pollutant migration and the suitability for submerged macrophyte growth; (2) Intense circulations in summer (July–September) enhance sediment resuspension and endogenous nutrient release, which are unfavorable for submerged plant colonization; (3) April–June is the optimal window for ecological restoration, with a mean flow velocity of 2.0–2.5 cm/s, TP ≤ 0.06 mg/L, NH₃-N ≤ 0.20 mg/L, COD_Mn ≤ 3.0 mg/L, and water temperature of 15–25 °C, providing favorable thresholds for submerged macrophyte recovery. This study reveals the coupled hydrodynamic–water environmental thresholds for shallow lake restoration, and offers a scientific basis for flow field regulation and ecological reconstruction of shallow lakes in the Yangtze River Delta. Full article

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces. Full article

Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances. Full article