Search Results (4,405)

Heavy metal(loid) pollution in watersheds surrounding mining areas originates from multiple and complex sources, posing persistent threats to terrestrial–aquatic ecosystems and human dietary safety. This study systematically investigated the pollution characteristics, spatial distribution, ecological risks and human health hazards of seven typical heavy metal(loid)s (As, Pb, Cr, Cd, Cu, Zn, and Ni) in the integrated water–soil–vegetable continuum of a mining-affected watershed in Southwest China. Field sampling was carried out in three functional zones with different mining disturbance intensities, and inductively coupled plasma mass spectrometry (ICP-MS) was used to detect heavy metal(loid) concentrations in all samples. Multiple pollution evaluation indices and the USEPA human health risk assessment model were adopted for comprehensive quantitative analysis. The results showed that 44.0% of surface water samples exceeded national permissible limits, with high-pollution areas concentrated in intensive mining zones, presenting moderate overall aquatic heavy metal(loid) pollution. Although the average concentrations of seven heavy metal(loid)s in riparian soils complied with Chinese agricultural soil screening standards, localized significant enrichment was observed for As (1.98 times), Cd (4.62 times), Cu (1.81 times), and Zn (2.72 times) compared with regional background values, causing mild comprehensive soil pollution. Farmland soils exhibited prominent Cu and Zn accumulation, and leafy vegetables in the study area suffered severe Pb and Cd pollution, with potential dietary exposure risks. Health risk assessment indicated that children face higher non-carcinogenic and carcinogenic risks than adults via soil hand-to-mouth exposure; dietary intake of vegetables leads to moderate carcinogenic risks for children caused by As and Ni exposure. Overall, this study clarifies the migration and enrichment rules of heavy metal(loid)s in the water–soil–vegetable system of mining watersheds, confirms the prominent ecological and human health risks of Cd, As and Pb in the study area, and provides targeted basic data for regional heavy metal(loid) pollution prevention and food safety management. Full article

Background: Fibromyalgia is a chronic condition characterised by widespread pain, fatigue, sleep disturbances, and psychological symptoms. Mindfulness-based approaches are increasingly used as complementary interventions for symptom management. This study aimed to investigate the effectiveness of mindfulness-based yoga (MBY) delivered via telerehabilitation in individuals with fibromyalgia. Methods: This trial included 64 women with fibromyalgia who were randomly assigned to an 8-week mindfulness-based yoga program delivered via telerehabilitation or active control group including walking and physiotherapy modalities. Both groups received patient education at the outset. Assessments were conducted before and after the intervention. Outcome measures included fatigue, anxiety, depression, sleep quality, symptoms associated with central sensitization, kinesiophobia, pain intensity, mindfulness level, impact of fibromyalgia on life, biopsychosocial status, and pain catastrophising. Data were analyzed using mixed-design analysis of variance (ANOVA), with additional t-tests and analysis of covariance (ANCOVA) conducted for post hoc analyses. Results: Compared to the control group, the mindfulness-based yoga (MBY) group showed more pronounced improvements in terms of fatigue, anxiety, symptoms associated with central sensitization, biopsychosocial status, symptom severity, catastrophising about pain, ruminative thoughts about pain, and cognitive dimensions of pain. Although no significant differences were found between groups for other variables, intra-group improvements were observed in the MBY group. Conclusions: It was concluded that the MBY intervention administered via telerehabilitation is a viable complementary approach to traditional treatments in reducing the symptom burden of fibromyalgia. It was thought that the effectiveness of the research could be increased by conducting studies involving long-term follow-up assessments and investigating the integration of different mindfulness-based telerehabilitation interventions into the clinical setting. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Cities are currently facing increasing challenges related to climate change, demographic pressure, and urban expansion. In this context, urban resilience has emerged as a strategic approach to anticipate, withstand, and adapt to environmental and social disturbances. The city of Salamanca, a UNESCO World Heritage Site, has implemented several green infrastructure strategies and climate adaptation initiatives, including the Integrated Sustainable Urban Development Strategy (EDUSI Tormes+), the Special Plan for the Protection of Green Infrastructure and Biodiversity (PEPIVB), and the programs SAVIA Red Verde Salamanca and LIFE Vía de la Plata. This study assesses the contribution of these initiatives to urban governance focused on response capacity by examining their level of implementation and the coherence among different municipal planning instruments. The analysis reveals that the municipal green infrastructure framework is explicitly planned and strategically designed with the objective to mitigate the urban heat island effect, regenerate the urban fabric, and establish structural pathways targeted to foster local biodiversity pathways. Overall, the results provide evidence that nature-based territorial management instruments can strengthen the adaptive capacity of heritage cities to climate change, offering a replicable model for other territories with similar characteristics. Full article

Direct grid-connected wireless charging based on direct AC–AC conversion is attractive for electric vehicles (EVs) because it can reduce power conversion stages and improve charger compactness. In matrix-converter-based wireless power transfer (WPT) systems, the grid-frequency AC voltage can be directly converted into high-frequency AC voltage without using bulky DC-link electrolytic capacitors. However, the removal of the intermediate energy-storage stage also makes the EV wireless charger more sensitive to grid-voltage fluctuation. For an LCC-S compensated WPT system, the voltage-source output characteristic makes the charging-side voltage sensitive to grid-voltage disturbance, resulting in severe MC output-current and battery charging-current overshoot. This transient overcurrent may threaten both the power converter and the EV battery charging process. In this paper, a dual-frequency state-space model is developed for the matrix-converter-based electrolytic-capacitor-less LCC-S WPT system to analyze the disturbance propagation from the grid side to the high-frequency resonant stage and the EV battery side. Based on the model, the current-overshoot suppression capability and bandwidth limitation of the conventional dual closed-loop control strategy are investigated. To further enhance transient current protection, a grid-voltage feedforward strategy is proposed to compensate for the disturbance before severe current overshoot is formed. Finally, experimental results verify that the proposed method effectively suppresses the MC output-current and battery charging-current overshoot under grid-voltage fluctuation, thereby improving the grid-disturbance resilience and dynamic safety of direct grid-connected EV wireless charging systems. Full article

A key mechanism in the pathogenesis of cerebral small vessel disease (CSVD) is endothelial dysfunction associated with impaired metabolism of nitric oxide (NO) and its main substrate, L-arginine. The aim of the study was to assess parameters of L-arginine metabolism and their association with MRI-defined brain damage in CSVD patients. A total of 100 CSVD patients (according to MRI STRIVE standards) and cognitive impairment (CI) of varying severity, as well as 20 healthy volunteers, were analyzed. Levels of L-arginine and its metabolites—L-ornithine, L-citrulline, and asymmetric dimethylarginine (ADMA)—were measured; diffusion tensor MRI, MRI volumetry, and morphometry were performed. A threshold level of L-arginine (51.25 μmol/L) was identified, above which an association with CI was observed. Patients with L-arginine ≥ 51.25 μmol/L demonstrated poorer performance on cognitive tests (Stroop test, trail-making test (TMT)-B, TMT B–A, 10-word test) and more severe brain damage, reflected by greater severity of MRI markers (white matter hyperintensities, microbleeds), changes in brain component volumes, cortical atrophy in specific regions, and impairment of white matter microstructural integrity. The obtained data indicate a pathogenetic link between disturbances in L-arginine homeostasis and the development of CSVD with CI and support the need for further studies aimed at refining approaches to their correction. Full article

The severe deformation and failure of reused roadways due to repeated mining disturbances pose considerable challenges to roadway maintenance. In this study, field measurements were taken at the 13092 reused roadway of Zhaozhuang Coal Mine to determine the deformation characteristics of its surrounding rock. Based on the equation for the plastic zone boundary of a circular roadway under a non-uniform stress field, the distribution characteristics of the plastic zone of the reused roadway under different stress conditions were analyzed, and their associated risk levels were assessed. Furthermore, the distribution characteristics of the plastic zone at different locations under primary and secondary mining, the non-uniform evolution of the mining-induced stress field, and the deformation behavior of the surrounding rock under repeated mining disturbances were investigated using FLAC3D 7.0 numerical simulations. The following conclusions were reached: Repeated mining is the primary cause of severe deformation and instability of the surrounding rock in the reused roadway, and there are marked spatial differences in severe deformation between different locations. Under a non-uniform stress field, the distribution of the plastic zone in the surrounding rock varies markedly with the ratio of the maximum principal stress to the minimum principal stress (λ). Specifically, as the ratio λ grows, the shape of the plastic zone evolves from circular to elliptical and ultimately to a butterfly shape. Once the plastic zone becomes butterfly-shaped, further increases in λ cause rapid expansion of the plastic zone. Under repeated mining disturbances, the plastic zone of the surrounding rock can be regarded as a superposition of plastic zones induced by multiple mining activities. The stress distribution of the surrounding rock is markedly different at different locations. The ratio λ, which is the dominant factor responsible for the distinct deformation and failure modes observed in different regions, also varies spatially. Based on these findings, a grouting reinforcement control technique was proposed. The grouting timing, grouting pressure, and grouting radius were determined to formulate a practical grouting control scheme for field application. Field tests demonstrate that the proposed grouting control method effectively covers the deformation range of the surrounding rock and achieves satisfactory control performance. The results of this study are expected to provide a valuable reference for grouting reinforcement control in similar mining scenarios. Full article

Anomaly detection models trained exclusively on healthy data are widely used in wind turbine condition monitoring because failure data are scarce, heterogeneous, and often unavailable. However, these models produce anomaly indicators that are sensitive not only to fault-related degradation but also to normal operational variability, transient disturbances, and changes in loading conditions. As a result, the practical behavior of an alarm system depends not only on the anomaly detection model but also on the decision rule used to activate and maintain alarm states. This study presents a decision-oriented evaluation of persistence-based alarm confirmation in wind turbine anomaly detection. Four representative techniques are analyzed within a unified framework: Isolation Forest, One-Class Support Vector Machine, Referenced Moving Window Principal Component Analysis using Q-statistic and percentage component weight indicators, and Autoencoder-based reconstruction error. The evaluation combines controlled OpenFAST simulations of rotor unbalance under different severity and noise conditions with an industrial SCADA case study involving a documented main bearing fault. Results show that temporal persistence strongly shapes alarm outcomes across methods and datasets. Low persistence values favor early detection but promote alarms from isolated threshold exceedances, whereas moderate persistence substantially reduces false positives while preserving detection capability in severe and well-observable faults. Excessive persistence increases detection latency and missed detections, particularly for weak, intermittent, or slowly evolving fault signatures. These findings indicate that persistence-based alarm confirmation should be treated as an explicit decision-level configuration variable, rather than as a fixed post-processing or alarm-state heuristic, when designing anomaly detection systems for wind turbine condition monitoring. Full article

Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. Full article

Triboelectric nanogenerators (TENG) show significant potential in pressure sensing by converting mechanical disturbances into electrical signals positively correlated with the magnitude of the applied force, yet their development as practical pressure sensors is severely hindered by the major drawback of only detecting transient mechanical inputs. Additionally, traditional dual-mode pressure sensors have typically required complex multilayer structures and time-consuming fabrication processes. Here, a simple dual-mode pressure sensor of novel structure integrated with TENG and anodic aluminum oxide (AAO) for both dynamic and static pressure detection is proposed. Nanoporous AAO is directly grown on an aluminum substrate to simplify the traditionally complex multi-layer structure of dual-mode pressure sensors. The AAO layer serves a dual functionality by acting as an active triboelectric layer that significantly enhances the triboelectric output performance while concurrently functioning as the capacitive dielectric layer. A polydimethylsiloxane (PDMS) film is employed as the elastic counterpart to pair with the AAO substrate. The influence of PDMS thickness on the charge accumulation and extraction of the TENG mode is investigated to optimize the device output. Under optimal configurations, the streamlined Al-AAO/PDMS sensor demonstrates good sensitivity and linearity (R² > 0.99) for both dynamic triboelectric voltage (1.05 V/kPa) and static capacitance (5.56 pF/kPa) over a wide sensing range of 1–73 kPa. This dual-mode sensor effectively overcomes the transient limitation of conventional single-mode TENGs and shows significant potential for future smart tactile applications. Full article

Conventional transient stability assessment in multi-machine power systems relies predominantly on fixed thresholds, which exhibit limited adaptability to varying operating conditions and fail to provide a unified analytical framework for rotor angle and voltage stability. To address these challenges, this paper proposes an adaptive threshold warning method based on geometric algebra. A multi-dimensional unified state vector incorporating generator rotor angles, speeds, electromagnetic powers and bus voltage magnitudes and phases is constructed to map system dynamics onto a high-dimensional geometric trajectory. The second- and third-order wedge products of this trajectory are computed to quantify disturbance severity and volumetric expansion preceding instability. An adaptive threshold mechanism is established utilizing sliding window robust statistics (Median Absolute Deviation) to track the trajectory’s instantaneous dimension in real time. Validation on the IEEE 39-bus system demonstrates that the proposed method issues a warning at t = 4.90 s, achieving a detection advance of 0.30 s relative to the conventional 30° rotor angle separation threshold. The method exhibits strong noise robustness with only 40 ms warning delay under 20 dB SNR conditions, and effectively captures rotor angle–voltage coupling characteristics. The geometric algebra framework offers a unified assessment tool with distinct advantages in computational speed, adaptivity, and interpretability. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

Rock pillars in deep underground mines are subjected to complex stress environments. The combined effects of in situ stress and cyclic disturbances from mining activities lead to a redistribution of the surrounding rock mass stress field, which readily triggers instability and failure, posing severe threats to mining engineering safety. To investigate the damage mechanism of cyclic loading on rock and its weakening effect on the bearing capacity of mine pillars, this study takes limestone as the research object. A series of uniaxial compression tests were conducted on limestone specimens subjected to triaxial cyclic pre-damage, complemented by numerical simulations to further characterize the energy and deformation evolution of the damaged limestone under cyclic loading conditions. The findings are as follows: (i) Triaxial cyclic tests on limestone show that both the input energy and dissipated energy follow similar trends, decreasing rapidly in the initial stage before stabilizing. The elastic strain energy remains largely constant, with most of the input energy being stored as elastic strain energy. Under constant stress levels and cycle numbers, increases in confining pressure and frequency reduce the rock’s input energy, elastic strain energy, and dissipated energy. (ii) The peak stress of damaged limestone exhibits a positive correlation with the pre-damage confining pressure and cyclic frequency, while it decreases with an increasing number of cycles. Higher confining pressure and frequency raise the input energy, elastic potential energy, and dissipated energy at the peak stress point. (iii) Deformation and failure in damaged limestone originate from the development and propagation of localized deformation zones. Increased lateral displacement within these zones promotes the formation of macroscopic fractures. Due to significant structural heterogeneity inside the localized areas, the evolution of deformation energy is influenced by regional characteristics. (iv) Simulation results indicate that the uniaxial compressive failure of limestone involves the accumulation and propagation of micro-scale tensile cracks, which ultimately coalesce into macro-scale shear fracture surfaces. During uniaxial loading of pre-damaged limestone, newly generated cracks predominantly initiate around pre-existing cracks, with only a limited number distributed randomly. Their peak intensity shows a positive correlation with the pre-damage confining pressure. Full article

City networks refer to the connections of physical or virtual flows among cities at different spatial scales, including population migration networks, economic networks, information networks and innovation networks. This concept has gradually evolved into an important paradigm for understanding the regional spatial structures. Based on Baidu Index data within the Yangtze River Economic Belt (YREB) in China, this paper constructs an information network and investigates its connection patterns. Using social network analysis, the structural differentiation of the information network is further investigated at both the overall and subregional scales. The results show that the connection patterns of the information network exhibit an obvious hierarchical structure, with the complexity of the spatial pattern gradually increasing from the upstream to the downstream regions. Furthermore, the structural assessment results suggest that the information network is characterized by high agglomeration, high mobility, high hierarchy and low disassortativity. These findings indicate that the information network in the YREB is dominated by several highly developed core city clusters. However, the inherently closed structure resulting from these characteristics may not be sufficiently counterbalanced by low disassortativity. Under sudden disturbances, such a structural configuration may exhibit limited adaptability, delayed response capacity, and slow reorganization and learning processes, thereby weakening structural resilience. This study provides a deeper understanding of intercity relationships within the YREB and offers policy implications for enhancing structural resilience across the Yangtze River Basin. Full article