Search Results (2,183)

Composite hydrogen storage vessels exhibit pronounced anisotropy, multilayered winding architectures, and strong ultrasonic attenuation, which severely degrade the focusing accuracy and defect visibility of the conventional isotropic total focusing method (TFM). To address these challenges, this study proposes an enhanced TFM framework for defect inspection in composite hydrogen storage vessels by integrating anisotropic delay correction, Gray-code coded excitation, and coherence-weighted reconstruction. First, an anisotropic propagation delay model is established using forward ray tracing to compensate for beam deviation and focusing mismatch induced by the anisotropic winding structure. Then, Gray-code excitation and pulse compression are introduced to improve signal energy and echo detectability under high-attenuation conditions. Finally, coherence-weighted imaging is applied to suppress incoherent background noise and structural artifacts, thereby enhancing defect contrast and image readability. The proposed method is validated on hydrogen storage vessel specimens containing artificial defects, with CT results used as references. Experimental results show that, compared with conventional isotropic TFM, the proposed collaborative approach significantly improves defect imaging quality for defects of different sizes and depths. The signal-to-noise ratio is increased from 7.2, 12.8, 14.8, and 7.4 dB for isotropic TFM to 32.5, 29.9, 52.6, and 42.7 dB, respectively, for the combined anisotropic, coded-excitation, and coherence-weighted TFM. In addition, the defect depth estimation remains stable and agrees well with the CT references, yielding approximately 9.0–9.6 mm for shallow defects and 18.7–19.3 mm for deeper defects. These results demonstrate that the proposed method can effectively improve defect detectability, image contrast, and depth characterization for embedded delamination-like artificial defects in composite hydrogen storage vessels, providing a promising ultrasonic imaging strategy for thick-walled anisotropic composite pressure structures. Full article

The reliability of dry-type transformers depends heavily on the coil winding process, which currently lacks effective automated monitoring and relies on error-prone manual inspection. Deploying vision-based monitoring on edge devices faces challenges due to complex industrial backgrounds, target occlusions, and strict lightweight processing requirements. To address these issues, we propose LCD-YOLOv11, a lightweight object detection model built upon the YOLOv11n architecture. First, a Lightweight Ghost Coordinate Attention (LGCA) block is introduced into the backbone to reduce computational redundancy while enhancing spatial localization. Second, a CA2RAFE operator replaces standard upsampling in the neck to improve complex boundary reconstruction. Finally, a dynamic detection head (Dy-Detect) is implemented to decouple the parameter scale from the computational cost. Evaluated on a custom dataset, LCD-YOLOv11 achieves an mAP@50 of 0.841, representing a 1.8% improvement over the baseline. Crucially, it significantly reduces the parameter count by 11.58% (to 2.29 M) and the computational load by 20.63% (to 5.0 GFLOPs). LCD-YOLOv11 achieves an effective accuracy–efficiency balance, providing a highly deployable and robust solution for automated compliance verification in industrial manufacturing. Full article

Local community knowledge is essential for understanding climate change impacts, as it provides valuable indicators to anticipate climatic behavior and assess risks through adaptation strategies. Surveys and participatory workshops were conducted with 52 producers to identify local climate change indicators and analyze implemented adaptation measures. A systematic indicator framework identified five main indicators: rising temperatures (30% of mentions), strong winds (25%), and three indicators with 15% each: increased rainfall, sudden changes in temperature, and seasonal variations. These indicators were validated against instrumental climate records, confirming the capacity of local knowledge systems to detect gradual and extreme climatic shifts. Rising temperatures stand out as the indicator with the greatest impact. Regarding adaptations, watershed reforestation emerges as the most implemented measure (22.2% of mentions), especially in response to increased temperature and rainfall. The social component shows the highest number of adaptive strategies, with incremental measures predominating. In contrast, soil and agriculture components show the lowest number of implemented measures. Strong winds are considered difficult to control, resulting in less adoption of adaptive strategies. The agricultural component records the lowest percentage of transformational adaptations. Structural barriers, including limited access to technical support, credit systems, and informal land tenure arrangements, constrain the transition toward transformational adaptation, leaving communities reliant on incremental responses that address immediate risks but fall short of reducing long-term vulnerability. These findings underscore the need to integrate traditional knowledge with scientific approaches within institutional frameworks that explicitly address governance gaps to develop sustainable and contextually relevant adaptation strategies. Full article

Atmospheric nitrogen dioxide (NO₂) is an important component of reactive nitrogen and plays a key role in the atmospheric nitrogen cycle outside major emission regions. However, its variability under remote background conditions remains poorly characterized, as most observational studies focus on urban or continental environments. This study investigates the background variability of in situ NO₂ measurements at a remote North Atlantic island (Azores) over the period 2015–2024 and examines its association with large-scale atmospheric transport regimes. Monthly NO₂ concentrations were classified into background Atlantic conditions and months classified under enhanced transport conditions using an objective PM₁₀ percentile-based criterion. Differences between regimes were assessed using non-parametric statistics. Although median NO₂ concentrations were slightly higher during months classified under enhanced transport conditions, the difference was not statistically significant. Wind speed analysis for the overlapping period 2018–2024 also indicated higher values during these months, but these differences were likewise not statistically significant. These results indicate that, at a monthly resolution, the influence of enhanced transport conditions on NO₂ at this remote marine site is weak and not statistically resolved by the present approach. The findings therefore provide limited statistical support for a transport-driven modulation of NO₂ and instead highlight the difficulty of detecting subtle reactive-nitrogen signals in clean marine environments. These findings contribute to improving the interpretation of reactive nitrogen variability in remote marine settings and highlight the value of island observatories for studying the atmospheric nitrogen cycle. Full article

Accurate validation of spaceborne lidar data is fundamental for reliable quantification of aerosol vertical distributions, which strongly influence air quality and climate effects. This study presents a comparative analysis of aerosol profiles from the 532 nm High-Spectral-Resolution Lidar (HSRL) onboard China’s DQ-1 satellite (ACDL) and ground-based observations from the Asian Dust and Aerosol Lidar Observation Network (AD-Net). Using one year of measurements under minimized spatiotemporal mismatches at three representative coastal stations (Matsue, Tokyo, Hedo), we quantify the sources of observational differences. Results show that discrepancies in detection targets (aerosols/clouds) dominate the total variance (>75%), while instrumental differences contribute 10–25%. Horizontal wind speed, particularly its north–south component, correlates more strongly with discrepancies than vertical wind speed, except in high-concentration aerosol layers where vertical motions become influential. Furthermore, larger differences are associated with increased aerosol extinction coefficients (α) and particle depolarization ratios (δ). This work demonstrates that integrated applications of multi-platform lidar data must account for both meteorological controls on aerosol transport and particle microphysical properties. These findings provide a quantitative validation framework for current and future spaceborne HSRL missions and support the integrated application of multi-platform lidar observations in regional aerosol monitoring, air quality assessment, and climate effect research. Full article

Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization. Full article

Solar radio bursts at very low frequencies are key phenomena in the Sun–Earth space environment, providing crucial diagnostics of the acceleration and propagation of solar wind, coronal mass ejection (CME), and non-thermal energetic particles and serving as important indicators for space weather forecasting. To meet the demand for rapid screening of burst events in large-scale observational datasets, we present an end-to-end automatic detection and evaluation framework tailored for Type III bursts, built upon long-term radio dynamic spectra from STEREO-A/SWAVES. We formulate radio burst detection as a one-dimensional interval localization task along the time axis and, in view of the scarcity of annotated samples, cast it as a few-shot object detection task. Building upon the Faster R-CNN architecture with a ResNet50-FPN backbone, we propose the Meta-FSOD framework, which adopts an episodic training paradigm to construct support–query episode pairs. The framework incorporates a metric-guided prototype learning branch to semantically align and calibrate region-of-interest (RoI) features via class prototypes, and integrates a dynamic Beta-Gating mechanism coupled with Soft-NMS to effectively suppress false positives while preserving high-recall performance. Experimental results demonstrate that, despite being trained on a significantly smaller dataset than comparable studies, Meta-FSOD achieves competitive performance, closely matching that of conventional supervised model. The proposed framework exhibits strong cross-temporal generalization capabilities and holds considerable potential for engineering applications in deep space exploration missions. Full article

Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent diagnosis method based on pulse-coupled injection. At the hardware level, a semi-ring capacitive coupling sensor is developed and designed, which realizes non-contact injection of high-frequency pulse signals and high-SNR extraction without a power outage. The reliability of the system under complex working conditions is verified by field experiments on multiple actual 110 kV transformers. At the algorithm level, an innovative MSCNN–Transformer–PGA deep composite model fused with prior electromagnetic physical knowledge is constructed and combined with the transformer equivalent circuit model. The model uses a multi-scale convolution to extract local details of frequency response signals, adopts Transformer to establish the global sequence dependence, and introduces a Physics-Guided Attention mechanism (PGA) to adaptively focus on the key fault physical frequency bands. The experimental results show that the proposed method effectively overcomes electromagnetic noise interference, and the fault classification accuracy of single-modal pulse frequency response data reaches 97.6%, providing a high-precision online monitoring solution for the safe operation and maintenance of transformers. Full article

In urban environments, large buildings influence air quality in their surroundings by altering natural wind patterns, obstructing airflow or creating high-velocity wind tunnels, often resulting in stagnant zones that trap pollutants. Furthermore, the extensive shadows cast by these structures reduce ground-level temperatures. For urban planners, accounting for these aerodynamic, thermal and air quality effects is important to fostering healthier, more livable cities. In this work, measurements assessing how shadow and micrometeorological conditions—driven by the proximity of large buildings—influence PM_2.5 levels were conducted in an urban commune of Santiago, Chile, during the winter and spring seasons. This commune is characterized by a mixture of one-story houses and high-rise buildings. PM_2.5 and meteorological parameters were measured outside three pairs of houses in winter of 2021, one of which received shadow from a nearby building and the other was under the sun. In one pair of houses, PM_2.5 concentrations were elevated in the shaded site exclusively during the winter months. This was attributed to shadow-induced temperature reductions, which likely increased local atmospheric stability and inhibited pollutant dispersion. However, this effect was limited to periods of low wind speed; during the spring, the transition to a higher wind speed regime facilitated sufficient mechanical mixing to neutralize the thermal influence of the shadow, resulting in no detectable difference between the sites. In another pair of houses, the result was attributed to the difference in wind speed in one of the houses, because the building acts as a windbreak, no shading effect were observed. Regarding the third pair of houses, no significant impact on PM_2.5 concentrations was observed in the whole period. This lack of variation is likely attributable to the absence of substantial micrometeorological differences between the two sites. Full article

Identifying the sources of Ultra-High Energy Cosmic Rays (UHECRs, $E>{10}^{18}$ eV) remains a fundamental challenge in astrophysics due to the significant deflections of charged particles by Galactic and extragalactic magnetic fields. Until now, dozens of events with energies over ${10}^{20}$ eV—Extreme Energy Cosmic Rays (EECRs)—were detected by the Pierre Auger Observatory and Telescope Array, but none of them showed a statistically significant association with potential sources. In this study, we investigate potential sources of EECRs with arrival directions from Local Void region. Since the energy loss lengths of such EECRs are of order of 20–40 Mpc, i.e., smaller than the Local Void extension (∼60 Mpc), potential sources should be predominantly Galactic ones. Since the most promising UHECR accelerators are mildly relativistic shocks, we consider Galactic microquasars, magnetars, and pulsar wind nebulae as potential sources of EECRs in the Local Void sky region. Using event-by-event reconstruction of trajectories of detected EECRs via CRPropa backtracking in the Galactic magnetic field, we find the potential Galactic sources and corresponding charges Z for some of the detected EECRs. The most promising coincidence is found between the EECR event triplet detected by PAO and TA and SGR 1900+14, a Galactic magnetar exhibiting high-energy flaring activity, with the inferred propagation time delay being consistent with the characteristic age of the magnetar. 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

High-Frequency (HF) radar remote sensing offers a unique capability to detect mesoscale air-sea interactions under strong monsoon conditions. This study leveraged HF radar-derived surface currents, buoy observations, and reanalysis data to systematically investigate the driving mechanism of the sea–land breeze (SLB) on surface currents in the Taiwan Strait during the strong winter monsoon. To address the challenge of extracting weak signals from a dominant background flow, we employed the Separation of the Regional Wind Field (SRWF) method and the complex demodulation spectrum shifting technique. The results demonstrate that HF radar observations confirm the presence of regular SLB activity even under the strong monsoon, with its intensity modulated by the land–sea temperature difference influenced by cloud cover. Spatial correlation analysis reveals that the SLB significantly drives diurnal variations in the surface current, with its impact extending up to 110 km offshore and a maximum amplitude of approximately 2.2 cm/s. Additionally, the analysis reveals that the duration of SLB events critically influences the current response: events lasting 7 days produce a stronger and more spatially coherent correlation with the diurnal currents than shorter 5-day events. Furthermore, harmonic analysis indicates that the SLB’s energy primarily affects the non-tidal residual current, with no significant impact on the principal diurnal tidal constituents ($O_1$, $K_1$). This work not only quantifies the SLB-current coupling during sustained SLB events in a strong monsoon regime but, more importantly, demonstrates the capability of HF radar remote sensing for resolving weak signals in complex, high-energy environments, providing a robust methodological framework and valuable insights for regional marine environmental forecasting. Full article

Urban areas in subtropical regions are increasingly exposed to heat stress as climate change intensifies extreme heat events. In high-density cities, urban renewal is widely implemented to upgrade aging building stock, yet its potential role as a passive heat adaptation strategy remains insufficiently understood, particularly for projects below environmental impact assessment thresholds. This study examines how urban renewal influences neighborhood-scale microclimates through a comparative analysis of six residential renewal cases using computational fluid dynamics (CFD) simulations. Pre- and post-renewal scenarios are evaluated to assess changes in wind environment and thermal conditions, with a particular focus on the spatial extent and distance–decay characteristics of renewal-induced effects. The results reveal a consistent distance–decay pattern of microclimate responses across all cases. The influence of urban renewal is strongest within 0–50 m, remains detectable up to approximately 100 m, and diminishes substantially beyond 100–150 m, indicating a clear neighborhood-scale impact radius. Ventilation performance improves systematically following renewal, while thermal responses are more heterogeneous. Localized cooling of up to 1.5 °C is observed in selected cases, whereas others exhibit negligible temperature change despite enhanced airflow. These findings demonstrate that improved ventilation alone does not guarantee thermal mitigation. Instead, thermal outcomes depend on the interaction between airflow, solar exposure, and surface thermal properties. Urban renewal can therefore function as a form of passive heat adaptation when morphological changes are coordinated with shading and surface design strategies. By quantifying the spatial limits of renewal-induced microclimate effects, this study provides empirical evidence for integrating microclimate considerations into neighborhood-scale planning. The identified influence radius offers a practical reference for climate-responsive urban renewal, particularly in high-density subtropical cities where incremental redevelopment plays a dominant role. Full article

Coastal dunes are uniquely dynamic environments continuously shaped by a complex network of physical and biotic factors. Due to its location, the northwest of Ireland presents a challenging coastline characterised by high waves and wind energy. Even though dune systems in this area are frequently subject to habitat loss and erosion processes, comprehensive ecological studies are scarce. With the primary objective of investigating species composition and morphometric variability, we selected 13 dune sites and collected 409 vegetation plots and multiple cross-shore profiles. We implemented multivariate analyses to detect the main patterns in vegetation and geomorphology, and functional traits (TRY database) to evaluate plant strategies along the natural gradient. We observed high geomorphological heterogeneity across the beach–dune area. The PCA results were linked to the width of the natural system and to erosion or progradation trend. Different habitats of conservation interest were identified, and the environmental gradient proved to be the primary influence on species composition. In addition, different patterns in functional traits were detected along the zonation in response to the intensity of abiotic factors. The application of a multidisciplinary approach was crucial in unravelling the complexity of these environments and highlighting the need for context-specific conservation strategies. Full article

The study focuses on the infrared nondestructive detection of inclusion-type internal defects in glass-fiber-reinforced plastic (GFRP) wind turbine blade specimens, which were designed to simulate the laminated material structure and typical hidden defects of in-service blades. To address the difficulty of detecting internal defects in in-service wind turbine blades, this paper establishes an active thermal imaging defect detection and recognition system using a halogen lamp as the infrared thermal excitation source and a high-resolution thermal imaging camera as the detection component. To improve the recognition of defect contour information in infrared images, a method combining bidimensional filtering empirical mode decomposition (BFEMD), Gaussian filtering, and Otsu threshold segmentation is proposed. The BFEMD procedure decomposes the infrared image into bidimensional intrinsic mode function components and residual components, Gaussian filtering suppresses noise in the selected components, and Otsu threshold segmentation extracts the defect contours. Experimental results show that the combined algorithm can enhance defect targets in infrared images, improve visibility and contour integrity, and provide a higher detection rate for wind turbine blade defects under different defect depths and materials. Full article