Search Results (586)

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

The characterization of airborne submicrometric composite structures’ debris is a challenge in the field of environmental monitoring and control. The work presented here aims to develop a new quantitative method to measure elemental mass concentrations via particle sampling and Transmission Electron Microscopy—Energy-Dispersive X-ray Spectroscopy (TEM-EDS). The principle is to collect airborne particles on a porous TEM grid, then add a certain mass of reference particles, and compare the relative mass percentages of elements from reference and sample particles via EDS. Diverse pairs of airborne particles (RbCl, CsCl, NaCl, SrCl₂, Ga(NO₃)₃, braking particles) were deposited on one TEM grid, and the experimental elemental mass ratios were measured by EDS and compared with the theoretical values. Results show that the quantitative and homogeneous collection of reference particles, such as RbCl, on the TEM grid could be suitable. For all the tested conditions, the absolute deviations between the theoretical elemental mass ratios and the experimental ratios remain lower than 8%. Thus, the mass concentration of Fe from the braking aerosol is calculated as 107 µg/m³. Compared to the cumbersome real-time instrument, this new method for mass characterization appears to be convenient, and requires a short time of aerosol sampling at the workplace. This approach ensures safety and practicability when assessing, e.g., the exposure risk of hazardous materials. Full article

Decentralized desalination systems driven by renewable energy sources have surfaced as a feasible way to alleviate water scarcity in arid and rural areas. This bibliometric study aims to clarify the research trends, conceptual frameworks, and cooperative dynamics in the scientific literature on decentralized renewable-powered desalination techniques. Using a thorough search approach, 1354 papers were found. Duplicates, thematically unrelated works, and entries with poor information were removed using the PRISMA 2020 framework. A selected 832 relevant papers from a filtered dataset were chosen for in-depth analysis. Quantitative measures were obtained by means of Bibliometrix; network visualisation was obtained by means of VOSviewer (version 1.6.19) and covered co-authorship, keyword co-occurrence, and citation structures. Over the previous 20 years, the data show a steady rise in academic production, especially in the fields of environmental science, renewable energy engineering, and water treatment technologies. Author keyword co-occurrence mapping revealed strong theme clusters centred on solar stills, thermoelectric modules, reverse osmosis, and off-grid systems. Emphasizing current research paths and emerging subject borders, this paper clarifies the intellectual and social structure of the field. The outcomes are expected to help policy creation, cooperative projects, and strategic planning meant to hasten innovation in sustainable and decentralized water desalination. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

Three-dimensional electric field sensors (3D EFSs) can simultaneously measure electric field components in three mutually orthogonal directions and comprehensively capture the spatial distribution and dynamic changes of the electric field. They can be widely used in atmospheric science, smart grids, aerospace, target detection, and other fields. This paper deeply analyzes the latest progress in 3D EFSs, focusing on four major types of sensors: DC field mill, electro-optic effect, capacitive sensing, and microelectromechanical system (MEMS). It elaborates on their working principles, structural design, and decoupling calibration methods. At the same time, the advantages and disadvantages of various types of 3D EFSs and their applications in different fields are analyzed. Finally, the challenges faced by 3D EFS technology and its future development direction are discussed. Full article

Extreme events such as tropical cyclones frequently occur in coastal areas in China. With high wind speeds and rainfall during such extreme events, the vehicles on sea-crossing bridges may face severe instability problems. In this study, the dynamics of vehicles on a cross-sea bridge under the wind–rain coupling effect were analyzed based on field measurement data using computational fluid dynamics (CFD). Wind field parameters of the coastal area in China were obtained using wind speed data from measurement towers. Based on CFD, the sliding grid method was applied to establish an aerodynamic analysis model of a container truck moving on a bridge under wind and rain conditions. The discrete phase model based on the Euler–Lagrange method was used to investigate the influence of rain and obtain the aerodynamic characteristics of the truck under the coupled wind and rain effects. Based on the computational analysis results, considering the turbulence intensity, the yaw angle peaks of the tractor and trailer increased by 5.2% and 3.8%, respectively, and the lateral displacement of the truck’s center of mass increased by 9.8%. Rainfall may cause the vehicle to have a higher response, resulting in a high risk of skidding. The results show that skidding occurs for the considered container truck when rainfall is at 9.8%. These results can provide parameters for traffic control strategies under such extreme climate events in coastal areas. Full article

Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids is challenging due to the uneven distribution of monitoring stations, data confidentiality restrictions, and the limitations of existing interpolation methods. In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. (CSPGRI) during the winter of 2023. Our method integrates static geographic information, dynamic meteorological factors, and ice_kriging values derived from parameter-optimized Empirical Bayesian Kriging Interpolation (EBKI) to create a spatiotemporally matched, multi-source fused icing thickness grid dataset. We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. The stacking model performed best, achieving an R-value of 0.634 (0.893), RMSE of 3.424 mm (2.834 mm), CSI of 0.514 (0.774), MAR of 0.309 (0.091), FAR of 0.332 (0.161), and fbias of 1.034 (1.084), respectively, when comparing predicted icing values with actual measurements on pylons. Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. Meteorological factors such as relative humidity (RH), 10 m wind speed (WS₁₀), 2 m temperature (T₂), and precipitation (PRE) demonstrated a range of positive and negative contributions consistent with the observed growth of icing. Thus, our multi-source remote-sensing data-fusion approach, combined with the stacking model, offers a highly accurate and interpretable solution for generating real-time icing grid fields. Full article

With the continuous improvement of China’s power grid, safety issues in substation operation and maintenance have become increasingly prominent. However, the existing electrical proximity early-warning devices are inadequate for the complex environments of substations, highlighting the urgent need to develop new electrical proximity early-warning technologies. Based on the safety needs of substation operators, this paper proposes an electrical proximity early-warning method that integrates ‘electric field + distance’. It combines MEMS electric field test technology with ultrasonic ranging technology and designs a double-criterion electrical proximity early-warning device. Based on the COMSOL 6.0 finite-element electric field simulation and the construction safety specification for substation equipment, a multistage electric-field early-warning threshold has been reasonably formulated. A field test conducted at a 220 kV substation demonstrates that this device can issue alerts for various electrical proximity threat levels of the circuit breaker within 0.1 s, which is faster and more accurate than existing commercial electrical proximity early-warning devices. The double-criterion early-warning system minimizes the risk of missed alarms during multi-distance measurements. Additionally, its flexible warning threshold accommodates the increasingly complex operational requirements of substations. Full article

The traditional finite element method deals with the temperature field around the cooling tube due to the computational efficiency problems caused by grid division and the uncertainty of the convective heat transfer coefficient, resulting in inaccurate calculation results around the cooling tube. We conducted experiments to study the thermal stress and temperature gradient caused by various factors such as different materials of cooling pipes, pipe diameters, cooling water temperatures, and flow rates. The results showed that aluminum alloy pipes had the highest cooling efficiency but also produced a large temperature gradient. Pipe diameter had the most significant impact on cooling efficiency. Additionally, it is recommended that the cooling water flow velocity is not less than 0.6 m/s to achieve the best efficiency for the cooling pipe of any pipe diameter. The influence range of the cooling pipe on concrete could vary with pipe material, flow rate, and ambient factors. Our experimental results were compared with other heat transfer formulas (the Dittus–Boelter formula and the Yang Joo-Kyoung formula). According to the measured results, the formula is modified). The modified formula can estimate the heat transfer coefficient more accurately according to the flow rate and pipeline characteristics. Finally, the applicability of the formula is further verified by comparing the concrete on the bottom plate of a dam. The proposed heat transfer prediction model can estimate the heat transfer coefficient according to the flow rate and pipeline characteristics, The accuracy of the convection coefficient under different working conditions is improved by 10–25%. It is convenient to predict concrete temperature in practical engineering. Full article

Understanding why some plant species become widespread while others remain restricted to limited ranges is a central challenge in ecology and biogeography. This study investigates how functional traits, including morphological, physiological, and nutrient-related traits, relate to the global registrability—defined as the likelihood of a species being observed and recorded—for 144 herbaceous plant species from Guangzhou, China. We combined field-measured morphological, physiological, and nutrient-related traits with occurrence data from the Global Biodiversity Information Facility (GBIF), quantified as the number of unique 10 km × 10 km grid cells per species. Our analyses reveal that resource-acquisitive traits—such as high leaf water content, chlorophyll concentration, and photosynthetic capacity—are positively associated with registrability, whereas traits linked to nutrient conservation (e.g., high leaf carbon content and leaf carbon-to-nitrogen ratios) show negative associations. Principal component analysis further indicates that multivariate trait axes characterized by acquisitive strategies are significantly and positively associated with higher registrability. These findings suggest that species with fast-growth, resource-intensive strategies are more likely to be encountered and reported, potentially due to both ecological generalism and observation bias. Full article

To evaluate the ecological remediation effect of Salix matsudana × alba on saline coastal soils, we established a five-year field experiment in Rudong County, Jiangsu Province, China. The experiment was designed with three salinity gradients (low, medium, and high) and five plant spacing treatments (2 × 2 m, 2 × 3 m, 3 × 3 m, 3 × 4 m, and 4 × 4 m). Soil samples were collected annually at a depth of 0–20 cm using grid and random sampling methods. Indicators of soil physicochemical properties and heavy metal content were measured, including soil organic matter (SOM), pH, total nitrogen (TN), total phosphorus (TP), total potassium (TK), electrical conductivity (EC), total salinity (TS), and bulk density (BD). Additionally, eight heavy metals were analyzed: zinc (Zn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg). Results showed that the hybrid willow significantly improved SOM content by up to 90% and reduced EC and TS by 52% and 60% over five years, especially under low and medium salinity conditions with dense planting (2 × 2 m, 2 × 3 m). The content of most heavy metals exhibited a decreasing trend or remained stable, indicating the plant’s phytostabilization potential (i.e., stabilization of heavy metals via plant-soil interaction). Principal component analysis (PCA) and random forest (RF) modeling identified SOM, EC, TS, and BD as the dominant factors influencing soil quality improvement. A soil quality index (SQI) was constructed based on PCA-derived weights, which further confirmed the positive ecological effect of this hybrid species on coastal saline soils. This study provides scientific evidence supporting the use of Salix matsudana × alba as a promising species for large-scale ecological restoration in coastal saline-alkaline lands. Full article