Search Results (1,112)

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

The Third Pole region, particularly the Hindu–Kush–Himalaya (HKH), is highly prone to lightning, causing thousands of fatalities annually. Skillful prediction and timely communication are essential for mitigating lightning-related losses in such observationally data-sparse regions. Therefore, this study evaluates kilometer-scale ICON-CLM-simulated atmospheric variables using six machine learning (ML) models to detect lightning activity over the Third Pole. Results from the ensemble boosting ML models show that ICON-CLM simulated variables such as relative humidity (RH), vorticity (vor), 2m temperature (t_2m), and surface pressure (sfc_pres) among a total of 25 variables allow better spatial and temporal prediction of lightning activities, achieving a Probability of Detection (POD) of ∼0.65. The Lightning Potential Index (LPI) and the product of convective available potential energy (CAPE) and precipitation (prec_con), referred to as CP (i.e., CP = CAPE × precipitation), serve as key physics aware predictors, maintaining a high Probability of Detection (POD) of ∼0.62 with a 1–2 h lead time. Sensitivity analyses additionally using climatological lightning data showed that while ML models maintain comparable accuracy and POD, climatology primarily supports broad spatial patterns rather than fine-scale prediction improvements. As LPI and CP reflect cloud microphysics and atmospheric stability, their inclusion, along with spatiotemporal averaging and climatology, offers slightly lower, yet comparable, predictive skill to that achieved by aggregating 25 atmospheric predictors. Model evaluation using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) highlights XGBoost as the best-performing diagnostic classification (yes/no lightning) model across all six ML tested configurations. Full article

Slotted roller buckets are energy dissipator structures designed to reduce the destructive power of high-velocity water flows in spillways, protecting downstream environments. This study aimed to estimate the critical role of slotted roller bucket design in downstream scour mitigation and hydraulic energy dissipation. The three-dimensional Navier–Stokes (N-St) equations were solved to simulate the jet flow over the roller bucket using CFD software. The free surface volume tracking using the volume of fluid (VOF) and non-equilibrium sediment transport equations was coupled with N-St to model the local scour downstream of the roller bucket system. Subsequently, the impact of bucket tooth lip angles, tailwater depth, and bucket radius on downstream scour were examined in a numerical 3D framework. The results showed that the 45- to 55-degree lip angle configuration significantly reduced the maximum scour depth by approximately 36%. Furthermore, the study quantified the effects of tailwater depth and bucket radius on scour dimensions and flow patterns. The optimal tailwater depth reduced scour depth by approximately 20% compared with the worst case, while variations in bucket radius led to more than a 50% difference in scour depth. We identified specific ranges for these parameters that further minimized erosion potential. The research also underscored the influence of transverse mixing on surging depth, revealing a crucial mechanism for energy dissipation. These findings contributed to a deeper understanding of the complex interplay between design parameters and scour. It offered practical insights for optimizing and operating hydraulic structures sustainably and understanding the scouring processes downstream of the dams. Full article

The Nam Phong Formation, a key unit of the pre-Khorat Group in the western Khorat Plateau, provides critical insights into the Mesozoic geological evolution of northeastern Thailand. This study presents the first integrated petrographic and geochemical investigation of the formation within Khon Kaen Geopark to reconstruct its Late Triassic–Early Jurassic depositional settings, provenance, and paleoclimate. A detailed stratigraphic section and five supplementary sites reveal litharenite and lithic wacke sandstones, interbedded with red paleosols and polymictic conglomerates. Sedimentary structures—such as trough and planar cross-bedding, erosional surfaces, and mature paleosols—indicate deposition in a high-energy braided fluvial system under semi-arid to subhumid conditions with episodic subaerial exposure. Petrographic analysis identifies abundant quartz, feldspar, and volcanic lithic fragments. Geochemical data and REE patterns, including diagnostic negative Ce anomalies, provide compelling evidence for provenance from active continental margins and oxidizing weathering conditions. These findings point to a tectonically active syn-rift basin influenced by climatic variability. Strikingly, the Nam Phong Formation exhibits paleoenvironmental and sedimentological features comparable to the modern Ebro Basin in northeastern Spain, highlighting the relevance of uniformitarian principles in interpreting ancient continental depositional systems. Full article

This study develops a vibration model for functionally graded material (FGM) plates with embedded planar cracks. Based on thin plate theory and von Kármán-type geometric nonlinear strain assumptions, the kinetic and potential energies of each region are derived. Displacement field trial functions are constructed according to boundary conditions, and the Ritz method is employed to determine natural frequencies and vibration modes under small deformation conditions. The investigation focuses on how crack parameters and material gradient coefficients affect vibration characteristics in exponentially graded FGM plates. The results show that natural frequencies decrease with increasing crack length, while crack presence alters nodal line patterns and mode symmetry. During free vibration, the upper and lower surfaces of the crack region exhibit relative displacement. Material gradient effects induce thickness–direction asymmetry, causing non-uniform displacements between the plate’s upper and lower sections. Full article

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants. Full article

The increasing demand for smart bone substitutes has boosted the implementation of biomaterials possibly endowed with both pro-osteogenic and pro-angiogenic capabilities, among which bioactive glasses hold great potential. Hence, two Poly(ε-caprolactone) (PCL)-based composites were loaded at 10 wt.%, with either pristine (SBA3) or copper-doped (SBA3_Cu) silica-based bioactive glasses, through a solvent casting method with chloroform. Neat PCL was used as a control. Samples produced by 3D printing underwent SEM and EDX analyses, and the following were measured: tensile strength and hardness, surface roughness, ion release through ICP-OES, surface free energy, and optical contact angle. Adipose-derived mesenchymal stem cells (ASCs) and human microvascular endothelial cells (HMEC-1) were used to test the biocompatibility of the materials through cell adhesion, spreading, and viability assays. A significant improvement in tensile strength and hardness was observed especially with Cu-doped composites. Both SBA3 and SBA3_Cu added to the PCL favored the early adhesion and the proliferation of HMEC-1 after 3 and 7 days, while ASCs proliferated significantly the most on the SBA-containing composite, at all the time points. Cellular morphology analysis highlighted interesting adaptation patterns to the samples. Further biological characterizations are needed to understand thoroughly how specific bioactive glasses may interact with different cellular types. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing antennas. The design methodology employs the lensmaker’s formula and Snell’s law to determine lens curvature for achieving a specified focal length of 100 mm. COMSOL Multiphysics simulations indicate that adding a PTFE lens increases power density concentration compared to a standard horn antenna, with a simulated focal point at approximately 100 mm. Surface roughness analysis based on the Rayleigh criterion supports 3D printing suitability for this application. Experimental validation includes radiation pattern measurements of the antenna without the lens and power density measurements versus distance with the lens, both showing good agreement with simulation results. The measured focal length was $95\pm 5$ mm, closely matching simulation predictions. This work presents an approach for implementing focused RF delivery solutions for medical treatments, wireless power transfer, and precision sensing at significantly lower costs than commercial alternatives. Full article

The increasing prevalence of antibiotic-resistant bacteria has intensified the need for innovative antibacterial surfaces, particularly in biomedical applications. Traditional approaches often rely on chemical agents alone, which may lead to diminishing efficacy over time. To address this, we investigated the development of a novel antibacterial surface by combining the inherent antimicrobial properties of copper with an engineered surface topography on a biopolymer matrix. A copper–poly-L-lactic acid (Cu-PLLA) composite system was fabricated using sputtering deposition followed by controlled thermal treatment to induce wrinkle-like micro- and nanostructures on the surface. The surface morphology was characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), confirming the formation of hierarchical wrinkle patterns. The chemical composition and distribution of copper were analyzed via energy-dispersive X-ray spectroscopy (EDS). Antibacterial performance was assessed against both Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus using standard colony count reduction assays. The Cu-PLLA wrinkled surfaces demonstrated significantly enhanced bactericidal activity compared with flat PLLA and copper-free controls, a finding attributed to a synergistic effect of mechanical membrane disruption and copper-mediated chemical toxicity. These findings suggest that biopolymer–metal hybrid surfaces with engineered topography offer a promising strategy for developing next-generation antibacterial materials suitable for biomedical and clinical use. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

A facile and versatile strategy to obtain NPs@ZnAlO nanocomposite materials, comprising controlled-size nanoparticles (NPs) within a ZnAlO matrix is reported. The mono-(Au, Ni) and bimetallic (Ni-Au) NPs serving as an active phase were prepared by the polyol-alkaline method, while the ZnAlO support was obtained via the thermal decomposition of its corresponding layered double hydroxide (LDH) precursors. X-ray diffraction (XRD) patterns confirmed the successful fabrication of the nanocomposites, including the synthesis of the metallic NPs, the formation of LDH-like structure, and the subsequent transformation to ZnO phase upon LDH calcination. The obtained nanostructures confirmed the nanoplate-like morphology inherited from the original LDH precursors, which tended to aggregate after the addition of gold NPs. According to the UV-Vis spectroscopy, loading NPs onto the ZnAlO support enhanced the light absorption and reduced the band gap energy. ATR-DRIFT spectroscopy, H₂-TPR measurements, and XPS analysis provided information about the functional groups, surface composition, and reducibility of the materials. The catalytic performance of the developed nanostructures was evaluated by the photodegradation of bisphenol A (BPA), under simulated solar irradiation. The conversion of BPA over the bimetallic Ni-Au@ZnAlO reached up to 95% after 180 min of irradiation, exceeding the monometallic Ni@ZnAlO and Au@ZnAlO catalysts. Its enhanced activity was correlated with good dispersion of the bimetals, narrower band gap, and efficient charge carrier separation of the photo-induced e⁻/h⁺ pairs. Full article

A square patch metasurface is designed, simulated, fabricated, and experimentally tested to investigate polarization characteristics quantitatively. The metasurface consists of one layer unit cell in the form of a square patch with one via and a lumped resistor, which is used for harvesting RF (radio frequency) energy. FR4 dielectric is used as a substrate supported by a metal ground plane. Polarization-dependent properties with specific surface current patterns and voltage dip are obtained when simulating under normal incidence of a plane wave. This characteristic results from changes in surface current conditions when the polarization angle is varied. A voltage dip appears at a specific polarization angle when the surface current pattern is symmetrical. This condition occurs when the position of the lumped resistor from the center of the patch is perpendicular to the linearly polarized incident electric field. A couple of 10 × 10 arrays with different resistor positions are fabricated and tested. The experimental results are in good agreement with the simulated results. The proposed design demonstrates a symmetric unit cell structure with one via and a resistor that exhibits polarization-dependent behavior for linear polarization. An asymmetric patch design is explored through both simulation and measurement to mitigate polarization dependence by suppressing the dip behavior, albeit at the expense of reduced absorption efficiency. This study provides a complete polarization analysis for both symmetric and asymmetric patch metasurfaces with a single via and a single lumped resistor, and introduces a predictive relation between the position of the resistor relative to the center of the patch and the resulting voltage dip behavior. Full article

Aramid fibre has become a critical material for individual soft body armour due to its lightweight nature and exceptional impact resistance. To investigate its energy absorption mechanism, quasi-static and dynamic tensile experiments were conducted on Kevlar^® 29 plain-woven fabric using a universal material testing machine and a Split Hopkinson Tensile Bar (SHTB) apparatus. Tensile mechanical responses were obtained under various strain rates. Fracture morphology was characterised using scanning electron microscopy (SEM) and ultra-depth three-dimensional microscopy, followed by an analysis of microstructural damage patterns. Considering the strain rate effect, a viscoelastic constitutive model was developed. The results indicate that the tensile mechanical properties of Kevlar^® 29 plain-woven fabric are strain-rate dependent. Tensile strength, elastic modulus, and toughness increase with strain rate, whereas fracture strain decreases. Under quasi-static loading, the fracture surface exhibits plastic flow, with slight axial splitting and tapered fibre ends, indicating ductile failure. In contrast, dynamic loading leads to pronounced axial splitting with reduced split depth, simultaneous rupture of fibre skin and core layers, and fibrillation phenomena, suggesting brittle fracture characteristics. The modified three-element viscoelastic constitutive model effectively captures the strain-rate effect and accurately describes the tensile behaviour of the plain-woven fabric across different strain rates. These findings provide valuable data support for research on ballistic mechanisms and the performance optimisation of protective materials. Full article