Search Results (940)

Recent advances in precision manufacturing and high-end equipment technologies have imposed ever more stringent requirements on the accuracy, real-time performance, and lightweight design of online steel strip surface defect detection systems. To reconcile the persistent trade-off between detection precision and inference efficiency in complex industrial environments, this study proposes StripSurface–YOLO, a novel real-time defect detection framework built upon YOLOv8n. The core architecture integrates an Efficient Cross-Stage Local Perception module (ResGSCSP), which synergistically combines GSConv lightweight convolutions with a one-shot aggregation strategy, thereby markedly reducing both model parameters and computational complexity. To further enhance multi-scale feature representation, this study introduces an Efficient Multi-Scale Attention (EMA) mechanism at the feature-fusion stage, enabling the network to more effectively attend to critical defect regions. Moreover, conventional nearest-neighbor upsampling is replaced by DySample, which produces deeper, high-resolution feature maps enriched with semantic content, improving both inference speed and fusion quality. To heighten sensitivity to small-scale and low-contrast defects, the model adopts Focal Loss, dynamically adjusting to sample difficulty. Extensive evaluations on the NEU-DET dataset demonstrate that StripSurface–YOLO reduces FLOPs by 11.6% and parameter count by 7.4% relative to the baseline YOLOv8n, while achieving respective improvements of 1.4%, 3.1%, 4.1%, and 3.0% in precision, recall, mAP₅₀, and mAP_50:95. Under adverse conditions—including contrast variations, brightness fluctuations, and Gaussian noise—SteelSurface-YOLO outperforms the baseline model, delivering improvements of 5.0% in mAP₅₀ and 4.7% in mAP_50:95, attesting to the model’s robust interference resistance. These findings underscore the potential of StripSurface–YOLO to meet the rigorous performance demands of real-time surface defect detection in the metal forging industry. Full article

Owing to the inherently lower sensitivity of microwave aperture synthesis radiometers (ASRs), Soil Moisture and Ocean Salinity (SMOS) satellite brightness temperature (TB) measurements exhibit significantly greater system noise than real-aperture microwave radiometers do. This paper introduces a novel noise mitigation method for the SMOS L1C multi-angle TB product. The proposed method develops a multi-angle sea surface TB relationship lookup table, enabling the mapping of SMOS L1C multi-angle TB data to any single-angle TB, thereby averaging to the measurements to reduce noise. Validation experiments demonstrate that the processed SMOS TB data achieve noise levels comparable to those of the Soil Moisture Active Passive (SMAP) satellite. Additionally, the salinity retrieval experiments indicate that the noise mitigation technique has a clear positive effect on SMOS salinity retrieval. Full article

Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances—the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability—have shown promise in addressing key challenges in SM estimation. Full article

This study investigates the linkage between the radar reflectivity slope and raindrop size distribution (DSD) in precipitation with bright bands through coordinated C-band/Ka-band radar and disdrometer observations in southern China. Precipitation is classified into three types based on the reflectivity slope (K-value) below the freezing level, revealing distinct microphysical regimes: Type 1 (K = 0 to −0.9) shows coalescence-dominated growth; Type 2 (|K| > 0.9) shows the balance between coalescence and evaporation/size sorting; and Type 3 (K = 0.9 to 0) demonstrates evaporation/size-sorting effects. Surface DSD analysis demonstrates distinct precipitation characteristics across classification types. Type 3 has the highest frequency of occurrence. A gradual decrease in the mean rain rates is observed from Type 1 to Type 3, with Type 3 exhibiting significantly lower rainfall intensities compared to Type 1. At equivalent rainfall rates, Type 2 exhibits unique microphysical signatures with larger mass-weighted mean diameters (D_m) compared to other types. These differences are due to Type 2 maintaining a high relative humidity above the freezing level (influencing initial D_m at bottom of melting layer) but experiencing limited D_m growth due to a dry warm rain layer and downdrafts. Type 1 shows opposite characteristics—a low initial D_m from the dry upper layers but maximum growth through the moist warm rain layer and updrafts. Type 3 features intermediate humidity throughout the column with updrafts and downdrafts coexisting in the warm rain layer, producing moderate growth. Full article

This study investigates the behavior of spark discharges under various environmental conditions to simulate aspects of early-stage lightning dynamics, with a focus on their spectral characteristics, propagation, and ionization behavior. In a laboratory setting, spark discharges generated by a Tesla coil operating with high-frequency alternating current (AC) were analyzed under varying air humidity and water surface conductivity. Spectral analysis revealed that the discharges are dominated by the second positive system of molecular nitrogen N₂ (2P) and also exhibit the first negative system of molecular nitrogen ions N₂⁺ (1N). Notably, the N₂ (2P) emissions show strong peaks in the 350–450 nm range, closely matching spectral features typically associated with corona and streamer discharges in natural lightning. Environmental factors significantly influenced discharge morphology: in dry air, sparks exhibited longer and more branched paths, while in moist air, the discharges were shorter and more confined. Over water surfaces, the sparks spread radially, forming star-shaped patterns. Deionized (DI) water, with low conductivity, supported wider lateral propagation, whereas higher conductivity in tap water and saltwater suppressed discharge spread. The gap between the electrode tip and the surface also affected discharge extent and brightness. These findings demonstrate that Tesla coil discharges reproduce key features of early lightning processes and offer insights into how environmental factors influence discharge development. Full article

Continuous, single-mode imaging systems fail to deliver true-color high-resolution imagery around the clock under extreme lighting. High-fidelity color and signal-to-noise ratio imaging across the full day–night cycle remains a critical challenge for surveillance, navigation, and environmental monitoring. We present a competitive dual-mode imaging platform that integrates a 155 mm f/6 telephoto daytime camera with a 52 mm f/1.5 large-aperture low-light full-color night-vision camera into a single, co-registered 26 cm housing. By employing a sixth-order aspheric surface to reduce the element count and weight, our system achieves near-diffraction-limited MTF (>0.5 at 90.9 lp/mm) in daylight and sub-pixel RMS blur < 7 μm at 38.5 lp/mm under low-light conditions. Field validation at 0.0009 lux confirms high-SNR, full-color capture from bright noon to the darkest nights, enabling seamless switching between long-range, high-resolution surveillance and sensitive, low-light color imaging. This compact, robust design promises to elevate applications in security monitoring, autonomous navigation, wildlife observation, and disaster response by providing uninterrupted, color-faithful vision in all lighting regimes. Full article

The article presents a three-stage methodology for calibrating 3D printing using mSLA technology, aimed at improving dimensional accuracy and print repeatability. The proposed approach is based on procedures that enable the collection and analysis of numerical data, thereby minimizing the influence of the operator’s subjective judgment, which is commonly relied upon in traditional calibration methods. In the first stage, compensation for the uneven illumination of the LCD matrix was performed by establishing a regression model that describes the relationship between UV radiation intensity and pixel brightness. Based on this model, a grayscale correction mask was developed. The second stage focused on determining the optimal exposure time, based on its effect on dimensional accuracy, detail reproduction, and model strength. The optimal exposure time is defined as the duration that provides the highest possible mechanical strength without significant loss of detail due to the light bleed phenomenon (i.e., diffusion of UV radiation beyond the mask edge). In the third stage, scale correction was applied to compensate for shrinkage and geometric distortions, further reducing the impact of light bleed on the dimensional fidelity of printed components. The proposed methodology was validated using an Anycubic Photon M3 Premium printer with Anycubic ABS-Like Resin Pro 2.0. Compensating for light intensity variation reduced the original standard deviation from 0.26 to 0.17 mW/cm², corresponding to a decrease of more than one third. The methodology reduced surface displacement due to shrinkage from 0.044% to 0.003%, and the residual internal dimensional error from 0.159 mm to 0.017 mm (a 72% reduction). Full article

In this study, an innovative dental functionalized magnetic nanomaterial was developed by incorporating hydrophilic magnetic clusters as an alternative to conventional isolated magnetic nanoparticles, introducing a novel structural and functional concept in dental applications. The ~100 nm magnetic clusters—composed of densely packed 7 nm Fe₃O₄ nanoparticles—were sequentially coated with a silica (SiO₂) layer (3–5 nm) to improve chemical and mechanical stability, followed by an outer calcium hydroxide [Ca(OH)₂] layer to enhance bioactivity and optical integration. This bilayer architecture enables magnetic field-assisted positioning and improved dispersion within dental resin matrices. Silver nanoparticles were incorporated to enhance antimicrobial activity and reduce biofilm formation. The synthesis process was environmentally friendly and scalable. Comprehensive physicochemical characterization confirmed the material’s functional performance. Saturation magnetization decreased progressively with surface functionalization, from 62 to 14 emu/g, while the zeta potential became increasingly negative (from −2.42 to −22.5 mV), supporting its ability to promote apatite nucleation. The thermal conductivity (0.527 W/m·K) closely matched that of human dentin (0.44 W/m·K), and the colorimetric analysis showed improved brightness (ΔL = 5.3) and good color compatibility (ΔE = 11.76). These results indicate that the functionalized magnetic nanomaterial meets essential criteria for restorative use and holds strong potential for future clinical applications. Full article

Stretchable display technology has rapidly evolved, enabling a new generation of flexible electronics with applications ranging from wearable healthcare and smart textiles to implantable biomedical devices and soft robotics. This review systematically presents recent advances in stretchable displays, focusing on intrinsic stretchable materials, wavy surface engineering, and hybrid integration strategies. The paper highlights critical breakthroughs in device architectures, energy-autonomous systems, durable encapsulation techniques, and the integration of artificial intelligence, which collectively address challenges in mechanical reliability, optical performance, and operational sustainability. Particular emphasis is placed on the development of high-resolution displays that maintain brightness and color fidelity under mechanical strain, and energy harvesting systems that facilitate self-powered operation. Durable encapsulation methods ensuring long-term stability against environmental factors such as moisture and oxygen are also examined. The fusion of stretchable electronics with AI offers transformative opportunities for intelligent sensing and adaptive human–machine interfaces. Despite significant progress, issues related to large-scale manufacturing, device miniaturization, and the trade-offs between stretchability and device performance remain. This review concludes by discussing future research directions aimed at overcoming these challenges and advancing multifunctional, robust, and scalable stretchable display systems poised to revolutionize flexible electronics applications. Full article

The coal reservoir in the Ordos Mizhi block is buried at a depth of over 2000 m. This study aims to obtain the characteristics of the coal reservoir in the Mizhi block through various experimental methods and combine the gas-bearing characteristics obtained from on-site desorption experiments to analyze the gas content and logging response characteristics of the study area. On this basis, a reservoir parameter interpretation model for the study area is established. This provides a reference for the exploration and development of coal-rock gas in the Mizhi block. The research results show that: (1) The study area is characterized by the development of the No. 8 coal reservoirs of the Benxi Formation, with a thickness ranging from 2 to 11.6 m, averaging 7.2 m. The thicker coal reservoirs provide favorable conditions for the formation and storage of coal-rock gas. The lithotypes are mainly semi-bright and semi-dark. The coal maceral is dominated by the content of the vitrinite, followed by the inertinite, and the exinite is the least. The degree of metamorphism is high, making it a high-grade coal. In the proximate analysis, the moisture ranges from 0.36 to 1.09%, averaging 0.65%. The ash ranges from 2.34 to 42.17%, averaging 16.57%. The volatile ranges from 9.18 to 15.7%, averaging 11.50%. The fixed carbon ranges from 45.24 to 87.51%, averaging 71.28%. (2) According to the results of scanning electron microscopy (SEM), the coal samples in the Mizhi block have developed fractures and pores. Based on the results of the carbon dioxide adsorption experiment, the micropore adsorption capacity is 7.8728–20.3395 cm³/g, with an average of 15.2621 cm³/g. The pore volume is 0.02492–0.063 cm³/g, with an average of 0.04799 cm³/g. The specific surface area of micropores is 79.514–202.3744 m²/g, with an average of 153.5118 m²/g. The micropore parameters are of great significance for the occurrence of coal-rock gas. Based on the results of the desorption experiment, the gas content of the coal rock samples in the study area is 12.97–33.96 m³/t, with an average of 21.8229 m³/t, which is relatively high. (3) Through the correlation analysis of the logging parameters of the coal reservoir, the main logging response parameters of the reservoir are obtained. Based on the results of the logging sensitivity analysis of the coal reservoir, the interpretation model of the reservoir parameters is constructed and verified. Logging interpretation models for parameters such as industrial components, microscopic components, micropore pore parameters, and gas content are obtained. The interpretation models have interpretation effects on the reservoir parameters in the study area. Full article

In deformation measurements, processing overexposed images poses challenges due to the welding process or metal reflection. To track the deformation surface, an Adaptive Brightness Global Digital Image Correlation method is proposed. First, the effective range is determined based on the extent of image overexposure. Second, an improved Dark Channel Prior method is employed to adjust the brightness of overexposed images. Third, by calculating the parameter results of Finite Element Partitioning, Adaptive Brightness Global Digital Image Correlation can be utilized to conduct deformation measurements. The proposed method can adjust both the image brightness and Finite Element Partitioning for Global Digital Image Correlation. The experimental results demonstrate that the improved dark channel method modifies the image brightness without altering its brightness distribution. The modified image can significantly increase the Mean Intensity Gradient within different partitions. This method overcomes the difficulty in measuring the weld deformation during the welding process and can achieve dynamic deformation measurement using overexposed images. Finally, the evolution processes of unstable deformation and angular deformation in the whole welding field are obtained, which can assist in optimizing the welding process. Full article

Numerous studies have demonstrated that appropriate use of daylight in educational spaces significantly enhances students’ health and academic performance. However, classrooms in Tehran still suffer from considerable daylighting challenges. In many cases, desks near windows are exposed to excessive brightness, while areas farther from the windows lack adequate illumination. This often leads to the use of curtains and artificial lighting, resulting in higher energy consumption and potential negative impacts on student learning. Light shelf systems have been proposed as effective daylighting solutions to improve light penetration and distribution. According to previous research, three key parameters—geometry, depth, and surface reflectance—play a critical role in the performance of light shelves. However, prior studies have typically focused on improving one or two of these parameters in isolation. There is a lack of research evaluating all three parameters simultaneously to determine season-specific configurations for optimal performance. Addressing this gap, the present study investigates the combined effects of light shelf geometry, depth, and reflectance across different seasons and proposes a system that dynamically adapts these parameters throughout the year. In winter, the system also integrates photovoltaic panels to reduce glare and generate electricity for its operation. Simulation results indicate that the proposed system leads to a 21% improvement in Useful Daylight Illuminance (UDI), a 65% increase in thermal comfort, and a 10% annual reduction in energy consumption. These findings highlight the potential of the proposed system as a practical and energy-efficient daylighting strategy for educational buildings in sunny regions such as Tehran. Full article

Advanced oxidation processes offer bright potential for eliminating organic pollutants from wastewater, where the development of efficient catalysts revolves around deep understanding of the microstructure–property–performance relationship. In this study, we explore how microstructural engineering influences the catalytic performance of nanoporous copper (NPC) in degrading organic contaminants. By systematically tailoring the NPC microstructure, we achieve tunable three-dimensional porous architectures with nanoscale pores and macroscopic grains. This results in a homogeneous, bicontinuous pore–ligament network that is crucial for the oxidative degradation of the model pollutant methylene blue in the presence of hydrogen peroxide. The catalytic efficiency is assessed using ultraviolet–visible spectroscopy, which reveals first-order degradation kinetics with a rate constant κ = 44 × 10⁻³ min⁻¹, a 30-fold improvement over bulk copper foil, and a fourfold increase over copper nanoparticles. The superior performance is attributed to the high surface area, abundant active sites, and multiscale porosity of NPC. Additionally, the high step-edge density, nanoscale curvature, and enhanced crystallinity contribute to the catalyst’s remarkable stability and reactivity. This study not only provides insights into microstructure–property–performance relationships in nanoporous catalysts but also offers an effective strategy for designing efficient and scalable materials for wastewater treatment and environmental applications. Full article

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article

Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones. Full article