Search Results (15,881)

This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Carassius auratus exhibits significant physiological and behavioral alterations under the combined stress of temperature and dissolved oxygen (DO) fluctuations, which are common challenges in aquaculture. In this investigation, we employed controlled thermal and DO gradients to characterize the multidimensional response profile of this species. The key findings revealed that thermal elevation profoundly influenced blood glucose and cortisol concentrations. Notably, exposure to hyperoxic conditions markedly attenuated stress responses relative to hypoxia at equivalent temperatures: cortisol levels were significantly suppressed (reductions of 60.11%, 118.06%, and 34.72%), while blood glucose levels exhibited concurrent increases (16.42%, 26.43%, and 26.34%). Distinctive behavioral patterns, including floating head behavior, surface swimming behavior, and rollover behavior, were identified as indicative behaviors of thermal–oxygen stress. Molecular analysis demonstrated the upregulated expression of stress-associated genes (HSP70, HSP90, HIF-1α, and Prdx3), which correlated temporally with elevated cortisol and glucose concentrations and the manifestation of stress behaviors. Furthermore, a muscle texture assessment indicated that increased DO availability mitigated the textural deterioration induced by heat stress. Collectively, this work establishes an authentic biomarker framework, providing crucial threshold parameters essential for the development of intelligent, real-time environmental monitoring and dynamic regulation systems to enhance climate-resilient aquaculture management. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from Cynara cardunculus (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract (PC) with traditional bovine rennet rich in chymosin (AC) during manufacture and 60-day ripening of Caciotta cheese. Classical compositional assays (ripening index, texture profile, color, solubility) were integrated with scanning electron microscopy, three-dimensional surface reconstruction, and descriptive sensory analysis. AC cheeses displayed slower but sustained proteolysis, yielding a higher and more linear ripening index, softer body, greater solubility, and brighter, more yellow appearance. Imaging revealed a continuous protein matrix with uniformly distributed, larger pores, consistent with a dairy-like sensory profile dominated by milky and umami notes. Conversely, PC cheeses underwent rapid early proteolysis that plateaued, producing firmer, chewier curds with lower solubility and darker color. Micrographs showed a fragmented matrix with smaller, heterogeneous pores; sensory evaluation highlighted vegetal, bitter, and astringent attributes. The data demonstrate that thistle coagulant can successfully replace animal rennet but generates cheeses with distinct structural and sensory fingerprints. The optimization of process parameters is therefore required when targeting specific product styles. Full article

The abrasion resistance of fabrics is one of the basic properties determining the utility performance and durability. The abrasion resistance of textile materials is measured using the Martindale device according to appropriate standards. The sample breakage method is the most commonly used of the three methods. The method is based on organoleptic assessment of fabric breakage. The method is time-consuming, and results may be subject to error resulting from the subjective nature of the assessment. The aim of the presented work was to check the possibility of the application of contactless 3D surface geometry measurement using an optical profilometer in an assessment of changes in fabrics’ surface due to the abrasion process. The obtained results confirmed that some parameters of the geometric structure of fabric surfaces, such as the highest height of the roughness profile Rz, the height of the highest pick of the roughness profile Rp, the depth of the lowest valley of the roughness profile Rv, the depth of the total height of the roughness profile Rt, and the kurtosis Rku, can be used to assess the abrasion resistance of fabrics. It is also stated that using the non-contact optical measurement of fabric surface geometry allows for an assessment of the directionality of surface texture. For this purpose, the autocorrelation function and angle distribution function can be applied. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

The drying process plays a crucial role in enhancing the shelf life of food products by reducing moisture content. As climate change contributes to rising temperatures, alternative drying methods, such as solar drying, offer promising solutions for sustainable food preservation. This study investigates the solar drying of eggplant (Solanum melongena L.) slices, with a focus on the effect of salting pretreatment on drying efficiency. Eggplant slices were subjected to salting pretreatment for partial moisture removal prior to drying. Drying kinetics were monitored to construct the characteristic drying curve. The dried eggplant slices were evaluated for their proximate composition and rehydration capacity, as well as textural and thermal properties. The results showed that salting pretreatment significantly enhanced the solar drying process by accelerating moisture removal. Notably, water activity (aw) decreased significantly from 0.978 to 0.554 for the control sample and to 0.534 for the saltedsample. Significant differences were observed between the dried and salted dried slices, particularly in rehydration capacity, which decreased following salting. Additionally, the salted dried samples showedreductions in protein, carbohydrate, and potassium contents. In contrast, ash content and hardness increased as a result ofosmotic pretreatment. These findings suggest that while dry salting pretreatment effectively reduces solar drying time, it may adversely affect several nutritional and textural properties. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

This study investigates the bond behavior of fabric-reinforced cementitious matrix (FRCM) composites with three common masonry substrates—solid clay bricks (SBs), perforated bricks (PBs), and concrete hollow blocks (HBs)—using knitted polyester grille (KPG) fabric. Through uniaxial tensile tests of the KPG fabric and FRCM system, along with single-lap and double-lap shear tests, the interfacial debonding modes, load-slip responses, and composite utilization ratio were evaluated. Key findings reveal that (i) SB and HB substrates predominantly exhibited fabric slippage (FS) or matrix–fabric (MF) debonding, while PB substrates consistently failed at the matrix–substrate (MS) interface, due to their smooth surface texture. (ii) Prism specimens with mortar joints showed enhanced interfacial friction, leading to higher load fluctuations compared to brick units. PB substrates demonstrated the lowest peak stress (69.64–74.33 MPa), while SB and HB achieved comparable peak stresses (133.91–155.95 MPa). (iii) The FRCM system only achieved a utilization rate of 12–30% in fabric and reinforcement systems. The debonding failure at the matrix–substrate interface is one of the reasons that cannot be ignored, and exploring methods to improve the bonding performance between the matrix–substrate interface is the next research direction. HB bricks have excellent bonding properties, and it is recommended to prioritize their use in retrofit applications, followed by SB bricks. These findings provide insights into optimizing the application of FRCM reinforcement systems in masonry structures. Full article

Accurate cloud segmentation is critical for astronomical observations and solar forecasting. However, traditional threshold- and texture-based methods suffer from limited accuracy (65–80%) under complex conditions such as thin cirrus or twilight transitions. Although the deep-learning segmentation method based on U-Net effectively captures low-level and high-level features and achieves significant progress in accuracy, current methods still lack interpretability and multi-scale feature integration and usually produce fuzzy boundaries or fragmented predictions. In this paper, we propose multi-scale CAM, an explainable AI (XAI) framework that integrates class activation mapping (CAM) with hierarchical feature fusion to quantify pixel-level attention across hierarchical features, thereby enhancing the model’s discriminative capability. To achieve precise segmentation, we integrate CAM into an improved U-Net architecture, incorporating multi-scale CAM attention for adaptive feature fusion and dilated residual modules for large-scale context extraction. Experimental results on the SWINSEG dataset demonstrate that our method outperforms existing state-of-the-art methods, improving recall by 3.06%, F1 score by 1.49%, and MIoU by 2.21% over the best baseline. The proposed framework balances accuracy, interpretability, and computational efficiency, offering a trustworthy solution for cloud detection systems in operational settings. Full article

Hydrogels are widely known for their ability to increase soil water retention and for their potential slow nutrient release mechanism. They have been constantly improved to meet the growing demand for sustainability in agriculture. Research focused on the development of biodegradable hydrogels, produced from industrial cellulose waste, are an ecological and efficient alternative soil ameliorant for the improvement of agricultural land. The objective of this study was to evaluate the impacts of two types of hydrogel (processed in a glass reactor versus a twin-screw extruder) on soils with different textures (clay and sandy loam), testing their water retention capacity, nitrogen leaching, and effects on seed germination. The methodology included the evaluation of water retention capacity at different pressures with different hydrogel addition rates in the soil, leaching tests in columns filled with soil and hydrogel layers, and germination tests of sorghum and corn. The results indicated that the addition of hydrogel significantly improved water retention, especially in sandy loam soils. The hydrogels also reduced nitrogen leaching, acting as nitrification inhibitors and limiting the conversion of ammonium to nitrate, with greater effectiveness in clayey soils. In the tested formulations, it was observed that the hydrogel doses applied to the columns favored nitrogen retention in the region close to the roots, directly influencing the initial stages of germination. This behavior highlights the potential of hydrogels as tools for directing nutrients in the soil profile, indicating that adjustments to the C:N ratio, nutrient release rate, and applied doses can optimize their application for different crops. Full article

This study focuses on the exchange of mono- and divalent metal cations in FAU-type zeolite and their behavior in gas-phase CO₂ adsorption measurements and liquid-phase methylene blue (MB) adsorption in the absence of oxidizing agents under dark conditions. Firstly, zeolites exchanged with different cations were characterized by several techniques, such as XRD, SEM, XRF, XPS, and N₂ adsorption–desorption, to reveal the impact of the cations on the zeolite texture and structure. The adsorption studies revealed a positive effect of cation exchange on the adsorption capacity of the zeolite, particularly for silver-loaded FAU zeolite. In liquid-phase experiments, Ag-Y zeolite also demonstrated the highest MB removal, with a value of 79 mg/g. Kinetic studies highlighted that Ag-Y could reach the MB adsorption equilibrium within 1 h, with its highest rate of adsorption occurring during the first 5 min. In gas-phase adsorption studies, the highest CO₂ adsorption capacity was also achieved over Ag-Y, yielding 10.4 µmol/m² of CO₂ captured. Full article