Search Results (3,831)

With the rapid advancement of energy storage technologies, there is a growing demand for affordable, efficient, and environmentally benign battery systems. Sodium-ion batteries (SIBs) present a promising alternative to lithium-ion systems due to sodium’s high abundance and similar electrochemical properties. Particular attention is given to developing NASICON -sodium (Na) super ionic conductor, type cathode materials, especially Na3V2(PO4)3, which exhibits high thermal and structural stability. This study focuses on the sol–gel synthesis of Na₃V₂(PO₄)₃ using citric acid and ethylene glycol, as well as investigating the effect of annealing temperature (400–1000 °C) on its structural and electrochemical properties. Phase composition, morphology, textural characteristics, and electrochemical performance were systematically analyzed. Above 700 °C, a highly crystalline NASICON phase free of secondary impurities was formed, as confirmed by X-ray diffraction (XRD). Microstructural evolution revealed a transition from a loose amorphous structure to a dense granular morphology, accompanied by changes in specific surface area and porosity. The highest surface area (67.40 m²/g) was achieved at 700 °C, while increasing the temperature to 1000 °C caused pore collapse due to sintering. X-ray photoelectron spectroscopy (XPS) confirmed the predominant presence of V³⁺ ions and the formation of V⁴⁺ at the highest temperature. The optimal balance of high crystallinity, uniform elemental distribution, and stable texture was achieved at 900 °C. Electrochemical testing in a Na/NVP half-cell configuration delivered an initial capacity of 70 mAh/g, which decayed to 55 mAh/g by the 100th cycle, attributed to solid-electrolyte interphase (SEI) formation and irreversible Na⁺ trapping. These results demonstrate that the proposed approach yields high-quality Na₃V₂(PO₄)₃ cathode materials with promising potential for sodium-ion battery applications. Full article

ASYMMETRIC LEAVES1 (AS1) and AS2 play essential roles in regulating leaf development in plants. However, their functional roles in soybean remain poorly understood. Here, we identified two members of the soybean AS1 gene family, GmAS1a and GmAS1c, which exhibit high expression levels in stem and leaf tissues. Using the CRISPR/Cas9 system, we targeted four GmAS1 and three GmAS2 genes, generating mutant lines with distinct leaf development phenotypes, including wrinkling (refers to fine lines and creases on the leaf surface, like aged skin texture), curling (describes the inward or outward rolling of leaf edges, deviating from the typical flat shape), and narrow. We found that functional redundancy exists among the four GmAS1 genes in soybean. GmAS1 and GmAS2 cooperatively regulate leaf curling, leaf crinkling phenotypes, and leaf width in soybean, with functional redundancy also observed between these two genes. Transcriptome sequencing analysis of w3 mutant (as1b as1c as1d as2a as2b as2c) identified 1801 differentially expressed genes (DEGs), including 192 transcription factors (TFs). Gene ontology enrichment analysis revealed significant enrichment of DEGs in pathways associated with plant hormone biosynthesis and signal transduction. A detailed examination of the DEGs showed several genes involved in the development of leaf lateral organs, such as KNOX (SHOOT MERISTEMLESS (STM), KNAT1, KNAT2, and KNAT6), LOB (LBD25, LBD30), and ARP5, were down-regulated in w3/WT (wild-type) comparison. CRISPR/Cas9-mediated targeted mutagenesis of the GmAS1/2 genes significantly impairs leaf development and polarity establishment in soybean, providing valuable germplasm resources and a theoretical framework for future studies on leaf morphogenesis. Full article

Fujian Tulou, a UNESCO World Heritage Site, is highly vulnerable to environmental and anthropogenic stresses, with its earthen walls prone to surface cracking that threatens both structural stability and cultural value. Traditional manual inspection is inefficient, subjective, and may disturb fragile surfaces, highlighting the need for non-destructive and automated solutions. This study proposes a dual-path framework that integrates lightweight crack detection with independent physical simulation. On the detection side, an improved YOLOv12 model is developed to achieve lightweight and accurate recognition of multiple crack types under complex wall textures. On the simulation side, a two-layer RFPA^3D model was employed to parameterize loading conditions and material thickness, reproducing the four-stage crack evolution process, and aligning well with field observations. Quantitative validation across paired samples demonstrates improved consistency in morphology, geometry, and topology compared with baseline models. Overall, the framework offers an effective and interpretable solution for standardized crack documentation and mechanistic interpretation, providing practical benefits for the preventive conservation and sustainable management of Fujian Tulou. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

Cave systems serve as key interfaces connecting surface and underground carbon cycles, and research on their carbon dynamics provides a unique perspective for revealing the mechanisms of carbon transport and transformation in karst critical zones. In this study, we established a multi-factor monitoring framework spanning the atmosphere–soil–cave continuum and associated meteorological conditions, continuously recorded cave microclimate parameters (temperature, relative humidity, atmospheric pressure, and cave winds) and CO₂ concentrations across atmospheric–soil–cave interfaces, and employed stable carbon isotope (δ¹³C) tracing in Mahuang Cave, a typical karst cave in southwestern China, from 2019 to 2023. The results show that the seasonal amplitude of atmospheric CO₂ and its δ¹³C is small, while soil–cave CO₂ and δ¹³C fluctuate synchronously, exhibiting “high concentration-light isotope” signatures during the rainy season and the opposite pattern during the dry season. Cave CO₂ concentrations drop by about 29.8% every November. Soil CO₂ production rates are jointly controlled by soil temperature and volumetric water content, showing a threshold effect. The δ¹³C response exhibits nonlinear behavior due to the combined effects of land-use type, vegetation cover, and soil texture. Quantitative analysis establishes atmospheric CO₂ as the dominant source in cave systems (66%), significantly exceeding soil-derived contributions (34%). At diurnal, seasonal, and annual scales, carbon-source composition, temperature and precipitation patterns, ventilation effects, and cave structure interact to control the rhythmic dynamics and spatial gradients of cave microclimate, CO₂ levels, and δ¹³C signals. Our findings enhance the understanding of carbon transfer processes across the karst critical zone. Full article

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data. Full article

This study evaluates the tribological properties of poly-DL-lactic acid (PDLLA) nanosheets attached to shark-skin surfaces with varying textures. The main goal was to assess friction reduction in samples with different surface textures and investigate the influence of PDLLA nanosheets on tribological behaviors. Biomimetic shark skin was created using a polydimethylsiloxane (PDMS)-embedded stamping method (PEES) that replicates shark skin’s unique texture, which reduces friction and drag in aquatic environments. PDLLA nanosheets, with a controlled thickness of several tens of nanometers, were fabricated and attached to the PDMS surfaces. The morphological characteristics of the materials were analyzed before and after attaching the PDLLA nanosheets using scanning electron microscopy (SEM), revealing the uniformity and adherence of the nanosheets to the PDMS surfaces. Friction tests were conducted using force transducers to measure the friction coefficients of biomimetic shark skin, biological models, and flat PDMS and silicon substrates, allowing a comprehensive comparison of frictional properties. Additionally, sliding tests with human fingers were performed to assess friction coefficients between various fingerprint shapes and sample surfaces. This aspect of the study is critical for understanding how human skin interacts with biomimetic materials in real-world applications, such as wearable devices. These findings clarify the relationship between surface texture, nanosheets, and their tribological performance against human skin, thereby contributing to the development of materials with enhanced friction-reducing properties for applications such as surface coatings, substrates for wearable devices, and wound dressings. Full article

Catalytic upgrading of vacuum residue (VR) is critical for enhancing fuel yield and reducing waste in petroleum refining. This study explores VR cracking over a novel cerium-loaded acidified metakaolinite catalyst (MKA800–20%Ce) prepared via calcination at 800 °C, acid leaching, and wet impregnation with 20 wt.% Ce. The catalyst was characterized using FTIR, BET, XRD, TGA, and GC–MS to assess structural, textural, and thermal properties. Catalytic cracking was carried out in a fixed-bed batch reactor at 350 °C, 400 °C, and 450 °C. The MKA800@Ce20% catalyst showed excellent thermal stability and surface activity, especially at higher temperatures. At 450 °C, the catalyst yielded approximately 11.72 g of total liquid product per 20 g of VR (representing a ~61% yield), with ~3.81 g of coke (~19.1%) and the rest as gaseous products (~19.2%). GC-MS analysis revealed enhanced production of light naphtha (LN), heavy naphtha (HN), and kerosene in the 400–450 °C range, with a clear temperature-dependent shift in product distribution. Structural analysis confirmed that cerium incorporation enhanced surface acidity, redox activity, and thermal stability, promoting deeper cracking and better product selectivity. Kinetics were investigated using an eight-lump first-order model comprising 28 reactions, with kinetic parameters optimized through a genetic algorithm implemented in MATLAB. The model demonstrated strong predictive accuracy taking into account the mean relative error (MRE = 9.64%) and the mean absolute error (MAE = 0.015) [MAE: It is the absolute difference between experimental and predicted values; MAE is dimensionless (reported simply as a number, not %. MRE is relative to the experimental value; it is usually expressed as a percentage (%)] across multiple operating conditions. The above findings highlight the potential of Ce-modified kaolinite-based catalysts for efficient atmospheric pressure VR upgrading and provide validated kinetic parameters for process optimization. Full article

Spent coffee grounds represent an abundant waste resource with potential for sustainable material applications. This study investigates the use of carbonized spent coffee grounds (CSCG) as fillers in polyurethane (PU) coatings for carbon fiber-reinforced polymer (CFRP) substrates to enhance mechanical durability and anti-icing performance. SCGs were dried, sieved (<100 µm), and oxidatively carbonized in air at 100–300 °C for 60–120 min, then incorporated into PU at 1 or 5 wt.% and applied by spray-coating. A full-factorial design was employed to evaluate the effects of carbonization temperature, particle size, and filler loading. The optimized formulation (300 °C, 100 µm, 5 wt.%) showed the highest water contact angle (103.5°), lowest work of adhesion (55.8 mJ/m²), and improved thermal stability with 60% char yield. Mechanical testing revealed increased tensile modulus with reduced strain, and differential scanning calorimetry indicated an upward shift in glass-transition temperature, suggesting restricted chain mobility. Ice formation at 0 °C was sparse and discontinuous, attributed to lowered polar surface energy, rough surface texture, and porous carbon morphology. These results demonstrate that CSCGs are effective sustainable fillers for PU coatings, offering combined improvements in mechanical, thermal, and anti-icing properties suitable for aerospace, wind power, and other icing-prone applications. Full article

Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems. Full article

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference between clue cells and normal epithelial cells lies in the surface texture and edge morphology. To address this specific problem, we propose an clue cell detection algorithm named CA-YOLO. The contributions of our approach lie in two synergistic and custom-designed feature extraction modules: the context-aware module (CAM) extracts and captures bacterial distribution patterns on the surface of clue cells; and the shuffle global attention mechanism (SGAM) enhances cell edge features and suppresses irrelevant information. In addition, we integrate focal loss into the classification loss to alleviate the severe class imbalance problem inherent in clinical samples. Experimental results show that the proposed CA-YOLO achieves a sensitivity of 0.778, which is 9.2% higher than the baseline model, making the automated BV detection more reliable and feasible. Full article

Surface defect detection serves as one of the crucial auxiliary components in the quality control of varistors, and it faces real challenges such as the scarcity of defect samples, high labelling cost, and insufficient a priori knowledge, which makes unsupervised deep learning-based detection methods attract attention. However, existing unsupervised models have problems such as inaccurate defect localisation and a low recognition rate of subtle defects in the detection results. To solve the above problems, an unsupervised detection method (Var-MNDR) is proposed to reconstruct the normal distribution of surface defects of varistors under mask constraints. Firstly, on the basis of colour space as well as morphology, an image preprocessing method is proposed to extract the main body image of the varistor, and a mask-constrained main body pseudo-anomaly generation strategy is adopted so that the model focuses on the texture distribution of the main body region of the image, reduces the model’s focus on the background region, and improves the defect localisation capability of the model. Secondly, Kolmogorov–Arnold Networks (KANs) are combined with the U-Network (U-Net) to construct a segmentation sub-network, and the Gaussian radial basis function is introduced as the learnable activation function of the KAN to improve the model’s ability to express the image features, so as to realise more accurate defect detection. Finally, by comparing the four unsupervised defect detection methods, the experimental results prove the superiority and generalisation of the proposed method. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

The increasing utilization of single-use plastics in the food sector poses serious environmental challenges. A circular economy approach, i.e., reusing packaging before recycling, offers a promising solution but raises concerns about cross-contamination between food products. This study investigates how repeated use and cleaning affect the surface topography of plastic food packaging and, in turn, how these changes influence cleaning efficiency and assessment. Recycled polyethylene terephthalate (rPET) trays were subjected to 20 industrial wash cycles with and without detergent concentration of 0.3% v/v at the following temperatures: 55 °C wash, 70 °C rinse. Surface roughness was measured using mechanical and optical techniques. Additionally, trays were roughened with sandpaper of varying grit sizes to simulate mechanical wear during consumer use. Cleanability was assessed using UV fluorescence imaging and adenosine triphosphate (ATP) assays. Results showed no significant increase in surface roughness after 20 wash cycles. However, artificially roughened surfaces retained more food residue, complicating cleaning. The application of UV fluorescence imaging proved more effective than ATP assays in detecting food residues on textured surfaces. These findings support the use of advanced imaging for evaluating the hygiene of reusable packaging and highlight key considerations for implementing circular reuse systems in food packaging. Full article

Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations. Full article