Search Results (4,767)

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article

The increasing demand for plant-based cheese alternatives reflects a shift toward healthier and more sustainable food choices. This study aimed to map technological trends, formulation strategies, and major challenges in the development of plant-based cheese analogues through a systematic review of the scientific literature and patents. Following the PRISMA protocol, searches were conducted in ScienceDirect and Lens.org between December 2024 and January 2025 using keywords related to cheese analogues. A total of 1553 scientific articles and 155 patents were initially retrieved. After applying inclusion and exclusion criteria, 88 articles and 66 patents were selected for detailed analysis. The results show a growing interest in this field since 2020, peaking in 2024. Data from 2025 may be limited due to the search period. Keywords were clustered into three main areas: (1) Formulation and Composition, (2) Texture and Processing, and (3) Food Safety and Consumer Acceptance. The United States leads in patent registrations (59). Valio Company and Cargill were the most active assignees, with nine and eight patents, respectively. This study highlights the importance of integrating food science and technology to improve the quality, sensory attributes, and market competitiveness of plant-based cheese analogues compared to traditional dairy products. Full article

Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation that integrates both spectral and texture features extracted from UAV-based multispectral imagery through the development of novel Spectral–Texture Fusion Indices (STFIs). Field data were collected under nitrogen gradient treatments across three critical growth stages: heading, early filling, and late filling. A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). The models—RFECV-RF, RFECV-XGBoost, SFS-SVR, and SFS-DNN—were evaluated using four feature configurations. The SFS-DNN model with STFIs achieved the highest prediction accuracy (R² = 0.874, RMSE = 2.621 mg/g). SHAP analysis revealed the significant contribution of STFIs to model predictions, underscoring the effectiveness of integrating spectral and texture information. The proposed STFI-based framework demonstrates strong generalization across phenological stages and offers a scalable, interpretable approach for UAV-based nitrogen monitoring in rice production systems. Full article

Due to the health-driven demand for fat replacers in meat products, Lycopene (Lyc)-loaded yeast protein (YP) high internal phase Pickering emulsions (HIPPEs) were explored as fat replacers for 3D-printed meat products. HIPPEs with varying Lyc concentrations were formulated, and their encapsulation efficiency and antioxidant activity (DPPH and ABTS assays) were evaluated. The encapsulation efficiency of Lyc exceeded 90% for all samples. Microscopic analysis revealed significant droplet enlargement in emulsions containing Lyc concentrations of 1.25 mg/mL and 1.50 mg/mL. Antioxidant activity peaked at a Lyc concentration of 1.00 mg/mL. Three-dimensional-printed meat products with different fat replacement ratios (0%, 25%, 50%, 75% and 100%) were prepared using both Lyc-loaded and non-loaded emulsions, and their printing precision, cooking loss, color, pH, texture, and lipid oxidation were assessed. The replacement ratio had no significant impact on printing precision, while cooking yield improved with higher fat replacement levels. Lyc emulsions notably influenced meat color, resulting in lower lightness and higher redness and yellowness. pH values remained stable across formulations. Lipid oxidation decreased with increasing fat replacement levels. The results indicate that Lyc-loaded YP Pickering emulsions have great potential as effective fat replacers for 3D-printed meat products, enhancing antioxidant performance while preserving product quality. Full article

In this study, two color morphs (red and green) of Asian swimming crab (Charybdis japonica) commonly distributed in the China Sea area were analyzed for their L*a*b* values, carapace and inner membrane histology, morphological characteristics, mitochondrial DNA sequences, muscle texture, and amino acid composition. The results showed that compared with the green morph group, the red morph group exhibits higher aggregation of melanocytes and fewer pigment cells in the inner membrane. In addition, L* and b* of the carapace, and L* values of the inner membrane were lower in red morph group. Both populations of C. japonica also exhibit significant differences in their morphological parameters, including carapace length, body weight, and pincer width. However, the coefficient of variation for these morphological parameters did not correspond to the subspecies level. The mitochondrial DNA analysis also revealed sequence identity of COI (98.96%) and ITS-1 (99.71%) genes in both groups, supporting them to belong to the same species. Both groups also presented significant differences in their muscle texture characteristics, including adhesiveness, springiness, and gumminess, but no significant differences were observed in the muscle amino acid composition. Overall, red and green morphs of C. japonica show differences in their body color, morphological characteristics, and muscle quality, but still belong to the same species. Full article

This study analyzes the recent scientific literature on advanced biocompatible materials for triboelectric nanogenerators (TENGs) in biomedical applications. Focusing on materials like synthetic polymers, carbon-based derivatives, and advanced hybrids, the study interprets findings regarding their triboelectric properties and performance influenced by surface texture and additive manufacturing techniques. Major findings reveal that precise control over surface morphology, enabled by additive manufacturing (AM) is promising for optimizing transferred charge density and maximizing TENG efficiency. The analysis highlights the relevance of these material systems and fabrication strategies for developing self-powered wearable and implantable biomedical devices through enabling biocompatible energy-harvesting components that can operate autonomously without external power, underscoring the need for stringent biocompatibility and performance stability. This work synthesizes current progress, identifying critical material and process design parameters for advancing the field of biocompatible TENGs. Full article

The present study aimed to characterize the total phenolic content and antioxidant activity of mesquite pods (Prosopis velutina) and evaluate the effect on meat qualities in a meat product, with a view to their application as a natural functional ingredient. Mesquite pods were subjected to chemical characterization, revealing the presence of polyphenol contents with antioxidant activity (reducing power and antiradical effect). In addition, pork patties were formulated with different levels of mesquite pods powder (MPP, 2% and 5%) and mesquite pods extract (MPE, 0.1% and 0.3%), and were compared with control (CN) samples. The proximate composition of mesquite pod powder revealed a high proportion of carbohydrates and a low fat content. Additionally, the presence of polyphenols with antioxidant activity, including antiradical and reducing power, was evident. No significant differences were observed in the pork patties’ proximate composition. During 9 days of storage at 2 °C, patties treated with MPP and MPE exhibited higher pH values and lower TBARS values compared to the CN, with MPE-0.3% being the most effective in retarding lipid oxidation. Color parameters (L*, a*, b*, C*, and h*) were positively influenced by MPP and MPE, and both treatments improved water-holding capacity and reduced cooking weight loss, especially at 5% MPP. Fracture texture analysis showed that 5% MPP enhances firmness. Sensory attributes did not differ significantly from the CN. These results indicate that MPP and MPE are promising natural ingredients for extending the shelf life and maintaining the quality of pork patties without compromising sensory acceptability. Full article

Silicon nitride (Si₃N₄) ceramic is widely used in the production of structural components. The surface wettability is closely related to the service life of materials. Laser surface texturing is considered an effective method for controlling surface wettability by processing specific patterns. This research focused on the laser surface texturing of a Si₃N₄ ceramic, employing rectangular patterns instead of the typical dimple designs, as these had promising applications in heat transfer and hydrodynamic lubrication. The effects of scanning speed and number of scans on the change of the morphologies and dimensions of the grooves were investigated. The results indicated that the higher scanning speed and fewer number of scans resulted in less damage to the textured surface. As the scanning speed increased, the width and depth of the grooves decreased significantly first, and then fluctuated. Conversely, increasing the number of scans led to an increase in the width and depth of the grooves, eventually stabilizing. The analysis of the elemental composition of different areas on the textured surface presented a notable increase in oxygen content at the grooves, while Si and N levels decreased. It was mainly caused by the chemical reaction between Si₃N₄ ceramic and oxygen during laser surface texturing in an air environment. This study also assessed the wettability of the textured surface, finding that the contact angle of the water droplet was significantly affected by the groove dimensions. After laser surface texturing, the contact angle increased from 35.51 ± 0.33° to 57.52 ± 1.83°. Improved wettability was associated with smaller groove volume, indicating better hydrophilicity at lower scanning speed and enhanced hydrophobicity with a fewer number of scans. Full article

Underground hydrogen storage (UHS) in carbonate and siliceous formations presents a promising solution for managing intermittent renewable energy. However, experimental data on hydrogen–rock interactions under representative subsurface conditions remain limited. This study systematically investigates mineralogical and petrophysical alterations in dolomite, calcite-rich limestone, and quartz-dominant siliceous cores subjected to high-pressure hydrogen (100 bar, 70 °C, 100 days). Distinct from prior research focused on diffraction peak shifts, our analysis prioritizes quantitative changes in mineral concentration (%) as a direct metric of reactivity and structural integrity, offering more robust insights into long-term storage viability. Hydrogen exposure induced significant dolomite dissolution, evidenced by reduced crystalline content (from 12.20% to 10.53%) and accessory phase loss, indicative of partial decarbonation and ankerite-like formation via cation exchange. Conversely, limestone exhibited more pronounced carbonate reduction (vaterite from 6.05% to 4.82% and calcite from 2.35% to 0%), signaling high reactivity, mineral instability, and potential pore clogging from secondary precipitation. In contrast, quartz-rich cores demonstrated exceptional chemical inertness, maintaining consistent mineral concentrations. Furthermore, Brunauer–Emmett–Teller (BET) surface area and Barrett–Joyner–Halenda (BJH) pore distribution analyses revealed enhanced porosity and permeability in dolomite (pore volume increased >10×), while calcite showed declining properties and quartz showed negligible changes. SEM-EDS supported these trends, detailing Fe migration and textural evolution in dolomite, microfissuring in calcite, and structural preservation in quartz. This research establishes a unique experimental framework for understanding hydrogen–rock interactions under reservoir-relevant conditions. It provides crucial insights into mineralogical compatibility and structural resilience for UHS, identifying dolomite as a highly promising host and highlighting calcitic rocks’ limitations for long-term hydrogen containment. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

Background: Cardiovascular diseases are the leading cause of global mortality, with 80% of coronary heart disease in patients over 65. Understanding aging cardiovascular structures is crucial. Photon-counting computed tomography (PCCT) offers improved spatial and temporal resolution and better signal-to-noise ratio, enabling texture analysis in clinical routines. Detecting structural changes in aging left-ventricular myocardium may help predict cardiovascular risk. Methods: In this retrospective, single-center, IRB-approved study, 90 patients underwent ECG-gated contrast-enhanced cardiac CT using dual-source PCCT (NAEOTOM Alpha, Siemens). Patients were divided into two age groups (50–60 years and 70–80 years). The left ventricular myocardium was segmented semi-automatically, and radiomics features were extracted using pyradiomics to compare myocardial texture features. Epicardial adipose tissue (EAT) density, thickness, and other clinical parameters were recorded. Statistical analysis was conducted with R and a Python-based random forest classifier. Results: The study assessed 90 patients (50–60 years, n = 54, and 70–80 years, n = 36) with a mean age of 63.6 years. No significant differences were found in mean Agatston score, gender distribution, or conditions like hypertension, diabetes, hypercholesterolemia, or nicotine abuse. EAT measurements showed no significant differences. The Random Forest Classifier achieved a training accuracy of 0.95 and a test accuracy of 0.74 for age group differentiation. Wavelet-HLH_glszm_GrayLevelNonUniformity was a key differentiator. Conclusions: Radiomics texture features of the left ventricular myocardium outperformed conventional parameters like EAT density and thickness in differentiating age groups, offering a potential imaging biomarker for myocardial aging. Radiomics analysis of left ventricular myocardium offers a unique opportunity to visualize changes in myocardial texture during aging and could serve as a cardiac risk predictor. Full article

This study investigates measurement methods for and the distribution characteristics of normal stress within tire–road contact areas. A novel measurement method, integrating 3D scanning technology with bearing area curve (BAC) analysis, is proposed. This method quantifies the rubber penetration depth and calculates contact stress based on rubber deformation. The key innovation of this method lies in this integrated methodology for high-precision stress mapping. In the spatial domain, stress distribution is characterized by the percentage of area occupied by different stress intervals, while in the frequency domain, stress levels are analyzed at various frequencies. The results demonstrate that as the Mean Profile Depth (MPD) of the road texture increases, the areas under stress greater than 1.0 MPa increase, while the areas under stress less than 0.8 MPa decrease. However, when the MPD exceeds 0.7 mm, this effect becomes less pronounced. Higher loads and harder rubber reduce the proportion of areas under lower stress and increase the proportion under higher stress. Low-frequency (<800 1/m) stress components increase with an MPD up to 0.7 mm, beyond which they exhibit diminished sensitivity. Stress at the same frequency is not significantly affected by load variation but increases markedly with increasing rubber hardness. This research provides crucial insights into contact stress distribution, establishing a foundation for analyzing road friction and optimizing surface texture design oriented towards high-friction pavements. Full article