Search Results (3,706)

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

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

Stainless steel is recognized for its excellent durability and anti-corrosion properties, which are essential qualities across various industrial applications. The machining of stainless steel, particularly under a dry environment to attain sustainability, poses several challenges. The poor heat conductivity and high ductility of stainless steel results in poor heat distribution, accelerating tool wear and problematic chip formation. To mitigate these challenges, the implementation of surface texturing has been identified as a beneficial strategy. This study investigates the impact of wave-type texturing patterns, developed on the flank surface of tungsten carbide ceramic tool inserts, on the machinability of AISI 316 stainless steel under dry cutting conditions. In this investigation, chip morphology and surface roughness were used as key indicators of machinability. Analysis of Variance (ANOVA) was conducted for chip thickness, chip thickness ratio, and surface roughness, while Taguchi mono-objective optimization was applied to chip thickness. The ANOVA results showed that linear models accounted for 71.92%, 83.13%, and 82.86% of the variability in chip thickness, chip thickness ratio, and surface roughness, respectively, indicating a strong fit to the experimental data. Microscopic analysis confirmed a substantial reduction in chip thickness, with a minimum observed value of 457.64 µm. The corresponding average surface roughness Ra value 1.645 µm represented the best finish across all experimental runs, highlighting the relationship between thinner chips and enhanced surface quality. In conclusion, wave textures on the cutting tool’s flank face have the potential to facilitate the dry machining of AISI 316 stainless steel to obtain favorable machinability. Full article

Pavement skid resistance is of vital importance for road safety. The objective of this study is to propose and validate a texture-based image indicator to predict pavement friction. This index enables pavement friction to be predicted easily and inexpensively using digital images, with predictions correlated to Dynamic Friction Tester (DFT) measurements. Three different types of asphalt surfaces (Dense-Grade Asphalt Concrete, Open-Grade Friction Course, and Chip Seal) were evaluated subject to various tire polishing cycles. Images were taken with corresponding friction coefficients obtained using DFT in the laboratory. The aggregate protrusion area is proposed as the indicator. Statistical models are established for each asphalt surface type to correlate the proposed indicator with friction coefficients. The results show that the adjusted R-squared values of all relationships are above 0.90. Compared to other image-based indicators in the literature, the proposed image indicator more accurately reflects the changes in pavement friction with the number of polishing cycles, proving its cost-effective use for considering pavement friction in the mix design stage. Full article

In the rapidly evolving fields of materials science, catalysis, electronics, drug delivery, and environmental remediation, the development of effective substrates for molecular deposition has become increasingly crucial. Ordered mesoporous silica materials have garnered significant attention due to their unique structural properties and exceptional potential as substrates for molecular immobilization across these diverse applications. This study compares three mesoporous silica powders: MCM-41, SBA-15, and SBA-16. A multi-technique characterization approach was employed, utilizing low- and wide-angle X-ray diffraction (XRD), nitrogen physisorption, and transmission electron microscopy (TEM) to elucidate the structure–property relationships of these materials. XRD analysis confirmed the amorphous nature of silica frameworks and revealed distinct pore symmetries: a two-dimensional hexagonal (P6mm) structure for MCM-41 and SBA-15, and three-dimensional cubic (Im$\overline{3}$m) structure for SBA-16. Nitrogen sorption measurements demonstrated significant variations in textural properties, with MCM-41 exhibiting uniform cylindrical mesopores and the highest surface area, SBA-15 displaying hierarchical meso- and microporosity confirmed by NLDFT analysis, and SBA-16 showing a complex 3D interconnected cage-like structure with broad pore size distribution. TEM imaging provided direct visualization of particle morphology and internal pore architecture, enabling estimation of lattice parameters and identification of structural gradients within individual particles. The integration of these complementary techniques proved essential for comprehensive material characterization, particularly for MCM-41, where its small particle size (45–75 nm) contributed to apparent structural inconsistencies between XRD and sorption data. This integrated analytical approach provides valuable insights into the fundamental structure–property relationships governing ordered mesoporous silica materials and demonstrates the necessity of combined characterization strategies for accurate structural determination. Full article

Low glycemic index (GI) cookie bars were prepared with soft wheat flour substituted with 10–50% soybean flour and 10–50% resistant starch. The effects of increased levels of soybean flour and resistant starch on the quality of low glycemic index cookie bars were investigated (i.e., moisture, cookie spread, texture (breaking force), surface color, and in vitro starch digestibility). It was found that increasing soybean flour substitution increased the breaking force, moisture, protein content, and yellowish color of the low GI cookie bars but decreased the cookie bar spread and the lightness of the cookie bars (p < 0.05). The addition of soybean flour and resistant starch by up to 50% did not significantly change the in vitro starch digestibility of the cookie bars. The overall acceptability of the cookie bars was lower when the soybean flour blend went beyond 10%. When soft wheat flour in the cookie bar formulation was replaced at the following levels (10%, 30%, and 50%) by resistant starch, the cookie spread and lightness of the cookie bars increased but the breaking force was decreased along with the yellowish color (p < 0.05). When resistant starch was combined with soft wheat flour at levels of up to 50%, this significantly increased the content of total dietary fiber and spread ratio of cookie bars. Sensorial analysis showed that resistant starch presence had an acceptable impact on overall acceptability of the low GI cookie bars. Resistant starch represents a viable dietary fiber source when substituted for 50% of soft wheat flour in formulations. While this substitution may result in increased spread ratio and decreased crispness in cookie bars, the addition of 10% soybean flour can mitigate these textural changes. Full article

This study proposes a laser texturing method to optimize adhesion and minimize residual stresses in CVD diamond films deposited on tungsten carbide (WC-Co). WC-5.8 wt% Co substrates were textured with quadrangular pyramidal patterns (35 µm) using a 1064 nm nanosecond-pulsed laser, followed by chemical treatment (Murakami’s solution + aqua regia) to remove surface cobalt. Diamond films were grown via HFCVD and characterized by Raman spectroscopy, EDS, and Rockwell indentation. The results demonstrate that pyramidal texturing increased the surface area by a factor of 58, promoting effective mechanical interlocking and reducing compressive stresses to −1.4 GPa. Indentation tests revealed suppression of interfacial cracks, with propagation paths deflected toward textured regions. The pyramidal geometry exhibited superior cutting post-deposition cooling time for stress relief from 3 to 1 h. These findings highlight the potential of laser texturing for high-performance machining tool applications. Full article

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials. Full article

Today, urban areas have started to grow and expand with the urbanization and industrialization processes brought about by rapid population growth. The increase in urban density brought about by this growth process has led to the destruction of natural areas and created surfaces such as concrete, asphalt, etc., that absorb solar energy. The expansion/proliferation of impervious surfaces in the city has changed the urban climate in the direction of temperature increase compared to the surrounding rural areas. When this change is combined with the temperature increases due to global climate change, it creates urban heat islands, especially in high density areas, and directly affects land surface temperatures. In this study, ground surface temperature analysis for the years 2012–2022 was carried out in order to determine the temperature changes in Denizli city. As a result of the analysis, eight urban textures with different characteristics with very high and high temperature increase were determined. Analyses were made in the context of urban heat island criteria in the determined textures, and the effect of the settlement pattern on urban heat island formation was examined by making use of the analysis results and related literature findings. Full article

As a carbon-rich material with sufficient inorganic nutrients, biochar is potentially an inexpensive and suitable additive to improve the quality of soil and achieve sustainable agriculture. However, the addition of biochar generally increases pesticide adsorption in soil because of the well-maintained porous structure, and the specific effects of the properties of biochar, soil, and pesticides on the adsorption capacity of pesticides remain unknown. In this study, a meta-analysis was conducted to investigate the effects of biochar addition on pesticide adsorption in soils, focusing on characteristics such as the biochar addition dosage, biochar properties (pH, specific surface area (SSA), pore diameter, (O+N)/C, H/C), and soil properties (texture, initial pH, cation exchange capacity). Overall, wood-derived biochar that was treated at ≥700 °C for 2–4 h, with a pH of 9–10 and a 2–4% addition rate led to the greatest enhancement in the pesticide adsorption capacity of soil. Additionally, the pyrolysis temperature of the biochar, the biochar’s pore diameter, and the soil’s pH significantly influenced the adsorption capacity. Based on this meta-analysis, we conclude that the (O+N)/C ratio of biochar is the most influential predictor of soil’s pesticide adsorption capacity. Full article

As manufacturing demands for challenging-to-machine metallic materials continue to evolve, the performance of cutting tools has emerged as a critical limiting factor. The synergistic application of micro-texture and coating in cutting tools can improve various properties. For the processing of existing micro-texture, because of the fast cooling and heating processing method of laser, there are defects such as remelted layer stacking and micro-cracks on the surface after processing. This study introduces a preheating-assisted technology aimed at optimizing the milling performance of textured coated tools. A milling test platform was established to evaluate the performance of these tools on titanium alloys under thermally assisted conditions. The face-centered cubic response surface methodology, as part of the central composite design (CCD) experimental framework, was employed to investigate the interaction effects of micro-texture preparation parameters and thermal assistance temperature on milling performance. The findings indicate a significant correlation between thermal assistance temperature and tool milling performance, suggesting that an appropriately selected thermal assistance temperature can enhance both the milling efficiency of the tool and the surface quality of the titanium alloy. Utilizing the response surface methodology, a multi-objective optimization of the textured coating tool-preparation process was conducted, resulting in the following optimized parameters: laser power of 45 W, scanning speed of 1576 mm/s, the number of scans was 7, micro-texture spacing of 130 μm, micro-texture diameter of 30 μm, and a heat-assisted temperature of 675.15 K. Finally, the experimental platform of optimization results is built, which proves that the optimization results are accurate and reliable, and provides theoretical basis and technical support for the preparation process of textured coating tools. It is of great significance to realize high-precision and high-quality machining of difficult-to-machine materials such as titanium alloy. Full article

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article