Search Results (3,073)

Rutting is a critical form of pavement distress that compromises driving safety and long-term structural integrity. Traditional detection methods predominantly rely on cross-sectional measurements and high-cost inertial navigation-assisted laser scanning, which limits their applicability for large-scale, full-section evaluation. To address these limitations, this study proposes a framework for full-section rutting detection leveraging an area-array structured light camera for efficient 3D data acquisition. A multi-scale texture enhancement strategy based on 2D wavelet transform is introduced to extract latent surface features, enabling robust and accurate point-cloud registration without the need for artificial markers. Additionally, an improved Random Sample Consensus—Density-Based Spatial Clustering of Applications with Noise (RANSAC-DBSCAN) algorithm is designed to enhance the precision and robustness of rutting region segmentation under real-world pavement conditions. The proposed method is experimentally validated using 102 multi-frame pavement point clouds. Compared to Fast Point Feature Histograms (FPFH) and Deep Closest Point (DCP), the registration approach achieves a 71.31% and 80.64% reduction in point-to-plane error, respectively. For rutting segmentation, the enhanced clustering method attains an average F1-score of 90.5%, outperforming baseline methods by over 15%. The proposed workflow can be seamlessly integrated into vehicle-mounted structured-light inspection systems, offering a low-cost and scalable solution for near real-time, full-lane rutting detection in routine pavement monitoring. Full article

This work presents a microstructural analysis and X-ray diffraction (XRD) investigation of the plastic deformation in commercially pure, single-phase hexagonal close-packed (hcp) Zn subjected to rolling and tensile tests up to failure. Samples were examined by optical microscope and XRD; hardness was assessed by Vickers microhardness. High-resolution diffraction profiles with Kα1/Kα2 deconvolution were used to identify deformation-induced texture and to estimate the dislocation density. Results show that rolling (40% thickness reduction) and tensile test change texture and cause peak shifts and broadening, with corresponding microstructural changes. Microhardness changes from 28–45 HV (annealed) to 30–50 HV after deformation. After rolling, the texture (002) is the most intense reflection and (004) increases without significant angular shifts. Tensile tests induce low-angle shifts of (101) and (004), as well as selective texture changes (appearance of (103) and (110)). The (101) full width at half maximum increases from β(2θ) = 0.115° (annealed) to 0.160° (rolled) and 0.140° (after tensile test), yielding dislocation densities from 2.73 × 10⁶ cm⁻² (annealed) to 3.03 × 10¹¹ cm⁻² (rolled) and 3.38 × 10¹⁰ cm⁻² (after tensile test). Finally, this study quantifies the XRD parameters (full width at half maximum, angular shifts and dislocation density). Plastic deformation of pure Zn leads to significant microstructural changes, including grain refinement, the generation of dislocations, and the formation of new crystallographic orientations, which are then observable in XRD patterns as peak broadening, shifts, and texture development. The severity of these effects depends on the level of deformation. Full article

Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems. Full article

In order to explore the influence of storage temperature and time on the quality of vacuum-packed water caltrop shell (WCS), this study investigated the changes in the quality of vacuum-packed WCS under different storage temperature (3 ± 1 °C, 5 ± 1 °C and 7 ± 1 °C) and time. The quality-related parameters of WCS, including sensory quality, moisture content, texture characteristics and microstructure, were examined. The results showed that at the storage temperature of 5 ± 1 °C, vacuum-packaged WCS could maintain high sensory quality within 21 days, while at 3 ± 1 °C and 7 ± 1 °C, the samples showed low sensory quality at 21 days and 14 days, respectively. For the same storage time, storage at 5 ± 1 °C resulted in the least significant decrease in elastic modulus and compressive strength of the samples. Among the three storage temperatures, storage at 7 ± 1 °C led to the most obvious change in pore structure, followed by storage at 3 ± 1 °C and then 5 ± 1 °C. The variance analysis suggested that storage time has significant effects on all the tested parameters, while storage temperature has significant effects on the sensory quality and texture characteristics of the samples but shows no significant effect on the moisture content. These findings provide a theoretical reference for the packaging and storage of WCS and the development of water caltrop sheller. Full article

Background: Dysphagia is a common problem in older adults, characterized as a swallowing disorder that prevents food from passing from the mouth to the esophagus. Besides impairing dietary intake and leading to malnutrition, dysphagia also severely restricts water intake. Purpose: This study aimed to develop polysaccharide-based hydrogels as dysphagia-friendly finger foods designed to provide high water content and enable controlled vitamin delivery to older persons with dysphagia. Procedures: Agar–carboxymethylcellulose (Agar-CMC) composite hydrogels with incorporated vitamins C, B₉, B, and D₃ were developed and tested for their textural and rheological properties, vitamin stability during storage, and vitamin release under simulated gastrointestinal conditions. Finally, a fiberoptic endoscopic swallowing assessment and sensory evaluation were conducted. Main Findings: Increasing the agar concentration in Agar-CMC hydrogels improved their internal structure and handling properties as finger foods, while still being easily breakable during swallowing. Agar-CMC hydrogels’ structure protected vitamins during processing and six weeks of storage. Vitamin release started immediately and remained steady in the gastric phase, with a noticeable increase at the beginning of the intestinal phase, resulting in 70–100% vitamin release by the end of this phase. The Fiberoptic Endoscopic Swallowing Evaluation confirmed their suitability for individuals with mild to moderate oropharyngeal dysphagia, with a low risk of aspiration (1 point on the Penetration-Aspiration Scale out of 8). Principal Conclusions: The developed Agar-CMC hydrogels present a promising dysphagia-friendly finger food alternative with high water content. They effectively deliver essential vitamins throughout the gastrointestinal tract, and notably demonstrate a low aspiration risk, making them suitable for individuals with mild to moderate oropharyngeal dysphagia. Full article

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

In this study, a biodegradable Mg-Zn-Nd-Gd alloy was processed via multi-directional forging (MDF) to evaluate its microstructural evolution, mechanical performance, and corrosion behavior. Electron backscattered diffraction (EBSD) analysis was conducted to evaluate the influence of grain size and texture on mechanical strength and corrosion resistance. The average grain size decreased significantly from 118 ± 5 μm in the homogenized state to 30 ± 10 μm after six MDF passes, primarily driven by discontinuous dynamic recrystallization (DDRX). Remarkably, this magnesium (Mg) alloy exhibited a rare synergistic enhancement in both strength and ductility, with ultimate tensile strength (UTS) increasing by ~59%, yield strength (YS) by ~90%, while elongation improved by ~44% unlike conventional severe plastic deformation (SPD) techniques that often sacrifice ductility for strength. This improvement is attributed to grain refinement, dispersion strengthening from finely distributed Mg₁₂Nd and Mg₇Zn₃ precipitates, and texture weakening, which facilitated the activation of non-basal slip systems. Despite the mechanical improvements, electrochemical corrosion testing in Hank’s balanced salt solution (HBSS) at 37 °C revealed an increased corrosion rate from 0.1165 mm/yr in homogenized condition to 0.2499 mm/yr (after six passes of MDF. This was due to the higher fraction of low-angle grain boundaries (LAGBs), weak basal texture, and the presence of electrochemically active fine Mg₇Zn₃ particles. However, the corrosion rate remained within the acceptable range for bioresorbable implant applications, indicating a favorable trade-off between mechanical performance and degradation behavior. These findings demonstrate that MDF processing effectively enhances the strength–ductility synergy of Mg-rare earth alloys while maintaining a clinically acceptable degradation rate, thereby presenting a promising route for next-generation biomedical implants. Full article

The ‘Annurca’ apple, a traditional Italian cultivar protected by the “Melannurca Campana” EU PGI designation, undergoes a mandatory, traditional postharvest reddening process in a melaio. While essential for developing its characteristic flavor and color, this process can also lead to significant textural degradation, resulting in a mealy and soft fruit that conflicts with modern consumer expectations. This study investigated an integrated postharvest strategy to resolve this quality trade-off. We evaluated the sensory profile and consumer acceptance of ‘Annurca’ apples subjected to three treatments: traditional melaio reddening (Melaio), a 1-methylcyclopropene treatment alone (MCP), and a combined treatment of MCP followed by melaio reddening (MCP+Melaio). A panel of 534 consumers evaluated the apples for overall liking and the intensity of seven key sensory attributes. The results showed that the integrated ‘MCP+Melaio’ treatment was significantly preferred (Mean liking = 6.61) over both the traditional ‘Melaio’ (M = 5.91) and ‘MCP’ alone (M = 5.91) treatments. This preference was driven by a superior sensory profile that combined the high crunchiness and low mealiness of the MCP treatment with the high perceived aroma intensity and sweetness developed during the melaio phase. Furthermore, consumer segmentation analysis identified four distinct preference clusters, revealing that the integrated treatment’s success derived from its ability to satisfy the divergent priorities of the two largest segments: “Melaio Fans” (37%) and “Texture & Flavor Seekers” (35%). Our findings demonstrate that combining 1-MCP with traditional practices creates a synergistic effect, producing a high-quality apple that is texturally superior, aromatically intense, and has an extended sensory shelf-life. This integrated approach offers a scientifically validated and practical solution to enhance the quality and consistency of ‘Annurca’ apple production. Full article

This study assessed the impact of commercial packaging on the stability and identity of Copioba cassava flour. Flour was packaged in low-density polyethylene (LDPE), polypropylene (PP), and metallized biaxially oriented polypropylene (BOPP) films. Quality changes over time were evaluated via moisture content, water activity (a_w), pH, titratable acidity, texture/hardness, color, fatty acid composition, and specific microorganisms. Moisture content and a_w increased in the LDPE-packaged flour and the control group. At the end of the storage period, levels of fatty acids had decreased by 55.81–68.28%, with only minor changes in a_w. There was a rise in yeast and mold levels up to 4 log CFU/g in flour packaged in LDPE films. In contrast, the levels of Bacillus cereus in flour packaged in PP and BOPP ranged from <1 to 2.30 log CFU/g. PP and BOPP films exhibited the most effective performance among the packaging materials evaluated. The results obtained in this study will contribute to the pursuit of a Geographical Indication GI certification by providing information about the best packaging type for preserving the unique characteristics of Copioba cassava flour, as no study has previously reported on the best type of packaging material for Copioba flour. Full article

Lead (Pb(II)) contamination in water poses severe environmental and health risks due to its toxicity and persistence. This study compares almond shell-derived biochars produced by slow pyrolysis (SP) and microwave pyrolysis (MW), with and without KOH activation, focusing on structural properties and Pb(II) adsorption performance. Biochars were characterized by proximate and elemental analysis, BET surface area, FTIR spectroscopy, and adsorption experiments including pH dependence, kinetics, and equilibrium isotherms. Non-activated SP exhibited the highest surface area (S_BET = 693 m²·g⁻¹), pronounced mesoporosity (≈73% of total pore volume), and the largest observed equilibrium capacities. KOH activation increased surface hydroxyl content but degraded textural properties; in MW samples, it induced severe pore collapse. Given the very fast uptake, kinetic modeling was treated cautiously: for non-activated biochars, Elovich adequately captured the time-course trend, whereas activated samples returned non-physical kinetic constants (e.g., negative k₂) likely due to high post-adsorption pH (>11) and probable Pb(OH)₂ precipitation. Equilibrium data (fitted over 50–500 mg·L⁻¹) were better captured by the Freundlich and Redlich–Peterson models, indicating a mixed adsorption behaviour with contributions from heterogeneous site distribution and site-specific interactions. Optimal Pb(II) removal occurred at pH 4, with no measurable leaching from the biochar matrix. Overall, non-activated SP biochar is the most effective, sustainable and low-cost option among the tested materials for Pb(II) removal from water, avoiding aggressive chemical activation while maximizing adsorption performance. Full article

Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A machine vision inspection method based on a two-stage fusion network is proposed in this paper, aiming to achieve accurate cutting of salmon parts and efficient recognition of defects. The fish body image is collected by building a visual inspection system, and the dataset is constructed by preprocessing and data enhancement. For the part cutting, the improved U-Net model that introduces the CBAM attention mechanism is used to strengthen the extraction ability of the fish body texture features. For defect detection, the two-stage fusion architecture is designed to quickly locate the defective region by adding the YOLOv5 of the P2 small target detection layer first, and then the cropped region is fed into the improved U-Net for accurate cutting. The experimental results demonstrate that the improved U-Net achieves a mean average precision (mAP) of 96.87% and a mean intersection over union (mIoU) of 94.33% in part cutting, representing improvements of 2.44% and 1.06%, respectively, over the base model. In defect detection, the fusion model attains an mAP of 94.28% with a processing speed of 7.30 fps, outperforming the single U-Net by 28.02% in accuracy and 236.4% in efficiency. This method provides a high-precision, high-efficiency solution for intelligent salmon processing, offering significant value for advancing automation in the aquatic product processing industry. Full article

Magnesium (Mg) alloys are prized as the lightest structural materials but often suffer from a strength–ductility trade-off that is particularly challenging for applications demanding high thermal conductivity. Aiming to resolve this issue, rolled ternary Mg-0.9Mn-1.9Ce (wt.%) alloy sheets were designed and fabricated, and the influence of rolling strain on optimizing the property balance was systematically investigated. The cast alloy was homogenized and rolled to two accumulated strains to obtain S10 (90%) and S20 (95%), followed by microstructure characterization and mechanical/thermal evaluation. Compared with S10, S20 developed finer, more equiaxed grains and a weaker basal texture via enhanced dynamic recrystallization; concurrent fragmentation and uniform dispersion of second-phase particles further contributed to strengthening. Consequently, S20 achieved 14.2% and 13.7% increases in yield and tensile strengths, respectively, with a slight improvement in elongation, while retaining high thermal conductivity (134.4 W·m⁻¹·K⁻¹ vs. 138.1 W·m⁻¹·K⁻¹ for S10). The minimal conductivity penalty is attributed to the low solute level in the α-Mg matrix, which limits electron scattering. These results provide experimental and mechanistic guidance for developing rolling Mg alloys that combine high mechanical performance with excellent thermal efficiency. Full article

Friction pairs in heavy-duty power-shift tractor wet clutches operate under complex conditions, making them vulnerable to damage and reducing reliability. Optimizing their tribological performance requires a trade-off between a high coefficient of friction (COF) for torque transmission and a low temperature rise ($∆T$) to prevent thermal damage. Surface texturing is an effective method for improving the tribological performance of friction pairs. This study simulated the friction of wet clutch pairs via pin-on-disk tests and designed micro-textures on the pin surface to enhance tribological performance. Based on the experimental data, a Gaussian Process Regression (GPR) surrogate model was developed to accurately predict COF and $∆T$ as a function of the clutch’s operating and micro-texture’s geometric parameters. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was then employed to obtain the optimal set of solutions. The obtained pareto front clearly revealed the COF–temperature rise trade-off. From the optimal solution set, optimal micro-texture parameters for two typical operating conditions of different clutches were extracted. Compared with the untextured surface, the optimal solutions increased COF by 2.6%/1.2% and reduced $∆T$ by 39.2%/12.1%. Relative to neighboring experimental points, COF further increased by 11.3%/2.7% and $∆T$ decreased by 16.6%/1.7%. This work establishes a method for balancing the frictional and thermal performance of friction pairs. Full article

This study employs a multi-technique approach to elucidate how graphene incorporation affects phase formation, microstructure, and thermal behavior in PVA-assisted sol–gel synthesized Ca_2.95Eu_0.05Co₄O_x nanomaterials. XRD confirms the preservation of the primary phases (hexagonal CaCO₃ and cubic CoO) alongside a distinct graphene (002) reflection; a systematic low-angle shift of the calcite (104) peak evidences partial relaxation of residual lattice strain with increasing graphene content, while Scherrer analysis indicates tunable crystallite size. Raman spectroscopy corroborates graphene incorporation through pronounced D (~1300 cm⁻¹) and G (~1580 cm⁻¹) bands and supports the XRD-identified phase coexistence via cobalt-oxide and calcite vibrations in the 200–700 cm⁻¹ region, also indicating increased defect/disorder with graphene loading. SEM shows grain refinement, denser/bridged lamellar textures, and reduced porosity at low–moderate graphene contents (1–3 wt.%), contrasted by agglomeration-driven heterogeneity at higher loadings (5–7 wt.%). EDX reveals increasing carbon with Ca/Co redistribution at accessible surfaces, and TG–DSC corroborates the removal of oxygen-containing groups and oxidative combustion of graphene at mid temperatures. Collectively, Raman–XRD-consistent evidence demonstrates that graphene provides a tunable handle over lattice strain, crystallite size, and grain-boundary architecture, establishing a processing–composition basis for optimizing functional (e.g., electrical/thermoelectric) performance. Full article

Structured CBN (cubic boron nitride) grinding wheels usually have a specially designed texture on their surface to reduce the grinding heat and grinding force. However, most structured grinding wheels are fabricated by electroplating, brazing, sintering, and mechanical or laser removal on the surface of conventional grinding wheels, which may have problems such as complicated processes, low processing efficiency, and unstable effects. In this paper, additive manufacturing was used to fabricate a radial straight groove-structured grinding wheel. Meanwhile, a corresponding mathematical model of the grinding wheel was also established considering the shape and position of the abrasive grains. Subsequently, the ground surface morphologies of the fabricated wheel and simulated wheel under different machining parameter conditions were compared to further prove the rationality of the simulated grinding wheel. The results showed that the ground surfaces of the fabricated wheel and simulated wheel had similar morphological characteristics. The trend in the surface roughness under the different machining parameter conditions was also analyzed and showed the same variation for fabricated and simulated wheels; the error rate was confined within 8%. This paper elucidates the grinding mechanism and surface morphology formation process of a radial straight groove-structured grinding wheel fabricated by additive manufacturing. Full article