Search

The effect of moisture on the adhesion strength at the asphalt and recycled concrete aggregate (RCA) interface plays an important role in the durability performance of asphalt pavements. In this study, a universal testing machine was used to analyse the effect of moisture on the asphalt adhesion rate at the interface. Scanning electron microscopy was utilised to observe the changes in the micro-morphology of the interface before and after the effect of moisture. The effect of moisture on the interface between asphalt and recycled aggregate was investigated using molecular dynamics (MD) simulation to explore the microscopic mechanism. The results showed that the residual rate of asphalt on the surface of concrete specimens decreased from 80.2% to 34.7% after 72 h of water bath curing. The micro-morphology at the asphalt–RCA interface changed significantly before and after the action of water. Interfacial water affects the concentration distribution of asphalt components at the interface mainly by changing the nanostructure within the asphalt binder. This leads to worse adhesion properties at the asphalt–RCA interface. This study provides deeper insights into understanding water damage at the asphalt–RCA interface and provides ideas for improving the durability of the material. [ABSTRACT FROM AUTHOR]

Fruit softening is a prominent attribute governing both longevity on shelves and commercial worth. Polygalacturonase (PG) plays a major role in strawberry fruit softening. However, the PG gene family in strawberry has not been comprehensively analyzed. In this study, 75 FaPG genes were identified in the octoploid strawberry genome, which were classified into three groups according to phylogenetic analysis. Subcellular localization prediction indicated that FaPGs are mostly localized to the plasma membrane, cytoplasm, and chloroplasts. Moreover, the expression of FaPGs during strawberry development and ripening of 'Benihoppe' and its softer mutant was estimated. The results showed that among all 75 FaPGs, most genes exhibited low expression across developmental stages, while two group c members (FxaC_21g15770 and FxaC_20g05360) and one group b member, FxaC_19g05040, displayed relatively higher and gradual increases in their expression trends during strawberry ripening and softening. FxaC_21g15770 was selected for subsequent silencing to validate its role in strawberry softening due to the fact that it exhibited the highest and most changed expression level across different developmental stages in 'Benihoppe' and its mutant. Silencing FxaC_21g15770 could significantly improve strawberry fruit firmness without affecting fruit color, soluble solids, cellulose, and hemicellulose. Conversely, silencing FxaC_21g15770 could significantly suppress the expression of other genes related to pectin degradation such as FaPG-like, FaPL, FaPME, FaCX, FaCel, FaGlu, FaXET, and FaEG. These findings provide basic information on the FaPG gene family for further functional research and indicate that FxaC_21g15770 plays a vital role in strawberry fruit softening. [ABSTRACT FROM AUTHOR]

Defects, such as scratches, frequently arise during the manufacturing of near-mirror workpieces, adversely affecting both their longevity and aesthetic appeal. The identification of these near-mirror defects through machine vision is fraught with challenges, including the scarcity of comprehensive datasets, variability in surface curvature, and the diminutive size of the defects. In response to these challenges, this study integrates Visual Parser and deep learning methodologies for the visual detection of near-mirror defects. A dataset conducive to deep learning was developed utilising a sliding window approach. The proposed model incorporates an Adaptive Weighted Convolution Structure (AWCS) and Convolutional Block Attention Module (CBAM) within the ResNet18 architecture. Additionally, we introduce an efficient and precise technique termed Visual Parser, which facilitates the detection and localisation of defect areas in near-mirror workpieces. When combined with a classification model, the Visual Parser allows for the online inference and detection of extensive near-mirror images. Experimental findings indicate that the performance metrics of the proposed ResNet18-AWCS-CBAM (ResNet18-ASCM) model significantly exceed those of established networks. The defect detection and localisation method based on Visual Parser is capable of fulfilling the online inspection requirements pertinent to the production of near-mirror workpieces. [ABSTRACT FROM AUTHOR]