Tianshui Normal University

Flesh mealiness, a textural disorder in apples, reduces storage quality and consumer acceptance. The ‘Delicious’ and ‘Fuji’, prominent apple cultivars in China, exhibit contrasting susceptibility to mealiness, though the underlying mechanisms remain unclear. This study compared cytological, physiological and cell wall metabolic changes between mealy ‘Oregon Spur II Delicious’ and non-mealy ‘Miyazaki Spur Fuji’ during ambient storage. Toluidine blue staining and scanning electron microscopy revealed that ‘Delicious’ exhibited larger intercellular spaces and cell separation in contrast to ‘Fuji’. This observation aligns with the earlier onset of mealiness in ‘Delicious’: its mealiness degree increased from 3.06% at harvest to 19.62% after 28 d of storage (a 6.4-fold rise), whereas that of ‘Fuji’ only increased from 2.13% to 3.90% (1.8-fold). This pronounced increase in ‘Delicious’ was accompanied by a significant increase in air space volume and a reduction in expressible juice. Furthermore, the occurrence of mealiness in ‘Delicious’ involved a sharp increase in respiration rate and ethylene production, alongside rapid declines in firmness and starch content. Notably, there was a substantial accumulation of water-soluble pectin (WSP) and chelator-soluble pectin (CSP) in ‘Delicious’, whereas the content of Na2CO3-soluble pectin (NSP) remained consistently lower. Monosaccharide composition analysis confirmed significantly reduced arabinose and galactose levels across pectin fractions (WSP, CSP, and NSP) in ‘Delicious’. Correspondingly, immunofluorescence labeling showed a pronounced degradation of arabinan and galactan within the side chains of rhamnogalacturonan-I (RG-I). In addition, the activities of pectin methylesterase, α-L-Arabinofuranosidase, and β-D-Galactosidase remained significantly elevated in ‘Delicious’. Collectively, these findings demonstrate that cultivar differences in flesh mealiness are attributable to divergent physiological senescence and cell wall disassembly processes.

In composite box girders with constant cross section corrugated steel webs, the shear stress in the web is typically calculated based on the applied shear force. For composite box girders with corrugated steel webs of variable cross sections, the calculation extends this approach by further incorporating additional shear effects induced by axial force and bending moment. The resulting web shear stress is obtained by superimposing the shear stress components arising from shear force, axial force, and bending moment. However, due to the unique folding effect of the corrugated steel web, its shear stiffness is significantly reduced. This effect also leads to discrepancies in the rotational angles among the top and bottom flanges, the web, and the overall cross section of the composite box girder. Although the traditional shear stress calculation method has clear physical meaning, it fails to accurately capture, through analytical expressions, the reduction in shear stiffness and the rotational angle discrepancies among these structural components. To address this, the present study derives a shear stress calculation equation for composite box girders with corrugated steel webs, accounting for the shear stiffness reduction caused by the folding effect. A rotational angle displacement function is introduced to reflect the angular discrepancies among the top and bottom flanges, the web, and the composite cross section. The web shear stress is then determined by combining the effects of shear stiffness reduction and rotational angle variation. Using the energy variational method, the shear contributions of the flanges and the web are decoupled. The shear force carried by the flanges is further decomposed into contributions from the top and bottom plates based on moment equivalence. In addition, the shear force distribution ratios among the top plate, bottom plate, and web are examined. Finally, a cantilever beam example is presented to validate the proposed equation by comparing it with existing shear stress calculation expressions for corrugated steel webs, thereby evaluating the shear performance of composite box girders.

The chemical recycling of plastic waste to value-added chemicals, with the advantages of reducing pollution and lower carbon emissions, shows great potential in the circular economy. However, traditional treatment methods have limitations such as high energy consumption, the need for toxic reagents, limited substrate scope, and low efficiency. Here, we present an efficient and sustainable flavin–scandium–thiourea upcycling method employing an easily available, green, and nontoxic flavin as a catalyst. This method enables the conversion of polystyrene with Mw as high as 1,700,000 g/mol and already-used plastics into benzoic acid with yields of up to 68%. Operating under mild conditions with visible light and ambient air, the protocol features a green catalyst, high efficiency, and broad substrate scope for the selective upcycling of PS from real-life plastic waste, aligning with green chemistry principles.

Abstract To enhance the intelligence of machining processes, accurate recognition of tool wear states has become a key issue in the manufacturing field. However, due to the non-stationary and high-dimensional nature of cutting signals, traditional methods face significant challenges in feature extraction and state classification. In the context of cutting processes, challenges such as difficulty in identifying tool wear states and the complex composition of monitoring information features persist. To address these issues, this paper proposes a deep learning model that integrates multi-scale feature extraction with a residual connection network (Multi-scale ResNet). Specifically, cutting vibration signals are processes using continuous wavelet transform (CWT), which enables the conversion of time-frequency information into images. The proposed deep learning model is then used for feature extraction and state identification. The proposed model is validated through cutting experiments conducted on γ -TiAl alloys. Experimental results show that the Multi-scale ResNet model achieves higher recognition accuracy than traditional models such as convolutional neural networks – support vector machines (CNN–SVM), Transformer, and ResNet in the initial and normal wear stages. It effectively mitigates misjudgments associated with initial and normal wear, achieving a prediction accuracy of 93.8 %, a recall rate of 94.2 %, and an F1 score of 94 %. This model offers a novel and effective approach for tool wear state monitoring, contributing to improved cutting processing efficiency and increased intelligence in production.