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Given that the conventional blockchain model for the fruit and vegetable supply chain has the problems of insufficient depth of traceability information, insufficient data correlation, and low query efficiency, a graph blockchain model was proposed in this study. Firstly, the graph theory was used to improve the block data structure of the traditional blockchain and change the storage structure of the sequence table in the traditional blockchain structure into a graph structure that can facilitate the storage of in-depth traceability information for associated storage of deep traceability information. Then, the mapping relationship between physical block data and graph block chain model was established through a graph block chain mapping model, and according to the logical structure of the fruit and vegetable supply chain, the graph block data structure was established. Customized design of smart contracts for graph blockchain data storage, query and authority management was performed to ensure the reliability of supply chain data. Finally, the graph blockchain model was developed based on the XuperChain platform and its performance was experimentally compared with that of the traditional blockchain. The results showed that the graph blockchain allows deep traceability of the fruit and vegetable supply chain and greatly improves the efficiency of data query, which can provide a new method and idea for the traceability information storage and fast query of the fruit and vegetable supply chain based on blockchain.

To improve the uniformity of the hot air drying quality for stacked flake materials, a heat and mass transfer study of stacked flake materials drying process based on multi-physical field coupling is carried out. A numerical simulation model of the stacked flake materials is developed based on the multi-physical field coupling theory, and the accuracy of the numerical simulation model is verified by the bidirectional alternating hot air drying test bed. The segmented hot air drying method is used to analyze the influence mechanism of single airflow direction and alternate airflow direction on the heat and mass transfer for stacked flake materials, and the optimization of drying parameters is studied. The results indicate that a single airflow direction may cause excessive drying, and the uneven stacking density significantly affects the drying quality. The increase of air flow direction change number will lead to the improvement of drying uniformity and moisture content of drying results, and at the same time, the flake material absorbs moisture significantly after changing the air flow direction. By adopting the down-down-up-up airflow direction and optimizing the parameters, the moisture content uniformity of the drying results is improved by 19%, while energy consumption is reduced. [ABSTRACT FROM AUTHOR]

[Objective] A lightweight SE-PPM algorithm (SAPMDSNet) was proposed to solve the problems of slow speed, misclassification and difficulty in feature extraction in traditional positive and subgroup classification of natural state tobacco leaves. [Method] Based on the lightweight ShuffleNetV2 net-work, the training speed of the network model was accelerated by reducing the network convolution depth and evolving the activation function. Then, the channel attention SE module was introduced to enhance the haracteristic differences between channels, improve the representation ability of the network model and a-void the group misclassification caused by the regionalization. Finally, the pyramid pooling module PPM was embedded to integrate the exposure characters and global information by aggregating context information, and the in-house tobacco leaf data set was used and compared with other models. [Result] The SAPMDSNet network model achieved relatively high classification results with accuracy of 91.09% and FLOPs of 151.70 M. Compared with the original ShuffleNetV2 model and the lightweight GhostNet model, the FLOPs were slightly increased by 2.65% and 2.84%, and the accuracy was increased by 2.72% and 21.13 %, respectively. MobileNetV2, DenseNet and SqueezeNet achieved recognition accuracies of 87.02%, 89.53% and 87.60%, which were close to the proposed model, but their FLOPs were significantly higher. [Conclusion] The constructed SAPMDSNet network model improved the classification accuracy of positive and sub-group tobacco leaves in natural state with better overall performance, which provides a new method for primary screening of tobacco leaves. [ABSTRACT FROM AUTHOR]

Hydrogen bonding is an important index to determine the viscosity of liquids containing a hydroxyl group. The change of hydrogen bond must change the viscosity of liquid. Glycerol is a kind of polyhydroxy high viscosity liquid. The rheological property is one of the biggest concerns for glycerol. Reduction of glycerol viscosity without diluting or heating to change its hydrogen bonding is considered to be challenging. Here, we demonstrate the effect of multiwalled carbon nanotubes (MWNTs) on the thinning of glycerol. The viscosity of glycerol was decreased at certain proportions, with a viscosity reduction range of 0.54%–1.17%. The hydrogen bond network was broken by the adsorption and space-limiting effect of MWNTs. Transmission electron microscopy and computer simulation results showed that glycerol molecular clusters were mainly dispersed on the surface of MWNTs and adsorbed into the internal pipe of MWNTs. A cavity between the glycerol molecules and MWNTs performed the space-limiting role of MWNTs. MWNTs simultaneously acted as a viscosifier and viscosity reducer. These two effects show a competitive relationship with the amount of additional changes. We expect these results to provide a new perspective in controlling rheological behavior and reveal the underlying interaction between hydroxy liquids and nanomaterials. [ABSTRACT FROM AUTHOR]