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The vesicular transport system is important for substance transport in plants. In recent years, the regulatory relationship between the vesicular transport system and plant disease resistance has received widespread attention; however, the underlying mechanism remains unclear. MdSYP121 is a key protein in the vesicular transport system. The overexpression of MdSYP121 decreased the B. dothidea resistance of apple, while silencing MdSYP121 resulted in the opposite phenotype. A metabolome and transcriptome dataset analysis showed that MdSYP121 regulated apple disease resistance by significantly affecting sugar metabolism. HPLC results showed that the levels of many soluble sugars were significantly higher in the MdSYP121-OE calli. Furthermore, the expression levels of genes related to sugar transport were significantly higher in the MdSYP121-OE calli after B. dothidea inoculation. In addition, the relationships between the MdSYP121 expression level, the soluble sugar content, and apple resistance to B. dothidea were verified in an F1 population derived from a cross between 'Golden Delicious' and 'Fuji Nagafu No. 2'. In conclusion, these results suggested that MdSYP121 negatively regulated apple resistance to B. dothidea by influencing the soluble sugar content. These technologies and methods allow us to investigate the molecular mechanism of the vesicular transport system regulating apple resistance to B. dothidea. [ABSTRACT FROM AUTHOR]

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results. [ABSTRACT FROM AUTHOR]

For the machining of aero-engine blades, factors such as machining residual stress, milling force, and heat deformation can result in poor blade profile accuracy. To address this issue, simulations of blade milling were completed using DEFORM11.0 and ABAQUS2020 software to analyze blade deformation under heat-force fields. Process parameters such as spindle speed, feed per tooth, depth of cut, and jet temperature are used to design both a single-factor control and BBD test scheme to study the influence of jet temperature and multiple changes in process parameters on blade deformation. The multiple quadratic regression method was applied to establish a mathematical model correlating blade deformation with process parameters, and a preferred set of process parameters was obtained through the particle swarm algorithm. Results from the single-factor test indicated that blade deformation rates were reduced by more than 31.36% in low-temperature milling (−190 °C to −10 °C) compared with dry milling (10 °C to 20 °C). However, the margin of the blade profile exceeded the permissible range (±50 µm); therefore, the particle swarm optimization algorithm was used to optimize machining process parameters, resulting in a maximum deformation of 0.0396 mm when the blade temperature was −160 °C~−180 °C, meeting the allowable blade profile deformation error. [ABSTRACT FROM AUTHOR]