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Deciphering drought resistance in crops is crucial for enhancing water productivity. Previous studies have highlighted the significant role of the transcription factor SlHB8 in regulating developmental processes in tomato plants but its involvement in drought resistance remains unclear. Here, gene overexpression (SlHB8-OE) and gene knockout (slhb8) tomato plants were utilized to study the role of SlHB8 in regulating drought resistance. Our findings showed that slhb8 plants exhibited a robust resistant phenotype under drought stress conditions. The stomata of slhb8 tomato leaves displayed significant closure, effectively mitigating the adverse effects of drought stress on photosynthetic efficiency. The slhb8 plants exhibited a decrease in oxidative damage and a substantial increase in antioxidant enzyme activity. Moreover, slhb8 effectively alleviated the degree of photoinhibition and chloroplast damage caused by drought stress. SlHB8 regulates the expression of numerous genes related to photosynthesis (such as SlPSAN, SlPSAL, SlPSBP, and SlTIC62) and stress signal transduction (such as SlCIPK25, SlABA4, and SlJA2) in response to drought stress. Additionally, slhb8 plants exhibited enhanced water absorption capacity and upregulated expression of several aquaporin genes including SlPIP1;3, SlPIP2;6, SlTIP3;1, SlNIP1;2, and SlXIP1;1. Collectively, our findings suggest that SlHB8 plays a negative regulatory role in the drought resistance of tomato plants. [ABSTRACT FROM AUTHOR]

The System #2 flow loop used in this study is a 4−inch−diameter, high−temperature, high−pressure system. In situ corrosion and electrochemical measurements were performed using a homemade flat corrosion specimen and a three−electrode probe. The experiment results show that temperature has an accelerated influence on the corrosion of antibacterial alloy steel. With the increase of temperature and the presence of O2 in the environment, a loose and porous corrosion product film was formed on the surface of the resistant steel, which made it easier for the corrosion medium to enter the corrosion product film from the pore, thus accelerating the corrosion. [ABSTRACT FROM AUTHOR]

As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional methods based on physical models. In this paper, we propose a novel data-driven method for predicting the remaining useful life of bearings based on a deep graph convolutional neural network with spatiotemporal domain convolution. This network uses the average sliding root mean square (ASRMS) as the health factor to identify the healthy and degraded states, and then uses correlation coefficient analysis on the hybrid features of the degraded data to construct a spatial graph according to the strength of the correlation between the obtained features. In the time domain, we introduce historical data as the input to the temporal convolution. After the data are processed by the spatial map and the temporal dimension, we perform the prediction of the remaining useful life. The experimental results show the accuracy of the method. [ABSTRACT FROM AUTHOR]