Kaempferol, also known as kaempferol-3 or kaempferide, is a flavonoid compound that naturally occurs in tea, as well as numerous common vegetables and fruits, including beans, broccoli, cabbage, gooseberries, grapes, kale, strawberries, tomatoes, citrus fruits, brussel sprouts, apples and grapefruit. The present review mainly summarizes the application of kaempferol in treating diseases and the underlying mechanisms that are currently being studied. Due to its anti-inflammatory properties, it may be used to treat numerous acute and chronic inflammation-induced diseases, including intervertebral disc degeneration and colitis, as well as post-menopausal bone loss and acute lung injury. In addition, it has beneficial effects against cancer, liver injury, obesity and diabetes, inhibits vascular endothelial inflammation, protects the cranial nerve and heart function, and may be used for treating fibroproliferative disorders, including hypertrophic scar.

To study the effect of fine particle size and volume concentration on the performance of solid-liquid two-phase centrifugal pump, the mixture multiphase flow model, RNG k-ε turbulence model, and SIMPLEC algorithm were used to simulate the two-phase flow of the centrifugal pump. The effects of particle size and volume concentration on internal pressure distribution, solid volume distribution, and external characteristics were analyzed. The results show that under the design discharge conditions, with the increase of particle size and volume concentration, the internal pressure of the flow field will decrease, and the volume fraction of solid phase in the impeller passage will also decrease as a whole. The solid particles gradually migrate from the suction surface to the pressure surface, and the particles in the volute channel are mainly concentrated in the flow channel near the outlet side of the volute. With the increase of particle size and volume concentration, the negative pressure value at the inlet of centrifugal pump increases, the total pressure difference at the inlet and outlet decreases, and the head and efficiency decrease accordingly.

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this paper, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.

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