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virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.

Our group has reported that Melan-A cells and lymphocytes undergo self-rotation in a homogeneous AC electric field, and found that the rotation velocity of these cells is a key indicator to characterize their physical properties. However, the determination of the rotation properties of a cell by human eyes is both gruesome and time consuming, and not always accurate. In this paper, a method is presented to more accurately determine the 3D cell rotation velocity and axis from a 2D image sequence captured by a single camera. Using the optical flow method, we obtained the 2D motion field data from the image sequence and back-project it onto a 3D sphere model, and then the rotation axis and velocity of the cell were calculated. After testing the algorithm on animated image sequences, experiments were also performed on image sequences of real rotating cells. All of these results indicate that this method is accurate, practical, and useful. Furthermore, the method presented there can also be used to determine the 3D rotation velocity of other types of spherical objects that are commonly used in microfluidic applications, such as beads and microparticles.

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