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Tissue engineering technology provides a revolutionary strategy to completely restore the structure and function of damaged tissues or organs. Digital light processing (DLP), as a kind of three-dimensional (3D) printing technology, has great advantages in printing resolution and efficiency, with low requirements for bioinks. This review introduces DLP-based printing and its development, as well as the manufacturing processes and printable materials. We also focus on tissue engineering products such as bone, tooth, cartilage, nerve, blood vessel, and so on. This review expounds on the difficulties and shortcomings of DLP printing technology in tissue engineering today. Perspectives are given on the current outlook on DLP-based 3D printing tissue engineering.

With the development of sensor technology and computer vision, the speed of video generation is increasing exponentially. The workload of video browsing and the amount of stored data are increasing. In order to achieve fast browsing and efficient storage, video structure technology is increasingly important. This paper focuses on the video structure technology in the construction of smart cities, outlines the system process of video structure, and studies the extraction algorithm of human and vehicle target attributes based on deep learning. And combined with the actual video monitoring scene, the experiment is carried out. Compared with traditional image processing methods, the video structure target attribute extraction model based on deep learning has high detection accuracy and strong algorithm generalization ability. The intelligent analysis and processing of data by video structure technology is conducive to quickly increasing the speed of target search, reducing storage capacity, solving the problem of long-term storage, and realizing full automation of surveillance video.

Automatic Machine Learning (AutoML) uses automated data-driven methods to realize the selection of hyper-parameters, neural network architectures, regularization methods, etc., making machine learning techniques easier to apply and reducing dependence on experienced human experts. And hyper-parameter search based on automatic machine learning is one of the current research hotspots in the industry and academia. We mainly introduce the hyper-parameter search framework based on automatic machine learning and the common hyper-parameter search strategies. Combined with specific data sets, the classification accuracy of the model under different hyper-parameter search strategies is compared to find the model parameter configuration that can maximize the classification accuracy. Compared with the experience-based parameter adjustment method, the hyper-parameter search based on automatic machine learning can reduce labor costs, improve training efficiency, and automatically construct a dedicated convolutional neural network to maximize the model effect.

A lattice Boltzmann model to simulate phase separation and two-phase flow is proposed. The nonideal force in multiphase flow is directly computed from the free energy. Thermodynamic consistency and Galilean invariance are theoretically analyzed and numerically verified. Remarkably, the theoretical simplicity endues the model with the advantages of high efficiency and easy implementation. We also find that it can work well together with various equations of state in order to simulate different kinds of multiphase flows. Importantly, it has a tunable parameter κ, which can be used to reduce the effect of spurious current and adjust the surface tension to meet the requirements of researches.