The growth of the mobile Internet, smartphones and social networks has brought in huge amounts of picture information, and traditional manual identification is not able to meet the demand well enough. Therefore, the automatical image recognition [1] has been proposed which can help us recognize the image efficiently and get the corresponding information. Although traditional machine learning methods [2] have already been widely used in the field of image recognition, most of these methods are designed to handle one-dimensional vector information. Thus, we should first stretch image matrix to one-dimensional vector or extract features from images to employ traditional image recognition methods, which would lose the adjacent information in images and miss some important features. With the development of computer technology, deep learning [3] is gradually applied to the field of image recognition. It can deal with two-dimensional image data naturally and extract features automatically. Compared with the traditional machine learning methods, deep learning is popular for its good learning ability and low generalization error. In this paper, we compare the differences between SVM [4] and deep learning on image recognition, with an application to handwritten digital images recognition. The results show that the deep learning method is more accurate and more stable in image recognition.

Microwave radiation has been widely used in various fields, such as communication, industry, medical treatment, and military applications. Microwave radiation may cause injuries to both the structures and functions of various organs, such as the brain, heart, reproductive organs, and endocrine organs, which endanger human health. Therefore, it is both theoretically and clinically important to conduct studies on the biological effects induced by microwave radiation. The successful establishment of injury models is of great importance to the reliability and reproducibility of these studies. In this article, we review the microwave exposure conditions, subjects used to establish injury models, the methods used for the assessment of the injuries, and the indicators implemented to evaluate the success of injury model establishment in studies on biological effects induced by microwave radiation.

Electromagnetic pulse (EMP) radiation was reported to be harmful to hippocampal neurons. However, the mechanism underlying EMP-induced neuronal damage remains unclear. In this paper, for the first time, we attempted to investigate the involvement of ferroptosis in EMP-induced neuronal damage and its underlying mechanism. In vivo studies were conducted with a rat model to examine the association of ferroptosis and EMP-induced hippocampal neuronal damage. Moreover, in vitro studies were conducted with HT22 neurons to investigate the underlying mechanism of EMP-induced neuronal ferroptosis. In vivo results showed that EMP could induce learning and memory impairment of rats, ferroptotic morphological damages to mitochondria, accumulation of malonaldehyde (MDA) and iron, overexpression of prostaglandin-endoperoxide synthase 2 (PTGS2) mRNA, and downregulation of GPX4 protein in rat hippocampus. In vitro results showed that EMP could induce neuronal death, MDA accumulation, iron overload, PTGS2 overexpression, and GPX4 downregulation in HT22 neurons. These adverse effects could be reversed by either lipid peroxides scavenger ferrostatin-1 or overexpression of GPX4. These results suggest that EMP radiation can induce ferroptosis in hippocampal neurons via a vicious cycle of lipid peroxides accumulation and GSH/GPX4 axis downregulation. Lipid peroxides and the GSH/GPX4 axis provide potential effective intervention targets to EMP-induced hippocampal neuronal damage.

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Urban underground rail transit in China serves people, but at the same time, there is a problem of insufficient utilization of resources. To make more effective use of subway resources and alleviate traffic pressure, aiming at the problem of insufficient use of time and space resources in the process of Metro operation, this paper proposes a logistics system of underground railway based on the underground logistics system of passenger and freight co-transported. On this basis, the mechanical structure design of the system is carried out in the aspects of metro transportation and transformation of the Metro station. Firstly, the paper analyses the shortcomings in the current situation of Metro operation, discusses the underground logistics system of passenger and freight co-transportation, then focuses on the mechanical structure design of the system, and uses SolidWorks to simulate and analyze the mechanical structure to ensure the feasibility of the system and improve the utilization rate of Metro resources.