In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.

Advanced glycation end products (AGEs) are a series of stable compounds produced under non-enzymatic conditions by the amino groups of biomacromolecules and the free carbonyl groups of glucose or other reducing sugars commonly produced by thermally processed foods. AGEs can cause various diseases, such as diabetes, atherosclerosis, neurodegeneration, and chronic kidney disease, by triggering the receptors of AGE (RAGEs) in the human body. There is evidence that AGEs can also affect the different structures and physiological functions of the skin. However, the mechanism is complicated and cumbersome and causes various harms to the skin. This article aims to identify and summarise the formation and characteristics of AGEs, focussing on the molecular mechanisms by which AGEs affect the composition and structure of normal skin substances at different skin layers and induce skin issues. We also discuss prevention and inhibition pathways, provide a systematic and comprehensive method for measuring the content of AGEs in human skin, and summarise and analyse their advantages and disadvantages. This work can help researchers acquire a deeper understanding of the relationship between AGEs and the skin and provides a basis for the development of effective ingredients that inhibit glycation.

Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

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