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It is noteworthy today that the creation and popularization of new energy has piqued the world's interest. As a result, new energy electric cars are liked and acknowledged by most customers as a representation of the development and use of new energy. The advancement of electric vehicles (EVs) has important implications for the sustainable use of energy resources. As the number of new energy EVs grows, so does the need for charging stations for these vehicles. Maximum simplification of charging station distribution may successfully satisfy the charging demands of EVs. As a result, determining the appropriate arrangement of EV charging stations has become an essential study issue. This paper proposed a novel algorithm for EV charging station optimization based on a neural network. The main idea is to optimize the cost of charging cost and the user's budget. Then, considering the target planning region of the charging station, the historical data is deployed to predict the time distribution of EVs based on the backpropagation neural network algorithm. Finally, the performance of swarm optimization is improved through the dynamic probability mutation method. Simulation results show that the proposed algorithm has better performance than existing algorithms in terms of global economic cost and low-power and high-power charging station's spatial location. [ABSTRACT FROM AUTHOR]

Massive loss of neurons following brain injury or disease is the primary cause of central nervous system dysfunction. Recently, much research has been conducted on how to compensate for neuronal loss in damaged parts of the nervous system and thus restore functional connectivity among neurons. Direct somatic cell differentiation into neurons using pro-neural transcription factors, small molecules, or microRNAs, individually or in association, is the most promising form of neural cell replacement therapy available. This method provides a potential remedy for cell loss in a variety of neurodegenerative illnesses, and the development of reprogramming technology has made this method feasible. This article provides a comprehensive review of reprogramming, including the selection and methods of reprogramming starting cell populations as well as the signaling methods involved in this process. Additionally, we thoroughly examine how reprogramming astrocytes into neurons can be applied to treat stroke and other neurodegenerative diseases. Finally, we discuss the challenges of neuronal reprogramming and offer insights about the field. [ABSTRACT FROM AUTHOR]