Cell therapy has been shown to be a key clinical therapeutic option for central nervous system disease or damage, and >30 types of cells have been identified through preclinical studies as having the capacity for neurorestoration. To standardize the clinical procedures of cell therapy as one of the strategies for treating neurological disorders, the first set of guidelines governing the clinical application of neurorestoration was completed in 2011 by the Chinese Branch of the International Association of Neurorestoratology. Given the rapidly advancing state of the field, the Neurorestoratology Professional Committee of Chinese Medical Doctor Association (Chinese Association of Neurorestoratology) and the Chinese Branch of the International Association of Neurorestoratology have approved the current version known as the "Clinical Cell Therapy Guidelines for Neurorestoration (China Version 2016)". We hope this guideline will reflect the most recent results demonstrated in preclinical research, transnational studies, and evidence-based clinical studies, as well as guide clinical practice in applying cell therapy for neurorestoration.

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This paper presents the possible periodic solutions and the solitons of the cubic–quintic nonlinear Schrödinger equation. Corresponding to five types of different structures of the pseudo-potentials, five types of periodic solutions are given explicitly. Five types of solitons are also obtained explicitly from the limiting procedures of the periodic solutions. This will benefit the study of the generation of fast ions or electrons, which are produced from the soliton breaking when the plasma is irradiated a high-intensity laser pulse.

For molecular and standard Bose-Einstein condensates and Fermi gases near Feshbach resonances, the general polytropic equation of states is 𝑃 ∝ 𝑛 𝛾+1 . According to the effective power 𝛾 ≈ 0.5 ∼ 1.3, we resolve the timedependent nonlinear Schrodinger equation and find series bright solitons. The analysis could help in the search for matter-wave soliton trains in degenerate Femi gas.

Experimental measurement of radiation temperature by a streaked optical pyrometer and a soft X-ray spectrometer viewing through the laser entrance hole are performed on SG-Ⅲ prototype laser facility. It was found that the two methods compares well. Using the power balance relation，the laser-hohlraum coupling efficiency for 1.0 mm×1.7 mm hohlraum and 1.0 mm×2.1 mm hohlraum is around 50%—55%.

In the wake of the continuous progress and development of the automotive industry, the automotive era has come. The development and progress of intelligent vehicle networking makes people spend more and more time in the car. Consumers’ demand and quality for automobiles are also gradually improving. While enjoying the improvement of work efficiency, rich material life and modern convenient transportation, consumers should also pay attention to driving safety, which makes the research on the direction of human-computer interaction of automobiles and their related products become an important part. In this text, the development status, related technology analysis and future development trend of intelligent vehicle multimedia Human-Computer Interaction system are discussed from various angles. Based on the research results of automobile human-computer interaction, different design concepts in different technical fields are put forward, that is, different technical fields are rooted in to provide the most efficient and safe interactive mode, such as gesture control technology, speech recognition technology, to achieve a full and convenient control interface, minimize distraction of drivers, reduce driving fatigue, and ensure driving safety.

With the increasingly close combination of the Internet and people’s production and life, the total amount of global data and information also grows increasingly. In order to save users the time to find their favorite music among many music types, the music recommendation service arises at the historic moment and is widely concerned by scholars. Traditional music recommendation system based on the collaborative filtering algorithm has low recommendation accuracy, poor real-time performance, sparsity, system cold start, and so on. Moreover, the traditional music recommendation algorithm only simply uses user behavior characteristics and does not make good use of user history for listening to audio characteristics. In view of the above question, this section based on the attention mechanism of the deep neural network music recommendation algorithm, through the use of improved MFCC audio data preprocessing, the extracted audio combined with the user’s own portrait features, through the AIN RNN network recommended list, by learning user history listening to songs, improves the model-recommended accuracy.