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In this paper the authors discuss the applications of LSTM Neural Network in Protein Structure Prediction. The main idea is to construct a LSTM neural network. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. In this article, a position-specific scoring matrices (PSSM) containing evolutionary information is linked to other features to construct a completely new feature set. The CB513 data set is selected to construct LSTM neural networks to predict the secondary structure of the sequence. Experiments have shown that the proposed method effectively improves the prediction accuracy and is better than the previous method. The idea in this paper can also be applied to the analysis of other sequences.

This paper compares the direct theory-Green's theory, and indirect method-potential theory, which aims at transiting the partial DE into the boundary IE for the harmonic transformation model. Comparing with the Green's theorem, potential theory is simple and easy to implement. By detecting the singularity point in the harmonic model, potential theory has been verified that it is more efficient. Experimental results illustrate that the indirect method applied for the harmonic transformation model has good performance comparing with the direct method.

This paper applies the indirect method - potential theory for converting the partial differential equation to the boundary integral equation for harmonic transformation model. Potential theory (PT) is simple and easy to implement comparing with Green's theorem. PT is designed specifically for integral equation. It has been verified that it is efficiency in detecting the singularity point to the harmonic transformation. Experimental results show that the potential theory has good performance comparing with the traditional one.

The future of mobile wireless communication networks will be experienced several generations as which have been experienced. This kind of development will drive the researches of information technology in industrial area. In this paper, we predict the future generations of mobile wireless communication networks including 4th, 5th, 6th and 7th generations. The main objective of this paper is to propose a technical frame for industry in the future. Thus, this paper is focused on the specification of future generations of wireless mobile communication networks.

The current existing multimedia services have been achieved mainly by fixed internet networks. The 4th Generation (4G) wireless mobile multimedia internet networks integrate with IPv6, LAS-CDMA, OFDM, MC-CDMA, UWB, Networks-LMDS and fixed internet networks to support mobile multimedia services as the same quality of service as fixed internet, which is an evolution not only to move beyond the limitations and problems of 3G, but also to enhance the quality of services, to increase the bandwidth and to reduce the cost of the resource. The 5th wireless mobile multimedia internet networks are real wireless world, which are completed wireless communication without limitation. In this paper, we propose Multi-network data path for 5G real wireless multimedia world.