Abstract This article explores the asymptotic stability of fractional delayed memristive neural networks with reaction-diffusion terms. A novel hybrid impulsive controller triggered by a specific event is proposed to stabilize the network, thereby replacing the conventional approach of modifying network parameters. The proposed controller is proven to prevent Zeno behavior. Sufficient conditions for the asymptotic stability of fractional delayed memristive neural networks with reaction-diffusion terms are established through Lyapunov direct method, inequality techniques, Green’s theorem and impulse analysis. Furthermore, the proposed controller is theoretically shown to be more resource-efficient than the conventional one, and our work extends existing research to make it more suitable for practical application such as pattern recognition, image processing and so on. Finally, an example is provided to illustrate the validity of the findings.

Sea surface temperature (SST) is an essential parameter for observing the marine environment. It directly reflects the state of heat storage and release in the ocean. The change in SST will cause many phenomena that profoundly affect human production and life. Therefore, predicting SST accurately and efficiently can help us avoid many risks. In this article, we present a method based on neural networks. First, we propose a finite-dimensional description of SST, which investigates SST in the finite-dimensional phase space. Based on phase space reconstruction technology and the autoreservoir neural network (ARNN), the Spatial Parallel ARNN (SPARNN) is proposed for predicting SST. Unlike the previous machine learning methods that require big data, our approach only needs a small amount of locally short-term data to catch the dynamic features of the SST field. It also has excellent parallelism and is easy to run on a large-scale computer platform.

BACKGROUND: Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition. RESULTS: In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed. CONCLUSIONS: Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

This paper discusses adaptive filtering algorithms and proposes a fast algorithm based on vector plots analysis, that is different from the previous adaptive filtering algorithms. By introducing approaches of mathematical geometrical analysis to study adaptive filtering, the paper inquires into vector plots structure of least mean squares (LMS) algorithm and geometrical feature of algorithm convergence and seeks effective fast algorithm on the basis of geometrical analysis. Numerical simulations are given to show the efficiency and superiority of the new algorithm.