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Piwi-interacting RNAs (piRNAs) are indispensable in the transposon silencing, including in germ cell formation, germline stem cell maintenance, spermatogenesis, and oogenesis. piRNA pathways are amongst the major genome defence mechanisms, which maintain genome integrity. They also have important functions in tumorigenesis, as indicated by aberrantly expressed piRNAs being recently shown to play roles in the process of cancer development. A number of computational methods for this have recently been proposed, but they still have not yielded satisfactory predictive performance. Moreover, only one computational method that identifies whether piRNAs function in inducting target mRNA deadenylation been reported in the literature. In this study, we developed a two-layered integrated classifier algorithm, 2lpiRNApred. It identifies piRNAs in the first layer and determines whether they function in inducting target mRNA deadenylation in the second layer. A new feature selection algorithm, which was based on Luca fuzzy entropy and Gaussian membership function (LFE-GM), was proposed to reduce the dimensionality of the features. Five feature extraction strategies, namely, Kmer, General parallel correlation pseudo-dinucleotide composition, General series correlation pseudo-dinucleotide composition, Normalized Moreau-Broto autocorrelation, and Geary autocorrelation, and two types of classifier, Sparse Representation Classifier (SRC) and support vector machine with Mahalanobis distancebased radial basis function (SVMMDRBF), were used to construct a two-layered integrated classifier algorithm, 2lpiRNApred. The results indicate that 2lpiRNApred performs significantly better than six other existing prediction tools.

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As one of the most important components in the semi-active damping system, the performance of MR damper directly determines the damping capacity of the damping system. In order to make the damping system has excellent damping effect, it is necessary to optimize the working performance of the MR damper. Therefore, Non-Dominated Sorting Genetic Algorithm version II (NSGA-II) was applied to optimize the structure of MR dampers in this paper. Firstly, the structural scheme of MR damper was proposed. Secondly, the design principle of MR damper was described, and the magnetic circuit material and MR fluid were selected. Thirdly, taking the maximum dynamic range and the minimum number of turns of electromagnetic coil as the optimization objective, the structure of MR damper was optimized by NSGA-II. The structural parameters of MR damper were determined in the Pareto optimal solution set based on the principle of minimum mass. Finally, through the magnetic simulation and the performance testing of the MR damper, it was verified that the MR damper has reasonable magnetic circuit and excellent performance. And the design results meet the requirements. The proposed optimization method can provide a theoretical basis for the optimal design of related damping devices.