Comparison study of typical algorithms for reconstructing time series from the recurrence plot of dynamical systems

The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested.

Quantum theory analysis of the action micromechanism exerted on bioparticles from laser microbeams

The effect of laser microbeam trapping the bioparticles has been applied widely in the biology .However the micromechanism of the acting that realizes the laser-microbeam trapping bioparticles is still lacking . In this paper ,the act microchenism of the gradiant force of laser microbeam for the bioparticles is analysed by means of quantum theory ,The result accords with our experiment.

ON BANACH-STONE TYPE THEOREMS IN UNIFORM MULTIPLICATIVE SUBGROUPS OF C(K )-SPACES

In this article, we study the preservation properties of （Silov） boundary of mul-tiplieative subgroups in C（X） spaces for non-surjective norm-preserving multiplieative maps.We also show a sufficient condition for surjective maps between groups of positive continuousfunctions to be a composition operator.

Chirped Bright and Kink Solitons in Nonlinear Optical Fibers with Weak Nonlocality and Cubic-Quantic-Septic Nonlinearity

This work focuses on chirped solitons in a higher-order nonlinear Schrodinger equation,including cubic-quinticseptic nonlinearity,weak nonlocal nonlinearity,self-frequency shift,and self-steepening effect.For the first time,analytical bright and kink solitons,as well as their corresponding chirping,are obtained.The influence of septic nonlinearity and weak nonlocality on the dynamical behaviors of those nonlinearly chirped solitons is thoroughly addressed.The findings of the study give an experimental basis for nonlinear-managed solitons in optical fibers.

Scaling Behaviour of Diffusion Limited Aggregation with Linear Seed

We present a computer model of diffusion limited aggregation with linear seed. The clusters with varying linear seed lengths are simulated, and their pattern structure, fractal dimension and multifractal spectrum are obtained. The simulation results show that the linear seed length has little effect on the pattern structure of the aggregation clusters if its length is comparatively shorter. With its increasing, the linear seed length has stronger effects on the pattern structure, while the dimension D f decreases. When the linear seed length is larger, the corresponding pattern structure is cross alike. The larger the linear seed length is, the more obvious the cross-like structure with more particles clustering at the two ends of the linear seed and along the vertical direction to the centre of the linear seed. Furthermore, the multifractal spectra curve becomes lower and the range of singularity narrower. The longer the length of a linear seed is, the less irregular and nonuniform the pattern becomes.

A novel knowledge-based potential for RNA 3D structure evaluation

Ribonucleic acids （RNAs） play a vital role in biology, and knowledge of their three-dimensional （3D） structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation.

An Averaging Principle for Caputo Fractional Stochastic Differential Equations with Compensated Poisson Random Measure

This article deals with an averaging principle for Caputo fractional stochastic differential equations with compensated Poisson random measure.The main contribution of this article is impose some new averaging conditions to deal with the averaging principle for Caputo fractional stochastic differential equations.Under these conditions,the solution to a Caputo fractional stochastic differential system can be approximated by that of a corresponding averaging equation in the sense ofmean square.

cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Knowledge of RNA 3-dimensional(3 D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3 D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3 D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4’ atom for backbone and N1(or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly,cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.