Multi-bright-dark soliton solutions to the AB system in nonlinear optics

The AB system is the basic integrable model to describe unstable baroclinic wave packets in geophysical fluids and the propagation of mesoscale gravity flows in nonlinear optics. On the basis of the spectral analysis of a Lax pair and the inverse scattering method, we establish the Riemann–Hilbert problem of the AB system. Then, the inverse problems are formulated and solved with the aid of the Riemann–Hilbert problem, from which the potentials can be reconstructed according to the asymptotic expansion of the sectional analytic function and the related symmetry relations. As an application, we obtain the multi-bright-dark soliton solutions to the AB system in the reflectionless case and discuss the dynamic behavior of elastic soliton collisions by choosing appropriate free parameters.     

Two-parameter estimation with three-mode NOON state in a symmetric triple-well potential

We propose a scheme to realize two-parameter estimation via Bose–Einstein condensates confined in a symmetric triple-well potential. The three-mode NOON state is prepared adiabatically as the initial state. The two parameters to be estimated are the phase differences between the wells. The sensitivity of this estimation scheme is studied by comparing quantum and classical Fisher information matrices. As a result, we find an optimal particle number measurement method. Moreover, the precision of this estimation scheme means that the Heisenberg scaling behaves under the optimal measurement.     

Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams

To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electron-laser and plasma wakefield acceleration, a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems. Due to their intrinsic one-to-many property, current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution. Here we propose a real-time solver for one-to-many problems of temporal shaping, with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN). We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles. This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.     

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.     

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.     

How much information is needed to be the majority during the binary-state opinion formation?

In this paper, we try to propose a toy model, which follows the majority rule with the Fermi function, to uncover the role of the heterogeneous interaction between individuals in opinion formation. In order to do this, we define the impact factor I_Fi$I{F}_i$, says individual i$i$, as the exponential function of its connectivity _ki$k_i$ with the tunable parameter β$\beta$. β$\beta$ also shows the public information that can be collected by individuals in the system. We realize our model in scale-free networks with mean connectivity 〈k〉$〈k〉$. We find that much more public information (β>_β2$\beta >{\beta }_2$) and less public information (β<_β1$\beta <{\beta }_1$) cannot let either of the two opinions be the majority during the opinion formation. Furthermore, _β1${\beta }_1$ is a constant and equal to −0.76(±0.04)$-0.76(\pm 0.04)$, and _β2${\beta }_2$ decreases as a power-law function of the mean connectivity 〈k〉$〈k〉$ of the network. Our work can provide some perspectives and tools to understand the diversity of opinion in social networks.     

Designing and Characterization of Biodegradable Multiblock Poly(L-Lactic Acid)/Polybutadiene Elastomers

A series of poly(L-lactic acid)/polybutadiene (PLA/PB) biodegradable multiblock elastomers was synthesized and characterized. A two-step process to prepare PLA/PB multiblock elastomers was applied. Melt polymerization was used to prepare poly(L-lactic acid) (PLA) terminated with hydroxyl groups and, at the same time, hydroxyl-terminated polybutadiene (HTPB) and 1,6-hexamethylene diisocyanate (HDI) were employed to synthesize diisocyanate-terminated polybutadiene (ITPB). Then, PLA and ITPB were reacted with different PLA/PB weight ratios. Consequently, a series of PLA/PB biodegradable poly(ester-urethane)s with crosslinked chains was obtained. Swelling characteristics and crosslink density of the crosslinked elastomer were investigated. DMA was applied to characterize its thermal properties. The measurement of mechanical properties showed that a PLA/PB elastomer with adjustable mechanical properties was synthesized. Micromorphology, hydrophobicity, and degradability of the material were also characterized.     

Mindlin板几何非线性分析的附加内部剪应变法

本文在几何非线性分析的Ｍｉｎｄｌｉｎ板元中引入单元附加内部剪应变，有效地解决了薄板情况下的剪切自锁问题．文中导出了相应的能量相容条件，给出了有限元非线性列式的全过程及有关簿板及中厚板大挠度问题的数值结果．     

Strongly Modular Subgroup in Finite Group

Ｓｔｒｏｎｇｌｙ　Ｍｏｄｕｌａｒ　Ｓｕｂｇｒｏｕｐ　ｉｎ　Ｆｉｎｉｔｅ　ＧｒｏｕｐＳｔｒｏｎｇｌｙＭｏｄｕｌａｒＳｕｂｇｒｏｕｐｉｎＦｉｎｉｔｅＧｒｏｕｐ￥ＹｕＪｉａｎｌｉ；ＪｉａｏＤｏｎｇｗｕ（ＬｕｏｙａｎｇＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇ...     

NO分子宏观气体热力学性质的理论研究

采用量子统计系综理论,研究了基态NO分子宏观气体摩尔熵、摩尔内能、摩尔热容等热力学性质.首先应用课题组前期建立的变分代数法(variational algebraic method, VAM)计算获得了基态NO分子的完全振动能级,得到的VAM振动能级作为振动部分,结合欧拉-麦克劳林渐进展开公式的转动贡献,应用于经典的热力学与统计物理公式中,从而计算得到了1000-5000 K温度范围内NO宏观气体的摩尔内能、摩尔熵和摩尔热容.将不同方法计算得到的摩尔热容结果分别与实验值进行比较,结果表明基于VAM完全振动能级获得的结果优于其他方法获得的理论结果.振动部分采用谐振子模型对无限能级求和计算热力学性质的方法有一定的局限性,应当使用有限的完全振动能级进行统计求和.