Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming

Science China Mathematics - In this paper, we accomplish the unified convergence analysis of a second-order method of multipliers (i.e., a second-order augmented Lagrangian method) for solving the...     

Graphene and graphene like 2D graphitic carbon nitride: Electrochemical detection of food colorants and toxic substances in environment

Excessive consumption of substances such as food colorants, exposure to doses of metal ions, antibiotic residues and pesticides residues above maximum tolerance limit have a detrimental effect on human health. Hence in detecting these harmful substances, the development of sensitive, selective and convenient analytical tools is an essential step. Graphene and graphene like 2D graphitic carbon nitride have shown great promise in the development of electrochemical sensors for determining the levels of these substances in different samples. In this paper, graphene and graphene like 2D graphitic carbon nitride applications on the determination of various food colorants in foods and drinks such as azo dyes (tartrazine, allura red, amaranth, carmine and sunset yellow); metal ions contaminants, antibiotic and pesticide residues in the environment are reviewed.     

Recent Advances in Spatial Self-Phase Modulation with 2D Materials and its Applications

In recent years, spatial self-phase modulation (SSPM) with two-dimensional (2D) materials has attracted the attention of many researchers as an emerging and ubiquitous nonlinear optical effect. In this review, the state of the art of 2D material-based SSPM is summarized. SSPM measures or tunes the nonlinearity of 2D materials, and it is also an effective approach to study the band structure of 2D materials. Several modified forms of SSPM, such as high-order, white-light-excited, vector field excited, and optically nonlinearly enhanced SSPM are also presented. Subsequently, the physical origin of the SSPM formation mechanism is compared and analyzed. Furthermore, the applications of SSPM with 2D materials, including passive photonic devices, generation of Bessel beams, and identifying the mode of the orbital angular momentum, are listed. Finally, several urgent problems of the SSPM with 2D materials, potential applications, and prospects for future development are presented.     

Ti0.33WO3电子结构和太阳辐射屏蔽性能第一性原理研究

节能减排已成为当今社会发展的主题,对节约能源、提高太阳能的高效综合利用的新型窗用透明隔热材料的理论设计和研究尤其重要.本文采用基于密度泛函理论的计算方法,研究了六方相三氧化钨Ti掺杂前、后的晶格参数、电子能带结构、形成能和光学性质.研究结果表明,Ti掺杂后晶格体积增大,系统能量降为负值,体系具有更好的稳定性;掺杂后电子能带结构发生很大的变化,但材料仍保持n型电导率;随之,其光学性质也发生改变,掺杂前h-WO3无近红外吸收性能,掺杂后的Ti0.33WO3具有很强的近红外吸收性能.在此基础上研究了Ti掺杂h-WO3前、后的太阳辐射屏蔽性能,掺杂前无太阳辐射屏蔽性能;掺杂后的Ti0.33WO3薄膜具有可见光高透明、近红外屏蔽的性能.计算结果为Ti掺杂h-WO3在窗用透明隔热材料方面的研究提供了理论依据.     

对《关于〈一维圆环上双原子链的马德隆常数〉的解析解》的讨论

本文绘制了一维等距离子晶体的累加法、埃夫琴法和平均值法的各级马德隆常数的曲线.本文推导了二聚化直线离子晶体马德隆常数的求和式,比较了两种解析式的优劣.本文详细地推导了圆环上等距和二聚化离子晶体的马德隆常数的解析式,使读者更易于理解.本文修正了《关于〈一维圆环上双原子链的马德隆常数〉的解析解》的两个笔误,还建议修正二聚化马德隆常数的定义式,使其物理意义更明显.     

Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.