Formation of Chlorotriophenoxy Radicals from Complete Series Reactions of Chlorotriophenols with H and OH Radicals

The chlorothiophenoxy radicals (CTPRs) are key intermediate species in the formation of polychlorinated dibenzothiophenes/thianthrenes (PCDT/TAs). In this work, the formation of CTPRs from the complete series reactions of 19 chlorothiophenol (CTP) congeners with H and OH radicals were investigated theoretically by using the density functional theory (DFT) method. The profiles of the potential energy surface were constructed at the MPWB1K/6-311+G(3df,2p)//MPWB1K/6-31+G(d,p) level. The rate constants were evaluated by the canonical variational transition-state (CVT) theory with the small curvature tunneling (SCT) contribution at 600–1200 K. The present study indicates that the structural parameters, thermal data, and rate constants as well as the formation potential of CTPRs from CTPs are strongly dominated by the chlorine substitution at the ortho-position of CTPs. Comparison with the study of formation of chlorophenoxy radicals (CPRs) from chlorophenols (CPs) clearly shows that the thiophenoxyl-hydrogen abstraction from CTPs by H is more efficient than the phenoxyl-hydrogen abstraction from CPs by H, whereas the thiophenoxyl-hydrogen abstraction from CTPs by OH is less impactful than the phenoxyl-hydrogen abstraction from CPs by OH. Reactions of CTPs with H can occur more readily than that of CTPs with OH, which is opposite to the reactivity comparison of CPs with H and OH.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

太赫兹超表面计算全息

全息术是一种三维成像技术,它已经被应用于多种实际场景。随着计算机科学与技术的迅猛发展,计算全息由于其方便和灵活的特性,已经成为一种广泛应用的全息成像方法。本文回顾了我们近期基于超表面的太赫兹计算全息研究进展。其中,作为全息板的超表面展示出了超越传统光学器件的独特性能。首先,利用超表面实现了对于全息板每个像素的相位振幅同时且独立的调控,进而实现了高质量全息成像。这种新的电磁波操控能力也带来了新的全息成像效果,如利用介质超表面实现了全息像沿传播方向上的连续变化。其次,对超表面在不同偏振态下的响应进行设计,分别实现了线偏振态与频率复用、圆偏振态复用、以及基于表面波的偏振复用超表面全息术。此外,本文提出了依赖于温度变化而主动可控的超表面全息术,为今后计算全息术的设计与实现提供了新的方案,也推动了超表面在实际应用方面的发展。     

Synthesis of Cellulose-Based Macromolecular Coupling Agent and its Utilization in Poly (butylene succinate)/Wood Flour Composites

A long fatty side chain was introduced into the macromolecule of hydroxyethyl cellulose (HEC) via esterification reaction. The hydrophobicity of hydroxyethyl cellulose lauric acid ester (HECLAE) was enhanced in comparison with HEC. The obtained HECLAE was used as macromolecular coupling agent in poly (butylene succinate)/wood flour composites and exhibited a positive influence on improving the mechanical performance of composites. Besides, HECLAE plays a role as a hydrophobic agent in composites. A significant increase in storage modulus (E’) was observed upon the incorporation of treated wood flour. SEM images showed that the dispersion of treated wood flour in PBS matrix was improved.     

基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。