Simple Sequence Repeat Markers in Genetic Divergence and Marker-Assisted Selection of Rice Cultivars: A Review

Sequencing of rice genome has facilitated the understanding of rice evolution and has been utilized extensively for mining of DNA markers to facilitate marker-assisted breeding. Simple sequence repeat (SSR) markers that are tandemly repeated nucleotide sequence motifs flanked by unique sequences are presently the maker of choice in rice improvement due to their abundance, co-dominant inheritance, high levels of allelic diversity, and simple reproducible assay. The current level of genome coverage by SSR markers in rice is sufficient to employ them for genotype identification and marker-assisted selection in breeding for mapping of genes and quantitative trait loci analysis. This review provides comprehensive information on the mapping and applications of SSR markers in investigation of rice cultivars to study their genetic divergence and marker-assisted selection of important agronomic traits.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Combination of nuclear magnetic resonance spectroscopy and nonlinear methods to analyze the copolymerization of phosphonic acid derivatives

We demonstrate in this study that the combination of modern inline monitoring methods [here: inline nuclear magnetic resonance (NMR)] with simulations gains more exact and profound kinetic results than previously used methods like linearization without that combination. The ¹H-NMR spectroscopic data (more than 100 data points) are used to construct the copolymerization diagram. The reactivity ratios are obtained applying the van Herks nonlinear least square method. The examination of the radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with (2-{[2-(ethoxycarbonyl)prop-2-en-1-yl]oxy}ethyl) phosphonic acid (ECPPA) as important adhesive monomer used in dentistry yields reactivity ratios of r_HEMA = 1.83; r_ECPPA = 0.42. The copolymerization diagram reflects nonideal, non-azeotropic copolymerization. The sequence distribution of the obtained by Monte Carlo simulation indicates the generation of statistical copolymers. As an important finding, it is demonstrated that the repeating units responsible for etching and adhesion are arranged over the whole polymer chain, which is necessary to achieve proper functionality. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48256.     

苏6井区下石盒子组高分辨率层序地层学特征

以高分辨率层序地层学理论和技术方法为指导，在苏里格气田苏6井区地表露头、钻井岩心、测井、地震资料综合研究的基础上，通过基准面旋回结构、叠加样式的沉积动力学分析，将该井区下石盒子组划分为2个长期、5个中期和14个短期旋回层序；并较为详细地讨论了各期旋回层序特征；建立了以长期旋回层序界面和最大洪泛面为等时年代地层框架，以中期旋回层序为等时地层单元的高分辨率层序地层格架；通过分析，认为下石盒子组盒₈下段天然气储、盖条件最为有利；有利于储层发育的河道砂体主要出现在长、中期上升半旋回的中、下部，层序界面上相当短期旋回级别的单河道砂体是下石盒子组天然气勘探开发的主要目标。     

FZ-870绕线机排线机构电控系统分析

文章分析了FZ-870绕线机排线机构电控系统，并就存在的问题提出了对策。     

一种大尺度二维直线度精密测量的新方法

本文提出了一种远距离、大范围二维直线度测量的新方法．该方法采用特殊设计的十字线准直激光器及线阵CCD，通过特殊的光学系统结构设计，能在0-20m距离内连续测量，直线度范围可达70mm，精度优于0．1mm，实际测量结果表明，该方法可适应野外作业环境。     

差分跳频通信系统的特点及性能

文章介绍了一种新型的短波跳频通信技术——差分跳频,分析了差分跳频技术区别于常规跳频技术的主要特点。针对按序列检测的信号接收方法,对差分跳频通信系统在AWGN信道下的性能进行了理论分析,同时做出相应的计算机仿真,证实了差分跳频通信技术和按序列检测方法的结合,使通信系统在AWGN信道下的性能得到了比较显著的提升。     

东海西湖凹陷平湖油气田花港组高分辨率层序地层特征

通过岩心观察、测井与录井资料,对平湖油气田花港组河流相地层进行了高分辨层序地层分析,识别出2个中期旋回(MSC1与MSC2)与20个短期旋回,建立了研究区的层序地层格架。短期旋回分4种类型并以非对称旋回为主。两个中期旋回为非对称旋回,但MSC2对称性更大,反映了可容纳空间的持续增加。在基准面旋回的控制下,沉积相呈现有规律的辫状河曲流河辫状河曲流河交替演化,在每个中期旋回均以辫状河开端,以曲河流结束,由于中期旋回在长期旋回中的位置不同,曲流河的特征也有所不同。位于中期旋回上升半旋回中部的曲流河及底部的辫状河是研究区的有利砂体,尤其是前者最为有利。     

层序单元体系域划分及勘探意义

体系域是层序地层学理论体系中的重要概念，在隐蔽油气藏的勘探中，勘探家们极为重视低位体系域。但体系域的成因意义、是否有不同级别层序的体系域以及如何划分，在实际工作中认识不一致。结合勃海湾盆地的勘探实例，分析体系域的成因机制、组合特征和勘探规律，认为：体系域是基准面变化等因素作用的产物，不同级别的层序都发育着与其对应的体系域：一般情况下，三级层序对应的体系域单元与含油气层的规模相匹配，但四级层序对应的体系域才是隐蔽油气藏赋存的有利部位。图5参6。     

应用AR模型的IMU误差系数漂移预测研究

将影响IMU误差系数漂移并难于分离的多种因素综合为一种因素，假定IMU误差系数的漂移由一特定系统所引起，从而采用时间序列建模方法进行IMU误差系数漂移的预测。该方法具有消除各因素具体分析中的不确定性并减少其工作员的优点。文中描述了其原理，对某惯组的33个误差系数进行了时间序列建模与预测并得到有效的结果。最后还阐述了存在的问题及其解决方法。