Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm

A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymorphisms in rice.     

A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters(e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.     

浅谈电力配网规划

随着电力系统自动化水平的提高,我国城市电网的发展也越来越快。电力配网作为电力企业电能输送的最后环节,其重要性也日益显著。本文所阐述的电力配网工作,主要包括配网的规划和建设、配网的运行和维护两个方面。     

高职院校学生体育运动与学习动机研究

文章通过对高职学生学习动机的分析，找出高职学生在体育运动学习过程中存在的问题，并提出解决这些问题的对策和措施。     

开展幼儿科学活动的思考

提高全体国民的科学素质是教育改革的主要任务,因为没有高科学素质的人,社会就不能进入高科技时代,就不能得到可持续发展.而这一切都要从幼儿抓起,从娃娃抓起.文章以此为背景,阐述了幼儿科学活动的重要性以及科学活动对幼儿教师提出的新要求,并说明了培养幼师学生科学活动能力的方法.     

未掺杂SI-GaAs退火行为的正电子湮没研究

用正电子湮没技术,研究了未掺杂的SI-GaAs退火行为。结果表明,长寿命分量τ_2的变化不仅与镓空位而且和多镓空位络合物有关。本文还讨论了电子和中子辐照的影响。     

Cu-Ni-Al-Ti合金时效早期的Spinodal分解

本文用TEM研究了Cu-Ni-Al-Ti合金时效早期的转变方式和时效特性,推出用δ条纹的衬度变化及其间距不变等特征,作为时效早期Spinodal分解的一个判据,证实了该合金时效早期就发生了Spinodal分解,以及观察到了Spinodal有序现象。     

机械故障时序诊断特性研究

本文讨论了采用时序建模方法诊断机械故障时，建模方法、判别函数、数据修正以及信噪比等因素对诊断结果的影响。提出在可以模拟故障工况时，对判为同类的工况数据不必进行修正。对时序诊断结果与频谱分析、灰色关联分析进行了比较。     

龙门刨床泵缸系统特性及爬行临界条件

建立了Ｂ２２８Ｙ液压龙门刨床液压系统主回路的数学模型，分析、评价了该系统的动态特性品质，指出了误差过大的问题，并提出了增加伺服阀的改进意见。确定了刨床工作台产生爬行现象的临界条件，提出了防止爬行的措施。