某型重机枪身管的多物理场耦合分析

为了研究具有不同厚度铬涂层的某型重机枪身管在高温、高压、高速火药流体作用下的温度场和应力场,采用基于多物理场耦合程序接口MpCCI联合结构计算软件Abaqus和流体计算软件Fluent进行耦合计算的方法,对涂层厚度分别为0.28mm、0.35mm、0.41mm的身管进行流-固-热多物理场耦合仿真计算。通过计算得到了不同铬涂层厚度下身管应力和温度的分布规律。结果表明,借助MpCCI联合Abaqus和Fluent进行多物理场耦合计算的方法是可行的;随着涂层厚度的增加身管内壁的温度和应力依次降低,但在涂层与身管结合处的集中应力逐渐增大。结论对身管的结构设计和寿命预测有一定的指导意义。     

Genome-wide association studies of 14 agronomic traits in rice landraces

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ～ 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ～ 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.     

月地高速再入返回飞行器陀螺在轨自主标定技术研究及实现

针对包含非正交安装陀螺的捷联惯导平台轻量化自主对准问题,建立了非正交陀螺组误差模型,提出一套基于最小二乘法的标定算法和地面试验验证方法,在计算能力受限的情况下完成了高精度在轨标定.地面数学仿真和试验验证结果表明,该方法与扩展卡尔曼滤波法精度相当,计算量却大幅减小.在此基础上,通过对标定算法进行逻辑设计,实现了在轨多组陀螺同步容错和标定.该方法已成功应用于探月工程三期月地高速再入返回飞行器的实际飞行,实现了2套惯性导航平台的高精度初始姿态同步对准.     

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.     

Genome-wide association study identifies a new melanoma susceptibility locus at 1q21.3

We performed a genome-wide association study of melanoma in a discovery cohort of 2,168 Australian individuals with melanoma and 4,387 control individuals. In this discovery phase, we confirm several previously characterized melanoma-associated loci at MC1R, ASIP and MTAP-CDKN2A. We selected variants at nine loci for replication in three independent case-control studies (Europe: 2,804 subjects with melanoma, 7,618 control subjects; United States 1: 1,804 subjects with melanoma, 1,026 control subjects; United States 2: 585 subjects with melanoma, 6,500 control subjects). The combined meta-analysis of all case-control studies identified a new susceptibility locus at 1q21.3 (rs7412746, P = 9.0 × 10(-11), OR in combined replication cohorts of 0.89 (95% CI 0.85-0.95)). We also show evidence suggesting that melanoma associates with 1q42.12 (rs3219090, P = 9.3 × 10(-8)). The associated variants at the 1q21.3 locus span a region with ten genes, and plausible candidate genes for melanoma susceptibility include ARNT and SETDB1. Variants at the 1q21.3 locus do not seem to be associated with human pigmentation or measures of nevus density.     

Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder

Transitional cell carcinoma (TCC) is the most common type of bladder cancer. Here we sequenced the exomes of nine individuals with TCC and screened all the somatically mutated genes in a prevalence set of 88 additional individuals with TCC with different tumor stages and grades. In our study, we discovered a variety of genes previously unknown to be mutated in TCC. Notably, we identified genetic aberrations of the chromatin remodeling genes (UTX, MLL-MLL3, CREBBP-EP300, NCOR1, ARID1A and CHD6) in 59% of our 97 subjects with TCC. Of these genes, we showed UTX to be altered substantially more frequently in tumors of low stages and grades, highlighting its potential role in the classification and diagnosis of bladder cancer. Our results provide an overview of the genetic basis of TCC and suggest that aberration of chromatin regulation might be a hallmark of bladder cancer.     

Behavior Model Construction for Client Side of Modern Web Applications

Most of the behavior models with respect to Web applications focus on sequencing of events,without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pages.As a result,these models are not sufficient to effectively represent the dynamic behavior of the Web2.0 application.Therefore,in this paper,to appropriately describe the dynamic behavior of the client side of Web applications,we define a novel Client-side Behavior Model(CBM) for Web applications and present a user behavior trace-based modeling method to automatically generate and optimize CBMs.To verify the effectiveness of our method,we conduct a series of experiments on six Web applications according to three types of user behavior traces.The experimental results show that our modeling method can construct CBMs automatically and effectively,and the CBMs built are more precise to represent the dynamic behavior of Web applications.     

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.