Mehrotra-type predictor-corrector algorithm,as one of most efficient interior point methods,has become the backbones of most optimization packages.Salahi et al.proposed a cut strategy based algorithm for linear optimization that enjoyed polynomial complexity and maintained its efficiency in practice.We extend their algorithm to P_*(κ)linear complementarity problems.The way of choosing corrector direction for our algorithm is different from theirs. The new algorithm has been proved to have an ο((1+4κ)(17+19κ) √(1+2κn)~(3/2)log[(x~0)~Ts~0/ε] worst case iteration complexity bound.An numerical experiment verifies the feasibility of the new algorithm.

Mehrotra-type predictor-corrector algorithm,as one of most efficient interior point methods,has become the backbones of most optimization packages.Salahi et al.proposed a cut strategy based algorithm for linear optimization that enjoyed polynomial complexity and maintained its efficiency in practice.We extend their algorithm to P* (κ) linear complementarity problems.The way of choosing corrector direction for our algorithm is different from theirs.The new algorithm has been proved to have an O((1 + 4κ)(17 + 19κ) √1+2κn3/2log (x0)Ts0/ε)worst case iteration complexity bound.An numerical experiment verifies the feasibility of the new algorithm.

对从煤液化残渣中萃取出的沥青类物质进行了固体13C-CP/MAS NMR分析、元素分析、红外光谱分析（FT-IR）和光电子能谱（XPS）分析,得到煤液化沥青的芳香结构单元信息及相关结构参数信息。结果表明,煤液化沥青芳香桥碳与周碳之比为0.115,芳香碳原子的存在形式以苯结构为主;脂肪结构多以甲基和环状亚甲基形式存在;氧主要以羰基、酯基的形式存在;氮主要以吡咯的形式存在。利用结构参数和分析表征结果构建了煤液化沥青的大分子结构模型,并运用13C-NMR预测软件ACD/CNMR Predictor计算了煤精制沥青大分子结构模型的13C化学位移。根据计算结果对大分子结构模型进行了修正,获得了与实验谱图吻合较好的大分子结构模型。

The asphaltene extracted from coal liquefaction residue was studied by 13 C-CP/ MAS NMR,elemental analysis , FT-IR and XPS to reveal its aromatic unit structure and relative structural parameters. The results show that the ratio of bridged carbon to the surrounding carbon of asphaltene is 0. 115. Benzene is themain form of aromatic carbon , and aliphatic structure exists mainly in the forms of alkanes and cyclic-methylene. Oxygen atoms present as carboxyl and ester group and nitrogen atom exits in the form of pyrrole. Based on structural parameters and analytic characterization, macromolecular structure model of refined asphaltene isconstructed. 13 C chemical shift of refined asphaltene is calculated by ACD/ CNMR predictor. According to the calculation results, macromolecular structure model of refined asphaltene is corrected, and finally the calculatedchemical shift diagram of model can tally well with the experimental result.

目的 在组学水平高通量、系统地预测及筛选细粒棘球绦虫的分泌蛋白组,研究分泌蛋白及其信号肽特征,为后续诊断和疫苗相关抗原(簇)研究奠定基础. 方法 利用SignalP4.1对细粒棘球绦虫全基因组蛋白序列进行信号肽预测,对含有信号肽的蛋白依次应用生物信息学软件TMHMMv2.0、Phobius、Big-PI predictor及TargetP1.1进行细粒棘球绦虫经典分泌蛋白质组筛选.随后利用LipoP1.0和TatP1.0分析分泌蛋白信号肽酶切位点类型,用SPSS19.0统计归纳信号肽和分泌蛋白的氨基酸序列特征,Blast2GO对分泌蛋白质组进行基因本体(gene ontology,GO)注释与聚类. 结果 在细粒棘球绦虫14 235条全基因组序列所编码的蛋白中,共发现984条含有信号肽的蛋白序列;其中有363条属于膜结合蛋白,另有25条定位于亚细胞器;最终筛选到596条经典分泌蛋白序列.分泌蛋白质组的信号肽长度集中于15～ 31个氨基酸残基,其中疏水性氨基酸含量为62％,主要被Ⅰ型信号肽酶所识别,且酶切位点在-3及-1位相对保守;分泌蛋白氨基酸含量集中于50～ 700个氨基酸残基,小于非分泌蛋白氨基酸含量(t=3.06,P＜0.01);GO分析显示分泌蛋白主要参与新陈代谢、细胞过程、调节和发育等生物过程,并行使催化、结合、抗氧化和酶调节功能. 结论 本研究预测筛选到含有596条蛋白序列的细粒棘球绦虫分泌蛋白组,可用于后续的诊断和疫苗相关抗原筛选研究.

Objective To predict and screen the secretome of Echinococcus granulosus by using genome-wide prediction and bioinformatic approaches,and systematically analyze the characteristics of signal peptides and secreted proteins so as to provide a platform for finding diagnostic and vaccine target antigen(s).Methods The genome-wide prediction and bioinformatic methods were used in the study to profile the secretome.The signal peptides were identified from the E.granulosus whole genome sequence by using SignalP4.1 program,and the proteins containing signal sequences were analyzed by TMHMM v2.0,Phobius,Big-PI predictor and TargetP1.1 in a stepwise way to identify the classical secreted proteins.Subsequently,the enzyme digestion sites of the signal sequences identified were analyzed by LipoP1.0 and TatP1.0,and the sequence features of both the signal peptides and the secreted proteins were statistically analyzed by SPSS19.0.Finally,the ontology(GO) annotations and clustering were performed for t

针对BP_Adaboost算法预测精度不高的问题,对算法作了改进:先用遗传算法对每个BP神经网络弱预测模型进行优化;然后把优化后的BP神经网络模型看作为新的弱预测器;再通过BP_Adaboost算法,用多个被遗传算法优化后的BP神经网络弱预测器组成强预测器模型;最后加权整合优化后用2 000组随机数据验证改进后算法的预测精度,用Matlab程序仿真实现改进后的BP_Adaboost算法,并与改进前的BP_Adaboost算法作比较。程序运行结果表明,改进后的BP_Adaboost算法预测精度有了明显提高。

The prediction problem is the core of large data.The existing BP_Adaboost algorithm is a fusion in BP neural network model of prediction model algorithm.As the accuracy of BP_Adaboost algorithm is not high, the BP_Adaboost algorithm is improved in our stndy.BP neural network model is put as a weak predictor,and the strong predictor of multiple BP neural network composed of weak predictor is obtained by BP_Adaboost algo-rithm.Genetic algorithm is used for each BP neural network prediction model for optimization.When optimized BP neural network model as a new weak predictor,and through the BP_Adaboost algorithm,the BP neural net-work by genetic algorithm optimization of weak predictor is composed of strong predictor model.From 2 000 groups of random experimental data,the prediction accuracy to verify the improved algorithm leads to improved BP_Adaboost algorithm simulation with Matlab program.The result is compared with the BP_Adaboost algo-rithm before improvement.From the res

为了解决因选择不同的静态代码属性子集而带来的不同软件缺陷预测性能的分歧,采用偏相关分析法,分析了静态代码属性之间的相关性,以及该相关性对预测算法的影响.在Eclipse数据集(包括发布前与发布后的缺陷数据)上的实验结果表明,静态代码属性之间存在偏相关.同时,也证实了偏相关是软件质量分析的非常重要的因素,为建立软件缺陷预测模型提供了可靠的分析方法.

This article analyzed the correlation between static code attributes and its effect upon predictor using partial correlation method in order to find a solution to the difference in the software defect predictors from using different static code attribute subsets. An experiment was performed using the public Eclipse dataset with both pre-release and post-release defects, and partial correlation was found to exist in the static code attributes. It has been proved to be a very important factor in software quality analysis, and provides a reliable analytical method to establish a model of the software defect predictors.