改进的TOPSIS决策方法在供应商选择中的应用

运用马氏距离替代欧式距离改进传统的TOPSIS方法,解决当属性间存在线性相关时欧式距离失效的缺陷;充分考虑对立集合并引入联系向量距离,解决可能存在的方案距离正理想解和负理想解距离都近的缺陷.然后通过决策者偏好系数将马氏距离和联系向量距离所得结果合成新的相对贴近度,从而同时克服传统TOPSIS方法的以上两个缺陷.最后通过供应商选择的实例来验证方法的有效性.     

含参集值向量拟均衡问题的适定性

首次对含参集值向量拟均衡问题的适定性进行了研究,并在适当的条件下建立了所研究问题适定性的充分条件.     

斜投影-子空间夹角算法快速分析农药中阿维菌素

采用斜投影-子空间夹角算法快速分析农药中阿维菌素的含量。用斜投影算法从阿维菌素对照品中提取阿维菌素B_(1a)纯光谱数据,用子空间夹角判据计算农药中阿维菌素的含量。阿维菌素的质量浓度在4~14μg/mL范围内与用斜投影法提取的阿维菌素B_(1a)纯光谱吸光度呈良好的线性关系,相关系数r=0.9994。斜投影-子空间夹角算法加标回收率在98.80%~101.67%之间,测定结果的相对标准偏差小于2%(n=6)。用该方法与高效液相色谱法对同一样品进行测定,两种方法测定结果相接近,相对误差小于2.20%。该方法满足快速检测农药中阿维菌素含量的分析要求。     

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.     

Predicting protein subcellular localization based on information content of gene ontology terms

Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.     

Globally generated vector bundles on the Segre threefold with Picard number two

We classify globally generated vector bundles on with small first Chern class, i.e. , . Our main method is to investigate the associated smooth curves to globally generated vector bundles via the Hartshorne–Serre correspondence.     

Solvable extensions of negative Ricci curvature of filiform Lie groups

We give necessary and sufficient conditions of the existence of a left‐invariant metric of strictly negative Ricci curvature on a solvable Lie group the nilradical of whose Lie algebra is a filiform Lie algebra . It turns out that such a metric always exists, except for in the two cases, when is one of the algebras of rank two, or , and is a one‐dimensional extension of , in which cases the conditions are given in terms of certain linear inequalities for the eigenvalues of the extension derivation.     

Electromagnetic eigenfields in checkerboard patterns

Here are described four solvers for time‐harmonic electromagnetic fields in checkerboard patterns. A pattern is built by four squares with constant permittivity, or . It is enclosed by conducting walls or is a unit cell of a periodic structure. The field is represented in two ways: by , the transverse component of the magnetic induction, and by , the magnetic vector potential in Lorenz gauge. and satisfy Helmholtz equations in each square as well as transmission and boundary conditions (BCs). These governing equations yield eigensolutions and , which are found to be at worst. Variational versions of the governing equations are introduced. The weak formulations for are standard, while those for are new. They imply that the derivative transmission and BCs are satisfied weakly on interfaces between regions with different permittivity. Eigenpairs are computed approximately by spectral element methods. They yield mutually consistent eigenpairs. However, only about half of the eigenpairs () correspond to eigenpairs (). For each set of BCs, the first few eigenfrequencies are given by tables, and some of the eigenfunctions are presented by contour plots. © 2015 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 32: 418–444, 2016     

环形线负载驱动变曲率反射镜技术

从薄板弹性理论出发,对可实现曲率变化的环形线负载驱动模型进行分析,给出了基于该模型的大镜厚比变曲率反射镜的形变方程.以较小的驱动力实现较大的中心形变为目标,利用MATLAB软件对不同反射镜厚度、驱动环半径下的反射镜形变情况进行模拟计算,结果表明,反射镜厚度范围在2~4 mm之间、驱动环半径数值在反射镜有效半径1/2处最佳.以此为依据,设计并研制了口径为100 mm、厚度为3 mm的铍青铜环形线负载驱动变曲率反射镜结构及原型样片,给出了变曲率反射镜整体结构前10阶的振动模态分析结果.完成装配后,反射镜原型样片的面形精度接近λ/30(λ为波长).对该结构进行极限曲率变化和面形精度保持的验证实验,通过对变曲率反射镜结构进行改进,环形线负载驱动能够实现超过30个波长(632.8 nm)的中心形变,且面形精度的变化与反射镜中心矢高的变化呈弱相关.     

基于支持向量机、支持向量回归和分子对接的CYP450 1A2抑制剂的发现研究

支持向量机,支持向量回归和分子对接的计算方法已广泛应用于化合物的药理活性计算。为了提高计算的准确性和可靠性,拟以细胞色素P450酶1A2为研究载体,运用建立的联合SVM-SVR-Docking计算模型预测潜在的CYP1A2抑制剂。其中,建立的最优SVM定性模型训练集,内部测试集和外部测试集的准确率分别为99.432%,97.727%和91.667%。最优SVR定量模型训练集和测试集的R和MSE分别为0.763,0.013和0.753,0.056。实验表明两个模型具有较高的准确性和可靠性。通过对SVM和SVR模型结果的比较分析,发现连接性指数、分子构成描述符和官能团数目等分子描述符可能与CYP1A2抑制剂的辨识和活性预测密切相关。随后利用分子对接技术分析化合物与CYP1A2的结合构象及相互作用的稳定性。形成氢键相互作用的关键氨基酸包括THR124,ASP320;形成疏水相互作用的关键氨基酸包括ALA317和GLY316。所获得模型可用于天然产物化学成分中CYP1A2潜在抑制剂的活性计算及其介导的药物-药物相互作用预测提供理论指导,也为合理联合用药提供一定参考。共获得20个对CYP1A2具有潜在抑制活性的化合物。部分结果与文献结果相互印证,进一步说明了模型的准确性和联合计算策略的可靠性.