导出的纯语言OWGA算子及其在的群决策中的应用

研究属性权重、专家权重和属性值均为纯语言变量的多属性群决策问题,利用纯语言变量的运算规律,提出一种新的数据信息集成算子:导出的纯语言有序加权几何平均(IPLOWGA)算子。并且给出纯语言环境下基于IPLOWGA算子和PLOWGA算子的一种群决策方法。最后,进行实例分析,说明该方法的实用性和有效性。     

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.     

基于偏序关系的Rough集模型及其应用

在标准Rough集理论的指导下,利用偏序关系性质构造了不同分类,并以此为基础探讨了上、下近似集,从而构建了基于偏序关系的Rough集模型。新模型将Rough集理论的应用范围由等价关系扩展到偏序关系。为了更好地增强模型的实用性和灵括性,一方面从程度、精度、概率等角度出发分别对其进行了扩展,另一方面引入依赖度使其适用于研究各种非严格的偏序关系。给出了实例分析,并结合现实生活中的现象阐述了模型的应用价值。     

紫外光谱法分析煤直接液化油族组成

煤液化油组成的测定方法以色谱法为主,但由于样品沸程长,组分性质不均一,色谱法无法实现简便快速地对液化油族组分进行定性/定量。为建立一种快速准确定量煤液化油中的酚类化合物、芳烃、脂肪烃的分析方法,本文选取具有代表性组成的煤液化油180～200 ℃馏分为研究对象,筛选了环己烷、乙醇、氢氧化钠-乙醇(50 Wt%,简称碱醇溶剂)三种分离溶剂。通过对煤液化油样品在200～400 nm波长间的特征吸收峰分析,发现碱醇溶剂可使芳烃化合物对酚类化合物的干扰减少到最小,可以有效避免吸收峰重叠问题。在此基础上,进一步对比分析了苯酚,间甲酚,邻甲酚,对甲酚等标准化合物与液化油酚类混合物在碱醇溶液中紫外吸收的标准曲线,以定量样品组成。选择间甲酚为标准化合物,根据其在290 nm处的标准曲线,得到煤液化油中酚类化合物的总量为32.14%,测定结果与宏量样品分离、称重、物料平衡后结果基本一致。在得到酚类化合物含量之后,以四氢萘为标准物,获得液化油中芳烃的总量为44.91%,脂肪烃的含量为22.95%。为确定方法的准确性,油样分别加入不同量的间甲酚和四氢萘标准物,酚的加标回收率为104.3～110.75%,芳香烃的加标回收率在84.3～91.75%。综上表明：利用紫外光谱法,以碱醇溶剂排除芳烃对酚吸收的影响,能够快速测定煤液化油中酚类和芳香烃的含量,脂肪烃的含量可差减得到。     

高光谱成像的垃圾分类识别研究

高光谱成像因光谱分辨率高、图谱合一、可实现快速无损检测等特点现已广泛应用于农业、医学、遥感等领域。现有的对可回收生活垃圾检测与分类的方法,都存在检测时间长,分类效率低,而大量多种垃圾无法同时快速分拣等问题。考虑到不同类别的生活垃圾由于其主要组成分子结构的差异,对不同波长的光有不同的吸收特性。高光谱图像在记录待分类垃圾的空间信息的同时,可以获得垃圾对不同波长的光的反射率光谱信息,通过建立识别分类模型对反射率光谱信息进行分析可以实现对高光谱图像中待分类垃圾的识别与分类。收集常见纸质、塑料、木质三种材料的可回收的垃圾样本,包括塑料瓶、食品包装袋、塑料玩具(饰品)碎片、一次性筷子、雪糕棒、木制家具碎片、木制包装盒、废旧课本、广告纸、办公用纸等多种物品共30个样本,进行清洗和裁剪处理,避免样本表面污渍对样本反射率产生影响。利用高光谱成像系统采集样本在近红外(780～1 000 nm)范围内的高光谱图像,其中18个样本做训练样本集,12个样本做测试样本集。对采集的样本图像数据做预处理,包括去噪声以及黑白校正反演反射率信息等处理;通过主成分分析(PCA)方法对训练样本集感兴趣区域(ROI)进行分析,提取到的特征波段为795.815,836.869,885.619,916.409,929.239,934.37,957.463,972.858和988.253 nm;在特征波段下分别提取这三种类别垃圾的参考光谱,通过光谱角度填图法(SAM)对测试样本ROI区域内提取的测试样本点集在特征波段下与参考光谱进行匹配,由匹配程度进行样本点归类,分析结果表明,测试样本集中纸制样本(A类别)、塑料样本(B类别)、木制样本(C类别)的分类准确度分别为100%,98%和100%,测试样本点集整体的分类准确度为99.33%;通过Fisher判别方法分析训练样本集得出判别函数式和判别准则,对测试样本点集分类,评价结果为A,B和C类样本分类准确度分别为100%,100%和97%,测试样本点集整体分类准确度为99%。通过SAM和Fisher两种判别方法对测试样本集的光谱图像进行目标物的检测与分类,结果表明,利用SAM判别方法在可回收垃圾的高光谱图像中实现检测与分类有更高的分类准确度,可达到99.33%。同时,也验证了使用高光谱成像进行可回收垃圾快速分类的科学性以及可行性,对未来系统化、机械化、智能化地解决生活中可回收垃圾的分类具有一定的实用意义。     

应用ICP-MS测定城市道路尘土中铂族元素

自然条件下,铂族元素(PGEs)一直被认为是表生条件下的痕量元素。但是,随着铂族元素在机动车排放催化剂及其他方面的广泛使用,最终导致Pt,Pd,Rh会排放到环境中。目前,铂族元素在环境中存在一定程度的积累现象,机动车尾气催化转换器被认为是其污染的主要来源。为研究北京市城区内道路尘土铂族元素的污染状况,于2010年11月采集了北京市内大型居民社区内的道路尘土样品。样品先经王水热消解以及阳离子交换树脂(Dowex AG50W-X8)分离纯化后,采用电感耦合等离子体质谱仪(ICP-MS)分析了道路尘土样品中三种铂族元素(铂(Pt)、钯(Pd)和铑(Rh))的浓度。结果表明：北京城区内居民社区道路尘土中Pd,Pt和Rh的含量范围分别为：14.20～161.80,9.39～70.80和3.18～17.05 ng·g-1,其平均浓度分别为50.76,23.82和7.54 ng·g-1,高于北京市土壤背景值, 说明北京城市居民社区内道路尘土中PGEs已经有了一定程度的富集。其中,Pd,Pt和Rh分别高于北京土壤背景值约49.8,32.6和5.1倍。为了定量评价道路尘土中PGEs的污染状况,采用地积累指数法对北京市道路尘土进行了评价。地积累指数法评价结果发现：大型居民社区内道路尘土中PGEs污染程度由强至弱的顺序依次为：Pd＞Pt＞Rh,总体而言,北京城区内大型居民社区内道路尘土存在较为明显的PGEs污染。     

Group inverse and core inverse in Banach and C*-algebras

Abstract

In this paper, we have focused our study on the acute perturbation of the group inverse for the elements of Banach algebra with respect to the spectral radius. We also give perturbation analysis for the core inverse in C*- algebra. The perturbation bounds for the core inverse under some conditions are presented. Additionally, this paper extends the results obtained in [11, 14].     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

Matrix Infrared Spectroscopy and Quantum‐Chemical Calculations for the Coinage‐Metal Fluorides: Comparisons of Ar?AuF,Ne?AuF,and Molecules MF2 and MF3

The reactions of laser‐ablated Au, Ag, and Cu atoms with F₂ in excess argon and neon gave new absorptions in the M? F stretching region of their IR spectra, which were assigned to metal‐fluoride species. For gold, a Ng? AuF bond was identified in mixed neon/argon samples. However, this bonding was much weaker with AgF and CuF. Molecules MF₂ and MF₃ (M=Au, Ag, Cu) were identified from the isotopic distribution of the Cu and Ag atoms, comparison of the frequencies for three metal fluorides, and theoretical frequency calculations. The AuF₅ molecule was characterized by its strongest stretching mode and theoretical frequency calculations. Additional evidence was observed for the formation of the Au₂F₆ molecule.