有机污染物降解的光化学及微生物降解方法探索

随着塑料用途的不断扩大和消费量的日益增长，塑料废弃物也与日俱增，环境污染和社会公害等问题也日趋严重。塑料废弃物具有不易降解性，它不仅污染环境（土壤、水源等），还会阻止其它物质的降解。文章试图从基本机理入手，找到有机物降解的最有效途径，以达到保护环境、节约能源的目的。     

从动词词义看双宾语构块式和与格构块式

本文从构块式语法的角度观察双宾语结构和与格结构间构块式意义的差异。用磁体构造模型,结合BROWN语料库统计数值,来阐释双及物动词词义类型与两种构块式选用的关联性,以便准确掌握选用两种构块式的规律。     

Computing maximum likelihood estimates from type II doubly censored exponential data

It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived. In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included.     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

求直射变换的二重元素问题的简便方法

本文对高等几何中直射变换的二重元素的关系进行探索,得出一个求解直射变换的二重元素问题的简便方法.     

Fuzzy K-means clustering models for triangular fuzzy time trajectories

We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fullyinformational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated.     

Local dependence estimation using semiparametric archimedean copulas

The authors define a new semiparametric Archimedean copula family which has a flexible dependence structure. The generator of the family is a local interpolation of existing generators. It has locally‐defined dependence parameters. The authors present a penalized constrained least‐squares method to estimate and smooth these parameters. They illustrate the flexibility of their dependence model in a bi‐variate survival example.     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

DNA分子近邻互补链碱基的非线性效应

DNA分子近邻互补链碱基的非线性动力学效应研究，综述了Yomosa及Zhang模型，并在改进后的平面转子模型基础上，利用阶乘矩分析方法分析了平面转子模型的非线性效应，结果说明该模型更好的近似性．     

神话再阐释:双重定义之上的本质属性

某种意义上 ,现代语义中的神话只是原始神话的语言形态。要科学地认识和理解人类文化之源的神话 ,仅仅着眼于神话的语言形态是不全面的 ,甚至是非本质的把握方式。文章尝试从本文与文本两个视角探讨神话定义 ,在艺术本体、传承主体、文化功能和思维机制等四个层面上重新阐释神话的本质属性 ,认为神话具有多维性、民族性、神圣性和不自觉性