热解吸直接进样气相色谱法分析室内空气中的总挥发性有机化合物

建立室内空气中总挥发性有机化合物的热解吸直接进样气相色谱分析方法。采集的空气样品吸附于Tenax吸附管中,将吸附管置于热解吸仪上,解吸气体直接由进样阀快速进入气相色谱仪进行分析,采用非极性石英毛细管柱,氢火焰离子化检测器检测。8种组分的线性范围为0.01～11.04μg,检测限为0.001～0.006μg,测定结果的相对标准偏差RSD≤4.8%。该方法符合室内空气中总挥发性有机化合物分析的要求。     

Confidence interval estimation by a joint pivot method

The Birnbaum–Saunders distribution is widely used in fields such as survival analysis. In this paper, we use a new method to construct confidence intervals for the shape parameter of the Birnbaum–Saunders distribution and find it superior to some existing methods. Our method can be called the “joint pivot method” as we obtain the confidence interval by uniting two pivots. Furthermore, we find that the joint pivot method can also be used to solve the Behrens–Fisher problem. Simulation studies show that the joint pivot method is comparable with some commonly practiced ones. Finally, we analyse two real data sets to illustrate the proposed procedure.     

Influence diagnostics and outlier tests for varying coefficient mixed models

In this paper, we consider subset deletion diagnostics for fixed effects (coefficient functions), random effects and one variance component in varying coefficient mixed models (VCMMs). Some simple updated formulas are obtained, and based on which, Cook’s distance, joint influence and conditional influence are also investigated. Besides, since mean shift outlier models (MSOMs) are also efficient to detect outliers, we establish an equivalence between deletion models and MSOMs, which is not only suitable for fixed effects but also for random effects, and test statistics for outliers are then constructed. As a byproduct, we obtain the nonparametric “delete = replace” identity. Our influence diagnostics methods are illustrated through a simulated example and a real data set.     

X射线CCD标定及模拟

 在北京同步辐射光源4B7B实验站上,采用透射光栅配X射线电荷耦合元件(CCD)的方法对单色光进行了检验,在优化高次谐波抑制方法后,开展了X射线CCD的灵敏度标定实验研究;在标定数据处理过程中,提出了等效曝光时间的概念,修正了快门开合造成的曝光时间误差。标定实验获得了100~1 500 eV能区CCD的灵敏度,补充了300~600 eV能区CCD灵敏度标定数据,验证了简化模型的正确性。     

毛细管气相色谱法同时测定白酒中的甲醇和杂醇油

采用PEG20000石英毛细管柱直接进样、氢火焰离子化检测器检测,建立了毛细管气相色谱同时测定白酒中的甲醇和杂醇油的分析方法。甲醇和杂醇油的检出限均为2.0×10-4g/(100mL),回收率分别为96.4%~99.1%、91.2%~98.3%,相对标准偏差不大于4.3%。该方法适用于白酒中甲醇和杂醇油的同时分析测定。     

Transformation-based model averaged tail area inference

In parameter estimation, it is not a good choice to select a “best model” by some criterion when there is model uncertainty. Model averaging is commonly used under this circumstance. In this paper, transformation-based model averaged tail area is proposed to construct confidence interval, which is an extension of model averaged tail area method in the literature. The transformation-based model averaged tail area method can be used for general parametric models and even non-parametric models. Also, it asymptotically has a simple formula when a certain transformation function is applied. Simulation studies are carried out to examine the performance of our method and compare with existing methods. A real data set is also analyzed to illustrate the methods.     

Global synchronization in arrays of coupled networks with one single time-varying delay coupling

Global synchronization in arrays of coupled networks with one single time-varying delay coupling is investigated in this Letter. A general linear coupled network with a time-varying coupling delay is proposed and its global synchronization is further discussed. Some sufficient criteria are derived based on Lyapunov functional and linear matrix inequality (LMI). It is shown that under one single delay coupling, the synchronized state changes, which is different from the conventional synchronized solution. Moreover, the degree of the nodes and the inner delayed coupling matrix play key roles in the synchronized state. In particular, the derivative of the time-varying delay can be any given value. Finally, numerical simulations are given to illustrate the theoretical results.     

Stability and bifurcation of a class of discrete-time neural networks

In this paper, a class of discrete-time system modelling a network with two neurons is considered. Its linear stability is investigated and Neimark–Sacker bifurcation (also called Hopf bifurcation for map) is demonstrated by analyzing the corresponding characteristic equation. In particular, the explicit formula for determining the direction of Neimark–Sacker bifurcation and the stability of periodic solution is obtained by using the normal form method and the center manifold theory for discrete time system developed by Kuznetsov. The theoretical analysis is verified by numerical simulations.     

���ھ���ֲ������ĸ�ά��̬�Լ���

??Kolmogorov-Smirnov (KS), Cramer-von Mises (CM) and Anderson-Darling (AD) test, which are based on empirical distribution function (EDF), are well-known statistics in testing univariate normality. In this paper, we focus on the high dimensional case and propose a family of generalized EDF based statistics to test the high-dimensional normal distribution by reducing the dimension of the variable. Not only can we approximate the corresponding critical values of three statistics by Monte Carlo method, we also can investigate the approximate distributions of proposed statistics based on approximate formulas in univariate case under null hypothesis. The Monte Carlo simulation is carried out to demonstrate that the performance of proposed statistics is more competitive than existing methods under some alternative hypotheses. Finally, the proposed tests are applied to real data to illustrate their utility.