确定原子基态的公式法

在能量最小原理、Ｐａｕｌｉ原理以及Ｈｕｎｄ定则的制约下，推导出计算原子基态谱项Ｌ和Ｓ的一套简洁公式，解决了Ｌ—Ｓ耦合下原子基态的确定问题     

基于WinCC和S7-200 PLC的转轮除湿多点温湿度监测系统

针对目前转轮除湿研究中,温湿度测量以干球温度、湿球温度测量为主,存在测量过程复杂,误差大等缺点,提出了基于WinCC和S7 -200PLC的转轮除湿多点温湿度监测系统。以转轮除湿机中处理风和再生风进出口及环境的温湿度作为研究对象,研华工控机IPC-610H作为上位机,西门子S7 -200作为下位机并作为控制器控制执行机构进行数据采集。通过EM231扩展模块进行数据交换,以SIMATIC WinCC组态软件做上位机监控画面,实现对转轮除湿机多个位置的温湿度监测。现场应用结果表明该系统便于管理,控制灵活,稳定性强。系统能简化测量过程,提高测量精度。     

The order statistics of exponential,power function and pareto distributions and some applications

Some relations between the exponential, the Pareto and the Power function distributions and their order statistics are given. These relations are employed to obtain some characterization theorems of Pareto and Power distributions.     

Estimation of regression coefficient from survey data based on test of significance

The ordinary least squares (OLS)estimator of regression coeffecient is implicitly based on I.I.D.assumption, which is rarely satisfied by survey data. Many approaches are proposed in the literature which can be classified in two broad categories as model based and design consistent.Du Mouchel and Duncan (1983) proposed a test statistic λwhich helps in testing the ignorability of sampling weights.In this article a preliminary test estimator based on λ is proposed. The model based properties of this estimator has been invetigated theoritically where as to study the design based properties simulation approach is adopted. It has been observed that the proposed estimator is a better cimpromise between model based and randomization based inferential frame work.     

Outlier detection using difference-based variance estimators in multiple regression

In this article, we propose an outlier detection approach in a multiple regression model using the properties of a difference-based variance estimator. This type of a difference-based variance estimator was originally used to estimate error variance in a non parametric regression model without estimating a non parametric function. This article first employed a difference-based error variance estimator to study the outlier detection problem in a multiple regression model. Our approach uses the leave-one-out type method based on difference-based error variance. The existing outlier detection approaches using the leave-one-out approach are highly affected by other outliers, while ours is not because our approach does not use the regression coefficient estimator. We compared our approach with several existing methods using a simulation study, suggesting the outperformance of our approach. The advantages of our approach are demonstrated using a real data application. Our approach can be extended to the non parametric regression model for outlier detection.     

Challenges related to the analytical process in realist evaluation and latest developments on the use of NVivo from a realist perspective

ABSTRACT

Realist evaluation (RE) is a research design increasingly used in program evaluation, that aims to explore and understand the influence of context and underlying mechanisms on intervention or program outcomes. Several methodological challenges, however, are associated with this approach. This article summarizes RE key principles and examines some documented challenges and solutions when analyzing RE data, including the development of Context-Mechanism-Outcome configurations. An analytic method using NVivo features is also presented. This method makes it possible to respond to certain analytic difficulties associated with RE by facilitating the identification of patterns and ensuring transparency in the analytical process.     

试论汉字结构对现代包装设计艺术的启示

汉字是世界文化艺术的瑰宝.它构形中所渗透的诸多美学因素与现代包装设计中的形式美感具有异曲同工之妙.从包装设计的角度论及汉字间架结构的美与现代包装设计艺术之间的相关性是本文的出发点.希望找到一个能将传统文化中的汉字构形艺术与当代包装设计艺术相结合起来的契合点,从而对现代包装设计的民族风格和发展研究有一定的参考价值和启示作用.     

Deriving the Least Squares Regression Equation

The article notes an alternative to calculus for deriving the least squares regression equation but also questions the need for proofs.     

Efficiency analysis of ten estimation procedures for quantitative linear models with autocorrelated errors

Many estimation procedures for quantitative linear models with autocorrelated errors have been proposed in the literature. A number of these procedures have been compared in various ways for different sample sizes and autocorrelation parameters values and for structured or random explanatory vaiables. In this paper, we revisit three situations that were considered to some extent in previous studies, by comparing ten estimation procedures: Ordinary Least Squares (OLS), Generalized Least Squares (GLS), estimated Generalized Least Squares (six procedures), Maximum Likelihood (ML), and First Differences (FD). The six estimated GLS procedures and the ML procedure differ in the way the error autocovariance matrix is estimated. The three situations can be defined as follows: Case 1, the explanatory variable x in the simple linear regression is fixed; Case 2,x is purely random; and Case 3x is first-order autoregressive. Following a theoretical presentation, the ten estimation procedures are compared in a Monte Carlo study conducted in the time domain, where the errors are first-order autoregressive in Cases 1-3. The measure of comparison for the estimation procedures is their efficiency relative to OLS. It is evaluated as a function of the time series length and the magnitude and sign of the error autocorrelation parameter. Overall, knowledge of the model of the time series process generating the errors enhances efficiency in estimated GLS. Differences in the efficiency of estimation procedures between Case 1 and Cases 2 and 3 as well as differences in efficiency among procedures in a given situation are observed and discussed.     

Bootstrap versus traditional hypothesis testing procedures for coefficients in least absolute value regression

Several approaches to hypothesis testing for coefficients in least absolute value regression are compared using a Monte Carlo simulation: likelihood ratio test, Lagrange multiplier test, and three versions of the bootstrap hypothesis test. Factors considered that might influence test performance include the disturbance distribution, the type of independent variable, and the sample size. Overall, the likelihood ratio and the bootstrap tests perform best, with the likelihood ratio test being marginally more powerful. Least absolute value tests are also compared to the standard t test and three versions of the bootstrapped t test for least squares regression.