碳素工具钢(T8)激光熔覆组织分析

本文研究了T8钢在不同热处理条件下激光熔覆镍基自熔合金粉后基体各区域的组织。实验结果表明：激光熔覆后基体各区域的组织形态与基材前期的热处理制度无关。基体热影响区的组织均为淬火马氏体，基体保持原热处理状态下的组织，在基体与熔覆层之间不存在因成分扩散而形成的过渡区，熔覆层与基体之间存在明显的界面。     

国外科技评估的历史、现状及其启示

文章简述了国外科技评估的发展历史,主要从立法情况、机构设置、评估方 法及评估种类等方面对国外科技评估进行介绍和比较研究,在此基础上分析中国科技评估的 现状及存在的问题,最后指出改善和推进中国科技评估工作需要借鉴的几个方面,以期能够 起到抛砖引玉的作用。     

Sample reuse simulation in optimal design for Tmax in pharmacokinetic experiments

T _max and C _max are important pharmacokinetic parameters in drug development processes. Often a nonparametric procedure is needed to estimate them when model independence is required. This paper proposes a simulation-based optimal design procedure for finding optimal sampling times for nonparametric estimates of T _max and C _max for each subject, assuming that the drug concentration follows a non-linear mixed model. The main difficulty of using standard optimal design procedures is that the property of the nonparametric estimate is very complicated. This procedure uses a sample reuse simulation to calculate the design criterion, which is an integral of multiple dimension, so that effective optimization procedures such as Newton-type procedures can be used directly to find optimal designs. This procedure is used to construct optimal designs for an open one-compartment model. An approximation based on the Taylor expansion is also derived and showed results that were consistent with those based on the sample reuse simulation.     

浅议21世纪大学英语课堂教学新模式

在大谈素质教育的今天,应试教育在大学英语教学中依然普遍存在。本文在分析目前大学英语教学现状的基础上,在教学理念、教学手段、教学方法等方面探讨英语课堂教学模式的新思路,并对3T教学法进行了系统的阐述。     

Decline of serum levels of free testosterone in aging healthy Chinese men

Objective.?To investigate the age-related change of serum androgen levels in healthy men and to define a cut-off value of serum testosterone for the diagnosis of androgen deficiency in the aging male.

Method.?1080 healthy men aged 20 to ?70 years old were enrolled in Beijing, Shanghai, Xian and Chongqing. Luteinizing hormone (LH), follicle-stimulating hormone (FSH), total testosterone (T), calculated free testosterone (cFT), sex hormone binding globulin (SHBG), 17beta-oestradiol (E2), the T/LH ratio, and T/SHBG as a free testosterone index (FTI) were all determined.

Results.?Serum total T did not significantly decline, but the cFT, T/LH and FTI progressively decreased with aging. To determine androgen deficiency, the 10th percentile value of men <40 years was defined as the lower cut-off value for cFT, T/LH or FTI, which were 0.3 nmol/L, 2.8 nmol/IU, and 0.4 nmol/IU respectively. With the median value of cFT of men aged between 20 and 49 years as the criterion, the level of cFT was lower in 2.82% of men from 40 to 49 years, in 19.53% from 50 to 59 years, in 22.57% from 60 to 69 years, and in 33.19% of men ?70 years. Taking the above value of cFT as the cut-off point, the prevalence of androgen deficiency in men 40–49 years was 13.0%, 31.8% in men 50–59 years, 30.1% in men 60 to 69 years, and 46.7% in men >70 years.

Conclusions.?(i). While serum total T values do not decline with aging, the levels of cFT gradually decline with aging; (ii) when using the value of cFT of the 10th percentile of men aged 20 to 39 years as the cut-off point, the prevalence of androgen deficiency was <15% before the age of 50 years, and about 30% thereafter, approaching 45% after the age of 70 years; and (iii) in this study the values of T/LH paralleled those of cFT closely; therefore, T/LH could serve as a surrogate for cFT.     

Power and Sample Size Determination When the Alternative Hypotheses are Given in Quantiles

The power of normal-theory tests about means depends on a noncentrality parameter which is a function of the unknown parameter σ. In order to calculate power and to solve sample-size problems based on power, differences between hypothesized and alternative values of the means are frequently selected as a multiple of σ, a choice which eliminates σ from the noncentrality parameter and permits a solution. Perhaps a more natural (but equivalent) way to express alternatives is to give one or more means as the quantile of order p (say Q_p ) of a distribution with another mean. As we will demonstrate, this kind of alternative also eliminates σ from the problem.     

Comparing variances and means when distributions have non-identical shapes

The paper compares several methods for computing robust 1-α confidence intervals for σ ₁ ²-σ ₂ ², or σ ₁ ²/σ ₂ ², where σ ₁ ² and σ ₂ ² are the population variances corresponding to two independent treatment groups. The emphasis is on a Box-Scheffe approach when distributions have different shapes, and so the results reported here have implications about comparing means. The main result is that for unequal sample sizes, a Box-Scheffe approach can be considerably less robust than indicated by past investigations. Several other procedures for comparing variances, not based on a Box-Scheffe approach, were also examined and found to be highly unsatisfactory although previously published papers found them to be robust when the distributions have identical shapes. Included is a new result on why the procedures examined here are not robust, and an illustration that increasing σ ₁ ²-σ ₂ ² can reduce power in certain situations. Constants needed to apply Dunnett’s robust comparison of means are included.     

Identifying Variables Contributing to Outliers in Phase I

When a process is monitored with a T ² control chart in a Phase II setting, the MYT decomposition is a valuable diagnostic tool for interpreting signals in terms of the process variables. The decomposition splits a signaling T ² statistic into independent components that can be associated with either individual variables or groups of variables. Since these components are T ² statistics with known distributions, they can be used to determine which of the process variable(s) contribute to the signal. However, this procedure cannot be applied directly to Phase I since the distributions of the individual components are unknown. In this article, we develop the MYT decomposition procedure for a Phase I operation, when monitoring a random sample of individual observations and identifying outliers. We use a relationship between the T ² statistic in Phase I with the corresponding T ² statistic resulting when an observation is omitted from this sample to derive the distributions of these components and demonstrate the Phase I application of the MYT decomposition.     

Monitoring Variation in a Multivariate Process When the Dimension is Large Relative to the Sample Size

A control procedure is presented for monitoring changes in variation for a multivariate normal process in a Phase II operation where the subgroup size, m, is less than p, the number of variates. The methodology is based on a form of Wilk' statistic, which can be expressed as a function of the ratio of the determinants of two separate estimates of the covariance matrix. One estimate is based on the historical data set from Phase I and the other is based on an augmented data set including new data obtained in Phase II. The proposed statistic is shown to be distributed as the product of independent beta distributions that can be approximated using either a chi-square or F-distribution. An ARL study of the statistic is presented for a range of conditions for the population covariance matrix. Cases are considered where a p-variate process is being monitored using a sample of m observations per subgroup and m < p. Data from an industrial multivariate process is used to illustrate the proposed technique.     

Shrinkage Estimation Under Multivariate Elliptic Models

The estimation of the location vector of a p-variate elliptically contoured distribution (ECD) is considered using independent random samples from two multivariate elliptically contoured populations when it is apriori suspected that the location vectors of the two populations are equal. For the setting where the covariance structure of the populations is the same, we define the maximum likelihood, Stein-type shrinkage and positive-rule shrinkage estimators. The exact expressions for the bias and quadratic risk functions of the estimators are derived. The comparison of the quadratic risk functions reveals the dominance of the Stein-type estimators if p ≥ 3. A graphical illustration of the risk functions under a “typical” member of the elliptically contoured family of distributions is provided to confirm the analytical results.