应用Monte Carlo模拟法探讨分层性物质的组合取样精度

应用MonteCarlo模拟法研究了分层性物质的组合取样精度,探讨了组合样中组分含量的分布规律、组合取样方差的分布规律、组合取样方差估计值的精度与组合样本数目之间的关系等.考察了组分含量服从正态分布、均匀随机分布及多项分布的分层性总体.结果表明,当样本数目较多时,组合取样误差规律对于不同原始分布的总体是相似的.     

分层性物质的组合取样误差与份样数目之间的关系及其Monte Carlo模拟

本文从理论上研究了分层性物质的组合取样误差与份样数目之间的定量关系，表明份样数目与组合取样相对标准偏差的平方之积为一常数，称为组合取样常数，它与Ｉｎｇａｍ ｅｌｌｓ 和Ｓｗ ｉｔｚｅｒ 提出的取样常数是相似的，表示６８％ 置信度下组合取样的相对标准偏差达到１％ 时的份样数目。应用Ｍｏｎｔｅ Ｃａｒｌｏ 模拟技术对该理论进行了验证，结果令人满意。     

可变误差多面体法同时测定复方新诺明中二组分的含量

以可变误差多面体法处理多波长吸光光度数据同时测定了复方新诺明制剂模样和五个批号实际样品中磺胺甲恶唑（ＳＭＺ）,甲氧苯氨嘧啶（ＴＭＰ）的含量,１１份模拟样中ＳＭＺ、ＴＭＰ的回收率分别为９９．８％和１００．３％,复方新诺明片和注射液的测定结果与药典方法一致。     

渐进取样法及其应用

提出了渐进取样法,通过样本数目的累积,使总方差估计值达到所需的精度。应用Ｍｏｎｔｅ Ｃａｒｌｏ模拟技术考察了满足一定取样精度的样本数目及其偏差。将该方法应用于散装生铁块中Ｓｉ、Ｍｎ、Ｃ、Ｓ、Ｐ含量分析的取样,结果令人满意。该项研究对于大宗货物的实际随机取样具有重要参考价值。     

海带中生命元素的ICP－AES研究

应用ＩＣＰ－ＡＥＳ技术测定了Ｓｒ、Ｃａ、Ｍｇ、Ｐ、Ｆｅ、Ｃｕ、Ｍｎ和Ｚｎ等８种生命元素在海带中的分布，发现三种分布模式：（１）Ｆｅ、Ｃｕ、Ｍｎ、Ｚｎ在叶状体末端和边缘、固着器末端含量高．其余部位含量低；（２）Ｓｒ和Ｃａ的分布规律与第一类相似，但固着器基部高于固着器末端；（３）Ｍｇ和Ｐ均匀分布．Ｓｒ和Ｃａ分布上的相似性提示了它们生理功能上的相似性．这些数据为研究微量元素在海带中的生理功能提供了新资料。     

海带中生命元素的ICP—AES研究

应用ＩＣＰ－ＡＥＳ技术测定了Ｓｒ，Ｃａ，Ｍｇ，Ｐ，Ｆｅ，Ｃｕ，Ｍｎ和Ｚｎ等８种生命元素在海带中的分布，发现三种分布模式；（１）Ｆｅ，Ｃｕ，Ｍｎ，Ｚｎ在叶状体末端和边缘，固着器末端含量高，其余部位含量低；（２）Ｓｒ和Ｃａ的分布规律与第一类相似，但固着器基部高于固着器末端；（３）Ｍｇ和Ｐ均匀分布。     

溶剂化金属原子浸渍法制备的Ni－Ag／γ－Al_2O_3催化剂的结构表征

应用溶剂化金属原子浸渍（ＳＭＡＩ）法和普通浸渍（ＣＩ）法制备了三种不同摩尔比的γ－Ａｌ２Ｏ３负载Ｎｉ－Ａｇ双金属催化剂。ＸＲＤ和磁测定结果表明ＳＭＡＩ催化剂中Ｎｉ和Ａｇ的粒度均小于金属含量相同的ＣＩ催化剂，ＳＭＡＩ催化剂中Ｎｉ和Ａｇ未形成合金，而ＣＩ催化剂中Ｎｉ和Ａｇ形成了合金。ＳＭＡＩ和ＣＩ催化剂都具有超顺磁性。ＸＰＳ测定结果表明ＳＭＡＩ催化剂中零价Ｎｉ和Ａｇ的含量均高于ＣＩ催化剂。ＳＭＡＩ催化剂Ｎｉ在表面含量高于体相，Ｎｉ在表面富集，而ＣＩ催化剂Ｎｉ在表面和体相的含量相同     

溶剂化金属原子浸渍法制备的Ni-Ag/γ-Al_2O_3催化剂的催化性质研究

应用溶剂化金属原子浸渍（ＳＭＡＩ）法和普通浸渍（ＣＩ）法制备了金属含量相同的γＡｌ２Ｏ３负载ＮｉＡｇ双金属催化剂。研究了这些催化剂在甲苯和二丙酮醇加氢以及ＣＯ２甲醇化反应中的催化性质，结果表明与组成相同的普通浸渍法催化剂相比，在所有这些反应中ＳＭＡＩ催化剂都显示出较高的催化活性，这是因为ＳＭＡＩ催化剂具有较高的分散度和还原度（零价金属百分比）。     

SMAI和CI法制备的Ni—Ag／SiO2催化剂的结构与催化性质研究

应用溶剂化金属原子浸渍（ＳＭＡＩ）法和普通浸渍（ＣＩ）法制备了金属含量相同的ＳｉＯ２负载Ｎｉ－Ａｇ双金属催化剂。ＸＲＤ和磁测定结果表明ＳＭＡＩ催化剂中Ｎｉ和Ａｇ的粒度均小于金属含量相同的ＣＩ催化剂，ＳＭＡＩ催化剂中Ｎｉ和Ａｇ未形成合金，而ＣＩ催化剂中Ｎｉ和Ａｇ形成了合金。ＳＭＡＩ和ＣＩ催化剂都具有超顺磁性。研究了这些催化剂在甲苯加氢反应的催化性质，结果表明与组成相同的普通浸渍法催化相比，ＳＭＡＩ催     

一种新型含酞侧基聚芳醚砜的稀溶液性质研究（I）——溶解行…

制备了窄分布的含酞侧基聚芳醚砜（ＰＥＳ－Ｃ）级份样品，用光散射法测定样品的重均分子量，粘度法测定样品在ＤＭＦ，ＣＨＣｌ３和１，２－Ｃ２Ｈ４Ｃｌ２中的特性粘数和Ｈｕｇｇｉｎｓ参数ｋ＾’值，Ｋ＾’值远大于０．５反映了体系中存在特殊的溶解行为，得到了ＰＥＳ－Ｃ在３种溶剂中的Ｍａｒｋ－Ｈｏｕｗｉｎｋ方程，（η）＝２．７９×１０＾－２Ｍ＾０．６１５ｗ，ｒ＝０．９９９８（ＤＭＦ，２５℃）（η）＝３．９６×１０     

Non-parametric estimation of reference intervals in small non-Gaussian sample sets

This study aimed at validating common bootstrap algorithms for reference interval calculation.We simulated 1500 random sets of 50–120 results originating from eight different statistical distributions. In total, 97.5 percentile reference limits were estimated from bootstrapping 5000 replicates, with confidence limits obtained by: (a) normal, (b) from standard error, (c) bootstrap percentile (as in RefVal) (d) BCa, (e) basic, or (f) student methods. Reference interval estimates obtained with ordinary bootstrapping and confidence intervals by percentile method were accurate for distributions close to normality and devoid of outliers, but not for log-normal distributions with outliers. Outlier removal and transformation to normality improved reference interval estimation, and the basic method was superior in such cases. In conclusions, if the neighborhood of the relevant percentile contains non-normally distributed results, bootstrapping fails. The distribution of bootstrap estimates should be plotted, and a non-normal distribution should warrant transformation or outlier removal. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

The comparison of parametric and nonparametric bootstrap methods for reference interval computation in small sample size groups

According to the IFCC, to determine the population-based reference interval (RI) of a test, 120 reference individuals are required. However, for some age groups such as newborns and preterm babies, it is difficult to obtain enough reference individuals. In this study, we consider both parametric and nonparametric bootstrap methods for estimating RIs and the associated confidence intervals (CIs) in small sample size groups. We used data from four different tests [glucose, creatinine, blood urea nitrogen (BUN), and triglycerides], each in 120 individuals, to calculate the RIs and the associated CIs using nonparametric and parametric approaches. Also for each test, we selected small groups (m = 20, 30,…, 120) from among the 120 individuals and applied parametric and nonparametric bootstrap methods. The glucose and creatinine data were normally distributed, and the parametric bootstrap method provided more precise RIs (i.e., the associated CIs were narrower). In contrast, the BUN and triglyceride data were not normally distributed, and the nonparametric bootstrap method provided better results. With the bootstrap methods, the RIs and CIs of small groups were similar to those of the 120 subjects required for the nonparametric method, with a slight loss of precision. For original data with normal or close to normal distribution, the parametric bootstrap approach should be used, instead of nonparametric methods. For original data that deviate significantly from a normal distribution, the nonparametric bootstrap should be applied. Using the bootstrap methods, fewer samples are required for computing RIs, with only a slightly increased uncertainty around the end points.     

Confidence intervals for calibration with neural networks

Based on neural network calibration the confidence intervals of aromaticity determination from infrared reflectance spectra of raw brown coals were estimated by means of the bootstrap method, a simplified Monte Carlo Simulation. The standard deviations and the confidence intervals were estimated to characterise the analysis error.It is shown that confidence intervals of non-linear analysis methods like Back Propagation Neural Networks (BPNN) can be estimated by the bootstrap method. The estimated confidence intervals of the calibration confirm the analysis by BPNN.     

Quantitative thin-film x-ray microanalysis by STEM/HAADF: statistical analysis for precision and accuracy determination.

Silicon-germanium thin films have been analyzed by EDS microanalysis in a field emission gun scanning transmission electron microscope (FEG-STEM) equipped with a high angular dark-field detector (STEM/HAADF). Several spectra have been acquired in the same homogeneous area of the cross-sectioned sample by drift-corrected linescan acquisitions. The Ge concentrations and the local film thickness have been obtained by using a previously described Monte Carlo based "two tilt angles" method. Although the concentrations are in excellent agreement with the known values, the resulting confidence intervals are not as good as expected from the precision in beam positioning and tilt angle position and readout offered by our state-of-the-art microscope. The Gaussian shape of the SiKalpha and GeKalpha X-ray intensities allows one to use the parametric bootstrap method of statistics, whereby it becomes possible to perform the same quantitative analysis in sample regions of different compositions and thicknesses, but by doing only one measurement at the two angles.     

Computer solutions for confidence intervals when the blank count time is an integer number of times greater than the sample count time

This paper extends past computer solutions for confidence intervals in paired counting. The net count is transformed to an integer; the resultant probability distribution is identical with that for detection limits when the blank is counted an integer number of times greater than the sample. Summation of the left tail of this distribution is used to determine left-hand and right-hand confidence limits with specified confidence levels for a given observed net count and known expected blank count in the sample count time. Poisson statistics are used throughout this paper and the Gaussian distribution is reserved for approximate results.     

Non-linear least-squares fitting with microsoft excel solver and related routines in HPLC modelling of retention

Summary Two problems related to non-linear regression, the evaluation of the best set of fitting parameters and the reliability of the methods used for the estimation of the standard errors of these parameters, are examined. It is shown that a non-linear curve fitting routine, like the Microsoft Excel Solver, may give more than one solution for the same data set and a simple Monte Carlo routine is described for the evaluation of the bestfit. For standard errors, the reliability of two procedures based on the conventional curvature matrix method, four Jackknife techniques and the bootstrap method are examined by comparing their results to those obtained from a Monte Carlo simulation of the experimental data. It is shown that a fitting parameter may follow a nonnormal distribution when the equation to be fitted is complicated, even if the errors on the data are normally distributed. In this case only Monte Carlo methods of data simulation can give accurate information about the standard errors and the confidence intervals of these parameters.     

组合取样的误差理论研究及其Monte Carlo模拟

基于多项分布理论,建立了组合取样方差与取样总体中各子区的大小及其组分含量之间的关系和计算公式,探讨了组合取样常数的物理意义.以颗粒物质为例,探讨了组合取样的逻辑质量单元的概念及其意义,为确定组合取样中的份样质量提供了理论依据.本文对于完善组合取样误差理论,保证组合取样的质量均具有重要意义.