An accurate method to predict the stress concentration in composite laminates with a circular hole under tensile loading

The paper presents a theoretical-numerical hybrid method for determining the stresses distribution in composite laminates containing a circular hole and subjected to uniaxial tensile loading. The method is based upon an appropriate corrective function allowing a simple and rapid evaluation of stress distributions in a generic plate of finite width with a hole based on the theoretical stresses distribution in an infinite plate with the same hole geometry and material. In order to verify the accuracy of the method proposed, various numerical and experimental tests have been performed by considering different laminate lay-ups; in particular, the experimental results have shown that a combined use of the method proposed and the well-know point-stress criterion leads to reliable strength predictions for GFRP or CFRP laminates with a circular hole. Russian translation published in Mekhanika Kompozitnykh Materialov, Vol. 43, No. 4, pp. 531–570, July–August, 2007.     

模糊神经网络在复合地基沉降量预测中的应用

复合地基后期沉降变形对于建筑物设计及安全具有重要意义,针对通过长期沉降观测以得到复合地基的最终沉降需要耗费较多资源的问题,提出了一种基于模糊神经网络的预测方法.该方法考虑沉降变化过程有较大的随机性和模糊性,直接将样本数据进行模糊化,所得的模糊数代表了样本点集与控制点集中各分量之间的相关度,并依此建立模糊BP神经网络进行学习和估算.实验结果表明了该方法对沉降进行预测是可行与有效的,且在相对误差的有效控制方面优于BP网络方法与灰色方法.     

价格波动对进出初级产品行业的影响

初级产品生产行业有两大特点：一是其初级投资有较大的不可逆转性，二是其产品价格具有较大的随机性（不确定性）。这两点的同时存在，给进入和退出初级产品行业造成壁垒。     

低场脉冲核磁共振测定二醋酸纤维素丝束中油剂

研究了采用低场脉冲核磁共振(NMR)测定二醋酸纤维素丝束油剂的新方法，给出NMR法测定二醋酸纤维素丝束油荆含量的标准曲线。同时对NMR法测量二醋酸纤维素丝束油荆不确定度进行了评估。结果表明，NMR法具有分析速度快、无污染、无损伤和准确度高的优点，适合于二醋酸纤维素丝束油荆的分析。     

Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using and nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets.     

Metrological role of neutron activation analysis. IB. Inherent characteristics of relative INAA as a primary ratio method of measurement

This is the second part of a paper which deals with the advantages and disadvantages of relative instrumental neutron activation analysis concerning traceability and uncertainty, and the current scope and possible future extensions of neutron activation analysis as a primary ratio method. The first part of this paper has been published in this Journal [1]. Received: 19 March 2001 Accepted: 2 October 2001     

论测量不确定度

讨论了测量不确定度的基本概念及应用。分析了误差和不确定度两个概念的区别，探讨了不确定度及分量的评定方法。     

Structure-property modelling of complex formation of strontium with organic ligands in water

The method of substructural molecular fragments based on representation of the molecular graph by ensembles of fragments and involving calculations of those contributions to the given property is applied to the modelling of stability constants of the complexes of strontium(II) with organic ligands in water. Reliability of predictions of developed structure-property models was examined using three different test sets of structurally diverse ligands. The obtained models have been used for generation and screening of combinatorial library of virtual ligands. Some hypothetical efficient Sr(II) binders were suggested.     

Setting up of recovery profiles: A tool to perform the compliance with recovery requirements for residue analysis

The use of the recovery term has presented some confusion in Analytical Chemistry. Recent IUPAC recommendations propose to distinguish between two terms: recovery or recovery factor, ℜ, and apparent recovery, ℜ*. Apparent recovery includes recovery factor and a new recovery term proposed in this paper, named calibration recovery, ℜ^C, which depends of the type of systematic error due to the matrix effect (constant and/or proportional) and is related to the applied calibration methodology. This paper highlights the dependence of the calibration recovery on the sample analyte concentration and, for extension, of the apparent recovery, defines the recovery profile, and makes evident the need to determine a “fit for purpose” analyte concentration interval to comply with a regulated recovery requirements. An approach to estimate the calibration recovery and its associated uncertainty in relation to the above-mentioned dependence is presented. The usefulness of the proposed methodology has been shown in the quantification of a pesticide by GC-ECD for assessing dermal exposure.     

Partial least-squares regression and fuzzy clustering — A joint approach

Chemical and physical analyses of malt, the main ingredient of beer, have been used to predict the concentration of certain volatile compounds in the finished beer.The prediction was done by means of the partial least squares regression (PLS2) in SIMCA. The total data set as well as individual malt clusters were submitted to PLS analysis. Best prediction was obtained by separating the total object matrix in classes according to similarity found by fuzzy pattern recognition (FCV). FCV was also used to separate the beer variables in classes and to select the subset of variables to be predicted.A joint approach of fuzzy pattern recognition to identify groups of samples and SIMCA-PLS2 to predict several dependent variables is suggested as a powerful tool in process-analytical chemistry.