Measurement of δ13C values of soil amino acids by GC–C–IRMS using trimethylsilylation: a critical assessment

In this study, we evaluated trimethylsilyl (TMS) derivatives as derivatization reagents for the compound-specific stable carbon isotope analysis of soil amino acids by gas chromatography–combustion–isotope ratio mass spectrometry (GC–C–IRMS). We used non-proteinogenic amino acids to show that the extraction–derivatization–analysis procedure provides a reliable method to measure δ¹³C values of amino acids extracted from soil. However, we found a number of drawbacks that significantly increase the final total uncertainty. These include the following:

• production of multiple peaks for each amino acid, identified as di-, tri- and tetra-TMS derivatives;

• a number of TMS-carbon (TMS-C) atoms added lower than the stoichiometric one, possibly due to incomplete combustion;

• different TMS-C δ¹³C for di-, tri- and tetra-TMS derivatives.

For soil samples, only four amino acids (leucine, valine, threonine and serine) provide reliable δ¹³C values with a total average uncertainty of 1.3?‰. We conclude that trimethylsilyl derivatives are only suitable for determining the ¹³C incorporation in amino acids within experiments using ¹³C-labelled tracers but cannot be applied for amino acids with natural carbon isotope abundance until the drawbacks described here are overcome and the measured total uncertainty significantly decreased.     

Mesure de la conductivite electrique totale du protoxyde de cobalt en fonction de sa composition definie par des electrodes d'alliage cobalt-platine,entre 1000 et 1400 K

The total electrical conductivity of cobaltous oxide has been measured from 1000 to 1400 K as a function of its composition, especially in the range of low oxygen pressures up to the limit of equilibrium with cobalt. These measurements have been carried out with monocrystalline samples of oxide placed between cobalt-platinum alloy electrodes; oxide composition is fixed by the activity of cobalt in the alloy which is also the activity of cobalt in oxide. Results are given by isotherm curves of the logarithm of conductivity versus the logarithm of oxygen pressure. The slope of these isotherms shows a regular variation between $\text{1}\text{4}$ and less than $\text{1}\text{6}$; this fact can only be explained by the successive effects of the formation of vacancies V″_Co, V′_Co and of intrinsic conductivity by electrons and free holes.     

面向并行设计的局部特征识别技术研究

为支持在并行设计过程中设计特征模型到加工特征模型的逐步转换，提出了局部特征识别的方法．在并行设计中，设计特征模型和加工特征模型通过面名历史图共享零件的实体模型，设计特征的变动通过局部特征识别自动地转换为相应的加工特征．局部特征识别是由基于最小条件子图特征识别方法改进来的，它以零件的局部区域为识别对象，通过搜索匹配局部区域构成的边界模式，识别出该局部区域中所包含的加工特征．局部特征识别方法的特点是只对设计中发生变动的区域进行识别．     

Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters

The accurate estimation of rare event probabilities is a crucial problem in engineering to characterize the reliability of complex systems. Several methods such as Importance Sampling or Importance Splitting have been proposed to perform the estimation of such events more accurately (i.e., with a lower variance) than crude Monte Carlo method. However, these methods assume that the probability distributions of the input variables are exactly defined (e.g., mean and covariance matrix perfectly known if the input variables are defined through Gaussian laws) and are not able to determine the impact of a change in the input distribution parameters on the probability of interest. The problem considered in this paper is the propagation of the input distribution parameter uncertainty defined by intervals to the rare event probability. This problem induces intricate optimization and numerous probability estimations in order to determine the upper and lower bounds of the probability estimate. The calculation of these bounds is often numerically intractable for rare event probability (say 10^?5), due to the high computational cost required. A new methodology is proposed to solve this problem with a reduced simulation budget, using the adaptive Importance Sampling. To this end, a method for estimating the Importance Sampling optimal auxiliary distribution is proposed, based on preceding Importance Sampling estimations. Furthermore, a Kriging-based adaptive Importance Sampling is used in order to minimize the number of evaluations of the computationally expensive simulation code. To determine the bounds of the probability estimate, an evolutionary algorithm is employed. This algorithm has been selected to deal with noisy problems since the Importance Sampling probability estimate is a random variable. The efficiency of the proposed approach, in terms of accuracy of the found results and computational cost, is assessed on academic and engineering test cases.