Proton Nuclear Magnetic Resonance-Based Method for the Quantification of Epoxidized Methyl Oleate

Epoxidized methyl esters (EMO) with their high oxirane ring reactivity, acts as a raw material in the synthesis of various industrial chemicals including polymers, stabilizers, plasticizers, glycols, polyols, carbonyl compounds, biolubricants etc. EMO has been generally quantified by the gas chromatography (GC) and high-performance liquid chromatography (HPLC) techniques. Taking into the account of the limitations of these techniques, two qHNMR-based equations have been proposed for the quantification of EMO in the mixture of EMO and methylesters (MO). The validity of the proposed method was determined using standard mixtures of MO and EMO having different molar concentrations. The developed equations have been applied on the samples of EMO prepared from oleic acid in two-step process viz., esterification followed by epoxidation. The qHNMR-based EMO quantification showed acceptable agreement with the results obtained from HPLC analysis.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

AHP FUZZY在残采工程方案优选中的应用

为探讨太白金矿回收原采场边部残余低品位矿体的方案，通过层次分析法（AHP）与模糊数学（FUZZY）理论，选取对残采工程影响较大的9个因素，建立优选模型。运用层次分析法确定了各评价指标的权重；采用模糊数学法分析计算各采矿方法的定性、定量指标，分别得到各评价指标的隶属度。最终计算出3种工程布置方案的综合优选度分别为68.1%、69.3%、58.1%，从而沿矿体走向双进路布置确定为最优开采方案。实践表明，采用该模型优选的工程布置方案具有一定的科学性、合理性，值得借鉴与推广。     

Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

实时荧光定量PCR鉴定肉制品中牛源性成分及其含量

建立一种快速、准确鉴定牛肉制品中牛源性成分并且量化牛肉成分含量的方法。以牛线粒体细胞色素b基因为靶基因,设计出具有特异性引物。选择真核生物核糖体16SrDNA为内参基因,采用实时荧光相对定量法。牛肉质量百分比的对数值与之对应的循环阈值差值△Ct呈良好线性关系。标准曲线回归公式为y=-3.3645x+0.8737,R2=0.9926,扩增效率达98.25%。通过模拟混合样品对标准曲线进行质量评估,证明该方法适用于对牛肉成分的鉴定以及含量的检测,为量化肉制品中牛肉成分研究提供参考意见。     

基于支持向量机和磁记忆技术的管道缺陷深度的定量化反演研究

金属管道表面往往存在不同深度的腐蚀缺陷。金属磁记忆检测技术是目前唯一能对铁磁性构件的早期损伤进行诊断的无损检测技术,然而磁记忆原始信号本身并不能直接实现对管道腐蚀缺陷深度特征的定量化识别,进而无法实现对管道腐蚀程度的预警。针对该问题,采用支持向量机方法建立了管道缺陷深度的定量化反演模型,利用该模型对管道上深度为1~15mm的腐蚀缺陷进行了多次反演,反演结果的平均误差为2.398mm,平均均方根误差为3.205mm,结果表明,模型对管道腐蚀缺陷深度的定量化反演是可行的。研究结果可为该领域的研究提供一定的参考,且具有较高的实际应用价值。     

Degradation rate quantification of solid oxide fuel cell performance with and without Al2TiO5 addition

Degradation rates of electrical current during constant voltage operation of SOFCs with anodes made using NiO precursor powders from two different manufacturers with and without the addition of aluminum titanate (ALT) added by either mechanical mixing or anode infiltration have been quantified using a novel MATLAB algorithm. Because the algorithm has been used to quantify degradation rates for many different SOFC tests, it is thought that the method can be applied to most measured SOFC data to quantify the instantaneous cell degradation rate as a function of time for the entire SOFC performance measurement. Degradation rates determined at different times have been plotted against varying concentrations of ALT addition, facilitating the estimation of optimum ALT concentration for SOFC anodes made with NiO from a specific manufacturer. The algorithm used to determine degradation rates is available upon request to the corresponding author.     

Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.