A class of multiple shrinkage estimators

Based on a sample of size n, we investigate a class of estimators of the mean of a p-variate normal distribution with independent components having unknown covariance. This class includes the James-Stein estimator and Lindley's estimator as special cases and was proposed by Stein. The mean squares error improves on that of the sample mean for p3. Simple approximations imations for this improvement are given for large n or p. Lindley's estimator improves on that of James and Stein if either n is large, and the coefficient of variation of is less than a certain increasing function of p, or if p is large. An adaptive estimator is given which for large samples always performs at least as well as these two estimators.     

Development of B‐doped Si multiple delta‐layer reference materials for SIMS profiling

B‐doped Si multiple delta‐layers (MDL) were developed as certified reference materials (CRM) for secondary ion mass spectrometry (SIMS) depth profiling analysis. Two CRMs with different delta‐layer spacing were grown by ion beam sputter deposition (IBSD). The nominal spacing of the MDL for shallow junction analysis is 10 nm and that for high energy SIMS is 50 nm. The total thickness of the film was certified by high resolution transmission electron microscopy (HR‐TEM). The B‐doped Si MDLs can be used to evaluate SIMS depth resolution and to calibrate the depth scale. A consistency check of the calibration of stylus profilometers for measurement of sputter depth is another possible application. The crater depths measured by a stylus profilometer showed a good linear relationship with the thickness measured from SIMS profiling using the calibrated film thickness for depth scale calibration. The sputtering rate of the amorphous Si thin film grown by sputter deposition was found to be the same as that of the crystalline Si substrate, which means that the sputtering rate measured with these CRMs can be applied to a real analysis of crystalline Si. Copyright © 2005 John Wiley & Sons, Ltd.     

通用模拟退火用于稳健多元分析校正

模拟退火是一种全局优化算法，具有跨越局部最优点的机制，最小一乘是一种较常用的最小二乘更为稳健的优化准则，更适用于可能偏离正态分布的实际数据集，本文探讨了用最小一乘为准则并利用模拟退火方法同时测定多组分体系的可能性。应用于2－3组分药物体系分析，获得了满意的结果，本文还探讨了改变步长提高模拟退火算法优化精度的方法。     

2D Burgers equation with large Reynolds number using POD/DEIM and calibration

Model order reduction of the two‐dimensional Burgers equation is investigated. The mathematical formulation of POD/discrete empirical interpolation method (DEIM)‐reduced order model (ROM) is derived based on the Galerkin projection and DEIM from the existing high fidelity‐implicit finite‐difference full model. For validation, we numerically compared the POD ROM, POD/DEIM, and the full model in two cases of Re = 100 and Re = 1000, respectively. We found that the POD/DEIM ROM leads to a speed‐up of CPU time by a factor of O(10). The computational stability of POD/DEIM ROM is maintained by means of a careful selection of POD modes and the DEIM interpolation points. The solution of POD/DEIM in the case of Re = 1000 has an accuracy with error O(10^?3) versus O(10^?4) in the case of Re = 100 when compared with the high fidelity model. For this turbulent flow, a closure model consisting of a Tikhonov regularization is carried out in order to recover the missing information and is developed to account for the small‐scale dissipation effect of the truncated POD modes. It is shown that the computational results of this calibrated ROM exhibit considerable agreement with the high fidelity model, which implies the efficiency of the closure model used. Copyright © 2016 John Wiley & Sons, Ltd.     

Carbon dots with strong excitation-dependent fluorescence changes towards pH. Application as nanosensors for a broad range of pH

In this study, preparation of novel pH-sensitive N-doped carbon dots (NCDs) using glucose and urea is reported. The prepared NCDs present strong excitation-dependent fluorescence changes towards the pH that is a new behavior from these nanomaterials. By taking advantage of this unique behavior, two separated ratiometric pH sensors using emission spectra of the NCDs for both acidic (pH 2.0 to 8.0) and basic (pH 7.0 to 14.0) ranges of pH are constructed. Additionally, by considering the entire Excitation–Emission Matrix (EEM) of NCDs as analytical signal and using a suitable multivariate calibration method, a broad range of pH from 2.0 to 14.0 was well calibrated. The multivariate calibration method was independent from the concentration of NCDs and resulted in a very low average prediction error of 0.067 pH units. No changes in the predicted pH under UV irradiation (for 3 h) and at high ionic strength (up to 2 M NaCl) indicated the high stability of this pH nanosensor. The practicality of this pH nanosensor for pH determination in real water samples was validated with good accuracy and repeatability.     

电学计量检定及测量的误差原因讨论构建

电学计量检定及测量在实际的操作过程中容易受到相关存在因素的影响，可能会产生系统误差，对于各种数据的真实有效性造成了较大的影响。因此，技术人员需要对电学计量检定及测量过程中误差产生的原因进行必要地分析，找出各种误差因素，为后续工作的顺利完成奠定坚实的基础。     

Simultaneous Determination of Acetaminophen and Phenobarbital in Suppositories by Partial Least-Squares Spectrophotometric Calibration

Abstract

The use of partial least-squares spectrophotometric calibration for the simultaneous determination of suppositories in a multicomponent formulation is presented. This method is applied to the deternination of acetaminophen and phenobarbital in suppository preparations. The results show that these components in a molar ratio of about 61:1 in suppositories have been determined simultaneously with high precision.     

Efficient computation of operator‐type response sensitivities for uncertainty quantification and predictive modeling: illustrative application to a spent nuclear fuel dissolver model

This work honors the 75th birthday of Professor Ionel Michael Navon by presenting original results highlighting the computational efficiency of the adjoint sensitivity analysis methodology for function‐valued operator responses by means of an illustrative paradigm dissolver model. The dissolver model analyzed in this work has been selected because of its applicability to material separations and its potential role in diversion activities associated with proliferation and international safeguards. This dissolver model comprises eight active compartments in which the 16 time‐dependent nonlinear differential equations modeling the physical and chemical processes comprise 619 scalar and time‐dependent model parameters, related to the model's equation of state and inflow conditions. The most important response for the dissolver model is the time‐dependent nitric acid in the compartment furthest away from the inlet, where measurements are available at 307 time instances over the transient's duration of 10.5 h. The sensitivities to all model parameters of the acid concentrations at each of these instances in time are computed efficiently by applying the adjoint sensitivity analysis methodology for operator‐valued responses. The uncertainties in the model parameters are propagated using the above‐mentioned sensitivities to compute the uncertainties in the computed responses. A predictive modeling formalism is subsequently used to combine the computational results with the experimental information measured in the compartment furthest from the inlet and then predict optimal values and uncertainties throughout the dissolver. This predictive modeling methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution for the a priori known mean values and uncertainties characterizing the model parameters and the computed and experimentally measured model responses. This approximate a priori distribution is subsequently combined using Bayes' theorem with the “likelihood” provided by the multi‐physics computational models. Finally, the posterior distribution is evaluated using the saddle‐point method to obtain analytical expressions for the optimally predicted values for the parameters and responses of both multi‐physics models, along with corresponding reduced uncertainties. This work shows that even though the experimental data pertains solely to the compartment furthest from the inlet (where the data were measured), the predictive modeling procedure used herein actually improves the predictions and reduces the predicted uncertainties for the entire dissolver, including the compartment furthest from the measurements, because this predictive modeling methodology combines and transmits information simultaneously over the entire phase‐space, comprising all time steps and spatial locations. Copyright © 2016 John Wiley & Sons, Ltd.     

Determination of Some Organochlorine Compounds in the Atmosphere

Abstract

Since plastics are suspected to adsorb orthophosphate, disposable-tip pipettes have been checked to determine their ability to be used in orthophosphate calibration. No adsorption has been detected through an experiment of standard preparation with and without changing the pipette tip. That is, the pipettes appear to be convenient for orthophosphate calibration.     

Nitrile Rubber Reactor Operation Troubleshooting with Principal Component Analysis

Principal Component Analysis (PCA) is employed as a tool in order to demonstrate yet another application of the technique, and, most importantly, to show that results from the statistical multivariate technique do make physico-chemical sense. The operation of a typical emulsion copolymerization of acrylonitrile and butadiene (nitrile butadiene rubber, NBR) is used as an example of process troubleshooting. In more general terms, a statistical tool is used to aid process data analysis and process operation (recipe, product property) troubleshooting.

The goal is to produce consistent Mooney Viscosity (MV) among different batches. The observation is that varying induction times lead to Mooney Viscosity inconsistencies. Firstly, we show results from the application of PCA to process data. Secondly, we deal with an even more important (and often ignored) question by examining whether the trends indicated by PCA make process sense.