Effect of annealing prior to cold rolling on magnetic and mechanical properties of low carbon non-oriented electrical steels

The effects of annealing prior to cold rolling on the microstructure, magnetic and mechanical properties of low-C grain non-oriented (GNO) electrical steels have been investigated. The grain structure of hot-rolled electrical steel strips is modified by annealing at temperatures between 700 and 1050 °C. Annealing at temperatures less than the ferrite to austenite+ferrite transformation temperature on heating (Ac₁) causes a marginal effect on the grain size. However, annealing in the intercritical region at temperatures between Ac₁ and Ac₃ (the ferrite+austenite to austenite transformation temperature on heating) causes rapid decarburization and development of large columnar ferrite grains free of carbide particles. This microstructure leads, after cold rolling and a fast annealing treatment, to carbide free, large ferrite grain microstructures with magnetic and mechanical properties superior to those observed typically in the same steel in the industrially fully processed condition. These results are attributed to the increment in grain size and to the {1 0 0} fiber texture developed during the final annealing at temperatures up to 850 °C. Annealing at higher temperatures, T>Ac₃, results in a strong {1 1 1} fiber texture and an increase of the quantity of second phase particles present in the microstructure, which lead to a negative effect on the final properties. The results suggest that annealing prior to cold rolling offers an attractive alternative processing route for the manufacture of fully processed low C GNO electrical steels strips.     

The role of matching thickness on the wideband electromagnetic wave suppresser using single layer doped barium ferrite

The effect of Mg²⁺, Co²⁺and Ti⁴⁺ substitution on microwave absorption has been studied for BaMg_0.5Co_0.5Ti_1.0Fe₁₀O₁₉ ferrite-acrylic resin composite in frequency range from 13 to 20 GHz. X-ray diffraction (XRD), scanning electron microscopy (SEM), vector network analysis and vibrating sample magnetometry (VSM) were employed to analyze structure, electromagnetic and microwave absorption properties of prepared ferrite. The obtained results of reflectivity demonstrate that by varying matching thickness along with weight percentage of ferrite to acrylic resin, the bandwidth coupled with reflection loss values of prepared composites can be easily tuned. Based on microwave measurement on reflectivity, it is found that BaMg_0.5Co_0.5Ti_1.0Fe₁₀O₁₉ is a good candidate for wideband electromagnetic compatibility and other practical applications at high frequency.     

Thermokinetic studies of the groups on TiO2 surface

The properties of groups on TiO₂ surface were evaluated by infrared (IR) spectra and thermal analysis. It could be found that water and ethanol are removed mainly between 343 and 363 K and the process is accompanied by an endothermic change. The results also show that ethanol could be removed completely below 673 K and the physically bonded or hydrogen‐bonded water could be removed almost completely above 873 K. The exothermic peaks ranging from 573 to 773 K are caused by the changes that chemisorbed water (Ti‐O‐H) on the surface condenses and evolves into water. There appears broad and unstable endothermic process in all temperature ranges due to the adhesions or transformations among the crystal particles. Thermogravimetry (TG) results show that with heat treatment at increasing temperatures, the groups on TiO₂ surface decreases gradually. The half peak breadth of X‐ray diffractometer (XRD) results suggest that TiO₂ crystallize better with higher temperature calcination. The photo‐catalytic efficiency was evaluated by UV‐Vis spectrophotometer. TiO₂ with heat treatment at higher temperature shows more effective photo‐catalytic property. Copyright © 2009 John Wiley & Sons, Ltd.     

An extended class of minimax generalized Bayes estimators of regression coefficients

We derive minimax generalized Bayes estimators of regression coefficients in the general linear model with spherically symmetric errors under invariant quadratic loss for the case of unknown scale. The class of estimators generalizes the class considered in Maruyama and Strawderman [Y. Maruyama, W.E. Strawderman, A new class of generalized Bayes minimax ridge regression estimators, Ann. Statist., 33 (2005) 1753–1770] to include non-monotone shrinkage functions.     

Packet loss characteristics for <Emphasis Type="Italic">M</Emphasis>/<Emphasis Type="Italic">G</Emphasis>/1/<Emphasis Type="Italic">N</Emphasis> queueing systems

In this contribution we investigate higher-order loss characteristics for M/G/1/N queueing systems. We focus on the lengths of the loss and non-loss periods as well as on the number of arrivals during these periods. For the analysis, we extend the Markovian state of the queueing system with the time and number of admitted arrivals since the instant where the last loss occurred. By combining transform and matrix techniques, expressions for the various moments of these loss characteristics are found. The approach also yields expressions for the loss probability and the conditional loss probability. Some numerical examples then illustrate our results.     

Pre-commitment or post-payment: Which worsens a line-sitting firm’s revenue?

Recently, Cui et al. (2018) investigated the economics of line-sitting, where a customer could hire a line-sitter to wait for her, and the line-sitting fee is paid at the completion of service. Usually, it is believed that the line-sitting firm benefits in the pre-commitment payment method, in which the customer still pays a pre-specified amount of fee for service even if the actual waiting time is less than the pre-committed wait. By comparing two payment mechanisms, we find that, surprisingly, under the optimal line-sitting rate, the revenue of firm worsens under pre-commitment case, but a better customer welfare can be obtained.     

Adjusting for High-Dimensional Covariates in Sparse Precision Matrix Estimation by Lasso Penalized D-Trace Loss

This paper develops a covariate-adjusted precision matrix estimation using a two-stage estimation procedure. Firstly, we identify the relevant covariates that affect the means by a joint l_1 penalization. Then, the estimated regression coefficients are used to estimate the mean values in a multivariate sub-Gaussian model in order to estimate the sparse precision matrix through a Lasso penalized D-trace loss. Under some assumptions, we establish the convergence rate of the precision matrix estimation under different norms and demonstrate the sparse recovery property with probability converging to one. Simulation shows that our methods have the finite-sample performance compared with other methods.     

Mass defect filter technique and its applications to drug metabolite identification by high‐resolution mass spectrometry

Identification of drug metabolites by liquid chromatography/mass spectrometry (LC/MS) involves metabolite detection in biological matrixes and structural characterization based on product ion spectra. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of metabolites using triple‐quadrupole and ion trap mass spectrometers. Recently, a novel mass defect filter (MDF) technique has been developed, which enables high‐resolution mass spectrometers to be utilized for detecting both predicted and unexpected drug metabolites based on narrow, well‐defined mass defect ranges for these metabolites. This is a new approach that is completely different from, but complementary to, traditional molecular mass‐ or MS/MS fragmentation‐based LC/MS approaches. This article reviews the mass defect patterns of various classes of drug metabolites and the basic principles of the MDF approach. Examples are given on the applications of the MDF technique to the detection of stable and chemically reactive metabolites in vitro and in vivo. Advantages, limitations, and future applications are also discussed on MDF and its combinations with other data mining techniques for the detection and identification of drug metabolites. Copyright © 2009 John Wiley & Sons, Ltd.     

一类比例保险模型的破产概率问题

研究了比例再保险的破产概率问题,推广了Gramer-Lundberg经典模型的有关结果,并证明了基于比例模型合保问题最低保费的一个结果。     

中国福利彩票销售额影响因素分析与基于残差主成分分析的预测

本文应用多元回归分析的方法讨论了中国各省份福利彩票销售额与国民经济、就业人员和职工工资、财政、固定资产投资、人民生活这五个大类中的22个预测变量之间的相关关系,找出了对彩票销售额有显著性影响的变量,此外我们发现,不同年份的数据所得到的残差存在强相关性,本文利用主成分分析的观点提取出了不同年份残差中的公共信息,最后得出彩票销售额的预测方程,利用上述方程对2007年各省份的彩票销售额进行预测,并与真实的销售额数据进行对比,获得了较小的平均相对预报误差.这说明本文所建立的模型确实可以对中国的福利彩票销售额进行有效的预测.