薄板坯连铸连轧流程Ti微合金化高强钢开发与应用

在薄板坯连铸连轧流程上，经过对Ti微合金化技术研究，有效解决了Ti微合金化强度性能波动大的问题，开发出屈服强度450～700MPa高强钢。对试制钢组织性能和工业应用分析研究，表明该产品通板性能良好，通板强度差30MPa左右；成形性能良好，d=0时冷弯测试全部合格；同时还具有良好焊接性能，焊接后，强度波动较小，均在母材部位断开；试制钢显微组织为珠光体和铁素体，晶粒均匀细小，平均尺寸为4～8／μm；实物质量满足集装箱和汽车制造行业要求，具有广阔市场前景。     

企业不但应该做大更应该做强

从入世的角度 ,分析了我国企业在重组过程中存在的问题 ,并指出了我国企业做强做大的紧迫性     

Formation of layered structure on bismuth-borate glass surface

X-ray diffraction analysis shows that the transformation during heat treatment from amorphous to crystalline phases of bismuth-borate glass samples takes place in sequences. After a short heat treatment, 5 min at 550 °C, a layered structure with a preferred orientation of crystallites on the surface is observed. After a long heat treatment, 8 h at the same temperature, normal polycrystalline bulk samples are obtained.     

基于DELPHI的镍氢电池管理系统软件设计

通过对镍氢电池性能的研究，给出了一个应用ISA总线技术构建的、基于DELPHI编程平台的镍氢电池管理系统的设计方案，重点介绍了该系统的软件技术和实现方法。     

Synthesis of LiFePO4 with fine particle by co-precipitation method

LiFePO₄ is a potential candidate for the cathode material of the lithium secondary batteries. A co-precipitation method was adopted to prepare LiFePO₄ because it is simple and cheap. Nitrogen gas was needed to prevent oxidation of Fe²⁺ in the aqueous solution. The co-precipitated precursor shows the high reactivity with the reductive gas, and the single phase of LiFePO₄ is successfully synthesized with the aid of carbon under less reductive conditions. LiFePO₄ fine powder prepared by co-precipitation method shows high rate capability, impressive specific capacity and cycle property.     

A new iterative order reduction (IOR) method for eigensolutions of large structures

Order reduction is a computationally efficient method to estimate some lowest eigenvalues and the corresponding eigenvectors of large structural systems by reducing the order of the original model to a smaller one. But its accuracy is limited to a small range of frequencies that depends on the selection of the retained degrees of freedom. This paper proposes a new iterative order reduction (IOR) technique to obtain accurately the eigensolutions of large structural systems. The technique retains all the inertia terms associated with the removed degrees of freedom. This hence leads to the reduced mass matrix being in an iterated form and the reduced stiffness matrix constant. From these mass and stiffness matrices, the eigensolutions of the reduced system can be obtained iteratively. On convergence the reduced system reproduces the eigensolutions of the original structure. A proof of the convergence property is also presented. Applications of the method to a practical GARTEUR structure as well as a plate have demonstrated that the proposed method is comparable to the commonly used Subspace Iteration method in terms of numerical accuracy. Moreover, it has been found that the proposed method is computationally more efficient than the Subspace Iteration method. Copyright © 2003 John Wiley & Sons, Ltd.     

真空制盐蒸发结晶器的设计与实践

真空蒸发制盐外热式强制逆循环轴向出料蒸发结晶器，经多个厂家生产应用实践证明是成功的，具有生命力的。这种新型结构，作为一项新技术新设备应加强研究，总结提高，推广应用。不断完善。文章从流体力学、结晶机理角度要求，到具体工程设计参数和材质选用。论述了该罐的特点。     

Nanostructured polymer blends: Synthesis and structure

Nanostructured polymer blends prepared via anionic ring opening polymerizations of cyclic monomers in the presence of a pre-made polymer melt exhibit a number of special properties over traditional polymer blends and homopolymers. Here, we report on a simple and versatile method of in situ polymerization of macrocyclic carbonates in the presence of a maleic anhydride polypropylene (mPP) matrix and a surface-active compatibilizer (i.e. PC grafted onto a mPP backbone generated in situ) to yield a micro- and nanostructured polymer blends consisting of a polycarbonate (PC) minor phase, and a polypropylene (PP) major phase. By varying the processing conditions and concentration of the macrocyclic carbonate it was possible to reduce the size of the PC dispersions to an average minor diameter of 150 nm. NMR and TEM characterizations indicate that the PC dispersions do not influence crystal content in the PP phase. Overall, the results point to a simple strategy and versatile route to new polymeric materials with enhanced benefits.     

Expressive Facial Gestures From Motion Capture Data

Human facial gestures often exhibit such natural stochastic variations as how often the eyes blink, how often the eyebrows and the nose twitch, and how the head moves while speaking. The stochastic movements of facial features are key ingredients for generating convincing facial expressions. Although such small variations have been simulated using noise functions in many graphics applications, modulating noise functions to match natural variations induced from the affective states and the personality of characters is difficult and not intuitive. We present a technique for generating subtle expressive facial gestures (facial expressions and head motion) semi‐automatically from motion capture data. Our approach is based on Markov random fields that are simulated in two levels. In the lower level, the coordinated movements of facial features are captured, parameterized, and transferred to synthetic faces using basis shapes. The upper level represents independent stochastic behavior of facial features. The experimental results show that our system generates expressive facial gestures synchronized with input speech.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.