Numerical solution of the three‐dimensional parabolic equation with an integral condition

Developement of numerical methods for obtaining approximate solutions to the three dimensional diffusion equation with an integral condition will be carried out. The numerical techniques discussed are based on the fully explicit (1,7) finite difference technique and the fully implicit (7,1) finite difference method and the (7,7) Crank‐Nicolson type finite difference formula. The new developed methods are tested on a problem. Truncation error analysis and numerical examples are used to illustrate the accuracy of the new algorithms. The results of numerical testing show that the numerical methods based on the finite difference techniques discussed in the present article produce good results. © 2002 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 18: 193–202, 2002; DOI 10.1002/num.1040     

基于MATLAB(SIMULINK)的电弧炉电极调节系统FUZZY-PD控制器的仿真

介绍了采用MATLAB V5.2提供的模糊逻辑工具箱来设计研究电孤纺电极调节系统中的模糊－－PD控制器，讨论了在SIMULINK环境下模糊－PD控制器的参数自调速原理、结构、建立模糊控制规则库和模糊推论方法，并给出仿真结果与结论。     

碳纳米管阵列、捆束的三步升温工艺制备及其形貌与结构

使用结构简单的单温炉设备，通过三步升温热解二茂铁、三聚氰氨混合物方法，在二氧化硅、多晶陶瓷基底上分别合成了碳纳米管阵列、碳纳米管捆束.使用扫描电子显微镜、透射电子显微镜、电子能量损失谱和x射线光电子能谱对合成样品进行了结构和成分分析.结果显示：两种基底上合成的纳米管均为多壁纯碳管；生长于光滑二氧化硅表面的碳纳米管具有高度取向性和一致的外径，长度为10—40μm.碳纳米管采取催化剂顶端生长模式并展示出类杯状形貌；生长于粗糙多晶陶瓷表面的碳纳米管捆束随机取向，碳纳米管直径为15—80nm，长度在几百微米，展示 关键词： 碳纳米管 热解法 三步升温工艺     

Some Aspects of the Implementation of Scaling Quantum Mechanical Molecular Force Fields

Some features of software implementation of the Pulay scaling procedure are considered. The advantages of the single value decomposition method for maintaining well-conditionality of the scale factor determination problem are demonstrated. The necessity of using a rational number of scale factors is shown. The possibility of obtaining transferable scale factors with the Pulay method and thus predict the vibrational spectra of related compounds is emphasized.     

逼近严格伪压缩映象不动点的Ishikawa迭代程序

Let(X,‖·‖ ) be a Banach space.Let K be a nonempty closed,convex subset of Xand T∶K→K.Assume that T is Lipschitzian,i.e.there exists L>0 such that‖ T(x) -T(y)‖≤ L‖ x -y‖for all x,y∈K.Withoutloss of generality,assume that L≥ 1 .Assume also that T is strictly pseudocontractive.According to[1 ] this may be statedas:there exists k∈ (0 ,1 ) such that‖ x -y‖≤‖ x -y + r[(I -T -k I) x -(I -T -k I) y]‖for all r>0 and all x,y∈ K.Throughout,let N denote the set of positive in…     

广义线性模型(六)

本讲座是广义线性模型这个题目的一个比较系统的介绍。主要分 3部分 :建模、统计分析与模型选择和诊断。写作时依据的主要参考资料是L .Fahrmeir等人的 :《MultivariateStatisticalModel ingBasedonGeneralizedLinearModles》。     

Validation of an optimization procedure to improve low frequency characteristics of rooms

A set of experiments was carried out to validate an optimization procedure based on finite element method (FEM). The idea of the procedure, fully presented in previous edition of this journal [Zhu X, Zhu Z, Cheng J. Using optimized surface modifications to improve low frequency response in a room. Appl Acoust 2004;65:841-60], is to produce an optimal geometry modification on the wall for improving low frequency sound uniformity in small rooms. Four experimental models were set up with a scale of 1:5. One was modified according to the optimized result and the others were treated with no optimization consideration. Measured frequency responses of four rooms were compared with numerical results calculated by FEM models. The transient responses in these rooms were also measured and analyzed. The agreements between calculation and measurement are satisfactory though the discrepancies due to the uncertainty of acoustic behavior of the room boundaries remain. The optimization procedure has been supported by the results that the optimized room produces the flattest frequency response and also the most smooth energy decay within the frequency range studied. The reductions of response fluctuation have reached 4.3 dB for prediction and 2.6 dB for measurement, respectively.     

Reference priors for exponential families with simple quadratic variance function

Reference analysis is one of the most successful general methods to derive noninformative prior distributions. In practice, however, reference priors are often difficult to obtain. Recently developed theory for conditionally reducible natural exponential families identifies an attractive reparameterization which allows one, among other things, to construct an enriched conjugate prior. In this paper, under the assumption that the variance function is simple quadratic, the order-invariant group reference prior for the above parameter is found. Furthermore, group reference priors for the mean- and natural parameter of the families are obtained. A brief discussion of the frequentist coverage properties is also presented. The theory is illustrated for the multinomial and negative-multinomial family. Posterior computations are especially straightforward due to the fact that the resulting reference distributions belong to the corresponding enriched conjugate family. A substantive application of the theory relates to the construction of reference priors for the Bayesian analysis of two-way contingency tables with respect to two alternative parameterizations.     

A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.