The spectrum of cyclic BSA(ν, 3, λ; α) with α = 2, 3

Balanced sampling plans excluding contiguous units (or BSEC) were first introduced by Hedayat, Rao, and Stufken in 1988. In this paper, we generalize the concept of a cyclic BSEC to a cyclic balanced sampling plan to avoid the selection of adjacent units (or CBSA for short) and use Langford and extended Langford sequences to construct a cyclic BSA(ν, 3, λ; α) with α = 2, 3. We finally establish the necessary and sufficient conditions for the existence of a cyclic BSA(ν, 3, λ; α) where α = 2, 3. © 2005 Wiley Periodicals, Inc. J Combin Designs.     

A unifying local-semilocal convergence analysis and applications for two-point Newton-like methods in Banach space

We provide a local as well as a semilocal convergence analysis for two-point Newton-like methods in a Banach space setting under very general Lipschitz type conditions. Our equation contains a Fréchet differentiable operator F and another operator G whose differentiability is not assumed. Using more precise majorizing sequences than before we provide sufficient convergence conditions for Newton-like methods to a locally unique solution of equation F(x)+G(x)=0. In the semilocal case we show under weaker conditions that our error estimates on the distances involved are finer and the information on the location of the solution at least as precise as in earlier results. In the local case a larger radius of convergence is obtained. Several numerical examples are provided to show that our results compare favorably with earlier ones. As a special case we show that the famous Newton-Kantorovich hypothesis is weakened under the same hypotheses as the ones contained in the Newton-Kantorovich theorem.     

基于部分变量反馈的混沌系统控制

提出了混沌系统部分变量逆序反馈控制器的定义，并以陈氏系统为例对该类控制器进行了研究，得出了部分变量正序和逆序控制器存在的条件，应用该条件扩展了有关文献的研究成果，得到了混沌系统的多种形式的反馈控制器.仿真结果显示，基于部分变量的混沌系统的控制是简单而有效的. 关键词： 部分变量反馈控制 逆序控制器 混沌系统     

赋Orlicz范数的Musielak-Orlicz序列空间的Gateaux可微点与Frechet可微点

该文给出赋0rlicz范数的Musielak-Orlicz序列空间中Gateaux可微点(光滑点)与Frechet可微点(强光滑点)的判定准则．在此基础上推出了该空间具有光滑性或强光滑性的充分必要条件．     

论国家对农民工群体的政策缺失

伴随着国家城市化和工业化进程的加快，农民工成为一个越来越受人们关注的群体。在我国以户籍制为基础的城乡二元结构下，农民工有着非常复杂的身份，他们生活、工作并居住在城市，但是他们的户籍在农村。在我国，社会福利保障制度是以户籍制为基础划分的，这样特殊的背景导致了农民工群体在很多方面被排除在社会福利保障制度之外。目前，我国正在积极构建社会主义和谐社会，在这个大背景的感召下，农民工群体作为和谐社会的一个特殊群体，对他们的社会政策有待进一步完善。本文分析了农民工群体在医疗保险、用工制度、子女教育等方面的政策缺失，并提出了相应的政策建议。     

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.     

Banach空间中拟-似变分包含解的存在与逼近问题

研究了Banach空间中拟-似变分包含解的存在与逼近问题.给出了一种寻求解的新的迭代算法,建立了具混合误差的Ishikawa型迭代序列强收敛到解的充要条件.所得结果推广了一些相关的结果.     

-混合序列的不变原理

给出一类较广泛的ρ-混合序列 ,并证明了在一定的矩条件下 ,ρ-混合序列的不变原理成立     

Perceptual learning of dot pattern

Dot pattern is a type of pattern defined by specific spatial relationship among some dots. As a compensation for concrete and meaningful visual stimuli, meaningless dot pattern can be used as stimuli in learning tasks. We performed an experiment for exploring the perceptual learning process of dot pattern against random-dot background. Participants were required to learn two types of dot patterns (9-dot and 11-dot). They were assigned to two groups: participants in Group 1 learned 9-dot pattern first and 11-dot pattern later, while those in Group 2 learned 11-dot pattern first and 9-dot pattern later. The results showed that participants could acquire the spatial relationship of dot pattern through perceptual learning in relatively short learning time. In comparison with 9-dot pattern, learning time of 11-dot pattern was slightly longer and its accuracy rate lower, but there was significant positive transfer effect from 11-dot pattern learning to 9-dot learning.