Generalized binomial and negative binomial distributions of order<Emphasis Type="Italic">k</Emphasis> by the<Emphasis Type="Italic">l</Emphasis>-overlapping enumeration scheme

In this paper, we investigate the exact distribution of the waiting time for ther-th ℓ-overlapping occurrence of success-runs of a specified length in a sequence of two state Markov dependent trials. The probability generating functions are derived explicitly, and as asymptotic results, relationships of a negative binomial distribution of orderk and an extended Poisson distribution of orderk are discussed. We provide further insights into the run-related problems from the viewpoint of the ℓ-overlapping enumeration scheme. We also study the exact distribution of the number of ℓ-overlapping occurrences of success-runs in a fixed number of trials and derive the probability generating functions. The present work extends several properties of distributions of orderk and leads us a new type of geneses of the discrete distributions.     

用数值方法分析微孔激光器出射光的近场特性

微孔激光器作为应用于近场光信息存储系统中的一种新型光源，它的出射光斑的近场特性对于近场光存储是十分重要的。针对纳米孔径运用角谱进行Fox-Li数值迭代，得到不同孔径微孔激光器的基模光强分布，然后运用二维非线性时域有限差分法分析微孔激光器出射端即微孔金属膜的近场光学性质，模拟计算了不同孔径和厚度的微孔金属膜的光强近场分布，从应用于近场光存储的角度，给出反映其近场光学特性的相关数据。发现由于TM模式下金属存在局域表面等离子增强效应，使得其出射强度比TE模式高一个数量级，从而更适于作为实际中近场光存储系统和原理试验的光学头。     

Near-Optimal Controls of Discrete-Time Dynamic Systems Driven by Singularly-Perturbed Markov Chains

This work is devoted to near-optimal controls of large-scale discrete-time nonlinear dynamic systems driven by Markov chains; the underlying problem is to minimize an expected cost function. Our main goal is to reduce the complexity of the underlying systems. To achieve this goal, discrete-time control models under singularly-perturbed Markov chains are introduced. Using a relaxed control representation, our effort is devoted to finding near-optimal controls. Lumping the states in each irreducible class into a single state gives rise to a limit system. Applying near-optimal controls of the limit system to the original system, near-optimal controls of the original system are derived.     

Improvement of real time diffraction efficiency of NGD holograms by introducing electron donors

Nongelatin dichromated holographic film(NGD) is a new holographic recordingmaterial.Holograms recorded on this material have better environmental stability, higherdiffraction efficiency and stronger real time effect, etc..An experimental study to ascertainthe influence of two electron donors, namely,N,N-dimethylformamide(DMF) and dimethyl-sulfoxide(DMSO),on the real time diffraction efficiency(RTDE) of NGD holograms is car-ried out.The pressence of electron donors not only improves the sensitivity,but also enhancesthe real time effect greatly.RTDE of above 60% at±1 orders of NGD plane transimissiongrating is achieved by introducing electron donors.     

LOCAL SPECTRA OF UNILATERAL OPERATOR WEIGHTED SHIFTS

In this note, the local spectral properties of unilateral operator weighted shifts arestudied.     

FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS WITH STOPPING TIME

The existence and uniqueness results of fully coupled forward-backward stochastic differential equations with stopping time (unbounded) is obtained. One kind of comparison theorem for this kind of equations is also proved.     

A solution procedure for the discrete time, cost and quality tradeoff problem using electromagnetic scatter search

Decisions concerning a project’s expedition, traditionally involved considerations regarding time and cost tradeoff. It was recently suggested that the quality of a project should also be taken into considerations. In this paper, we propose a meta-heuristic solution procedure for the discrete time, cost and quality tradeoff problem. This problem involves the scheduling of project activities in order to minimize the total cost of the project while maximizing the quality of the project and also meeting a given deadline. We apply a so called electromagnetic scatter search to solve this problem. In this process, we initially generate a population of feasible solutions. In so doing, we use frequency memory to well sample the feasible region. A number of these solutions are then selected and improved locally. The improved solutions are then combined to generate new set of solutions. The combination process utilizes attraction–repulsion mechanisms borrowed from the electromagnetism theory. The whole process is stopped when no significant improvement in the set of solutions are observed. The validity of the proposed solution procedure is demonstrated, and its applicability is tested on a randomly generated large and complex problem having 19,900 activities.     

Global and exploding solutions in a model of self-gravitating systems

We study asymptotic properties of solutions to an extension to arbitrary dimensions of the astrophysical model proposed by Chavanis et al. to explain phenomena of gravitational collapse in clouds of self-gravitating particles. In particular, we show that in the two-dimensional case the solutions can be continued to global ones, while in three space dimensions large data of negative energy blow up in a finite time. Relations between isothermal, Streater's energy-transport and the present models are also studied.     

Randomly censored partially linear single-index models

This paper proposes a method for estimation of a class of partially linear single-index models with randomly censored samples. The method provides a flexible way for modelling the association between a response and a set of predictor variables when the response variable is randomly censored. It presents a technique for “dimension reduction” in semiparametric censored regression models and generalizes the existing accelerated failure-time models for survival analysis. The estimation procedure involves three stages: first, transform the censored data into synthetic data or pseudo-responses unbiasedly; second, obtain quasi-likelihood estimates of the regression coefficients in both linear and single-index components by an iteratively algorithm; finally, estimate the unknown nonparametric regression function using techniques for univariate censored nonparametric regression. The estimators for the regression coefficients are shown to be jointly root-n consistent and asymptotically normal. In addition, the estimator for the unknown regression function is a local linear kernel regression estimator and can be estimated with the same efficiency as all the parameters are known. Monte Carlo simulations are conducted to illustrate the proposed methodology.     

Local polynomial fitting under association

We consider the estimation of multivariate regression functions r(x₁,…,x_d) and their partial derivatives up to a total order p1 using high-order local polynomial fitting. The processes {Y_i,X_i} are assumed to be (jointly) associated. Joint asymptotic normality is established for the estimates of the regression function r and all its partial derivatives up to the total order p. Expressions for the bias and variance/covariance matrix (of the asymptotic distribution) are given.