基于多属性决策的复杂网络节点重要性综合评价方法

复杂网络中的节点重要性评价在实际应用中有着重要意义.现有的一些重要性评价指标如度、介数等存在适用范围有限,评价结果不够全面等缺点,因为节点在复杂网络中的重要性不仅仅受单一因素的影响.为此,本文提出了一种基于多属性决策的复杂网络节点重要性综合评价方法.该方法将复杂网络中的每一个节点看作一个方案,其多个重要性评价指标作为该方案的属性,通过计算每个方案到理想方案的接近程度,最终得到该节点的重要性综合评价结果.该方法不仅可以用于不同类型复杂网络的节点重要性评价,而且便于扩展,实验结果表明了该方法的有效性.     

Wick-Itô Formula for Gaussian Processes

Abstract

A Wick-Itô formula for Gaussian processes is obtained. This is a change of variables formula, which is to Wick-Itô integrals what the usual Itô formula is to Itô integrals. The conditions are weak enough to allow processes with infinite quadratic variation. They are satisfied by fractional Brownian motion with parameter 1/4 < H < 1.     

Some inequalities for martingales and stochastic convolutions

We give a stopped Doob inequality for a right continuous martingale in Hilbert space,, Using this we obtain inequalities for p-th moments with 0 < p < in terms of the Meyer process and the quadratic variation of the pure jump part. We also consider the convolution of a contraction type semigroup and a right continuous martingale and obtain inequalities similar to those of a martingale     

Topological Solutions of Noncommutative Stochastic Differential Equations

Abstract

A general approach is introduced to studying the properties of solutions of an arbitrary noncommutative stochastic differential equation (NSDE) in several interesting locally convex operator topologies, grouped into two main sets comprising the strong/λ^?-topologies and the weak topologies. Results concerning the existence and uniqueness of solutions in these topologies are established. The approach is based on two reformulations of the NSDE, corresponding to the two sets of topologies, and is well-suited for characterizing, both analytically and numerically, various topological features of the solutions of an NSDE.     

The First Attempt on the Stochastic Calculus on Time Scale

The aim of this article is to study the Doob–Meyer decomposition theorem, ?-stochastic integration and Ito's formula for stochastic processes defined on time scale. The obtained results can be considered as a first attempt on the stochastic calculus on time scale.     

Maximum Likelihood for Generalized Linear Models with Nested Random Effects via High-Order,Multivariate Laplace Approximation

Abstract

Nested random effects models are often used to represent similar processes occurring in each of many clusters. Suppose that, given cluster-specific random effects b, the data y are distributed according to f(y|b, Θ), while b follows a density p(b|Θ). Likelihood inference requires maximization of ∫ f(y|b, Θ)p(b|Θdb with respect to Θ. Evaluation of this integral often proves difficult, making likelihood inference difficult to obtain. We propose a multivariate Taylor series approximation of the log of the integrand that can be made as accurate as desired if the integrand and all its partial derivatives with respect to b are continuous in the neighborhood of the posterior mode of b|Θ,y. We then apply a Laplace approximation to the integral and maximize the approximate integrated likelihood via Fisher scoring. We develop computational formulas that implement this approach for two-level generalized linear models with canonical link and multivariate normal random effects. A comparison with approximations based on penalized quasi-likelihood, Gauss—Hermite quadrature, and adaptive Gauss-Hermite quadrature reveals that, for the hierarchical logistic regression model under the simulated conditions, the sixth-order Laplace approach is remarkably accurate and computationally fast.     

MCMC Strategies for Computing Bayesian Predictive Densities for Censored Multivariate Data

Traditional criteria for comparing alternative Bayesian hierarchical models, such as cross-validation sums of squares, are inappropriate for nonstandard data structures. More flexible cross-validation criteria such as predictive densities facilitate effective evaluations across a broader range of data structures, but do so at the expense of introducing computational challenges. This article considers Markov chain Monte Carlo strategies for calculating Bayesian predictive densities for vector measurements subject to differential component-wise censoring. It discusses computational obstacles in Bayesian computations resulting from both the multivariate and incomplete nature of the data, and suggests two Monte Carlo approaches for implementing predictive density calculations. It illustrates the value of the proposed methods in the context of comparing alternative models for joint distributions of contaminant concentration measurements.     

Modelling of Heat Transfer and Vehicle Dynamics for Thermal Load Reduction by Hypersonic Flight Optimization

Heat input reduction by appropriate, optimal trajectory control is considered for the range cruise and the return-to-base cruise of a hypersonic vehicle propelled by a turbo/ram jet engines combination. A mathematical model is developed for describing the unsteady heat transfer through the thermal protection system. This model is coupled to the model of the dynamics of the vehicle. An efficient optimization technique is applied for constructing a solution for the two cruise problems. The results show that significant heat input reductions can be achieved with only a small penalty in fuel consumption.     

矩形空腔内Stokes流的状态空间有限元法

基于Hellinger-Reissner二类变分原理,从平面Stokes流问题的平衡方程、连续性要求和边界条件出发,得到相应的Hamilton函数,建立Hamilton正则方程后,采用分离变量法对场变量进行离散求解:在x方向采用有限元插值,在y方向采用状态空间法给出控制坐标方向的解析解。计算过程中的指数矩阵均采用精细积分法求解,使得本文算法具有高效率、高精度、对步长不敏感的优点。通过对侧边自由液面边界条件的单板驱动矩形空腔Stokes流问题的求解,得到与文献相同的结果,从而验证了本文方法的有效性。本文旨在将弹性力学状态空间有限元法的思想引入到低雷诺数流体力学中,为Hamilton体系下研究复杂边界Stokes流问题提供新的途径。     

Atomistic simulation of CO2 solubility in poly(ethylene oxide) oligomers

We have performed atomistic molecular dynamics simulations coupled with thermodynamic integration to obtain the excess chemical potential and pressure-composition phase diagrams for CO₂ in poly(ethylene oxide) oligomers. Poly(ethylene oxide) dimethyl ether, CH₃O(CH₂CH₂O)_nCH₃ (PEO for short) is a widely applied physical solvent that forms the major organic constituent of a class of novel nanoparticle-based absorbents. Good predictions were obtained for pressure–composition–density relations for CO₂ + PEO oligomers (2 ≤ n ≤ 12), using the Potoff force field for PEO [J. Chem. Phys. 136, 044514 (2012)] together with the TraPPE model for CO₂ [AIChE J. 47, 1676 (2001)]. Water effects on Henry’s constant of CO₂ in PEO have also been investigated. Addition of modest amounts of water in PEO produces a relatively small increase in Henry’s constant. Dependence of the calculated Henry’s constant on the weight percentage of water falls on a temperature-dependent master curve, irrespective of PEO chain length.