Analytical Modeling of Rheological Postbuckling Behavior of Wood-Based Composite Panels Under Cyclic Hygro-Loading

This study was conducted to develop analytical models to predict postbuckling behavior of woodbased composite panels under cyclic humidity conditions. Both the Rayleigh method and von Karman theory of nonlinear plate with imperfection were used to obtain a closed form solution to the hygrobuckling and postbuckling. In addition, mechano-sorptive creep effects were also taken into account for the derivation of analytical models. The closed-form solutions derived for both isotropic and orthotropic materials showed a good agreement with the experimental results in terms of the center deformation of hardboard, especially in the case of the edge movements. The unrecovery deformation was much greater at the first cycle and then decreased as the number of cyclic hygro-loading increased

Growth of ultra-uniform graphene using a Ni/W bilayer metal catalyst

We investigated a bilayer catalyst system consisting of polycrystalline Ni and W films for growing mono-layer graphene over large areas. Highly uniform graphene was grown on Ni/W bilayer film with 100% coverage. The graphene grown on Ni/W bilayer film and transferred onto an insulating substrate exhibited average hole and electron mobilities of 727 and 340 cm(2)V(-1)s(-1), respectively. A probable growth mechanism is proposed based on X-ray diffractometry and transmission electron microscopy, which suggests that the reaction between diffused carbon and tungsten atoms results in formation of tungsten carbides. This reaction allows the control of carbon precipitation and prevents the growth of non-uniform multilayer graphene on the Ni surface; this has not been straightforwardly achieved before. These results could be of importance in better understanding mono-layer graphene growth, and suggest a facile fabrication route for electronic applications. (C) 2015 AIP Publishing LLCopen0

Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems

A series of novel filters for probabilistic inference that propose an alternative way of performing Bayesian updates, called particle flow filters, have been attracting recent interest. These filters provide approximate solutions to nonlinear filtering problems. They do so by defining a continuum of densities between the prior probability density and the posterior, i.e. the filtering density. Building on these methods' successes, we propose a novel filter. The new filter aims to address the shortcomings of sequential Monte Carlo methods when applied to important nonlinear high-dimensional filtering problems. The novel filter uses equally weighted samples, each of which is associated with a local solution of the Fokker-Planck equation. This hybrid of Monte Carlo and local parametric approximation gives rise to a global approximation of the filtering density of interest. We show that, when compared with state-of-the-art methods, the Gaussian-mixture implementation of the new filtering technique, which we call Stochastic Particle Flow, has utility in the context of benchmark nonlinear high-dimensional filtering problems. In addition, we extend the original particle flow filters for tackling multi-target multi-sensor tracking problems to enable a comparison with the new filter