Characterisation of different commercial reactive magnesia

Reactive magnesia (MgO) has emerged as an essential component in a new family of cements with significantly superior technical and environmental performance over Portland cement. The physical characteristics of different reactive magnesia, which are likely to affect their engineering performance, vary considerably depending on their origin and manufacturing processes. To appropriately utilise such a material, it is essential to develop a better understanding of the characteristics of different magnesia from various sources. In this study, the detailed characterisation of 14 commercial magnesia in terms of reactivity, textural properties, X-ray diffraction pattern, pH value and hydration behaviour and morphology is presented and correlation between them is developed. Relationships were developed between the reactivity, specific surface area, agglomeration ratio and hydration rate based on the experimental observations. As a result, the reactive magnesia used in this study were grouped into three categories and their characteristics and anticipated performances in different applications were discussed.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.