Reversible jump sampler for autoregressive time series

We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

High-resolution TEM characterization of ZnO core-shell nanowires for dye-sensitized solar cells

Recently ZnO nanowire films have been used in very promising and inexpensive dye-sensitized solar cells (DSSC). It was found that the performance of the devices can be enhanced by functionalising the nanowires with a thin metal oxide coating. This nm-scale shell is believed to tailor the electronic structure of the nanowire, and help the absorption of the dye. Core-shell ZnO nanowire structures are synthesised at low temperature (below 120°C) by consecutive hydrothermal growth steps. Different materials are investigated for the coating, including Mg, Al, Cs and Zr oxides. High resolution TEM is used to characterise the quality of both the nanowire core and the shell, and to monitor the thickness and the degree of crystallisation of the oxide coating. The interface between the nanowire core and the outer shell is investigated in order to understand the adhesion of the coating, and give valuable feedback for the synthesis process. Nanowire films are packaged into dye-sensitised solar cell prototypes; samples coated with ZrO2 and MgO show the largest enhancement in the photocurrent and open-circuit voltage and look very promising for further improvement. © 2010 IOP Publishing Ltd.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Microstructural modelling in friction stir welding of 2000 series aluminium alloys

Simple process models are applied to predict microstructural changes due to the thermal cycle imposed in friction stir welding. A softening model developed for heat-treatable aluminium alloys of the 6000 series is applied to the aerospace alloy 2014 in the peak-aged (T6) condition. It is found that the model is not readily applicable to alloy 2024 in the naturally aged (T3) temper, but the softening behaviour can still be described semi-empirically. Both analytical and numerical (finite element) thermal models are used to predict the thermal histories in trial welds. These are coupled to the microstructural model to investigate: (a) the hardness profile across the welded plate; (b) alloy softening ahead of the approaching welding tool. By incorporating the softening model applied to 6082-T6 alloy, the hardness profile of friction stir welds in dissimilar alloys is also predicted. © AFM, EDP Sciences 2005.