Distribution of critical current density in large YBa2Cu3O7-δ grains fabricated using seeded peritectic solidification

High quality large grain high Tc superconducting ceramics offer enormous potential as 'permanent' magnets and in magnetic screening applications at 77K. This requires sample dimensions -cm with uniform high critical current densities of the order 105 A/cm2 in applied magnetic fields of IT. We report a study of the magnetic characterisation of a typical large YBa2Cu3O7-δ grain, prepared by seeded peritectic solidification, and correlate the magnetically determined critical current density, Jc, with microstuctural features from different regions of the bulk sample. From this data we extract the temperature, field and positional dependence of the critical current density of the samples and the irreversibility line. We find that whilst the bulk sample exhibits a good Jc of order 104 A/cm2 (77K, 1T), the local Jc is strongly correlated with the sample microstructure towards the edge of the sample and more severely at the centre of the sample by the presence of SmBa2Cu3O7-δ seed crystal. © 1997 IEEE.

A dynamic view of the spread and intracellular distribution of Salmonella enterica.

The events that determine the dynamics of proliferation, spread and distribution of microbial pathogens within their hosts are surprisingly heterogeneous and poorly understood. We contend that understanding these phenomena at a sophisticated level with the help of mathematical models is a prerequisite for the development of truly novel, targeted preventative measures and drug regimes. We describe here recent studies of Salmonella enterica infections in mice which suggest that bacteria resist the antimicrobial environment inside host cells and spread to new sites, where infection foci develop, and thus avoid local escalation of the adaptive immune response. We further describe implications for our understanding of the pathogenic mechanism inside the host.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.