Electrochemically Deposited Cadmium Electrode for Sealed Ni-cd Cells

An investigation into the work on electrochemical cadmium deposition processes is describred. A beaker impregnation system is constructed to investigate the practical limits of loading and the effect of various process parameters. Reasonably high loadings of cadmium are obtained and the process appears amenable to tight control and the production of uniform consistent electrodes. A pilot impregnation facility is built to further investigate electrodeposition processes. Both the inert anode and consummable anode processes are investigated. Results of this evaluation and an analysis of associated problems are presented

The Formation of Crystalline Dust in AGB Winds from Binary Induced Spiral Shocks

As stars evolve along the Asymptotic Giant Branch, strong winds are driven from the outer envelope. These winds form a shell, which may ultimately become a planetary nebula. Many planetary nebulae are highly asymmetric, hinting at the presence of a binary companion. Some post-Asymptotic Giant Branch objects are surrounded by torii of crystalline dust, but there is no generally accepted mechanism for annealing the amorphous grains in the wind to crystals. In this Letter, we show that the shaping of the wind by a binary companion is likely to lead to the formation of crystalline dust in the orbital plane of the binary.Comment: Submitted to ApJ

Inference of Temporally Varying Bayesian Networks

When analysing gene expression time series data an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Whilst some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data. Here we present a method that allows us to infer regulatory network structures that may vary between time points, utilising a set of hidden states that describe the network structure at a given time point. To model the distribution of the hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov Model, a nonparametric extension of the traditional Hidden Markov Model, that does not require us to fix the number of hidden states in advance. We apply our method to exisiting microarray expression data as well as demonstrating is efficacy on simulated test data