Bayesian sample size for exploratory clinical trials incorporating historical data

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored. Copyright (C) 2007 John Wiley & Sons, Ltd.

An overview and historical look back at the solvent extraction using nitrogen donor ligands to extract and separate An(III) from Ln(III)

The partitioning of minor trivalent actinides (An) from lanthanides (Ln) is one of the challenges in the chemical treatment of nuclear waste. The optimal ligand to carry out the separation of An(III) and Ln(III) using solvent extraction has to meet several important criteria: high selectivity towards the solute, chemical and radiolytic stability, stripping possibilities and recycling of the organic phase, high separation factors and good distribution ratio, to name just a few of them. A chronological line can be drawn along the development of each extraction ligand family and some milestones are emphasized in this overview. Further developments in organic synthesis of extracting ligands are expected.

Historical and current machine intelligence

In this article, we provide an initial insight into the study of MI and what it means for a machine to be intelligent. We discuss how MI has progressed to date and consider future scenarios in a realistic and logical way as much as possible. To do this, we unravel one of the major stumbling blocks to the study of MI, which is the field that has become widely known as "artificial intelligence"