A hierarchical distributed approach for mining molecular fragments

Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

Best Available Techniques (BAT) and coal fired power stations: Can the energy gap be plugged without increasing emissions?

At present, there is much anxiety regarding the security of energy supplies; for example, the UK and other European States are set to become increasingly dependant upon imports of natural gas from states with which political relations are often strained. These uncertainties are felt acutely by the electricity generating sector, which is facing major challenges regarding the choice of fuel mix in the years ahead. Nuclear energy may provide an alternative; however, in the UK, progress in replacing the first generation reactors is exceedingly slow. A number of operators are looking to coal as a means of plugging the energy gap. However, in the light of ever more stringent legal controls on emissions, this step cannot be taken without the adoption of sophisticated pollution abatement technology. This article examines the role which legal concepts such as Best Available Techniques (BAT) must play in bringing about these changes.

Asymptotic normality in mixtures of power series distributions

The problem of estimating the individual probabilities of a discrete distribution is considered. The true distribution of the independent observations is a mixture of a family of power series distributions. First, we ensure identifiability of the mixing distribution assuming mild conditions. Next, the mixing distribution is estimated by non-parametric maximum likelihood and an estimator for individual probabilities is obtained from the corresponding marginal mixture density. We establish asymptotic normality for the estimator of individual probabilities by showing that, under certain conditions, the difference between this estimator and the empirical proportions is asymptotically negligible. Our framework includes Poisson, negative binomial and logarithmic series as well as binomial mixture models. Simulations highlight the benefit in achieving normality when using the proposed marginal mixture density approach instead of the empirical one, especially for small sample sizes and/or when interest is in the tail areas. A real data example is given to illustrate the use of the methodology.