Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Understanding construction competitiveness: the initial results of a Delphi survey in Finland, Sweden and the UK

The competitiveness of the construction industry is an important issue for many countries as the industry makes up a substantial part of their GDP – about 8% in the UK. A number of competitiveness studies have been undertaken at company, industry and national levels. However, there has been little focus on sustainable competitiveness and the many factors that are involved. This paper addresses that need by investigating what construction industry experts consider to be the most important factors of construction industry competitiveness. It does so by conducting a Delphi survey among industry experts in Finland, Sweden and the UK. A list of 158 factors was compiled from competitiveness reports by institutions such as World Economic Forum and International Institute of Management Development, as well as from explorative workshops in the countries involved in the study. For each of the countries, experts with different perspectives of the industry, including, consultants, contractors and clients, were asked to select their 30 most influential factors. They then ranked their chosen factors in order of importance for the competitiveness of their construction industry. The findings after the first round of the Delphi process underline the complexity of the term competitiveness and the wide range of factors that are considered important contributors to competitiveness. The results also indicate that what are considered to be the most important factors of competitiveness is likely to differ from one country to another.

Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations

The Eyjafjallajökull volcano in Iceland erupted explosively on 14 April 2010, emitting a plume of ash into the atmosphere. The ash was transported from Iceland toward Europe where mostly cloud-free skies allowed ground-based lidars at Chilbolton in England and Leipzig in Germany to estimate the mass concentration in the ash cloud as it passed overhead. The UK Met Office's Numerical Atmospheric-dispersion Modeling Environment (NAME) has been used to simulate the evolution of the ash cloud from the Eyjafjallajökull volcano during the initial phase of the ash emissions, 14–16 April 2010. NAME captures the timing and sloped structure of the ash layer observed over Leipzig, close to the central axis of the ash cloud. Relatively small errors in the ash cloud position, probably caused by the cumulative effect of errors in the driving meteorology en route, result in a timing error at distances far from the central axis of the ash cloud. Taking the timing error into account, NAME is able to capture the sloped ash layer over the UK. Comparison of the lidar observations and NAME simulations has allowed an estimation of the plume height time series to be made. It is necessary to include in the model input the large variations in plume height in order to accurately predict the ash cloud structure at long range. Quantitative comparison with the mass concentrations at Leipzig and Chilbolton suggest that around 3% of the total emitted mass is transported as far as these sites by small (<100 μm diameter) ash particles.