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An operational prototype of the Integrated Bulgarian Chemical Weather Forecasting and Information System is presented. This version of the system is limited to relatively low resolution (10 km) but covers all Bulgaria. It is based on the US EPA Models-3 System (MM5, SMOKE and CMAQ). The meteorological input to the system is the Bulgarian operational numerical weather forecast. The boundary conditions are taken from analogous Greek system (Aristotle University of Thessaloniki). Bulgarian system runs automatically twice a day (00 and 12 UTC) and produces 48-hour forecast. The part of the results of each System's run is post-processed in a way to be visualized and uploaded to a respective web site. In the paper, description of the System is given together with a demonstration of its products. In addition highlights of Systems upgrade will be given. [ABSTRACT FROM AUTHOR]

Text mining, sometimes alternately referred to as text analytics, refers to the process of extracting high-quality knowledge from the analysis of textual data. Text mining has wide variety of applications in areas such as biomedical science, news analysis, and homeland security. In this paper, we describe an approach and some relatively small-scale experiments which apply text mining to neuroscience research literature to find novel associations among a diverse set of entities. Neuroscience is a discipline which encompasses an exceptionally wide range of experimental approaches and rapidly growing interest. This combination results in an overwhelmingly large and often diffuse literature which makes a comprehensive synthesis difficult. Understanding the relations or associations among the entities appearing in the literature not only improves the researchers current understanding of recent advances in their field, but also provides an important computational tool to formulate novel hypotheses and thereby assist in scientific discoveries. We describe a methodology to automatically mine the literature and form novel associations through direct analysis of published texts. The method first retrieves a set of documents from databases such as PubMed using a set of relevant domain terms. In the current study these terms yielded a set of documents ranging from 160,909 to 367,214 documents. Each document is then represented in a numerical vector form from which an Association Graph is computed which represents relationships between all pairs of domain terms, based on co-occurrence. Association graphs can then be subjected to various graph theoretic algorithms such as transitive closure and cycle (circuit) detection to derive additional information, and can also be visually presented to a human researcher for understanding. In this paper, we present three relatively small-scale problem-specific case studies to demonstrate that such an approach is very successful in replicating a neuroscience expert's mental model of object-object associations entirely by means of text mining. These preliminary results provide the confidence that this type of text mining based research approach provides an extremely powerful tool to better understand the literature and drive novel discovery for the neuroscience community. [ABSTRACT FROM AUTHOR]

On 2011-10-01, WSA-Enlil became the first operational numerical space weather prediction model to be used by the United States National Weather Service. One of the primary purposes of this heliospheric model is to provide advance warning of geomagnetic storms from Earth-directed CMEs; such a forecast requires an accurate characterization of a CME. Almost a decade of published literature had suggested that the single viewpoint, flat-fronted cone model of Xie et al. [1] could supply the quantitative and consistent characterization of CMEs necessary for operational space weather forecasting. However, early in the transition-to-operations process, it became clear that this basic cone model is fundamentally flawed as an operational forecasting tool. We will describe the problems associated with this model, focusing in particular on the problems that arise because an unrealistic model shape is used to describe CME morphology. One of the consequences of the shape mismatch is that the flat-fronted cone model underestimates the speed of a CME. [ABSTRACT FROM AUTHOR]

We develop efficient mesh free methods to solve European and American option pricing problems. Using radial basis functions, these methods leads to systems of difference equations which are solved by using a [phonetic_theta]-method. Numerical results describing the payoff functions and option values are also presents. [ABSTRACT FROM AUTHOR]

Advances in parallel processing in recent years are helping to improve the cost of numerical simulation. Breakthroughs in Graphical Processing Unit (GPU) based computation now offer the prospect of further drastic improvements. The introduction of 'compute unified device architecture' (CUDA) by NVIDIA (the global technology company based in Santa Clara, California, USA) has made programming GPUs for general purpose computing accessible to the average programmer. Here we use CUDA to develop parallel finite difference schemes as applicable to two problems of interest to NDE community, namely heat diffusion and elastic wave propagation. The implementations are for two-dimensions. Performance improvement of the GPU implementation against serial CPU implementation is then discussed. [ABSTRACT FROM AUTHOR]