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The atmospheric dispersion of NOx and PM10 was simulated with a second generation Gaussian model over a medium-size south-European city. Microscopic traffic models calibrated with GPS data were used to derive typical driving cycles for each road link, while instantaneous emissions were estimated applying a combined Vehicle Specific Power/Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (VSP/EMEP) methodology. Site-specific background concentrations were estimated using time series analysis and a low-pass filter applied to local observations. Air quality modelling results are compared against measurements at two locations for a 1 week period. 78% of the results are within a factor of two of the observations for 1-h average concentrations, increasing to 94% for daily averages. Correlation significantly improves when background is added, with an average of 0.89 for the 24 h record. The results highlight the potential of detailed traffic and instantaneous exhaust emissions estimates, together with filtered urban background, to provide accurate input data to Gaussian models applied at the urban scale.

Abstract
The corner stone of a language development environment is an editor. For programming languages, several code editors are readily available to be integrated in Web applications. However, only few editors exist for diagrammatic languages. Eshu is an extensible diagram editor, embeddable in Web applications that require diagram interaction, such as modeling tools or e-learning environments. Eshu is a JavaScript library with an API that supports its integration with other components, including importing/exporting diagrams in JSON. Eshu was already integrated in a pedagogical environment with automated diagram assessment, configured for extended entityrelationship diagrams, that served as basis for an usability evaluation. © José Paulo Leal, Helder Correia, and José Carlos Paiva;licensed under Creative Commons License CC-BY.

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The United States, Brazil, and Argentina are responsible for 83% of world’s soybean production. Together, they respond to more than 80% of soybean grains and soybean meal exported and for more than 60% of soybean oil exportation. This paper studies the soybean trade of these three major exporters with the top ten commercial partners of each one in order to examine the main factors that influence this relationship. We follow a network analysis approach to evaluate the level of interdependence between exporters and importers. Our research studies the three main soybean products: grain, meal, and oil. The findings seem to indicate that countries prefer importing soybean grains to process inside their borders due to commodity prices and logistics costs.

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