Consequences of selecting different subsets of meteorological data to utilize in deterministic safety analysis.

Atmospheric dispersion calculation of radiological releases can be done for different purposes such as deterministic or probabilistic safety analysis, environmental impact assessment, emergency preparedness and response. The characteristics of the weather conditions used in such assessments have a significant effect on the results, thus it is vital to select appropriate meteorological data for the calculation. In this paper, we conduct a study on deterministic safety analysis of radiological releases and investigate the effects of using different subsets of a meteorological database for such assessments. We demonstrate that conducting deterministic dose assessment with a large site specific dataset of meteorological measurements and the use of a dose percentile is more beneficial than using one set of meteorological parameters. This is because variations in the meteorological condition have considerable effect on the dose results when using one set of meteorological parameters (e.g. worst case scenario) and less when a large meteorological database is used. We show that there can be a significant difference in the maximum dose computed with a large (at least annual with hourly resolution) meteorological database when there is a lack of data points or conversion of the parameters is needed, thus the 100th dose percentile is not optimal for verification of safety criteria fulfillment. It is better to use a relatively high percentile (e.g. from 80th to 99th), partly because it behaves more robustly and also because the use of the maximum dose (100th percentile) would be overly conservative. In case of meteorological data not being available for a sufficient temporal domain (e.g. data available for only one year or less), a multiplication factor - determined based on conservative assumptions and extensive studies on the possible spread of meteorological conditions and their effect at a given location - can be used for the comparison of a selected dose percentile with the safety limit. A 5-years long meteorological database provided by a meteorological measurement system was used in this study as an example to demonstrate and calibrate the methodology on a real database. The methods presented in this work are universal, they can be used in deterministic safety analysis of other nuclear facilities, and the results can facilitate the development of optimal meteorological databases.

Understanding and coping with extremism in an online collaborative environment: A data-driven modeling.

The Internet has provided us with great opportunities for large scale collaborative public good projects. Wikipedia is a predominant example of such projects where conflicts emerge and get resolved through bottom-up mechanisms leading to the emergence of the largest encyclopedia in human history. Disaccord arises whenever editors with different opinions try to produce an article reflecting a consensual view. The debates are mainly heated by editors with extreme views. Using a model of common value production, we show that the consensus can only be reached if groups with extreme views can actively take part in the discussion and if their views are also represented in the common outcome, at least temporarily. We show that banning problematic editors mostly hinders the consensus as it delays discussion and thus the whole consensus building process. To validate the model, relevant quantities are measured both in simulations and Wikipedia, which show satisfactory agreement. We also consider the role of direct communication between editors both in the model and in Wikipedia data (by analyzing the Wikipedia talk pages). While the model suggests that in certain conditions there is an optimal rate of "talking" vs "editing", it correctly predicts that in the current settings of Wikipedia, more activity in talk pages is associated with more controversy.