Coupling JOREK and STARWALL for Non-linear Resistive-wall Simulations

The implementation of a resistive-wall extension to the non-linear MHD-code JOREK via a coupling to the vacuum-field code STARWALL is presented along with first applications and benchmark results. Also, non-linear saturation in the presence of a resistive wall is demonstrated. After completion of the ongoing verification process, this code extension will allow to perform non-linear simulations of MHD instabilities in the presence of three-dimensional resistive walls with holes for limited and X-point plasmas.Comment: Contribution for "Theory Of Fusion Plasmas, Joint Varenna - Lausanne International Workshop, Villa Monastero, Varenna, Italy (27.-31.8.2012)", accepted for publication in Journal of Physics Conference Serie

Cost-benefit analysis of abatement measures for nutrient emission from agriculture

In intensive animal husbandry areas surface water N and P concentrations often remain too high. The Water Framework Directive calls for additional nutrient emission abatement measures. Therefore, costs and benefits for possible agricultural measures in Flanders were first analysed in terms of soil balance surplus. Finally, abatement measures for agriculture, households and industry were set off against each other and ranked according to their cost-efficiency by the Environmental Costing Model. Increased dairy cattle efficiency, winter cover crops and increased pig feed efficiency turn out very cost efficient. Other agricultural measures are less cost efficient than for instance collective treatment for households and industry.nitrogen and phosphorus abatement, surface water, cost efficiency, Environmental Economics and Policy, Livestock Production/Industries,

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity