In the European fusion roadmap, reliable power handling has been defined as one of the most criticalchallenges for realizing a commercially viable fusion power. In this context, the divertor is the key in-vessel component, as it is responsible for power exhaust and impurity removal for which divertor targetis subjected to very high heat flux loads. To this end, an integrated R&D project was launched in theEUROfusion Consortium in order to deliver a holistic conceptual design solution together with the coretechnologies for the entire divertor system of a DEMO reactor. The work package ‘Divertor’ consistsof two project areas: ‘Cassette design and integration’ and ‘Target development’. The essential missionof the project is to develop and verify advanced design concepts and the required technologies for adivertor system being capable of meeting the physical and system requirements defined for the next-generation European DEMO reactor. In this contribution, a brief overview is presented of the works fromthe first project year (2014). Focus is put on the loads specification, design boundary conditions, materialsrequirements, design approaches, and R&D strategy. Initial ideas and first estimates are presented

Abstract. In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the masses appearance is the main obstacle of building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; on the contrary, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first SVM classifier. The detection task is here considered as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.

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