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The JRC‘s Centre for Advanced Studies (CAS) was created in 2016 to help improve and bridge the interface between science and policy in order to enhance the JRC‘s capacity to better inform and influence the regulatory frameworks needed to address the new and emerging societal challenges confronting the EU and our societies as a whole. By creating the conditions necessary for innovative and interdisciplinary research, as well as offering a creative and generative space in which ideas and knowledge in emerging thematic fields across different scientific and technological disciplines can thrive and flourish, CAS has become an incubator for formal inquiry, stimulating ideas and activities and providing the JRC with new insights, data projections and solutions for the increasingly complex medium and longterm challenges facing the EU, especially in the fields of demography, big data and digital transformation. Through the performance of advanced, cutting edge research, ranging from applied research to topics of a more academic character, all within a stimulating trans- and interdisciplinary environment, CAS allows external researchers and scientists to work together with the JRC to explore and exchange new ideas and knowledge on scientific research in emerging fields of strategic societal importance, which might otherwise fall outside the policy support activities undertaken by the JRC on behalf of the European Commission.

The JRC's Centre for Advanced Studies (CAS) was created in 2016 to help improve and bridge the interface between science and policy in order to enhance the JRC's capacity to better inform and influence the regulatory frameworks needed to address the new and emerging societal challenges confronting the EU and our societies as a whole. By creating the conditions necessary for innovative and interdisciplinary research, as well as offering a creative and generative space in which ideas and knowledge in emerging thematic fields across different scientific and technological disciplines can thrive and flourish, CAS has become an incubator for formal inquiry, stimulating ideas and activities and providing the JRC with new insights, data projections and solutions for the increasingly complex medium and longterm challenges facing the EU, especially in the fields of demography, big data and digital transformation. Through the performance of advanced, cutting edge research, ranging from applied research to topics of a more academic character, all within a stimulating trans- and interdisciplinary environment, CAS allows external researchers and scientists to work together with the JRC to explore and exchange new ideas and knowledge on scientific research in emerging fields of strategic societal importance, which might otherwise fall outside the policy support activities undertaken by the JRC on behalf of the European Commission. Projects are typically led by a senior scientist with an established reputation in the research area and have a limited duration of a maximum of three years, after which they may be integrated into the JRC‘s core research activities.

Impaired emotional abilities (higher scores of alexithymia and lower levels of emotional awareness) were found in patients with skin-restricted lupus, warranting examination of the relationship between these abilities and the evolution of skin-restricted lupus, using longitudinal data. A total of 75 consecutive outpatients with skin-restricted lupus were recruited and assessed by a dermatologist and a psychiatrist every 6 months over a period of 2.5 years. Alexithymia and emotional awareness were evaluated with the French versions of the Toronto Alexithymia Scale (TAS-20) and the Levels of Emotional Awareness Scale (LEAS). During follow-up, good stability of the LEAS scores was observed, whereas TAS-20 scores varied; those variations were positively associated both with lupus duration and current psychiatric and personality disorders, but not with lupus remission. Such findings regarding 2 complementary aspects of emotional functioning are of direct interest for the management of patients with skin-restricted lupus.

Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.

The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain. © Springer Nature Switzerland AG 2019.

The High Pressure Diecasting process is very attractive tothe casting buyer, offering fast production rates coupledto optimised production costs. As castings becomemore complex and wall sections become heavier so theadvantage of the very rapid cooling rate is reduced andcasting defects more commonly seen in gravity diecastingand sand casting are now being experienced. The needto heat treat and weld castings also means that castingquality levels must be improved.A fresh approach to the treatment of the Aluminium meltis therefore required and this paper describes the designof a test piece and a test programme to examine theinfluence that metal treatment can have on casting quality.Simulation is used in the design of the test piece andvarious non-destructive tests are used to measure thequality of the Aluminium melt, prior to casting. FinallyX-Ray, SEM and image analysis and mechanical testingare used to assess the quality of the resulting castings.

Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several metaheuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search metaheuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times.