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Nuclear Science and Engineering
Fusion Science and Technology
New U.K. report: Young people want to know more about nuclear
Almost two-thirds of 14- to 18-year-olds in the United Kingdom would consider a career in nuclear if they knew more about it, according to a new report, Nuclear Energy: Young People’s Views on Nuclear Energy and Careers in the Nuclear Sector, from the British Science Association (BSA).
About the report: The report was conducted as part of the BSA’s Future Forum program and was funded by Urenco, an international supplier of uranium enrichment services and fuel cycle products, as part of its commitment to education and skills development.
The report centered around an initial survey of 1,000 14- to 18-year-olds in England, Scotland, and Wales, with two follow-up workshops that were attended by 39 young people, providing the opportunity for more detailed responses.
Geert Verdoolaege, Giorgos Karagounis, Andrea Murari, Jesús Vega, Guido Van Oost, JET-EFDA Contributors
Fusion Science and Technology | Volume 62 | Number 2 | October 2012 | Pages 356-365
Selected Paper from the Seventh Fusion Data Validation Workshop 2012 (Part 1) | doi.org/10.13182/FST12-A14627
Articles are hosted by Taylor and Francis Online.
Pattern recognition is becoming an increasingly important tool for making inferences from the massive amounts of data produced in fusion experiments. In this work, we present an integrated framework for (real-time) pattern recognition for fusion data. The main starting point is the inherent probabilistic nature of measurements of plasma quantities. Since pattern recognition is essentially based on geometric concepts such as the notion of distance, this necessitates a geometric formalism for probability distributions. To this end, we apply information geometry for calculating geodesic distances on probabilistic manifolds. This provides a natural and theoretically motivated similarity measure between data points for use in pattern recognition techniques. We apply this formalism to classification for the automated identification of plasma confinement regimes in an international database and the prediction of plasma disruptions at JET. We show the distinct advantage in terms of classification performance that is obtained by considering the measurement uncertainty and its geometry. We hence advocate the essential role played by measurement uncertainty for data interpretation in fusion experiments. Finally, we discuss future applications such as the establishment of scaling laws.