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This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2021)
February 9–11, 2021
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Former NRC chairs issue vaccine timeline recommendation to CDC
Five former chairmen of the U.S. Nuclear Regulatory Commission—Stephen Burns, Allison Macfarlane, Nils Diaz, Richard Meserve, and Dale Klein—signed a letter to José Romero, Arkansas health secretary and chair of the Centers for Disease Control and Prevention (CDC) immunization advisory committee, requesting that the advisory committee update its recommendation for COVID-19 vaccine allocation guidance for the energy workforce (including nuclear energy workers).
Currently, the CDC has four phases for the COVID-19 vaccine rollout. Those phases are numbered:
Keisuke Fujii, Ichihiro Yamada, Masahiro Hasuo
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 57-64
Technical Paper | dx.doi.org/10.1080/15361055.2017.1396179
Articles are hosted by Taylor and Francis Online.
Manual uncertainty propagation from possible noise sources has often been adopted for data analysis in many fields of science, including the analysis of Thomson scattering measurement data in fusion plasma science. However, it is not possible to perfectly model all the noise sources and their distributions. In this work, we propose a more data-driven approach for the noise modeling of multichannel measurement systems. We directly modeled the noise distribution by tractable density distributions parameterized with neural networks and trained their weights from a vast amount of measurement data. We demonstrated an application of this method in Thomson scattering measurement data for the Large Helical Device project. This method enabled us to make a realistic inference even without sufficient prior knowledge about the noise.