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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.
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A day in the life of the nuclear community
The November issue of Nuclear News is focused on the individuals who make up our nuclear community.
We invited a small group of those individuals to tell us about their day-to-day work in some of the many occupations and applications of nuclear science and technology, and they responded generously. They were ready to tell us about the part they play, together with colleagues and team members, in supplying clean energy, advancing technology, protecting safety and health, and exploring fundamental science.
In these pages, we see a community that can celebrate both those workdays that record progress moving at a steady pace and the exceptional days when a goal is reached, a briefing is delivered, a contract goes through, a discovery is made, or an unforeseen challenge is overcome.
The Nuclear News staff hopes that you enjoy meeting these members of our community—or maybe get reacquainted with friends—through their words and photos.
Yunfei Zhao, Xiaoxu Diao, Jonathon Huang, Carol Smidts
Nuclear Technology | Volume 205 | Number 8 | August 2019 | Pages 1021-1034
Technical Paper – Special section on Big Data for Nuclear Power Plants | dx.doi.org/10.1080/00295450.2019.1580967
Articles are hosted by Taylor and Francis Online.
A large number of licensee event reports are available in the nuclear power generation sector. A comprehensive analysis of the reports will provide valuable insights for improving nuclear power plant operation and safety. However, the free-text format of the reports poses great challenges to the analysis of the tens of thousands of reports generated. To address this issue, we propose an automated method for the analysis based on natural language processing techniques. Specifically, the objective is to automatically extract the causal relationships from free-text reports. The proposed method relies on a set of keywords that indicates causal relationships and the rules associated with the keywords for identifying the causal relationships, both of which can be identified based on manual analysis of sampled reports and sentences. The rules are described using the parts of speech of the words in a sentence and the dependencies between these words. The keywords and the rules constitute a rule-based expert system, Causal Relationship Identification (CaRI). The proposed method is applied to the analysis of the abstract section of the reports from the U.S. Nuclear Regulatory Commission Licensee Event Report database. We identified 11 keywords and developed 184 rules. The developed system, CaRI, is tested and the result shows that 86% of the causal relationships in the test data can be captured automatically. Application of the proposed method is foreseen in a number of areas, for instance, in the analysis of performance-shaping factors and in reconstruction of the scenario in an event.