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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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The RAIN scale: A good intention that falls short
Radiation protection specialists agree that clear communication of radiation risks remains a vexing challenge that cannot be solved solely by finding new ways to convey technical information.
Earlier this year, an article in Nuclear News described a new radiation risk communication tool, known as the Radiation Index, or, RAIN (“Let it RAIN: A new approach to radiation communication,” NN, Jan. 2025, p. 36). The authors of the article created the RAIN scale to improve radiation risk communication to the general public who are not well-versed in important aspects of radiation exposures, including radiation dose quantities, units, and values; associated health consequences; and the benefits derived from radiation exposures.
J. Wesley Hines, Brandon Rasmussen
Nuclear Technology | Volume 151 | Number 3 | September 2005 | Pages 281-288
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT05-A3650
Articles are hosted by Taylor and Francis Online.
Empirical modeling techniques have been applied to online process monitoring to detect equipment and instrumentation degradations. However, few applications provide prediction uncertainty estimates, which can provide a measure of confidence in decisions. This paper presents the development of analytical prediction interval estimation methods for three common nonlinear empirical modeling strategies: artificial neural networks, neural network partial least squares, and local polynomial regression. The techniques are applied to nuclear power plant operational data for sensor calibration monitoring, and the prediction intervals are verified via bootstrap simulation studies.