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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.
M. Marseguerra, E. Zio
Nuclear Science and Engineering | Volume 117 | Number 3 | July 1994 | Pages 194-200
Technical Paper | doi.org/10.13182/NSE94-A28534
Articles are hosted by Taylor and Francis Online.
The Boltzmann machine is a general-purpose artificial neural network that can be used as an associative memory as well as a mapping tool. The usual information entropy is introduced, and a network energy function is suitably defined. The network’s training procedure is based on the simulated annealing during which a combination of energy minimization and entropy maximization is achieved.,An application in the nuclear reactor field is presented in which the Boltzmann input-output machine is used to detect and diagnose a pipe break in a simulated auxiliary feedwater system feeding two coupled steam generators. The break may occur on either the hot or the cold leg of any of the two steam generators. The binary input data to the network encode only the trends of the thermohydraulic signals so that the network is actually a polarity device. The results indicate that the trained neural network is actually capable of performing its task. The method appears to be robust enough so that it may also be applied with success in the presence of substantial amounts of noise that cause the network to be fed with wrong signals.