Spectrum prediction in X-ray fluorescence analysis using Bayesian estimation

Matsuyama, Tsugufumi*; Nakae, Masanori*; Murakami, Masashi; Yoshida, Yukihiko; Machida, Masahiko; Tsuji, Koichi*

Spectrochimica Acta, Part B, 199, p.106593_1 - 106593_6, 2023/01

https://doi.org/10.1016/j.sab.2022.106593

Study on Assessment of Cancer Risk due to Radiation using the State Space Model; Merge cancer model with epidemiology data

Shimada, Kazumasa

no journal, , 

Cancer risk assessment of radiation is used a dose response based on epidemiology data. To develop more scientific and reasonable risk assessment, it is important to introduce cancer model considering cell mutation to risk assessment. However, it is not easy to gain cell mutation parameters by experiments. In this research, I used the State Space Model to combine the epidemiology data of atomic bomb survivor and cancer model considering cell mutation to calculate model parameters each radiation doses and ages.

Investigation on distribution of radioactive substances in Fukushima, 6; Integration of air dose rate data measured by different observation methods

Takemiya, Hiroshi; Nemoto, Miho*; Hayashi, Hiroko*; Seki, Akiyuki; Saito, Kimiaki

no journal, , 

no abstracts in English

Development of a source term estimation method based on Bayesian inference with multi-scale atmospheric dispersion simulations

Terada, Hiroaki; Nagai, Haruyasu; Tsuzuki, Katsunori; Kadowaki, Masanao; Furuno, Akiko

no journal, , 

We have developed a method for estimation of source term of radioactive materials released into the atmosphere in nuclear accidents by comprehensive comparison of atmospheric dispersion simulations with different spatial scales and various types of environmental measurements. By applying this method to the Fukushima Daiichi Nuclear Power Station accident, the release rate estimated by the previous study was optimized and the effectiveness was examined.

Characterisation of radioactive boundary wastes; A Bayesian solution

Hiller, P.*; Pyke, C.*; Koma, Yoshikazu; Oki, Keiichi

no journal, , 

Bayesian statistics is complementary to the DQO approach due to their underlying iterative principles. For waste characterisation this provides an opportunity for greater information for decision makers when analytical data approaches a waste boundary. The Bayesian t-test is analogous to the current statistical approach advised by CL:AIRE with the benefit of more completely using Prior information and allowing for the introduction of adaptive sampling strategies based on developing knowledge. This iterative approach provides a more fully underpinned justification for sampling numbers and provides increased flexibility for the DQO team than the traditional statistical approach. Developed in a UK regulatory context and translated to fallen trees from the Fukushima Daiichi NPS, this paper demonstrates potential benefits of this methods for a waste nearing the characterisation boundary and shows how the approach can be used to support decision making on waste disposal in a global context.