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2026 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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My Story: John L. Swanson—ANS member since 1978
. . . and in 2019, on his 90th birthday.
Swanson in 1951, the year of his college graduation . . .
My pre-college years were spent in a rural suburb of Tacoma, Wash. In 1947, I enrolled in Reed College, a small liberal arts school in Portland, Ore.; I majored in chemistry and graduated in 1951. While at Reed, I met and married a young lady with whom I would raise 3 children and spend the next 68 years of my life—almost all of them in Richland, Wash., where I still live.
I was fortunate to have a job each of my “college summers” that provided enough money to cover my college costs for the next year; I don’t think that is possible these days. My job was in the kitchen/dining hall of a salmon cannery in Alaska. Room and board were provided and the cannery was in an isolated location, so I could save almost every dollar of my salary.
Fuga Nishioka, Tomohiro Endo, Akio Yamamoto, Masao Yamanaka, Cheol Ho Pyeon
Nuclear Science and Engineering | Volume 196 | Number 2 | February 2022 | Pages 133-143
Technical Paper | doi.org/10.1080/00295639.2021.1968225
Articles are hosted by Taylor and Francis Online.
To robustly estimate the fundamental mode component of prompt neutron decay constant α in a subcritical system, dynamic mode decomposition (DMD) is applied to time-series data obtained by the pulsed-neutron source (PNS) and Rossi-α methods. For the statistical uncertainty quantification of α by DMD, randomly sampled virtual data are used for the DMD procedure. The applicability of DMD is demonstrated by analyzing the experimental results by the PNS and Rossi-α methods, which are performed at the Kyoto University Critical Assembly (KUCA). When applying the DMD to the PNS and Rossi-α experimental data, a constant signal was added to the experimental data to remove the background constant component. The application results indicate that DMD enables one to robustly estimate the fundamental mode component of α in the PNS and Rossi-α methods.