ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Winter Conference & Expo
November 15–18, 2026
Phoenix, AZ|Arizona Grand Resort & Spa
Latest Magazine Issues
Aug 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
October 2026
Nuclear Technology
September 2026
Fusion Science and Technology
August 2026
Latest News
What’s reshaping nuclear licensing and compliance today?
Mark Reidmeyer
It is the convergence of urgency, innovation, and modernization that is reshaping nuclear licensing and compliance today.
For decades, nuclear licensing operated in a relatively stable environment built around large light water reactors, predictable review cycles, and well-established regulatory pathways. Today, that model is evolving rapidly. Advanced reactors, AI-enabled tools, digital engineering platforms, grid reliability concerns, and aggressive decarbonization goals are all pushing the industry—and regulators—to move faster and think differently.
J. R. Wolberg, G. Hetsroni
Nuclear Technology | Volume 4 | Number 3 | March 1968 | Pages 187-189
Technical Paper and Note | doi.org/10.13182/NT68-A26384
Articles are hosted by Taylor and Francis Online.
Prediction analysis is applied to the design of experiments for measuring the half-life of a radioactive species. The half-life is assumed to be determined by fitting the exponential-plus-background function to the data points. Results can predict the experimental accuracy to which the half-life will be determined in a proposed experiment. The predicted accuracy is a function of the number of data points, the range of time values, the initial count rate, the amplitude-to-background ratio, and the uncertainties (in the time value as well as in the counts per channel) associated with each data point.