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.
Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Oklo completes end-to-end demonstration of advanced fuel recycling
Oklo Inc. has announced that it has completed the first end-to-end demonstration of its advanced fuel recycling process as part of an ongoing $5 million project in collaboration with Argonne and Idaho National Laboratories. Oklo’s goal: scaling up its fuel recycling capabilities to deploy a commercial-scale recycling facility that would increase advanced reactor fuel supplies and enhance fuel cost effectiveness for its planned sodium fast reactors.
Tim H. J. J. van der Hagen
Nuclear Technology | Volume 106 | Number 1 | April 1994 | Pages 135-138
Technical Note | Reactor Control | doi.org/10.13182/NT94-A34955
Articles are hosted by Taylor and Francis Online.
The processing elements of an artificial neural network apply a transfer function to the weighted sum of their inputs. A very commonly used transfer function is the sigmoid. It is shown that the recently published idea of changing the socalled scaling parameter of this function during training of the network is in effect identical to two well-known techniques in function fitting: shaking the parameters to be fitted and adjusting the learning parameter. The effect of modifying the scaling parameter is understood and explained.