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
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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!
Latest Magazine Issues
May 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
June 2024
Nuclear Technology
Fusion Science and Technology
Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Arvind Sundaram, Hany Abdel-Khalik
Nuclear Science and Engineering | Volume 195 | Number 9 | September 2021 | Pages 977-989
Technical Paper | doi.org/10.1080/00295639.2021.1897731
Articles are hosted by Taylor and Francis Online.
In the face of advanced persistent threat actors, existing information technology (IT) defenses as well as some of the more recent operational technology (OT) defenses have been shown to become increasingly vulnerable, especially for critical infrastructure systems with well-established technical know-how. For example, data deception attacks have demonstrated their ability to mislead human operators and statistical detectors alike for a wide range of systems, e.g., electric grid, chemical and nuclear plants, etc. To combat this challenge, our previous work has introduced a new modeling paradigm, called covert cognizance (C2), serving as an active OT defense that allows a critical system to build self-awareness about its past performance, with the awareness parameters covertly embedded into its own state function, precluding the need for additional courier variables. Further, the embedding process employs one-time-pad randomization to blind artificial intelligence (AI)–based learning and ensures zero impact on system state. This paper employs one of the competing AI-based learning algorithms, i.e., the long short-term memory neural network in a supervised learning setting, to validate the C2 embedding process. This is achieved by presenting the network with many labeled samples, distinguishing the original state function from the one containing the embedded self-awareness parameters. A nuclear reactor model is employed for demonstration.