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
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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!
Latest Magazine Issues
Jul 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
Latest News
ARPA-E announces $40 million to develop transmutation technologies for UNF
The Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) announced $40 million in funding to develop cutting-edge technologies to enable the transmutation of used nuclear fuel into less-radioactive substances. According to ARPA-E, the new initiative addresses one of the agency’s core goals as outlined by Congress: to provide transformative solutions to improve the management, cleanup, and disposal of radioactive waste and spent nuclear fuel.
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.