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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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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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.
Pola Lydia Lagari, Styliani Pantopoulou, Miltos Alamaniotis, Lefteri H. Tsoukalas
Nuclear Technology | Volume 207 | Number 8 | August 2021 | Pages 1270-1279
Technical Paper | doi.org/10.1080/00295450.2020.1816743
Articles are hosted by Taylor and Francis Online.
Since radionuclides have unique characteristic gamma-ray spectra, usually maintained as a set of (energy, counts/energy) ordered pairs, an explicit functional representation would be indisputably useful. In this paper, the Gamma Detector Response and Analysis Software has been used to simulate the gamma-ray spectra as it would be collected by an NaI detector for a set of 70 radionuclides. Gaussian radial basis function (RBF) networks that offer simple, closed-form expressions are then trained to represent each of these spectra. Hence, a library consisting of 70 RBF networks for the corresponding radionuclides has been built. The presence of these library-contained radionuclides in a given gamma-ray spectrum of an unknown source is identified by an algorithm that employs a linear combination of the library spectra to approximate the unknown spectrum. The combination coefficients are then determined by minimizing the squared deviation error function under convexity constraints.