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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.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Natalie Baughan, Alexis Poitrasson-Rivière, Jonathan B. Moody, Benjamin C. Lee, Edward P. Ficaro
Nuclear Technology | Volume 206 | Number 7 | July 2020 | Pages 977-983
Technical Paper – Special section on the 2019 ANS Student Conference | doi.org/10.1080/00295450.2019.1708142
Articles are hosted by Taylor and Francis Online.
Traditional patient selection criteria for cardiac resynchronization therapy (CRT) could be improved to predict patient response to CRT. Assessment of cardiac dyssynchrony using gated myocardial perfusion single-photon emission computed tomography (SPECT) or positron emission tomography (PET) in quantification software programs can be a reliable alternative. Quantitative parameters that describe the left ventricular phase analysis histogram such as phase standard deviation, bandwidth, and entropy aid in physician decision making. Entropy has been found in previous studies to be an effective parameter in identifying patients with left ventricular cardiac dyssynchrony. In this paper, we describe the characteristics of the entropy parameter with respect to other parameters such as phase standard deviation and histogram bandwidth. The implementation and testing of the entropy metric in the Corridor4DM (4DM) software package is also described. Algorithm testing and characterization were performed using computer-generated pseudorandom normal distributions. Implementation testing in 4DM was performed with two groups of patient data: patients with a left bundle branch block (LBBB) and patients with low pretest likelihood (LLk) for coronary artery disease. Entropy was found to monotonically increase in a semilogarithmic fashion with respect to phase standard deviation. For pseudorandom normal distributions with a constant standard deviation, the number of histogram bins used in calculating the entropy metric varied the metric by up to 61.3%; on average, an increase in histogram bins from 60 to 100 increased the mean entropy value by 11.0%. Implementation testing in 4DM showed agreement with the preliminary algorithm results and found a clear separation in entropy values between LLk and LBBB patients.