Results 11 to 20 of about 407,221 (296)
Nuclear Fusion, 2023 Plasma disruption is a very dangerous event for future tokamaks and fusion reactors. Therefore, predicting disruption is crucial for ensuring the safety and performance of reactors.
B.H. Guo +10 more
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Predicting Resilience of Interdependent Urban Infrastructure Systems
IEEE Access, 2022 Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions.
Beatrice Cassottana +5 more
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Prediction errors disrupt hippocampal representations and update episodic memories [PDF]
Proceedings of the National Academy of Sciences, 2020 Significance Our brains draw on memories to predict the future; when our predictions are incorrect, we must update our memories to improve future predictions. Past studies have demonstrated that the hippocampus signals prediction error (i.e., surprise) but have not linked this neural signal to memory updating.
Alyssa H. Sinclair +4 more
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Initial analytical theory of plasma disruption and experimental evidence
Scientific Reports, 2023 It is a great physical challenge to achieve controlled nuclear fusion in magnetic confinement tokamak and solve energy shortage problem for decades. In tokamak plasma, large-scale plasma instability called disruption will halt power production of reactor
Huibin Qiu +13 more
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Spectroscopic Constraints on the Form of the Stellar Cluster Mass Function [PDF]
, 2012 This contribution addresses the question of whether the initial cluster mass function (ICMF) has a fundamental limit (or truncation) at high masses. The shape of the ICMF at high masses can be studied using the most massive young (
Adamo +47 more
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Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data
Plasma, 2023 Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared.
Joost Croonen +2 more
doaj +1 more source
Modeling the Impact of Baryons on Subhalo Populations with Machine Learning [PDF]
, 2018 We identify subhalos in dark matter-only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)-mass host halos from the Latte suite ...
Garrison-Kimmel, Shea +4 more
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Prediction of the endocrine disruption profile of pesticides [PDF]
SAR and QSAR in Environmental Research, 2015 Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to ...
J, Devillers, E, Bro, F, Millot
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Комплексный подход к управлению адаптацией иностранных студентов [PDF]
, 2009 This article describes an integrated approach to the assessment, prediction and management of adaptation of foreign students studying at the university.
Breuer, Ruth +3 more
core +1 more source
Disruption prediction for future tokamaks using parameter-based transfer learning
Communications Physics, 2023 Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device.
Wei Zheng +14 more
doaj +1 more source