Results 1 to 10 of about 138,109 (218)

Multiplex Analysis Platform for Endocrine Disruption Prediction Using Zebrafish [PDF]

open access: yesInternational Journal of Molecular Sciences, 2019
Maria Rubio-Brotons   +2 more
exaly   +2 more sources

Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

open access: yesNuclear Fusion, 2023
The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors ...
J.X. Zhu   +6 more
doaj   +1 more source

Big data analytics and anomaly prediction in the cold chain to supply chain resilience [PDF]

open access: yesFME Transactions, 2021
The purpose of the research was to develop a prediction method to prevent disruption related to temperature anomaly in the cold chain supply. The analysed data covers the period of the entire working cycle of the thermal container.
Lorenc Augustyn   +2 more
doaj   +1 more source

Performance Comparison of Machine Learning Disruption Predictors at JET

open access: yesApplied Sciences, 2023
Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant ...
Enrico Aymerich   +8 more
doaj   +1 more source

IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction

open access: yesNuclear Fusion, 2023
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us
C. Shen   +12 more
doaj   +1 more source

Disruption prediction on EAST with different wall conditions based on a multi-scale deep hybrid neural network

open access: yesNuclear 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
doaj   +1 more source

Predicting Resilience of Interdependent Urban Infrastructure Systems

open access: yesIEEE 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
doaj   +1 more source

Initial analytical theory of plasma disruption and experimental evidence

open access: yesScientific 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
doaj   +1 more source

Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data

open access: yesPlasma, 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

Disruption prediction for future tokamaks using parameter-based transfer learning

open access: yesCommunications 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

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