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1. Full Text Determining the prediction limits of models and classifiers with applications for disruption prediction in JET
by Murari, A and Peluso, E and Vega, J and Gelfusa, M and Lungaroni, M and Gaudio, P and Martínez, F. J and JET Contributors
Nuclear fusion, ISSN 0029-5515, 11/2016, Volume 57, Issue 1, p. 16024
conditional entropy | sawteeth | prediction factor | disruptions | predictability | ELMs | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology
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2. Full Text Disruption prediction using a full convolutional neural network on EAST
by Guo, B H and Shen, B and Chen, D L and Rea, C and Granetz, R S and Huang, Y and Zeng, L and Zhang, H and Qian, J P and Sun, Y W and Xiao, B J
Plasma physics and controlled fusion, ISSN 0741-3335, 12/2020, Volume 63, Issue 2
deep learning | plasma | convolutional neural network | disruption prediction | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology
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3. Full Text Multi-year prediction of estrogenicity in municipal wastewater effluents
by Arlos, Maricor J and Parker, Wayne J and Bicudo, José R and Law, Pam and Marjan, Patricija and Andrews, Susan A and Servos, Mark R
The Science of the total environment, ISSN 0048-9697, 01/2018, Volume 610-611, pp. 1103 - 1112
Endocrine disruption | Model | Water treatment | Wastewater | Estrogenicity | Effluent | Environmental Sciences & Ecology | Life Sciences & Biomedicine | Science & Technology | Environmental Sciences | Strategic planning (Business) | Sewage | Purification | Analysis | Estrogen | Green technology | Index Medicus
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4. Full Text In Silico Predictions of Endocrine Disruptors Properties
by Schneider, Melanie and Pons, Jean-Luc and Labesse, Gilles and Bourguet, William
Endocrinology (Philadelphia), ISSN 0013-7227, 11/2019, Volume 160, Issue 11, pp. 2709 - 2716
Life Sciences & Biomedicine | Endocrinology & Metabolism | Science & Technology | Animals | Endocrine Disruptors - chemistry | Computer Simulation | Humans | Ligands | Quantitative Structure-Activity Relationship | Endocrine Disruptors - toxicity | Endocrine disruptors | Physiological aspects | Environmental aspects | Analysis | Structure-activity relationships (Biochemistry) | Environmental assessment | Biodegradation | Toxicity | Endocrine system | Topology | Hydrophobicity | Chemical compounds | Environmental risk | Biological activity | Chemicals | Molecular weight | Organic chemistry | Properties (attributes) | Risk assessment | Biocompatibility | In vivo methods and tests | Disruption | Cellular structure | Crystal structure | Structural analysis | Index Medicus | Abridged Index Medicus
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5. Full Text Adaptive learning for disruption prediction in non-stationary conditions
by Murari, A and Lungaroni, M and Gelfusa, M and Peluso, E and Vega, J and JET Contributors
Nuclear fusion, ISSN 0029-5515, 7/2019, Volume 59, Issue 8, p. 86037
de-learning | machine learning predictors | obsolescence | ensembles of classifiers | disruptions | adaptive training | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology
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6. Full Text Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction
by Zorn, Kimberley M and Foil, Daniel H and Lane, Thomas R and Russo, Daniel P and Hillwalker, Wendy and Feifarek, David J and Jones, Frank and Klaren, William D and Brinkman, Ashley M and Ekins, Sean
Environmental science & technology, ISSN 0013-936X, 10/2020, Volume 54, Issue 19, pp. 12202 - 12213
Contaminants in Aquatic and Terrestrial Environments | Engineering | Engineering, Environmental | Life Sciences & Biomedicine | Technology | Environmental Sciences & Ecology | Science & Technology | Environmental Sciences | Endocrine disruptors | Molecular structure | Estrogens | Estrogen receptors | Training | Learning | Receptors | Biomedical materials | Xenoestrogens | Mathematical analysis | Chemical activity | Mathematical models | Test sets | Biological activity | Chemicals | Learning algorithms | Algorithms | Data sets | Computer applications | Machine learning | Predictions | Agonists | In vivo methods and tests | Disruption | In vitro methods and tests | Bayesian analysis | Estrogenic activity | Environmental protection | Index Medicus
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7. Full Text Multiplex Analysis Platform for Endocrine Disruption Prediction Using Zebrafish
by Jarque, Sergio and Ibarra, Jone and Rubio-Brotons, Maria and Garcia-Fernandez, Jessica and Terriente, Javier
International journal of molecular sciences, ISSN 1422-0067, 04/2019, Volume 20, Issue 7, p. 1739
Biochemistry & Molecular Biology | Physical Sciences | Chemistry | Life Sciences & Biomedicine | Chemistry, Multidisciplinary | Science & Technology | Endocrine disruptors | Deactivation | Transcription | Toxicity | Endocrine system | Genes | Iodide peroxidase | Zebrafish | Thyroid gland | Gene expression | Embryos | Experiments | Design | Androgens | Thyroglobulin | Thyroxine | Biomarkers | Ligands | Peroxidase | Triiodothyronine | Disruption | Design of experiments | Thyroid | transcriptomics | estrogens | zebrafish | androgens | thyroid | endocrine disruption
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8. Full Text A rat α-fetoprotein binding activity prediction model to facilitate assessment of the endocrine disruption potential of environmental chemicals
by Hong, Huixiao and Shen, Jie and Ng, Hui Wen and Sakkiah, Sugunadevi and Ye, Hao and Ge, Weigong and Gong, Ping and Xiao, Wenming and Tong, Weida and Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)
International journal of environmental research and public health, ISSN 1661-7827, 03/2016, Volume 13, Issue 4, pp. 372 - 372
Binding | Assessment | Prediction | Model | Endocrine | α-fetoprotein | Disruption | Public, Environmental & Occupational Health | Environmental Sciences & Ecology | Life Sciences & Biomedicine | Science & Technology | Environmental Sciences | Animals | Models, Biological | Computer Simulation | Rats | Environmental Exposure | Environmental Pollutants - toxicity | Endocrine Disruptors - toxicity | alpha-Fetoproteins - metabolism | Index Medicus | assessment | MATHEMATICS AND COMPUTING | BASIC BIOLOGICAL SCIENCES | disruption | prediction | binding | model | endocrine
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9. Full Text Improvements in disruption prediction at ASDEX Upgrade
by Aledda, R and Cannas, B and Fanni, A and Pau, A and Sias, G and the ASDEX Upgrade Team
Fusion engineering and design, ISSN 0920-3796, 10/2015, Volume 96-97, pp. 698 - 702
Disruption prediction | Nuclear fusion | Logistic regressor | Mahalanobis distance | Availability | Design engineering | Upgrading | Machine learning | Disruption | Assessments | Temporal logic | Logistics
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10. Full Text Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak
by Zheng, W and Hu, F.R and Zhang, M and Chen, Z.Y and Zhao, X.Q and Wang, X.L and Shi, P and Zhang, X.L and Zhang, X.Q and Zhou, Y.N and Wei, Y.N and Pan, Y and J-TEXT Team and J-TEXT team
Nuclear fusion, ISSN 0029-5515, 3/2018, Volume 58, Issue 5, p. 56016
density limit | real-time | disruption | neural network | disruption prediction | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology
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11. Full Text A multidimensional linear model for disruption prediction in JET
by Rattá, G.A and Vega, J and Murari, A and JET Contributors
Fusion engineering and design, ISSN 0920-3796, 09/2019, Volume 146, pp. 2393 - 2396
Disruption prediction | JET | Linear equation | Machine learning | Technology | Nuclear Science & Technology | Science & Technology | Analysis | Algorithms | Disruption | Linear equations | False alarms | Genetic algorithms
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12. Full Text Optimization of DEMO geometry and disruption location prediction
by Maviglia, Francesco and Albanese, Raffaele and Ambrosino, Roberto and Bachmann, Christian and Federici, Gianfranco and Villone, Fabio
Fusion engineering and design, ISSN 0920-3796, 09/2019, Volume 146, pp. 967 - 971
Electromagnetic modelling | Vertical stabilization | DEMO | Disruption simulations | Design parameters | Disruption | Economic impact | Time constant | Vertical orientation | Elongation | Optimization
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13. Full Text Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers
by Jayakumar Chandrasekar and Surendar Madhawa and J Sangeetha
Journal of intelligent & fuzzy systems, ISSN 1064-1246, 01/2020, Volume 39, Issue 6, p. 8365
Classifiers | Algorithms | Prediction models | Stacking | Error reduction | Machine learning | Structural damage | Tokamak devices | Disruption
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