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Harnessing Disruptive Technologies for Flood Prediction and Advisory Systems through Persuasive Modeling

Ugochukwu Onwudebelu, John O. Ugah, Olusanjo Olugbemi Fasola   +2 more
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In Vitro Evaluation and Network Pharmacology Analysis of the Antimicrobial Activity of <i>Pistacia lentiscus</i>. [PDF]

Int J Dent
Ganeshkumar A, Sabarish R, Shakir N, Sankar DS, Franklin AJ, Gandhi N, Kumar K, Abu Hasna A.   +7 more
europepmc   +1 more source

Graph Analysis and Prediction-Based Timetable Rescheduling Framework to Reduce Metro Line Capacity Loss after Partial Disruption

Shuang Zhang, Yaoxin Wu, Laurens Bliek, Yanqiu Cheng, Yingqian Zhang   +4 more
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AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets. [PDF]

J Oral Biol Craniofac Res
Keskar V, Shenoy A, Desai S.
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Predicting and Early Detection of Delirium through Motion Patterns: A Narrative Review. [PDF]

Clin Psychopharmacol Neurosci
Hong JS, Kim NY, Kim HR, Han DH, Kim SM.
europepmc   +1 more source

Adaptive learning for disruption prediction in non-stationary conditions

Nuclear Fusion, 2019
For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same ...
A. Murari, M. Lungaroni, M. Gelfusa, E. Peluso, Jesús Vega, J. Contributors   +5 more
semanticscholar   +9 more sources

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Recent progress on deep learning-based disruption prediction algorithm in HL-2A tokamak

Chinese Physics B, 2023
Disruption prediction and mitigation is a crucial topic, especially for future large-scale tokamaks, due to disruption’s concomitant harmful effects on the devices.
Zongyu 宗谕 Yang 杨, Yuhang 宇航 Liu 刘, X. Zhu 朱, Z. Chen 陈, F. Xia 夏, W. Zhong 钟, Z. Gao 高, Y. Zhang 张, Y. Liu 刘   +8 more
semanticscholar   +1 more source