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Naseer Muhammad Khan, Liqiang Ma, Muhammad Zaka Emad, Tariq Feroze, Qiangqiang Gao, Saad S. Alarifi, Li Sun, Sajjad Hussain and Hui Wang
The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock b...
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Yucheng Yang, Guohua Xu, Yongjie Shi and Zhiyuan Hu
This study develops a hybrid solver with reversed overset assembly technology (ROAT), a viscous vortex particle method (VVPM), and a CFD program based on the URNS method, in order to study the aerodynamic and acoustic characteristics of coaxial rigid rot...
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Sipho G. Thango, Georgios A. Drosopoulos, Siphesihle M. Motsa and Georgios E. Stavroulakis
A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex d...
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Caosen Xu, Jingyuan Li, Bing Feng and Baoli Lu
Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention me...
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Ayushi Chahal, Preeti Gulia, Nasib Singh Gill and Ishaani Priyadarshini
IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. ...
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