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Yang Lu, Xiaochun Wang, Yijun He, Jiping Liu, Jiangbo Jin, Jian Cao, Juanxiong He, Yongqiang Yu, Xin Gao, Mirong Song and Yiming Zhang
Two coupled climate models that participated in the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0), and the Nanjing University of Information Science and Technol...
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Robert J. Weaver and Abigail L. Stehno
Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy dissipation. Their intricate root systems play a key role in safeguarding shorelines from tsu...
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Jiaming Bian, Ye Liu and Jun Chen
In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network?s performance ...
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María Elena Tejeda-del-Cueto, Manuel Alberto Flores-Alfaro, Miguel Toledo-Velázquez, Lorena del Carmen Santos-Cortes, José Hernández-Hernández and Marco Osvaldo Vigueras-Zúñiga
The objective of this study is to develop a genetic algorithm that uses the IGP parameterization to increase the lift coefficient (CL) of three airfoils to be used on wings of unmanned aerial vehicles (UAVs). The geometry of three baseline airfoils was m...
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Evangelos Filippou, Spyridon Kilimtzidis, Athanasios Kotzakolios and Vassilis Kostopoulos
The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can...
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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Timothy O. Hodson, Keith J. Doore, Terry A. Kenney, Thomas M. Over and Muluken B. Yeheyis
Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and so...
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Haipeng Lin, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie and Huhu Chen
Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we dev...
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Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh...
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Mahsa Kashani, Stefan Jespersen and Zhenyu Yang
The application of deoiling hydrocyclone systems as the downstream of three-phase gravity separator (TPGS) systems is one of the most commonly deployed produced water treatment processes in offshore oil and gas production. Due to the compact system?s com...
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