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https://www.selleckchem.com/products/itacnosertib.html Most of the segregation occurs at the grain-air free surface and is driven by large velocity gradients. In the silo with an insert, the velocity gradient at the free surface is greatly reduced, hence, so is the degree of segregation.Predicting motions of vessels in extreme sea states represents one of the most challenging problems in naval hydrodynamics. It involves computing complex nonlinear wave-body interactions, hence taxing heavily computational resources. Here, we put forward a new simulation paradigm by training recurrent type neural networks (RNNs) that take as input the stochastic wave elevation at a certain sea state and output the main vessel motions, e.g. pitch, heave and roll. We first compare the performance of standard RNNs versus GRU and LSTM neural networks (NNs) and show that LSTM NNs lead to the best performance. We then examine the testing error of two representative vessels, a catamaran in sea state 1 and a battleship in sea state 8. We demonstrate that good accuracy is achieved for both cases in predicting the vessel motions for unseen wave elevations. We train the NNs with expensive CFD simulations offline, but upon training, the prediction of the vessel dynamics online can be obtained at a fraction of a second. This work is motivated by the universal approximation theorem for functionals (Chen & Chen, 1993. IEEE Trans. Neural Netw.4, 910-918 (doi10.1109/72.286886)), and it is the first implementation of such theory to realistic engineering problems.Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage

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