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Coping with Covid-19 Economy in The GCC CountriesThis paper examines how the GCC countries are dealing with COVID-19. This paper shows how this outbreak creates difficult conditions for the economy of the GCC countries. From February 2020, the number of confirmed cases continues to increase around the globe. This pandemic situation creates a very alarming situation for the economic cycle. GCC countries depend on hydrocarbon exports, and this outbreak not only reduces the epidemic but also reduces oil prices. In this situation, the GCC countries have a considerable challenge to maintain growth. As a matter of concern, this paper examines how the GDP of the GCC countries has decreased and how demands and supply shortages are occurring. The decrease in oil prices indicates that it takes time to cover these circumstances. Research also suggests that it seems difficult for oil prices to reach $45 per barrel. This pandemic is reducing travel, and this is the main cause of this shortfall. Thus, in this case, the GCC countries must manage the fiscal deficit with a fiscal stimulus. The government is already working on public financing and is providing a fiscal stimulus to the industry.
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On the Theory of the Arrhenius-Normal Model with Applications to the Life Distribution of Lithium-Ion BatteriesTypically, in accelerated life testing analysis, only probability distributions possessing shape parameters are used to fit the experimental data, and many distributions with no shape parameters have been excluded, including the fundamental ones like the normal distribution, even when they are good fitters to the data. This work shows that the coefficient of variation is a replacement for the shape parameter and allows using normal distributions in this context. The work focuses on the Arrhenius-normal model as a life-stress relationship for lithium-ion (Li-ion) batteries and precisely derives the estimating equations of its accelerating parameters. Real and simulated lives of Li-ion batteries are used to validate our results.
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Numerical Modeling and Analysis of Harvesting Atmospheric Water Using Porous MaterialsNowadays, harvesting water from the atmosphere is becoming a new alternative for generating fresh water. To the author’s best knowledge, no mathematical model has been established to describe the process of harvesting water from the atmosphere using porous materials. This research seeks to develop a new mathematical model for water moisture absorption in porous materials to simulate and assess harvesting atmospheric water. The mathematical model consists of a set of governing partial differential equations, including mass conservation equation, momentum equation, associated parameterizations, and initial/boundary conditions. Moreover, the model represents a two-phase fluid flow that contains phase-change gas–liquid physics. A dataset has been collected from the literature containing five porous materials that have been experimentally used in water generation from the air. The five porous materials include copper chloride, copper sulfate, magnesium sulfate, manganese oxides, and crystallites of lithium bromide. A group of empirical models to relate the relative humidity and water content have been suggested and combined with the governing to close the mathematical system. The mathematical model has been solved numerically for different times, thicknesses, and other critical parameters. A comparison with experimental findings was made to demonstrate the validity of the simulation model. The results show that the proposed mathematical model precisely predicts the water content during the absorption process. In addition, the simulation results show that; during the absorption process, when the depth is smaller, the water content reaches a higher saturation point quickly and at a lower time, i.e., quick process. Finally, the highest average error of the harvesting atmospheric water model is around 1.9% compared to experimental data observed in manganese oxides
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Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous MediaReservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.
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The conditional average entropiesThis paper introduces two more definitions of the conditional average entropy. Some properties of the three definitions are studied and some mistakes in the preceding literature are corrected.
