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Analytical solutions for harvesting atmospheric water using desiccant materialsAtmospheric water generation using desiccant materials is a promising technology for producing clean drinking water in waterscarce regions. While experimental research on this topic has been extensive, modeling and simulation research are still in their nascent stages. The development of accurate models and simulations is crucial for predicting performance and refining system design. This paper presents analytical solutions for predicting and improving the behavior of water absorption and desorption by the calcium chloride (CaCl2) desiccant, which is commonly used in atmospheric water generation systems. The model considers several physical effects, such as mass transfer, and diffusion. The model considers a linear relationship between the collected water content and relative humidity. Based on this assumption the model has been solved analytically for different cases of boundary conditions including, Dirichlet boundary conditions and Dirichlet–Neumann boundary conditions. Several physical scenarios have been calculated and the results have been discussed.

Comparative Study B etween Lognormal and Weibull Distributions in Modeling Commercial Concentrator III–V TripleJunction Solar Cells LifetimesIn this paper, we show that modeling the accelerated lifetime of commercial concentrator lattice match triplejunction GaInP/GaInAs/Ge cells by a lognormal distribution is better fitted than by Weibull distribution has been adopted by most of the research literature in the field. A fair number of statistical tests are used to analyze realtime datasets from accelerated life testing that significantly favors the lognormal distribution. For comparison purposes, the ArrheniusWeibull and lognormal stress relationships are used to predict the lifetime model under nominal conditions. They provide comparable estimates to the nominal meantime to failure and activation energy of the cells, yet, the two models possess different behaviors, especially at their tails and peaks. Moreover, an intensive Monte Carlo simulation is conducted to examine the distribution robustness towards the censoring scheme. The results again affirm that type I censored samples of Lognormal are more efficient than those of Weibull in estimating the distribution parameters.

The variance entropy multilevel thresholding methodThis paper proposes a new multilevel entropybased image thresholding method. The key principle of the proposed method depends on the minimum of the variance entropy. The method is fully automated at all stages of implementation. It produces competitive segmentation results as compared to the generalized Otsu’s method, which is one of the most powerful multilevel thresholding techniques that requires human intervention. In addition, the method significantly outperforms the generalized Kapur’s method, which is one of the benchmarking entropybased thresholding techniques. The method is successfully applied to several scenarios of trial histograms and real images, and its performance is checked using a variety of classification measures and quality metrics.

On the Theory of the ArrheniusNormal Model with Applications to the Life Distribution of LithiumIon 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 Arrheniusnormal model as a lifestress relationship for lithiumion (Liion) batteries and precisely derives the estimating equations of its accelerating parameters. Real and simulated lives of Liion batteries are used to validate our results.

On the inverse power lawnormal model for life prediction of organic light emitting diodesIn accelerated life testing analysis with nonthermal accelerating stress, the inverse power law (IPL) is often solely merged with a particular lifetime probability distribution with a shape parameter. Although many fundamental lifetime distributions, such as the normal distribution, are excellent fits to the experimental lifetime data, they have not been considered as they lack the shape parameter. As such, this paper, for the first time, demonstrates that the shape parameter can be replaced by the coefficient of variation, allowing the use of normal distributions in this context. The work further introduces the IPLnormal model in a rigorous mathematical setup that precisely leads to the least squares estimating equations and maximum likelihood estimates of the IPLnormal accelerating parameters and the general coefficient of variation. The proposed model uses accelerated experimental data to successfully predict the lifetime of organic lightemitting diodes (OLEDs) at use conditions. Based on these fundamentals, the predictions are benchmarked with prior works that were validated by market studies.

Choosing the best lifetime model for commercial lithiumion batteriesThis paper explores threelifetime models for the commercial LithiumIon Batteries, namely, Weibull, Lognormal and Normal distributions. A comparative study is performed on the censored data of an accelerated lifetime test (ALT) to select the best of the proposed models. Although the three models fit the real experimental data and provide similar estimations to the mean time to failure of the batteries, the effect of censoring in estimating the parameters of the distributions is entirely different. The work uses three standard criteria to determine the preferable model. In brief, it is found that the lognormal distribution is the best among the three suggested models.

Experimental Investigations and Modeling of Atmospheric Water Generation Using a Desiccant MaterialHarvesting atmospheric water by solar regenerated desiccants is a promising water source that is energyefficient, environmentally clean, and viable. However, the generated amounts of water are still insignificant. Therefore, more intensive fundamental research must be undertaken involving experiments and modeling. This paper describes several experiments, which were conducted to predict and improve the behavior of water absorption/desorption by the Calcium Chloride (CaCl2) desiccant, where the uncertainty did not exceed ±3.5%. The absorption effect in a deep container was studied experimentally and then amplified by pumping air into the solution. The latter measured water absorption/desorption by a thin solution layer under variable ambient conditions. Pumping air inside deep liquid desiccant containers increased the water absorption rate to 3.75% per hour, yet when using a thin layer of the solution, it was found to have increased to 6.5% per hour under the same conditions. The maximum amount of absorbed water and water vapor partial pressure relation was investigated, and the mean absolute error between the proposed formula and measured water content was 6.9%. An empirical formula, a onedimensional mathematical model, was then developed by coupling three differential equations and compared to experimental data. The mean absolute error of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. Governing mathematical conservation equations were subsequently formulated. The mathematical and empirical models were combined and solved numerically. Findings obtained from the simulation were compared to experimental data. Additionally, several scenarios were modeled and tested for Jeddah, Saudi Arabia, under various conditions.

Machine Learning Prediction of Nanoparticle Transport with TwoPhase Flow in Porous MediaReservoir simulation is a timeconsuming 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 twophase 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 scikitlearn 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, Rsquared correlation, mean squared error, and root mean 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.

Machine Learning Prediction of Nanoparticle Transport with TwoPhase Flow in Porous MediaReservoir simulation is a timeconsuming 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 twophase 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 scikitlearn 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, Rsquared 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.

Joule Heating and Viscous Dissipation Effects on a Stretching/Shrinking Cannel Filled by Micropolar Hybrid Nanofluid in Presence Thermal/Solar RadiationThe main goal for this research is to investigate the effect of two composed hybrid nanoparticle materials in heat transfer with account several parameters and in two cases. In addition, exploring how the micropolar hybrid nanofluid (Cu–TiO2) behaves in a shrinking and expansion of the channel. The model considers external factors such as magnetic fields, heat radiation, and solar radiation. The boundary layer approach has been utilized to create transformations that pout the equations of the system in the dimensionless form. The shooting method has been combined with the fourthorder RungeKuttaGill to numerically solve the modified ordinary differential equations. The impacts of the nanoparticles transport on the heat transfer and fluid flow are addressed, and the results are compared to the case of pure water. The velocity, isotherms, angularvelocity, and concentration distributions, are given in tables or graphs. It was found that the effect of heat on the hybrid nanofluids is directly proportional to its velocity and angular velocity. For mass fraction of the two nanofluids φ 1 and φ 2, the velocity profile f′(η) has a comparable influence for both hybrid nanofluid and nanofluid. The larger quantity of the factors φ 1, φ 2, M and Q enhance the temperature. For M, φ 1 and φ 2, the angular velocity profile g(η) has a comparable influence for both hybrid and magnetic parameter. The absorption parameter storing the radiation energy and augmentation of the solar irradiance immersion capacity leads to a greater heat transfer.

Experimental Investigations and Modeling of Atmospheric Water Generation Using a Desiccant MaterialHarvesting atmospheric water by solar regenerated desiccants is a promising water source that is energyefficient, environmentally clean, and viable. However, the generated amounts of water are still insignificant. Therefore, more intensive fundamental research must be undertaken involving experiments and modeling. This paper describes several experiments, which were conducted to predict and improve the behavior of water absorption/desorption by the Calcium Chloride (CaCl2) desiccant, where the uncertainty did not exceed ±3.5%. The absorption effect in a deep container was studied experimentally and then amplified by pumping air into the solution. The latter measured water absorption/desorption by a thin solution layer under variable ambient conditions. Pumping air inside deep liquid desiccant containers increased the water absorption rate to 3.75% per hour, yet when using a thin layer of the solution, it was found to have increased to 6.5% per hour under the same conditions. The maximum amount of absorbed water and water vapor partial pressure relation was investigated, and the mean absolute error between the proposed formula and measured water content was 6.9%. An empirical formula, a onedimensional mathematical model, was then developed by coupling three differential equations and compared to experimental data. The mean absolute error of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. Governing mathematical conservation equations were subsequently formulated. The mathematical and empirical models were combined and solved numerically. Findings obtained from the simulation were compared to experimental data. Additionally, several scenarios were modeled and tested for Jeddah, Saudi Arabia, under various conditions.

Joule Heating and Viscous Dissipation Effects on a Stretching/Shrinking Cannel Filled by Micropolar Hybrid Nanofluid in Presence Thermal/Solar RadiationThe main goal for this research is to investigate the effect of two composed hybrid nanoparticle materials in heat transfer with account several parameters and in two cases. In addition, exploring how the micropolar hybrid nanofluid (Cu–TiO2) behaves in a shrinking and expansion of the channel. The model considers external factors such as magnetic fields, heat radiation, and solar radiation. The boundary layer approach has been utilized to create transformations that pout the equations of the system in the dimensionless form. The shooting method has been combined with the fourthorder RungeKuttaGill to numerically solve the modified ordinary differential equations. The impacts of the nanoparticles transport on the heat transfer and fluid flow are addressed, and the results are compared to the case of pure water. The velocity, isotherms, angularvelocity, and concentration distributions, are given in tables or graphs. It was found that the effect of heat on the hybrid nanofluids is directly proportional to its velocity and angular velocity. For mass fraction of the two nanofluids φ 1 and φ 2, the velocity profile f′(η) has a comparable influence for both hybrid nanofluid and nanofluid. The larger quantity of the factors φ 1, φ 2, M and Q enhance the temperature. For M, φ 1 and φ 2, the angular velocity profile g(η) has a comparable influence for both hybrid and magnetic parameter. The absorption parameter storing the radiation energy and augmentation of the solar irradiance immersion capacity leads to a greater heat transfer.

Sustainable Development Goals in Saudi ArabiaThis paper presents a discussion on the sustainable development goals in Saudi Arabia objectives that will be executed by the year 2030 according to their vision 2030. There are 17 sustainable development goals set. These objectives will provide the citizen and the nation with the structure to be assigned to the activity and to make another general public appearing as a goal of development. This work has focus on the efforts being made by the Kingdom of SaudiArabia towards the implementation of the Sustainable Development Goals (SDGs), Goal #7 (Renewable Energy). This SGD is important as the old society move on to a new level of innovation and, at the same time, to develop SDGs that will help to have a workable effect that will motivate the shared activity of individuals, the planet and success. With the implementation of Goal 7 the renewable energy sector will enable the nation of Saudi Arabia to move towards sustainability and green energy consumption.

Coping with Covid19 Economy in The GCC CountriesThis paper examines how the GCC countries are dealing with COVID19. 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.

On the Theory of the ArrheniusNormal Model with Applications to the Life Distribution of LithiumIon 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 Arrheniusnormal model as a lifestress relationship for lithiumion (Liion) batteries and precisely derives the estimating equations of its accelerating parameters. Real and simulated lives of Liion batteries are used to validate our results.

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 twophase fluid flow that contains phasechange 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

Machine Learning Prediction of Nanoparticle Transport with TwoPhase Flow in Porous MediaReservoir simulation is a timeconsuming 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 twophase 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 scikitlearn 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, Rsquared 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.

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.