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Physics‐based and data‐driven approaches for lifetime estimation under variable conditions: Application to organic light‐emitting diodesThe prognosis of organic lightemitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physicsbased models? In this paper, datadriven methods, feedforward neural networks (FFNN), support vector machines (SVMs), knearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the datadriven and our physicsbased OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN.

Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson’s disease in an Egyptian cohortParkinson’s disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify pathomechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls.

Fates of a Nonwetting Slug in Tapered Microcapillaries under Gravity and Zero Gravity Conditions: Dynamics, Asymptotic Equilibrium Analysis, and Computational Fluid Dynamics VerificationsIt has been determined experimentally and numerically that a nonwetting slug in a tapered capillary tube, under the sole action of capillary force, selfpropels itself toward the wider end of the tube until an equilibrium state is reached. The aim of this work is to highlight the state of the slug at equilibrium in terms of configuration and location. Furthermore, it turns out that gravity adds richness to this phenomenon, and more fates become possible. A modified Bond number is developed that determines the relative importance of gravity and capillarity for this system. According to the magnitude of the Bond number, three more fates are possible. Therefore, in a tapered capillary tube held vertically upward with its wider end at the top, in the absence of gravity or under microgravity conditions, the nonwetting slug moves upward toward the wider end of the tube until it reaches equilibrium with the two menisci part of a single sphere. The location of the slug at equilibrium in this case represents the farthest fate among the other fates. When gravity exists yet capillarity dominates, the slug still moves upward toward the wider end. However, in this case, the two menisci become parts of two different spheres of different curvatures. For this scenario, the slug climbs upward but reaches a lower level compared to the previous scenario. On the other hand, when gravity dominates, the slug experiences a net downward pull toward the narrower end of the tube and starts to move in the direction of gravity until capillary force establishes a balance, then it stops. When gravity sufficiently dominates, it pulls the slug downward until it completely drains off the tube. A computational fluid dynamics (CFD) analysis is conducted in order to build a framework for verification exercises. Excellent agreements between the results of the developed model and the CFD analysis are obtained. A fate map and a scheme are developed to identify these four fates based on two Bond numbers; namely, the initial Bond number and that associated with the slug when it is at the exit.

Mapping the Research Landscape of Organizational Climate and Performance Using Bibliometric AnalysisThis study aims to address the limited understanding of rganizational climate and performance by conducting a omprehensive bibliometric analysis of scholarly publications. The methodology involves analyzing publications using bibliometric techniques and VOSviewer. The results indicate that organizational performance, employee engagement, job satisfaction, leadership, and leadership culture are prominent topics within the field. The top five countries in terms of published documents and citations are the USA, India, the UK, Australia, and Malaysia. Recent publications have prioritized topics such as quality of work life, innovation, productivity, wellbeing, organizational commitment, work engagement, and corporate social responsibility. This study provides valuable insights for researchers, practitioners, and organizations to improve employee performance and productivity. The significance of this work lies in its ability to inform future research directions and guide collaboration efforts. Ultimately, this study advances the understanding of organizational climate and performance with practical implications for various organizational settings.

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.

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.