RSS 1.0Natural Science, Math and Tech Unit (NSMTU)
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A bibliometric analysis of GCC healthcare digital transformationFrom diagnosing to preventing the spread of coronavirus, digital transformation and innovative technology have demonstrated their ability to play a key role in every aspect of the COVID-19 pandemic. Today, digital transformation goes beyond the application of artificial intelligence (AI) to increase productivity; it is currently reaching the large population in the Gulf Cooperation Council (GCC) and has a significant impact on both work and daily life. This study aims to evaluate the GCC’s various contributions to scientific publications on digital transformation, focusing on the methods used to combat the COVID-19 pandemic and protect community well-being, including the most recent AI applications for COVID-19 safety measures, symptom detection, and remote healthcare. The research methodology used in this study is based on bibliometric analysis, a collection of strategies for analyzing vast amounts of bibliographic data by combining mathematical, statistical, and computer techniques. A set of publications is retrieved from three databases, Scopus, Web of Science (WoS), and Lens.org Then, VOS viewer is used to extract quantitative publication metrics and visualize coexisting networks of key terms extracted during the last 5years. This study focuses on the Scopus database while the WoS and the Lens databases are left for the user as an active learning process with some research directions in exploring bibliometric analysis in healthcare and digital transformation. From 2017 to 2021, 1520 healthcare, AI, and digital transformation documents were retrieved from the Scopus database using the journals’ abstract, title, and keywords “TILE-ABSKEY” sections. Results show that the total number of published documents in the GCC in healthcare and AI increased from 107 papers in 2017 to 720 papers in 2021. Furthermore, the number of citations jumped from 44 in 2017 to more than 4600 in 2021. The most active country was Saudi Arabia, followed by United Arab Emirates (UAE), Qatar, Oman, Kuwait, and Bahrain. Three of the top five most active institutions were from Saudi Arabia—King Saud University, King Abdulaziz University, and Imam Abdulrahman University—followed by the University of Sharjah from the UAE and Qatar University. Out of the 1520 documents retrieved, 20.6% were published in medicine, 18.6% in computer science, and 9.4% in engineering. Our findings indicate that AI and healthcare research are generally well established within each country, with more advancement in Saudi Arabia and UAE, but need more collaborative research between the GCC. This study provides a comprehensive overview of the bibliometric analysis of GCC healthcare digital transformation and AI, which may help researchers, policymakers, and practitioners better understand healthcare needs and development within the GCC
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Comparative Study B etween Lognormal and Weibull Distributions in Modeling Commercial Concentrator III–V Triple-Junction Solar Cells LifetimesIn this paper, we show that modeling the accelerated lifetime of commercial concentrator lattice match triple-junction 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 real-time datasets from accelerated life testing that significantly favors the lognormal distribution. For comparison purposes, the Arrhenius-Weibull 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.
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Powerful Mathematica Codes for Goodness-of-Fit Tests for Censored DataIn reliability studies of energy and electrical systems life data are often censored, because life tests are terminated, and life data are analyzed before the failure of all sample units. The most important task to accomplish a successful reliability analysis is to choose, through statistical goodness-of-fit tests, the correct or nearly correct probability distribution to describe the failure mechanism of given experimental data. However, due to censoring, this task would not be as easy as testing complete samples. Unfortunately, the built-in functions and codes of the available computation programs are not valid to test for incomplete or censored samples and give completely wrong results if they are used for that purpose, even on the most sophisticated ones like Mathematica and MATLAB. On the other hand, there is a high chance to slip up when trying to perform this type of tests by someone with humble probabilistic and mathematical background. Correct performance of such tests requires a deep knowledge in how to treat the estimating equations of the candidate distribution’s parameters from a censored sample. This type of equations is usually implicit, which often needs a careful numerical treatment to be successfully solved. Also, we should keep in mind that the test statistics formulas of censored samples are different from those of complete samples. The corresponding critical value of the test must be modified according to the type of the distribution nominated, the degree of censoring, and the complete sample size. Therefore, there is a crucial need to have codes that safely run the tests and give reliable results. This book chapter is devoted to introducing efficient Mathematica codes for two of the best goodness-of-fit tests for censored data, the Cramér–von Mises and Anderson-Darling tests for Weibull and lognormal distributions, which are useful in a great variety of applications in energy studies, particularly as models for product life. The codes are presented together with some practical examples extracted from the literature in various topics of energy systems and related fields.
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The variance entropy multi-level thresholding methodThis paper proposes a new multi-level entropy-based 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 multi-level thresholding techniques that requires human intervention. In addition, the method significantly outperforms the generalized Kapur’s method, which is one of the benchmarking entropy-based 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.
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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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On the inverse power law-normal 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 IPL-normal model in a rigorous mathematical setup that precisely leads to the least squares estimating equations and maximum likelihood estimates of the IPL-normal accelerating parameters and the general coefficient of variation. The proposed model uses accelerated experimental data to successfully predict the lifetime of organic light-emitting diodes (OLEDs) at use conditions. Based on these fundamentals, the predictions are benchmarked with prior works that were validated by market studies.
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Choosing the best lifetime model for commercial lithium-ion batteriesThis paper explores three-lifetime models for the commercial Lithium-Ion 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.
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Mathematical Modeling and Simulation of Metal Hydride Hydrogen StorageAbstract. Energy storage is considered one of the most challenging with the rising energy demands. High energy density requires more extended storage than traditional storage. Recently, hydrogen has been considered one of the solutions for the future energy transition in terms of several aspects, such as hydrogen generation, safety, and storage. Metal hydride hydrogen storage has gained much interest as a possible medium for ample storage. This study introduces mathematical modeling and numerical simulation of metal hydride hydrogen storage. The metal hydride is a porous medium that absorbs or releases hydrogen under different heat transfer conditions. The mathematical model should clearly describe the complicated physics in the metal hydride hydrogen storage, such as absorbent, transport in porous media, and extracting the hydrogen from the absorbent material. Therefore, heat and mass transfer to and from the metal hydride is critical in the storage process. Moreover, since the pressure inside the bed is moderate, hydrogen is considered an ideal gas. It is assumed that the solid phase is isotropic and uniformly porous. The Van't Hoff equation expresses the gas pressure equilibrium. The hydrogen invades the alloy bed, and metal grains absorb it, which leads to density variation. This change in hydride density produces both absorption reaction and diffusive movement due to the hydride concentration, where the porosity and diffusion movement coefficient will be constant. Based on that, governing partial differential equations are provided in polar coordinates along with algebraic empirical relationships and initial/boundary conditions. Then, the developed mathematical system was solved numerically using the mixed finite element. The results have been presented and discussed by considering possible heating/cooling scenarios. It was found that hydrogen reaction with the porous hybrid metal, including density variation, depends mainly on the heating and cooling efficiency.
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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 energy-efficient, 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 one-dimensional 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.
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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 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.
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Detection of Hydrogen Leakage Using Different Machine Learning TechniquesWhen employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R 2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.
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Mathematical Modeling of Desiccant-Based Atmospheric Water Generation AuthorsChallenges of population growth, rapid urbanization, climate change, depletion of local water supplies, and rising demand from agriculture, industry, and the energy sectors are all contributing to water scarcity and the lack of access to clean water. The UN reported that in 2021, over 45% of the world population would suffer from accessing safely managed sanitation facilities, and over 25% would live in water-stress areas. Under the current trend, by 2030, the world would be faced with a 40% water deficit, forcing governments, companies, and research scientists, to find a solution to the critical problem of water scarcity. Over the years, seawater desalination has been considered the most viable solution, but it has a large eco-logical footprint, and high energy consumption and only a few countries can afford it. Recently, significant efforts have been devoted to harvesting water from clouds, fog, or water vapor in the atmosphere, particularly in dry regions. Harvesting atmospheric water by solar regeneration desiccants is a promising water source due to its low energy cost and low impact on the environment. However, the actual published results of the amount of water generated are still insignificant. This paper attempts to predict and improve the behavior of water absorption and desorption by the Calcium Chloride (CaCl2) desiccant by developing a mathematical model coupled with an empirical formula, including a phase change mass conservation equation, a momentum conservation equation, and an energy equation. The mathematical and empirical models were combined and solved numerically using the MATLAB® PDE solver pdepe; Results were compared to experimental investigations conducted at Effat University Lab in Jeddah. The comparison in the cases of absorption and desorption shows good agreement between simulations and experiments in terms of water content, average temperature, relative humidity, and solution depth. It was found that using an air pump boosts the absorption in deep containers, however, the use of a thin layer is still more effective. The mean absolute error (MAE) of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. The results of this research highlight the potential and promise of desiccant-based atmospheric water generation as a viable solution to water scarcity, and help achieve UN Sustainable Development Goal 6, “Ensure availability and sustainable management of water and sanitation for all."
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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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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 fourth-order Runge-Kutta-Gill 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, angular-velocity, 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.
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Machine Learning Prediction for Nanoparticles Behavior in Hydrocarbon ReservoirsThe use of machine learning to forecast how nanoparticles would migrate through porous material is covered in this research. We employed the random forest, decision tree, artificial neural network, and gradient boosting regression machine learning techniques. Since there are not many experimental datasets available, it is easier to create artificial datasets using verified numerical simulators. Additionally, covered in the paper are data preprocessing, correlations, the importance of features, and hyperparameter adjustment. Moreover, different error metrics and R 2 -correlation are used to gauge how well the predictive models perform. Finally, examples of the findings are presented. The decision tree model is determined to have the highest accuracy, the best performance, and the lowest root mean squared error.
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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 energy-efficient, 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 one-dimensional 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.
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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 fourth-order Runge-Kutta-Gill 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, angular-velocity, 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.
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Numerical Modeling of Nanoparticle Transport in Porous Media MATLAB/PYTHON ApproachNumerical Modeling of Nanoparticle Transport in Porous Media: MATLAB/PYTHON Approach focuses on modeling and numerical aspects of nanoparticle transport within single- and two-phase flow in porous media. The book discusses modeling development, dimensional analysis, numerical solutions and convergence analysis. Actual types of porous media have been considered, including heterogeneous, fractured, and anisotropic. Moreover, different interactions with nanoparticles are studied, such as magnetic nanoparticles, ferrofluids and polymers. Finally, several machine learning techniques are implemented to predict nanoparticle transport in porous media. This book provides a complete full reference in mathematical modeling and numerical aspects of nanoparticle transport in porous media. It is an important reference source for engineers, mathematicians, and materials scientists who are looking to increase their understanding of modeling, simulation, and analysis at the nanoscale.
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Data Mining and Visualization of Space Technology Research Trends in the Arab WorldSpace exploration has become a crucial field in recent years, with many countries investing heavily in research and development to enhance their capabilities. Nevertheless, there has been a lack of research on the trends and advancements in aerospace research in the Arab world. This study aims to address this gap by conducting a bibliometric analysis of scientific publications using the Scopus database. The analyzed data covers the period from 1980 to 2022 and focuses on identifying the historical foundations, evolution, and emergence of space programs through citation, occurrence, collaboration, and clustering. The United Arab Emirates is found to be the most active country in terms of publications, followed by Saudi Arabia and Egypt. The results indicate that the majority of the publications are centered on remote sensing and the use of optical systems in space exploration. This study provides valuable insights into technological innovation in the aerospace sector in the Arab world and highlights potential research directions for future studies.
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Mapping the Scientific Landscape of Metaverse Using VOSviewer and BibliometrixThe concept of the Metaverse has gained increasing attention as advances in virtual and augmented reality (AR) technologies have enabled the creation of immersive and interactive virtual environments. However, most of these studies remain independent and only a few studies attempted to investigate their relationships. To analyze key trends in Metaverse research and conduct a thorough bibliometric analysis, we used VOSviewer and Bibliometrix, R-tool package on the Scopus database. Our co-occurrence analysis revealed that the hot topics are related to virtual reality, augmented reality, the Internet of Things, and blockchain, and there are potential areas for future research, such as privacy, security, and education in the Metaverse. In addition, our analysis identified the most active countries and institutions in the field, the top subject areas, as well as potential gaps in the literature that could be explored in future research. This study provides valuable insights into Metaverse research and can help guide future research in this field.
