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  • Artificial Intelligence and Machine Learning Research: Towards Digital Transformation at a Global Scale

    Sarirete, Akila; Balfagih, Zain; Brahimi, Tayeb; Lytras, Miltiadis; Visvizi, Anna; Department Collaboration; Energy Lab; NSMTU; Sarirete, Akila (Springer, 2022-04-17)
  • Design and Simulation of a Standalone PV System for a Mosque in NEOM City

    Alzharani, Amani; Brahimi, Tayeb; Rajeh, Mashael; Banjar, Shaima; No Collaboration; Energy Lab; NSMTU; 3; Alzharani, Amani (2021-03-07)
    The current state and the future potentials of renewable energy have increased widely globally to reduce the usage of other resources such as fossil fuel, which affect the environment. NEOM city in the Kingdom of Saudi Arabia (KSA) is a case where renewable energy will count for 100% of its energy consumption. Solar energy will play a prominent role in NEOM city. This project aims to present a design and simulation of a standalone photovoltaic (PV) system for a mosque located in NEOM city to meet the people's need for electricity. The system harvests solar energy and converts it into electrical energy to cover the electrical power needed for a typical mosque in NEOM. The design and simulation have been implemented using PVsyst software, powerful software for designing, simulating and generating PV systems reports. The designed standalone PV system could produce yearly electrical energy of 300 MWh. This study's significance relies on initiating research and development of building PV systems to harvest solar energy in NEOM city.
  • Coping with Covid-19 Economy in The GCC Countries

    Albadi, Assel; Brahimi, Tayeb; College collaboration; Energy Lab; 1; NSMTU; Albadi, Assel (PalArch, 2021-05-07)
    This 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.
  • Wind Energy in Saudi Arabia Opportunities and Challenges

    Alkinani, Sadeem; Alghazi, Omnia; Asfour, Rawan; Brahimi, Tayeb; Department Collaboration; Energy Lab; NSMTU; 3; Alkinani, Sadeem (IEOM, 2022-04-05)
    Wind energy has been recognized as one of the fastest-growing energy sources in world and as a key role in reducing greenhouse gas emissions, reducing dependence on oil, diversifying energy supply, and providing electricity at a low cost. In its 2030 Vision, the Kingdom of Saudi Arabia has recently set ambitious targets to move away from oil dependency and redirect the efforts towards more higher-value exploitation of oil and gas, mainly meeting 10 percent of its energy demand through renewable energy sources. This paper attempts to explore the feasibility of using wind turbine machines for energy generation in Saudi Arabia by presenting a study of the opportunities and challenges that can arise while installing these machines. The paper also highlights several technical challenges and gaps that have been anticipated for this design motivation. Observations and upcoming trends show that by 2030 renewable energy, including solar and wind, will provide up to 50% of electricity production in the Kingdom.
  • A Bibliometric of Sentiment Analysis in Tourism Industry during COVID-19 Pandemic

    Yaqub, Asra; Huda, Talib; Brahimi, Tayeb; Sarirete, Akila; Department Collaboration; Energy Lab; 2; Master of Science in Urban Design; Asra, Yaqub (IEOM, 2022-03-07)
    The purpose of this paper is to explore, visualize, and analyze the state-of-the-art research of sentiment analysis in the tourism industry during COVID-19 using bibliometrics. The information source is obtained from the Scopus database from January 1, 2020, to November 27, 2021. A sample of 1690 documents was obtained within this period. Topic areas with titles, keywords, and abstract criteria in sentiment analysis and travel and tourism were used as a reference for extracting results. There was a significant increase in papers published in 2020 and 2021 related to COVID-19 and sentiment analysis. The top five most active countries were the United States (310 documents and 1799 citations), followed by the United Kingdom (170 documents and 12674 citations), China (240 documents and 1297 citations), Australia (120 documents and 430 citations), and Germany (44 documents and 172 citations). The maximum number of occurrences was "social media (401)" followed by "sentiment analysis (149)", "tourism (116)", "Covid-19 (131)", and "Twitter (77)". This study provides an overview of sentiment analysis and shows the trends in the research and publication with the tourism industry.
  • On the Theory of the Arrhenius-Normal Model with Applications to the Life Distribution of Lithium-Ion Batteries

    Kittaneh, Omar; No Collaboration; NSMTU
    Typically, 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.
  • A Data Mining Analysis of Cognitive Science and Artificial Intelligence

    Brahimi, Tayeb; Haneya, Hala; Yaqub, Asra; Al Salem, Fatmah; Bathallath, Joudi; No Collaboration; Energy Lab; 4; NSMTU; Brahimi, Tayeb (IEEE, 2023-01-13)
    Cognitive science borrows from fields such as Artificial Intelligence (AI) which helps in simulating and modeling the human brain. Recently, there has been an increase in the number of research and applications involving cognitive science and AI cooperation. Based on data extracted from the Scopus database. This paper uses the Visualization Of Similarities Method (VOS) between objects in VOSviewer 1.6.18 to look at, evaluate, and find relevant literature, trends, and the scope of research in the fields of cognitive science and AI. The results showed that the USA, the UK, China, Germany, and Canada are the top 5 most active countries in terms of publications. The University of Calgary came out on top of the active institution while the top funding source came from the National Science Foundation in the USA. The study's results will serve as a road map for future academics and researchers developing theory and practice in artificial intelligence and cognitive science.
  • The Practicality of the Internet of Underwater Things: MATLAB & Simulink AUV Application IoT

    Al Talib, Rabab; Rabih Fatayerji, Hala; Alqurashi, Asmaa; Brahimi, Tayeb; No Collaboration; Energy Lab; 3; NSMTU; Al Talib, Rabab (IEOM, 2022-06-12)
    As Oceans cover approximately 72% of the Earth's surface, it is imperative to extend the Internet of Things (IoT) principles to the existing bodies, paving the way for a new digital trend: the Internet of Underwater Things (IoUT). The IoUT refers to the worldwide network of smart interconnected underwater objects, which enhances monitoring unexplored and vast water areas. It enables various applications like environmental monitoring, disaster prevention, and underground exploration. The Internet of Underground things with such applications is among the leading applied technologies in smart cities. This paper aims to focus specifically on determining the potential applications of the IoUT applications, which can help in examining the challenges that are and will be faced during its modeling and implementation. The study aims to investigate the challenges of designing and implementing such a network. The research includes communication networks and MATLAB & Simulink simulation. Based on previous reviews, an optimal network of IoUT enhances the development and integration of various systems for the robust evaluation of water ecosystems. The study's outcomes include a tested AUV simulation model, which is a crucial part of an IoUT system that can be adjusted to suit local water conditions. The idea of IoUT should be embraced due to its positive impact on social and economic factors.
  • Numerical Modeling and Analysis of Harvesting Atmospheric Water Using Porous Materials

    Alkinani, Sadeem; El-Amin, Mohamed; Brahimi, Tayeb; Department Collaboration; Energy Lab; NSMTU; 1; Alkinani, Sadeem (MDPI, 2022-11-10)
    Nowadays, 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
  • Modeling and Numerical Analysis of Harvesting Atmospheric Water Using Copper Chloride

    Brahimi, Tayeb; El-Amin, Mohamed; Alkinani, Sadeem; Department Collaboration; Energy Lab; NSMTU; 1; Alkinan, Sadeem (IEEE, 2022-06-13)
    Access to freshwater is becoming increasingly difficult due to a lack of sources, such as lakes, rivers, and groundwater. To resolve water scarcity, Harvesting Atmospheric Water (HAW) has emerged as a promising alternative water source, particularly for arid areas. However, the efficiency of an atmospheric water harvesting system depends on relative humidity, temperature, water sorption capacity based on the adsorption phenomenon, and other factors. This paper aims to develop a mathematical model to simulate and analyze atmospheric water harvesting using copper chloride. The simulation results show that the proposed mathematical modeling predicts well the water content. The water content reaches saturation at different times, depending on the depth. More work is underway to simulate and analyze atmospheric water harvesting using other types of salts, for example, magnesium sulfate and copper sulfate and compare the results with experimental data, as well as conducting sensitivity analysis to explore how depths, porosities, hydraulic conductivities, temperatures, and relative humidity affect moisture absorption.
  • Stress, anxiety, and depression among students and employees during the pandemic

    Brahimi, Tayeb; Bathallath, Joudi; No Collaboration; Energy Lab; 1; NSMTU; Bathallath, Joudi (Routledge, 2021-09-30)
    Psychological and social implications due to COVID-19 pandemic are particularly relevant in Higher Education Institutions (HEI). The objective of this chapter is to examine and analyze the level of depression, anxiety, and stress between university students and employees at the HEI in Saudi Arabia due to COVID-19. The method used in this study is based on a narrative review of recent literature on related mental health symptoms and interventions due to the pandemic and a survey conducted on 51 students and 72 employees using the Patient Health Questionnaire for depression, Generalized Anxiety Disorder (GAD), and Perceived Stress Scale (PSS). Results show that psychological well-being is crucial to overcoming COVID-19 and avoiding mental illness and emotional coping. Higher levels of stress, anxiety, and depression were found to be higher in adults than adolescents. The study concluded that there is a need for mental health awareness regarding COVID-19, and it is suggested that there should be an online therapy session with people who have severe levels of stress, anxiety, and depression.
  • Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media

    El-Amin, Mohamed F.; Alwated, Budoor; Hoteit, Hussein; External Collaboration; Energy Lab; NSMTU; 1; El-Amin, Mohamed F. (MDPI, 2023-01-06)
    Reservoir 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.
  • An Outlook to Childhood Obesity in Saudi Arabia using VOSviewer

    Haneya, Hala; Balbaid, Hanin; Ranjha, Hafsa; Brahimi, Tayeb; Khateeb, Kholoud; Department Collaboration; Energy Lab; 3; NSMTU; Brahimi, Tayeb (IEOM, 2022-03-07)
    Childhood Obesity (CHO) is a global epidemic that has increased in recent years. According to the World Health Organization (WHO), 39 million children under five will be overweight or obese in 2020. In Saudi Arabia, the prevalence of obesity reached its peak at 35.5%. WHO also anticipated that by 2030, 30% of all deaths worldwide would be caused by lifestyle diseases. Extensive studies have been published on the fundamental causes of obesity, its treatment, and its prevention. As a result of the efforts and resources invested in childhood obesity research, control, and prevention, it is necessary to assess and analyze these studies and provide valuable insights to public health officials and healthcare institutions. Using the Scopus database, this paper uses bibliometric analysis to evaluate and identify trends and studies in childhood obesity in Saudi Arabia based on VOSviewer. Results revealed that the top 5 active affiliations in childhood obesity are King Saud University (165 documents), King Abdulaziz University (139), King Saud University for Health Sciences (47), Imam Abdulrahman Bin Faisal University(42), and Princess Nourah Bint Abdulrahman University (39). King Saud University sponsored the highest number of published documents. Out of 643 documents retrieved from the Scopus database, 42.5% were in Medicine and 11.8% in biochemistry. Understanding state-of-the-art childhood obesity is critical in planning future measures to control and prevent highrisk children early and future obesity complications. This study aims to provide insight for Saudi Arabian authorities to consider boosting health measures to reduce the incidence and prevalence of CHO.
  • Research and Trends in COVID-19 Vaccines Using VOSviewer

    Brahimi, Tayeb; Abbas, Hagar; No Collaboration; Energy Lab; 1; NSMTU; Brahimi, Tayeb (Springer, 2022-02-18)
    The World Health Organization (WHO) classified the latest coronavirus (COVID-19) as a pandemic on March 11, 2020. As a result, the pandemic has spread to practically every country on the planet. WHO’s major goals for 2021 are to fight COVID-19, strengthen current health systems, increase access to COVID-19 treatment, and provide equitable and safe vaccines for all. As the number of scientific publications continues to expand, there is an increasing need to analyze factors and characteristics that contribute to highly published documents and highly cited articles. This study evaluates and identifies trends and studies in COVID-19 vaccines using the SCOPUS database and VOSviewer. The top five active countries on COVID-19 vaccines publication are the United States with 4168 documents, China with 2245 documents, Italy with 1512 documents, the United Kingdom with 1370 documents, and Spain with 663 documents. Results of network visualizations indicate that understanding the state-of-the-art COVID-19 pandemic is essential in planning future measures to fight COVID-19 and improve vaccination uptake
  • Wind Farm Layout: Modeling and Optimization Using Genetic Algorithm

    Brahimi, Tayeb; El-Amin, Mohamed; Asfour, Rawan; Department Collaboration; Energy Lab; NSMTU; 1; Asfour, Rawan (IOP Publishing Ltd, 2022-01-08)
    . Wind Farm Layout Optimization (WFLO) is a complex multidisciplinary topic that requires a lot of expertise and is becoming an essential part of today's wind farm planning. Yet, selecting optimum wind farm locations is complex, time-consuming, and influenced by environmental factors and upstream turbines inflow wind. The present study attempts to develop an optimization approach based on the Genetic Approach (GA) to determine the most suitable wind turbine locations that maximize the net energy production while minimizing the Cost of Energy (COE) ($/kWh). The WFLO for the optimized objective function was performed for 500, 1000, and 1500 iterations. The best output was obtained for 1500 iterations with the lowest value for the objective function.
  • Aerodynamic Analysis and Performance Prediction of VAWT and HAWT Using CARDAAV and Qblade Computer Codes

    Brahimi, Tayeb; Praschivoiu, Ion; External Collaboration; Energy Lab; NSMTU; Brahimi, Tayeb (IntechOpen, 2021-04-21)
    Wind energy researchers have recently invited the scientific community to tackle three significant wind energy challenges to transform wind power into one of the more substantial, low-cost energy sources. The first challenge is to understand the physics behind wind energy resources better. The second challenge is to study and investigate the aerodynamics, structural, and dynamics of large-scale wind turbine machines. The third challenge is to enhance grid integration, network stability, and optimization. This chapter book attempts to tackle the second challenge by detailing the physics and mathematical modeling of wind turbine aerodynamic loads and the performance of horizontal and vertical axis wind turbines (HAWT & VAWT). This work underlines success in the development of the aerodynamic codes CARDAAV and Qbalde, with a focus on Blade Element Method (BEM) for studying the aerodynamic of wind turbines rotor blades, calculating the induced velocity fields, the aerodynamic normal and tangential forces, and the generated power as a function of a tip speed ration including dynamic stall and atmospheric turbulence. The codes have been successfully applied in HAWT and VAWT machines, and results show good agreement compared to experimental data. The strength of the BEM modeling lies in its simplicity and ability to include secondary effects and dynamic stall phenomena and require less computer time than vortex or CFD models. More work is now needed for the simulation of wind farms, the influence of the wake, the atmospheric wind flow, the structure and dynamics of large-scale machines, and the enhancement of energy capture, control, stability, optimization, and reliability.
  • The conditional average entropies

    Kittaneh, Omar; No Collaboration; NSMTU; Kittaneh, Omar (Taylor and Francis, 2021)
    This 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.