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Publication Metadata only A comprehensive review of data analytics and storage methods in geothermal energy operations(ELSEVIER, 2025) Basem, Ali; Al-Nussairi, Ahmed Kateb Jumaah; Khidhir, Dana Mohammad; Singh, Narinderjit Singh Sawaran; Baghoolizadeh, Mohammadreza; Fazilati, Mohammad Ali; Salahshour, Soheil; Sajadi, S. Mohammad; Hasanabad, Ali Mohammadi; University of Warith Alanbiyaa; University of Manara; Knowledge University; INTI International University; Shahrekord University; Islamic Azad University; Okan University; Bahcesehir University; Ministry of Education of Azerbaijan Republic; Khazar UniversityGeothermal energy storage (GES) systems are thoroughly examined in this research, with a focus on methods like borehole thermal energy storage (BTES), underground thermal energy storage (UTES), and aquifer thermal energy storage (ATES). It highlights the importance of thermal energy storage (TES) systems in addressing global energy challenges. The feasibility of UTES for large-scale energy storage and its integration with geothermal power plants is investigated. The ATES, with the advantage of large storage capacity and low operating costs has could be employed in regions with suitable aquifers. The adaptability of BTES to different ground conditions and its small land footprint made it a spotlight for the researchers. The study emphasizes the role of TES technologies in meeting the growing demand for renewable energy, reducing the impact of climate change, and providing efficient energy solutions for heating, ventilating, and air conditioning. HVAC systems. Also, the application of geothermal power plants and TES systems in decreasing the dependence on nonrenewable energy sources and increasing energy efficiency increase investigated. The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications.Publication Metadata only The impact of acute and chronic aerobic and resistance exercise on stem cell mobilization: A review of effects in healthy and diseased individuals across different age groups(ELSEVIER, 2024) Li, Wei; Chen, Lingzhen; Sajadi, S. Mohammad; Baghaei, Sh.; Salahshour, Soheil; Chinese People's Liberation Army General Hospital; Zhejiang Gongshang University; Cihan University-Erbil; Islamic Azad University; Okan University; Bahcesehir University; Lebanese American UniversityStem cells (SCs) play a crucial role in tissue repair, regeneration, and maintaining physiological homeostasis. Exercise mobilizes and enhances the function of SCs. This review examines the effects of acute and chronic aerobic and resistance exercise on the population of SCs in healthy and diseased individuals across different age groups. Both acute intense exercise and moderate regular training increase circulating precursor cells CD34+ and, in particular, the subset of angiogenic progenitor cells (APCs) CD34+/ KDR+. Conversely, chronic exercise training has conflicting effects on circulating CD34+ cells and their function, which are likely influenced by exercise dosage, the health status of the participants, and the methodologies employed. While acute activity promotes transient mobilization, regular exercise often leads to an increased number of progenitors and more sustainable functionality. Short interventions lasting 10-21 days mobilize CD34+/KDR + APCs in sedentary elderly individuals, indicating the inherent capacity of the body to rapidly activate tissue-reparative SCs during activity. However, further investigation is needed to determine the optimal exercise regimens for enhancing SC mobilization, elucidating the underlying mechanisms, and establishing functional benefits for health and disease prevention. Current evidence supports the integration of intense exercise with chronic training in exercise protocols aimed at activating the inherent regenerative potential through SC mobilization. The physical activity promotes endogenous repair processes, and research on exercise protocols that effectively mobilize SCs can provide innovative guidelines designed for lifelong tissue regeneration. An artificial neural network (ANN) was developed to estimate the effects of modifying elderly individuals and implementing chronic resistance exercise on stem cell mobilization and its impact on individuals and exercise. The network's predictions were validated using linear regression and found to be acceptable compared to experimental results. (c) 2024, The Japanese Society for Regenerative Medicine. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Publication Metadata only Preparation and identification of a novel 1,1′-(1,4-phenylenebis (methylene) bis (4-cyanopyridin-1-ium) bromide as a corrosion inhibitor for C1018 in highly acidic media(Elsevier B.V., 2024) Kuraimid, Zaidoun Khalaf; El-Sayed Fouda, Abd Aziz; Sajadi, S. Mohammad; Abid, Dawood S.; Wahba, Ahmed Mohamed; Jasim, Dehyaa J.; Salahshour, Soheil; Kuraimid, Zaidoun Khalaf, Department of Chemistry, Faculty of Science, Mansoura, Egypt; El-Sayed Fouda, Abd Aziz, Department of Chemistry, Faculty of Science, Mansoura, Egypt; Sajadi, S. Mohammad, Department of Nutrition, Cihan University-Erbil, Erbil, Iraq; Abid, Dawood S., Department of Chemistry, University of Basrah, Basra, Iraq; Wahba, Ahmed Mohamed, Department of Medical Sciences and Preparation Year, Northern College of Nursing, Arar, Saudi Arabia; Jasim, Dehyaa J., Department of Petroleum Engineering, Al-Amarah University College, Amarah, Iraq; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon1,1′-(1,4-phenylene-bis (methylene)) bis(4-cyanopyridin-1-ium) bromide (PCB) was synthesized and identified via spectral methods: Fourier-transform infrared (FTIR) spectroscopy, proton nuclear magnetic resonance hydrogen (1HNMR), and proton nuclear magnetic resonance carbon (13CNMR). The inhibitory effect (% IE) was determined using weight loss (WL) method, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) techniques for the corrosion of C1018 in strong (6 M) HCl. % IE reached 98.9 % at 200 mg/L, 313 K. The effects of the PCB concentration, HCl concentration, and temperature on the corrosion rate of C1018 were then confirmed using WL. The PDP curves indicate that PCB acts as mixed type- inhibitor. The adsorption of PCB obeyed the Langmuir adsorption isotherm. The adsorption of PCB on C1018 revealed that the adsorption process exhibiting physical and chemical adsorption. Theoretical modeling revealed the correlation between the QAS molecular chemical structure and its anticorrosive property. All the experimental and theoretical calculations were in good agreement. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only A comprehensive review of data analytics and storage methods in geothermal energy operations(Elsevier B.V., 2025) Basem, Ali A.; Al-Nussairi, Ahmed Kateb Jumaah; Khidhir, Dana Mohammad; Sawaran Singh, Narinderjit Singh; Baghoolizadeh, Mohammadreza; Fazilati, Mohammad Ali; Salahshour, Soheil; Sajadi, S. Mohammad; Hasanabad, Ali Mohammadi; Basem, Ali A., Faculty of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq; Al-Nussairi, Ahmed Kateb Jumaah, Al-Manara College for Medical Sciences, Amarah, Iraq; Khidhir, Dana Mohammad, Department of Petroleum Engineering, Knowledge University, Erbil, Iraq; Sawaran Singh, Narinderjit Singh, Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia; Baghoolizadeh, Mohammadreza, Department of Mechanical Engineering, Shahrekord University, Shahr-e Kord, Iran; Fazilati, Mohammad Ali, Efficiency and Smartization of Energy Systems Research Center, Khomeyni Shahr, Iran; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, Research Center of Applied Mathematics, Khazar University, Baku, Azerbaijan; Sajadi, S. Mohammad, Department of Chemistry, Payame Noor University, Tehran, Iran; Hasanabad, Ali Mohammadi, Fast Computing Center, Tehran, IranGeothermal energy storage (GES) systems are thoroughly examined in this research, with a focus on methods like borehole thermal energy storage (BTES), underground thermal energy storage (UTES), and aquifer thermal energy storage (ATES). It highlights the importance of thermal energy storage (TES) systems in addressing global energy challenges. The feasibility of UTES for large-scale energy storage and its integration with geothermal power plants is investigated. The ATES, with the advantage of large storage capacity and low operating costs has could be employed in regions with suitable aquifers. The adaptability of BTES to different ground conditions and its small land footprint made it a spotlight for the researchers. The study emphasizes the role of TES technologies in meeting the growing demand for renewable energy, reducing the impact of climate change, and providing efficient energy solutions for heating, ventilating, and air conditioning. HVAC systems. Also, the application of geothermal power plants and TES systems in decreasing the dependence on nonrenewable energy sources and increasing energy efficiency increase investigated. The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications. © 2025 Elsevier B.V., All rights reserved.