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Publication Metadata only The effect of the initial temperature, pressure, and shape of carbon nanopores on the separation process of SiO2 molecules from water vapor by molecular dynamics simulation(Elsevier Ltd, 2024) Mei, Bing; Jasim, Dehyaa J.; Alizadeh, As'ad; Hekmatifar, Maboud; Nasajpour-Esfahani, Navid; Salahshour, Soheil; Sabetvand, Roozbeh; Toghraie, Davood; Mei, Bing, College of Construction Engineering, Yunnan Agricultural University, Kunming, China; Jasim, Dehyaa J., Department of Petroleum Engineering, Al-Amarah University College, Amarah, Iraq; Alizadeh, As'ad, Department of Civil Engineering, Cihan University-Erbil, Erbil, Iraq; Hekmatifar, Maboud, Department of Mechanical Engineering, Islamic Azad University, Tehran, Iran; Nasajpour-Esfahani, Navid, College of Engineering, Atlanta, United States; 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, Lebanon; Sabetvand, Roozbeh, Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran; Toghraie, Davood, Department of Mechanical Engineering, Islamic Azad University, Tehran, IranToday, with the advancement of science in nanotechnology, it is possible to remove dust nanostructures from the air breathed by humans or other fluids. In the present study, the separation of SiO2 molecules from H2O vapor is studied using molecular dynamics (MD) simulation. This research studied the effect of initial temperature, nanopore geometry, and initial pressure on the separation of SiO2 molecules. The obtained results show that by increasing the temperature to 500 K, the maximum velocity (Max-Vel) of the samples reached 2.47 Å/fs. Regarding the increasing velocity of particles, more particles pass via the nanopores. Moreover, the shape of the nanopore could affect the number of passing particles. The results show that in the samples with a cylindrical nanopore, 20 and 40% of SiO2 molecules, and with the sphere cavity, about 32 and 38% of SiO2 particles passed in the simulated structure. So, it can be concluded that the performance of carbon nanosheets with a cylindrical pore and 450 K was more optimal. Also, the results show that an increase in initial pressure leads to a decrease in the passage of SiO2 particles. The results reveal that about 14 and 54% of Silica particles passed via the carbon membrane with increasing pressure. Therefore, for use in industry, in terms of separating dust particles, in addition to applying an EF, temperature, nanopore geometry, and initial pressure should be controlled. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis(Elsevier B.V., 2024) Ezhilarasan, N.; Augustin, Felix; Saraswathy, Ranganathan; Narayanamoorthy, Samayan; Salahshour, Soheil; Ahmadian, Ali; Kang, Daekook; Ezhilarasan, N., Division of Mathematics, Vellore Institute of Technology, Chennai, Chennai, India; Augustin, Felix, Division of Mathematics, Vellore Institute of Technology, Chennai, Chennai, India; Saraswathy, Ranganathan, Department of Radiology, Karpagam Faculty of Medical Sciences and Research, Coimbatore, India; Narayanamoorthy, Samayan, Department of Mathematics, Bharathiar University, Coimbatore, India; 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, Lebanon; Ahmadian, Ali, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Decisions Lab, Università degli Studi di Reggio Calabria, Reggio Calabria, Italy; Kang, Daekook, Department of Industrial Engineering and Management, Inje University, Gimhae, South KoreaObjectives: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a novel decision-support system under a bipolar intuitionistic fuzzy environment to examine an effective TB diagnosing method. Methods: To achieve the aim, a novel fuzzy decision support system is derived by integrating PROMETHEE and ARAS techniques. This technique evaluates TB diagnostic methods under the bipolar intuitionistic fuzzy context. Moreover, the defuzzification algorithm is proposed to convert the bipolar intuitionistic fuzzy score into crisp score. Results: The proposed method found that the sputum test (T3) is the most accurate in diagnosing TB. Additionally, comparative and sensitivity analyses are derived to show the proposed method's efficiency. Conclusion: The proposed bipolar intuitionistic fuzzy sets, combined with the PROMETHEE-ARAS techniques, proved to be a valuable tool for assessing effective TB diagnosing methods. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only A novel radial basis neural network for the Zika virus spreading model(Elsevier Ltd, 2024) sabir, Zulqurnain; Rada, Tino Bou; Kassem, Zeinab; Umar, Muhammad Awais; Salahshour, Soheil; sabir, Zulqurnain, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon; Rada, Tino Bou, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon; Kassem, Zeinab, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon; Umar, Muhammad Awais, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe motive of current investigations is to design a novel radial basis neural network stochastic structure to present the numerical representations of the Zika virus spreading model (ZVSM). The mathematical ZVSM is categorized into humans and vectors based on the susceptible S(q), exposed E(q), infected I(q) and recovered R(q), i.e., SEIR. The stochastic performances are designed using the radial basis activation function, feed forward neural network, twenty-two numbers of neurons along with the optimization of Bayesian regularization in order to solve the ZVSM. A dataset is achieved using the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) based on the process of training for solving the nonlinear ZVSM. The division of the data is categorized into training, which is taken as 78 %, while 11 % for both authentication and testing. Three different cases of the nonlinear ZVSM have been taken, while the scheme's correctness is performed through the matching of the results. Furthermore, the reliability of the scheme is observed by applying different performances of regression, MSE, error histograms and state transition. © 2024 Elsevier B.V., All rights reserved.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(Japanese Society of Regenerative Medicine, 2024) Li, Wei; Chen, Lingzhen; Sajadi, S. Mohammad; Baghaei, Sh; Salahshour, Soheil; Li, Wei, Department of Sports Medicine, General Hospital of People's Liberation Army, Beijing, China; Chen, Lingzhen, Department of Sports, Zhejiang Gongshang University, Hangzhou, China; Sajadi, S. Mohammad, Department of Nutrition, Cihan University-Erbil, Erbil, Iraq; Baghaei, Sh, Department of Mechanical Engineering, Islamic Azad University, Tehran, Iran; 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, LebanonStem 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. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Using hardystonite as a biomaterial in biomedical and bone tissue engineering applications(Elsevier Ltd, 2024) Wang, Haoyu; Sanghvi, Gaurav V.; Arefpour, Ahmadreza R.; Alkhayyat, Ahmed Hussein R.; Soheily, Ali; Jabbarzare, Saeid; Salahshour, Soheil; Alizadeh, As'ad; Baghaei, Sh; Wang, Haoyu, Medical College, Xijing University, Xi'an, China, Department of Orthopaedics, Xi'an Jiaotong University, Xi'an, China; Sanghvi, Gaurav V., Department of Microbiology, Marwadi University, Rajkot, India; Arefpour, Ahmadreza R., Department of Materials Engineering, Isfahan University of Technology, Isfahan, Iran; Alkhayyat, Ahmed Hussein R., Department of Computers Techniques Engineering, The Islamic University, Najaf, Najaf, Iraq, Department of Computers Techniques Engineering, The Islamic University, Najaf, Najaf, Iraq, Department of Computers Techniques Engineering, The Islamic University, Najaf, Najaf, Iraq; Soheily, Ali, Department of Materials Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran; Jabbarzare, Saeid, Department of Materials Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran; 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, Lebanon; Alizadeh, As'ad, Department of Mechanical Engineering, Urmia University, Urmia, Iran; Baghaei, Sh, Department of Mechanical Engineering, Islamic Azad University, Tehran, IranWidespread adoption for substitutes of artificial bone grafts based on proper bioceramics has been generated in recent years. Among them, calcium-silicate-based bioceramics, which possess osteoconductive properties and can directly attach to biological organs, have attracted substantial attention for broad ranges of applications in bone tissue engineering. Approaches exist for a novel strategy to promote the drawbacks of bioceramics such as the incorporation of Zn2+, Mg2+, and Zr4+ ions into calcium-silicate networks, and the improvement of their physical, mechanical, and biological properties. Recently, hardystonite (Ca2ZnSi2O7) bioceramics, as one of the most proper calcium-silicate-based bioceramics, has presented excellent biocompatibility, bioactivity, and interaction. Due to its physical, mechanical, and biological behaviors and ability to be shaped utilizing a variety of fabrication techniques, hardystonite possesses the potential to be applied in biomedical and tissue engineering, mainly bone tissue engineering. A notable potential exists for the newly developed bioceramics to help therapies supply clinical outputs. The promising review paper has been presented by considering major aims to summarize and discuss the most applicable studies carried out for its physical, mechanical, and biological behaviors. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only A numerical treatment through Bayesian regularization neural network for the chickenpox disease model(Elsevier Ltd, 2025) sabir, Zulqurnain; Mehmood, Muhammad Athar; Umar, Muhammad Awais; Salahshour, Soheil; Altun, Yener; Arbi, Adnène; Ali, Mohamed R.; sabir, Zulqurnain, Department of Mathematics and Statistics, Hazara University Pakistan, Mansehra, Pakistan; Mehmood, Muhammad Athar, Department of Mathematics, University of Gujrat, Gujrat, Pakistan; Umar, Muhammad Awais, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Altun, Yener, Faculty of Economic and Administrative Sciences, Van Yüzüncü Yıl Üniversitesi, Van, Turkey; Arbi, Adnène, Department of LIM (LR01ES13), University of Carthage, Tunis, Tunisia; Ali, Mohamed R., Faculty of Engineering, Benha National University, Benha, Egypt, Department of Basic Engineering Sciences, Faculty of Engineering at Benha, Benha, EgyptObjectives: The current research investigations designates the numerical solutions of the chickenpox disease model by applying a proficient optimization framework based on the artificial neural network. The mathematical form of the chickenpox disease model is divided into different categories of individuals, susceptible, vaccinated, infected, exposed, recovered, and infected with/without complications. Method: The construction of neural network is performed by using the single hidden layer and the optimization of Bayesian regularization. A dataset is assembled using the explicit Runge-Kutta technique for reducing the mean square error using the training 76 %, while 12 %, 12 % for validation and testing. The whole stochastic procedure is based on logistic sigmoid fitness function, single hidden layer structure with thirty neurons, along with the optimization capability of Bayesian regularization. Finding: The designed procedure's correctness and reliability is observed by results matching, negligible absolute error around 10−04 to 10−06, regression, error histogram, and state transmission. Moreover, the best performance values based on the mean square error are performed as 10−09 to 10−11. Novelty: The current neural network framework using the construction of a single hidden layer and the optimization of Bayesian regularization is applied first time to solve the chickenpox disease model. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only Harnessing the power of nanotechnology and intelligent wound dressings to transform sports injury recovery and healing(Editions de Sante, 2025) Wei, Feng; Siyu, Rong; Baghaei, Sh; Salahshour, Soheil; Wei, Feng, Department of Physical Education and Research, Central South University, Changsha, China; Siyu, Rong, Department of Physical Education and Research, Central South University, Changsha, China; Baghaei, Sh, Fast Computing Center, Tehran, Iran, Ceramic Engineering Research Center, Scientific and Research Town, Isfahan, 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, AzerbaijanAs the field of sports medicine continues to evolve, the integration of nanoparticle-based technologies and intelligent wound dressings is poised to revolutionize the way athletes recover from injuries. In the coming years, we can expect to see a surge in the development of highly sophisticated, multifunctional wound care solutions tailored specifically for the unique demands of athletic populations. Advancements in smart materials, such as stimuli-responsive hydrogels and self-healing dressings, will enable precise control over the wound microenvironment, promoting accelerated tissue regeneration and minimizing the risk of complications. The incorporation of wireless sensors and real-time monitoring capabilities into these intelligent dressings will empower clinicians to make data-driven decisions, optimizing treatment strategies and ensuring timely interventions. Furthermore, the integration of novel drug delivery systems (DDS), including biodegradable nanoparticles and transdermal patches, will facilitate the targeted administration of therapeutic agents, enhancing the efficacy of wound healing while reducing systemic side effects. Innovations in gas-releasing dressings and nanoenzyme-based therapies will expand the arsenal of tools available to sports medicine professionals, addressing a wider range of wound types and complexities. As these cutting-edge technologies mature and transition into clinical practice, athletes will benefit from expedited recovery times, improved functional outcomes, and a swifter return to their respective sports. The convergence of nanotechnology, smart materials, and data-driven healthcare is poised to usher in a new era of personalized, precision-based wound care in the world of sports medicine. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only A stochastic neural network procedure for the nonlinear typhoid fever disease system(Springer, 2025) sabir, Zulqurnain; Akkilic, Ayse Nur; Bulut, Hasan; Umar, Muhammad Awais; Salahshour, Soheil; Saba, Iram; sabir, Zulqurnain, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon; Akkilic, Ayse Nur, Department of Mathematics, Firat Üniversitesi, Elazig, Turkey; Bulut, Hasan, Department of Mathematics, Firat Üniversitesi, Elazig, Turkey, Azerbaijan University, Baku, Azerbaijan; Umar, Muhammad Awais, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Saba, Iram, Department of Chemistry, Government College Women University Sialkot, Sialkot, PakistanThe aim of this work is to provide the numerical results of the typhoid fever disease system by applying an artificial neural network. The nonlinear typhoid fever disease system is considered into susceptible, exposed, infected, and recovered. The typhoid fever disease system is one of the nonlinear models and numerical results of the system are accomplished via stochastic computing scheme. The optimization is performed by using the Levenberg-Marquardt backpropagation (LMQBP) neural network for solving the nonlinear typhoid fever disease system. An explicit Runge-Kutta solver implemented to calculate the dataset, which is used to lessen the mean square error by data separating into testing (10%), training (70%), and validation (20%). The proposed stochastic scheme is implemented by taking sixteen neurons, log-sigmoid transfer function in the hidden layer, with the input and output layer structure for solving the typhoid fever disease system. The exactness of the scheme is validated by applying the assessment of reference and obtained outputs along with negligible values of the absolute error. Furthermore, the statistical presentations using various disciplines are implemented to indorse the approach’s consistency. The proposed stochastic scheme is implemented first time to solve the nonlinear typhoid fever disease system. © 2025 Elsevier B.V., All rights reserved.