Soheila Ghasemi | Mathematical Chemistry | Best Researcher Award

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Assoc. Prof. Dr. Soheila Ghasemi | Mathematical Chemistry | Best Researcher Award

Soheila Ghasemi | Shiraz University | Iran

Assoc. Prof. Dr. Soheila Ghasemi is a materials chemist specializing in polymer-based nanocatalysts, smart drug-delivery systems, and environmentally responsive nanomaterials. Her research focuses on designing functionalized polymers, metal nanoparticle catalysts, and thermo/pH-responsive nanogels for applications in catalysis, environmental remediation, and cancer therapeutics. She has contributed significantly to the development of sustainable catalytic systems, including Pd- and Au-based nanocomposites, as well as advanced polymer networks for targeted drug delivery. Her work demonstrates strong interdisciplinary integration across polymer chemistry, nanotechnology, catalysis, and biomedical materials, and has been cited across leading journals in chemistry and materials science.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  1. Kafshboran, H. R., & Ghasemi, S. (2023). Au nanoparticles decorated on EDTA-functionalized poly(NIPAM-co-allylamine) grafted Fe₃O₄ for reduction of nitroarenes. Materials Chemistry and Physics, 308, 128237. Citations: 9
    Year: 2023

  2. Ghasemi, S., Zare, N., Ghezelsofloo, M., Dehghani, A., & Kafshboran, H. R. (2025). Thermo-responsive poly(N-isopropyl acrylamide-b-vinylimidazole)/Pd catalyst: Catalytic application of Suzuki–Miyaura coupling reaction in water. Catalysis Surveys from Asia, 29(2), 139–154. Citations: 8
    Year: 2025

  3. Ghasemi, S., Najafi, M., Doroudian, M., Rastegari, B., Behzad-Behbahani, A., et al. (2025). Temperature- and pH-responsive poly(NIPAM-co-HEMA-co-AAm) nanogel as a smart vehicle for doxorubicin delivery: Combating colorectal cancer. Gels, 11(4), 227. Citations: 8
    Year: 2025

  4. Tamami, B., & Ghasemi, S. (2012). Copper- and amine-free Sonogashira–Hagihara coupling reaction catalyzed by Pd(0) nanoparticles supported on modified crosslinked polyacrylamide. Collection of Czechoslovak Chemical Communications, 76(12), 1967–1978. Citations: 7
    Year: 2012

  5. Ghasemi, S., & Ghezelsofloo, M. (2023). Isocyanate-free urethane vinyl ester resin: Preparation, characterization, and investigation of thermal and mechanical properties. Chemical Papers, 77(2), 1165–1180. Citations: 6
    Year: 2023

Fuat Türk | Optimization | Best Researcher Award

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Assist. Prof. Dr. Fuat Türk | Optimization | Best Researcher Award

Researcher| Gazi University | Turkey

Assist. Prof. Dr. Fuat Türk is a researcher in artificial intelligence, medical image analysis, and machine learning, currently affiliated with Gazi University, Turkey. His research focuses on developing intelligent diagnostic and segmentation models for healthcare applications, including kidney and renal tumor detection, lung opacity analysis, heart disease prediction, and cancer diagnosis. He has contributed to advancing hybrid deep-learning architectures, CNN-based image fusion models, machine learning–driven feature selection, and optimization techniques for clinical decision support systems. Dr. Türk’s work integrates computational modeling with real-world medical datasets, aiming to improve diagnostic accuracy, automate radiological workflows, and support early disease detection. His published works demonstrate a strong interdisciplinary approach bridging mathematics, computer vision, and biomedical engineering, and his studies have been cited widely in the fields of medical imaging and machine learning.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  1. Türk, F., Lüy, M., & Barışçı, N. (2020). Kidney and renal tumor segmentation using a hybrid V-Net-based model. Mathematics, 8(10), 1772. Citations: 98

  2. Türk, F. (2023). Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(2), 465–477. Citations: 59

  3. Türk, F., & Kökver, Y. (2023). Detection of lung opacity and treatment planning with three-channel fusion CNN model. Arabian Journal for Science and Engineering, 49, 2973–2985. Citations: 17

  4. Türk, F. (2024). Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection. Signal, Image and Video Processing. Citations: 15

  5. Akkur, O. E. E., & Türk, F. (2022). Breast cancer diagnosis using feature selection approaches and Bayesian optimization. Computer Systems Science and Engineering, 45(2), 1017–1031. Citations: 13

Jose F Carinena | Mathematical Physics | Editorial Board Member

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Prof. Jose F Carinena | Mathematical Physics | Editorial Board Member

Emeritus Professor | Universidad de Zaragoza | Spain

Prof. José F. Cariñena is a distinguished mathematical physicist at the University of Zaragoza, Spain, recognized for his foundational contributions to differential geometry, classical and quantum mechanics, and geometric methods in dynamical systems. His work explores the geometry underlying Lagrangian and Hamiltonian mechanics, Lie systems, symmetries, and variational principles. Through influential publications on canonoid transformations, master symmetries, fibre bundle connections, and the geometric formulation of Noether’s theorem, Prof. Cariñena has advanced the mathematical structures that support modern theoretical physics. His research continues to shape the geometric understanding of dynamical systems and their applications across physics and mathematical analysis.

Profiles: Scopus | Google Scholar

Featured Publications

  1. Cariñena, J. F., & Fernández Núñez, J. (2016). Geometric approach to dynamics obtained by deformation of Lagrangians. Nonlinear Dynamics, 83(1), 457–461.
    Citations: 27
    Year: 2016

  2. Cariñena, J. F., Falceto, F., & Ranada, M. F. (2013). Canonoid transformations and master symmetries. arXiv preprint arXiv:1303.6225.
    Citations: 27
    Year: 2013

  3. Cariñena, J. F., & Ramos, A. (2005). Lie systems and connections in fibre bundles: Applications in quantum mechanics. In Differential Geometry and Its Applications (pp. 437–452).
    Citations: 27
    Year: 2005

  4. Cariñena, J. F., & Figueroa, H. (1994). A geometrical version of Noether’s theorem in supermechanics. Reports on Mathematical Physics, 34(3), 277–303.
    Citations: 27
    Year: 1994

  5. Cariñena, J. F., López, C., & Román-Roy, N. (1988). Origin of the Lagrangian constraints and their relation with the Hamiltonian formulation. Journal of Mathematical Physics, 29, 1143.
    Citations: 27
    Year: 1988

Shinichi Watanabe | Statistics | Best Researcher Award

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Assist. Prof. Dr. Shinichi Watanabe | Statistics | Best Researcher Award

Associate Professor| Gifu University of Health Science | Japan

Dr. Shinichi Watanabe is an Associate Professor of Physical Therapy at Gifu University of Health Science, specializing in ICU early rehabilitation, mobilization dose quantification, diaphragm ultrasound assessment, and functional outcome prediction. He has contributed extensively to national multicenter networks including IPAM, IPAMICS, BRIDGE, and J-RELIFE, completing more than 15 large-scale cohort and observational studies. His research focuses on rehabilitation metrics, respiratory and critical care rehabilitation, and data-driven prognostication. With over 100 peer-reviewed publications, he also serves as a reviewer and guest editor for multiple international journals and collaborates with academic hospitals and ICU research groups across Japan.

Profiles: Scopus | Orcid

Featured Publications

1. One-year outcomes in sepsis: a prospective multicenter cohort study in Japan

Watanabe, S., et al. (2025). One-year outcomes in sepsis: A prospective multicenter cohort study in Japan. Journal of Intensive Care, 13(1), 23.
Citations: 3
Year: 2025

2. Correction to: One-year outcomes in sepsis: a prospective multicenter cohort study in Japan

Watanabe, S., et al. (2025). Correction to: One-year outcomes in sepsis: A prospective multicenter cohort study in Japan. Journal of Intensive Care, 13(1), Article 23 Correction.
Citations: 0
Year: 2025

3. The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024

Watanabe, S., et al. (2025). The Japanese clinical practice guidelines for management of sepsis and septic shock 2024. Journal of Intensive Care, 13(1).
Citations: 14
Year: 2025

Yaser Damchi | Faculty of Electrical Engineering | Editorial Board Member

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Assoc. Prof. Dr. Yaser Damchi | Faculty of Electrical Engineering | Editorial Board Member

Faculty of Electrical Engineering | Shahrood University of Technology | Iran

Dr. Yaser Damchi is an Associate Professor in the Faculty of Electrical Engineering at Shahrood University of Technology, Iran, specializing in advanced power system analysis. His research focuses on power system protection, relay coordination, fault detection, power swing analysis, power system reliability, and renewable energy integration.

He has developed innovative methods for detecting high-impedance and low-impedance faults, identifying abnormal power swing conditions, improving reliability-centered maintenance practices, and enhancing fault-location algorithms in modern transmission systems. His work bridges theoretical modeling and practical applications, with solutions applicable to distribution networks, transmission lines, and renewable-integrated grids.

Dr. Damchi’s contributions have appeared in leading journals such as Electric Power Systems Research, Computers and Electrical Engineering, International Journal of Electrical Power and Energy Systems, and International Transactions on Electrical Energy Systems. His research continues to support the evolution of secure, intelligent, and reliable power systems.

Profiles: Scopus | Orcid

Featured Publications

Alaei, S. A., & Damchi, Y. (2023). A new method based on the discrete time energy separation algorithm for high and low impedance faults detection in distribution systems. Electric Power Systems Research. https://doi.org/10.1016/j.epsr.2023.109200

Taheri, R., Eslami, M., & Damchi, Y. (2023). A current-based algorithm for one-end fault location in series capacitor compensated double-circuit transmission lines. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2023.10861

Kaffashbashi, A., & Damchi, Y. (2023). Statistical approach for detection of fault and stable and unstable power swings based on signal energy. International Journal of Electrical Power and Energy Systems. https://doi.org/10.1016/j.ijepes.2022.108638

Abbasghorbani, M., Damchi, Y., & Mashhadi, H. R. (2022). Reliability-centered maintenance for overhead transmission lines in composite power system. International Transactions on Electrical Energy Systems. https://doi.org/10.1155/2022/1170269

Damchi, Y., & Eivazi, A. (2022). Power swing and fault detection in the presence of wind farms using generator speed zero-crossing moment. International Transactions on Electrical Energy Systems. https://doi.org/10.1155/2022/2569810

A. Eswari | Differential Equation | Editorial Board Member

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Dr. A. Eswari | Differential Equation | Editorial Board Member

Assistant Professor | HC&RI, Tamil Nadu Agricultural University, Periyakulam | India

Dr. A. Eswari is a researcher in applied mathematics with strong expertise in mathematical modeling, reaction–diffusion systems, biosensor modeling, and heat and mass transfer analysis. Her doctoral work focused on deriving analytical solutions for systems of coupled reaction–diffusion equations in microelectrodes, contributing to advancements in electrochemical modeling. Over the years, she has expanded her research into interdisciplinary areas including thermal engineering, enzyme kinetics, nanofluid dynamics, and agricultural economics.

Her research output reflects a blend of theoretical rigor and real-world application, especially in chemical kinetics, thermal systems, and biosensor technology. She has published in reputable international journals and edited volumes, collaborating with researchers across mathematics, engineering, and applied sciences. Her contributions include analytical methods for nonlinear differential equations, studies on temperature-dependent thermal conductivity, and modeling of biological and industrial processes.

Dr. Eswari has also served in various academic and research roles across engineering and agricultural institutions, contributing to teaching, mentoring, and curriculum development. Her achievements include multiple merit awards for academic excellence at undergraduate, postgraduate, and M.Phil. levels.

With publications spanning over a decade, Dr. Eswari continues to work in applied mathematical analysis, focusing on deriving analytical and semi-analytical solutions for complex physical, chemical, and biological systems.

Profile: Google Scholar

Featured Publications

  1. Ananthi, S. P., Manimozhi, P., Praveen, T., Eswari, A., & Rajendran, L. (2013). Mathematical modeling and analysis of the kinetics of thermal inactivation of enzyme. International Journal of Engineering Mathematics, 2013(1), Article 132827. (Citations: 7)

  2. Meena, A., Eswari, A., & Rajendran, L. (2011). Mathematical modeling of biosensors: Enzyme–substrate interaction and biomolecular interaction. In New perspectives in biosensors technology and applications (Vol. 1). (Citations: 7)

  3. Saravanakumar, S., Eswari, A., Makinde, O. D., Anbazhagan, N., Joshi, G. P., et al. (2023). Analysis of temperature-dependent thermal conductivity and fin efficiency: Direct Akbari–Ganji method. Case Studies in Thermal Engineering, 51, Article 103627. (Citations: 6)

  4. Srinivasan, M., Raj, S. V., & Eswari, A. (2018). Demand and supply analysis of roundwood in India. Madras Agricultural Journal, 105(1–3), 1. (Citations: 6)

  5. Eswari, A., Maragatham, L., Anbazhagan, N., Joshi, G. P., & Cho, W. (2024). Analytical investigation of heat and mass transfer in MHD nanofluid flow past a moving vertical plate. Case Studies in Thermal Engineering, 60, Article 104642. (Citations: 5)

Senthilkumar R | Data Science | Editorial Board Member

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Dr. Senthilkumar R | Data Science | Editorial Board Member

Assistant Professor / CSE | Hindustan Institute of Technology | India

Dr. Senthilkumar R is a researcher and academic specializing in Internet of Things (IoT), artificial intelligence, fog and edge computing, machine learning, and big data analytics. With over 12 years of academic experience, he has contributed to innovative system designs including AI-enabled monitoring systems, IoT-based automation, embedded intelligence, and deep learning–driven applications. His work spans smart environments, air quality monitoring, emergency alert systems, and AI-powered automation solutions. He has published in reputed international journals such as Elsevier and IOS Press, with research touching multiple domains including communication systems, intelligent sensing, and emotion detection. His achievements include industry-recognized innovation projects, government honors such as the Chief Minister’s Award of Excellence, and contributions to books and edited volumes in artificial intelligence and psychological computing.

Profiles: Scopus | Orcid 

Featured Publications

1. Senthilkumar. (2024). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, Article 103934. Citation count: 0. Year: 2024.

2. Senthilkumar. (2024). Machine and deep learning techniques for emotion detection. In Advances in Psychology, Mental Health, and Behavioral Studies (Edited book). IGI Global. Citation count: 0. Year: 2024.

3. Senthilkumar, R., Venkatakrishnan, P., & Balaji, N. (2022). IoT-based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques. Journal of Intelligent & Fuzzy Systems. Citation count: 0. Year: 2022.

4. Senthilkumar, R., Venkatakrishnan, P., & Balaji, N. (2020). Intelligent-based novel embedded system: IoT-enabled air pollution monitoring system. Microprocessors and Microsystems, 103172. Citation count: 0. Year: 2020.

yanan Camaraza-Medina | Integracion Numerica | Editorial Board Member

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Dr. yanan Camaraza-Medina | Integracion Numerica | Editorial Board Member

Postdoctoral Research | University of Guanajuato | Mexico

Yanan Camaraza Medina is a mechanical engineer with 15 years of experience in thermal and energy systems, specializing in convective heat transfer, thermal radiation, and industrial heat-transfer equipment. His work integrates experimental, analytical, and numerical methods to improve the performance, safety, and efficiency of thermal systems used in energy generation and industrial applications. With academic and industrial expertise, he has served as a department head in thermal engineering, university professor, and researcher, contributing to advancements in thermophysical property modeling, phase-change processes, and multiphysics simulations. His research includes the development of predictive correlations, heat-transfer models, and computational strategies applied to real-world engineering problems.

Profiles: Scopus | Orcid 

Featured Publications

Camaraza Medina, Y. (2025). Proximate and ultimate analysis, higher heating value and inorganic chemical composition of woods from central region of Cuba. Sustainable Chemistry One World.

Camaraza Medina, Y., et al. (2025). Heat transfer modeling during condensation inside tubes with arbitrary geometrical orientations. Heat Transfer.

Camaraza Medina, Y. (2025). Experimental correlation of the steel’s thermophysical properties for thermal engineering applications. Heat Transfer.

Camaraza Medina, Y., et al. (2025). Multiphysics analysis of electric arc extinction in low voltage switchgear: Electromagnetic, thermal, and fluid dynamics interactions. Thermal Science and Engineering Progress.

Camaraza Medina, Y., et al. (2025). Analysis of transient heat conduction in tubes under convective boundary conditions. Heat Transfer.

Josep Maria Bofill | Mathematical Chemistry | Best Researcher Award

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Prof. Dr. Josep Maria Bofill | Mathematical Chemistry | Best Researcher Award

Chair Professor | University of Barcelona | Spain

Prof. Dr. Josep Maria Bofill is a renowned theoretical chemist at the University of Barcelona, Spain, recognized for his pioneering work in computational chemistry, reaction mechanisms, and molecular dynamics. His research explores the quantum mechanical modeling of chemical reactions, mechanochemical pathways, and solvent effects in molecular systems. With decades of impactful publications and international collaborations, Prof. Bofill has significantly advanced understanding in theoretical and computational chemistry, particularly in reaction dynamics and atmospheric chemistry. His interdisciplinary approach combines physics, chemistry, and computation to explain complex molecular behavior and energy transformations at the atomic level.

Profiles: Orcid | Google Scholar 

Featured Publications

Coral, J., Urbiola, M., Sabaté, E., Bofill, J., Lleixà, T., & Vilà Baños, R. (2020). Does the teaching of physical education in a foreign language jeopardise children’s physical activity time? International Journal of Bilingual Education and Bilingualism, 23(8), 839–854. Citation count: 58.

Luque, F. J., Bofill, J. M., & Orozco, M. (1995). New strategies to incorporate the solvent polarization in self-consistent reaction field and free-energy perturbation simulations. The Journal of Chemical Physics, 103(23), 10183–10191. Citation count: 54.

Olivella, S., Solé, A., & Bofill, J. M. (2009). Theoretical mechanistic study of the oxidative degradation of benzene in the troposphere: Reaction of benzene–HO radical adduct with O₂. Journal of Chemical Theory and Computation, 5(6), 1607–1623. Citation count: 52.

Moreira, I. P. R., Bofill, J. M., Anglada, J. M., Solsona, J. G., Nebot, J., & Romea, P. (2008). Unconventional biradical character of titanium enolates. Journal of the American Chemical Society, 130(11), 3242–3243. Citation count: 50.

Quapp, W., & Bofill, J. M. (2016). A contribution to a theory of mechanochemical pathways by means of Newton trajectories. Theoretical Chemistry Accounts, 135(4), 113. Citation count: 44.

Heba Kurdi | Nature-inspired computing | Best Academic Researcher Award

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Prof. Heba Kurdi | Multi-View Data | Best Academic Researcher Award

Full Professor | King Saud University | Saudi Arabia

Prof. Heba Kurdi is a leading researcher in distributed artificial intelligence and cloud computing at King Saud University, Saudi Arabia. A Stanford-ranked top 2% scientist (2022–2024), she has produced over 120 research publications and holds eight patents in AI-driven systems, multi-agent technologies, and computational optimization. Her work spans distributed AI, swarm intelligence, and smart system design, with applications in robotics, cloud infrastructures, and cyber-physical systems. Prof. Kurdi has led more than 15 funded projects and consulted on national-level research initiatives with institutions such as KACST and Aramco. As an MIT Fellow and IEEE Senior Member, she continues to advance the integration of bio-inspired algorithms and AI-driven models to enhance system efficiency, scalability, and intelligence in both industrial and academic settings.

Profiles: Orcid | Google Scholar 

Featured Publications

Al-Rayis, E., & Kurdi, H. (2013). Performance analysis of load balancing architectures in cloud computing. 2013 European Modelling Symposium, 520–524. Citation count: 44.

Aljalaud, F., Kurdi, H., & Youcef-Toumi, K. (2023). Bio-inspired multi-UAV path planning heuristics: A review. Mathematics, 11(10), 2356. Citation count: 41.

Kurdi, H., Almulifi, A., Al-Megren, S., & Youcef-Toumi, K. (2021). A balanced evacuation algorithm for facilities with multiple exits. European Journal of Operational Research, 289(1), 285–296. Citation count: 39.

Kurdi, H., Alkhaider, S., & Alfaifi, N. (2014). Development and evaluation of a web-based question answering system for Arabic language. Computer Science & Information Technology (CS & IT), 4(2), 187–202. Citation count: 38.

Althnian, A., Aloboud, N., Alkharashi, N., Alduwaish, F., Alrshoud, M., & Kurdi, H. (2020). Face gender recognition in the wild: An extensive performance comparison of deep-learned, hand-crafted, and fused features with deep and traditional models. Applied Sciences, 11(1), 89. Citation count: 36.