Pascal Vollenweider | Software Engineering | Best Researcher Award

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Mr. Pascal Vollenweider| Software Engineering | Best Researcher Award

Biomechanical Engineer | Straumann Group| Switzerland

Mr. Pascal Vollenweider is a highly skilled biomechanical engineer whose work bridges mechanical engineering and biomedical science, with a strong focus on orthodontic biomechanics and laser-based material processing. He currently serves as a Biomechanical Engineer at Ortho RDI, Institut Straumann AG in Basel, where he specializes in in-vitro testing of clear aligners and the development of advanced experimental methods. He holds a Master of Science in Life Science with a specialization in Biomedical Engineering from FHNW Muttenz (2020–2023), where his thesis on the Biomechanical Investigation of Orthodontic Tooth Movements Induced by Clear Aligners earned the highest distinction, demonstrating his ability to integrate engineering precision with biological understanding to enhance clinical orthodontic applications. His earlier academic training includes a Bachelor of Science in Mechanical Engineering from FHNW Brugg-Windisch, with a focus on Production Engineering, where his research on Laser Machining of PEEK for Security Features on Surfaces reflected his proficiency in high-precision manufacturing and materials science. Professionally, he has contributed to the Institute for Product and Production Engineering at FHNW, engaging in laser-based surface structuring, supervising student research, and supporting industrial collaborations. His multidisciplinary expertise enables him to contribute to the development of next-generation orthodontic devices and biomedical solutions that combine mechanical innovation with patient-centered design. Fluent in German, English, and French, Vollenweider demonstrates strong potential for global collaboration and continued research excellence. His work embodies a commitment to innovation, scientific rigor, and societal benefit through engineering-driven advancements in healthcare technology.

Featured Publication

Vollenweider, P. (2023). Validation of the quantitative case analysis method for measuring orthodontic tooth movement. Journal of Biomedical Engineering and Orthodontic Research, 15(2), 145–156.

Hawazin Elani | Machine Learning | Best Researcher Award

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Dr. Hawazin Elani | Machine Learning | Best Researcher Award

Harvard University | United States

Dr. Hawazin W. Elani, Ph.D., is an accomplished scholar and academic leader whose research integrates dentistry, epidemiology, and health policy to advance oral health equity through data-driven, interdisciplinary approaches. She serves as an Associate Professor in the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health and in the Department of Oral Health Policy and Epidemiology at the Harvard School of Dental Medicine, with additional affiliations at the Harvard Data Science Initiative and the Kempner Institute for the Study of Natural and Artificial Intelligence. Dr. Elani earned her Ph.D. in Dental Sciences with a concentration in Epidemiology and Population Health and an M.Sc. from McGill University, as well as an MMSc in Oral Biology and a Clinical Certificate in Prosthodontics from Harvard. Her research explores health disparities, oral health policy, and the application of artificial intelligence and machine learning in predicting oral health outcomes. She has authored over 30 peer-reviewed publications in high-impact journals such as Health Services Research, JAMA Network Open, and Journal of Dental Research, with her work cited widely for shaping discussions on healthcare access and reform. As principal investigator on multiple NIH and foundation-funded projects, including R01 and K-series grants, she has led innovative studies assessing the effects of Medicaid expansion and socioeconomic factors on dental care utilization. Recognized with Harvard’s Young Mentor Award and Distinguished Junior Faculty Award in 2024, Dr. Elani also contributes to national and international committees, including the NIH, the National Academies of Sciences, and the Medicaid Policy Research Advisory Group. Through her leadership, global collaborations, and dedication to mentoring, she continues to advance the intersection of artificial intelligence, population health, and oral health policy, driving forward equitable and sustainable improvements in healthcare delivery worldwide.

Profiles: Scopus | ORCID
Featured Publication

lani, H. W., Kawachi, I., & Sommers, B. D. (2020). Changes in emergency department dental visits after Medicaid expansion. Health Services Research, 55(1), 76–84.

Elani, H. W., Simon, L., Ticku, S., Bain, P. A., Barrow, J., & Riedy, C. A. (2018). Does providing dental services reduce overall health care costs? A systematic review of the literature. Journal of the American Dental Association (1939), 149(6), 430–438.e10.

Elani, H. W., Starr, J. R., Da Silva, J. D., & Gallucci, G. O. (2018). Trends in dental implant use in the U.S., 1999–2016, and projections to 2026. Journal of Dental Research, 97(13), 1424–1430.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., Cleber, S., & Bittner, N. (2019). Comparison of the color appearance of peri-implant soft tissue with natural gingiva using anodized pink-neck implants and pink abutments: A prospective clinical trial. The International Journal of Oral & Maxillofacial Implants, 34(1), 168–175.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., & Bittner, N. (2017). A prospective clinical trial to assess the optical efficacy of pink neck implants and pink abutments on soft tissue esthetics. Journal of Esthetic and Restorative Dentistry, 29(3), 213–219.

Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

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Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Daniel Atnafu Chekole | Computational Theory | Best Researcher Award

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Mr. Daniel Atnafu Chekole | Computational Theory | Best Researcher Award

Researcher | Space Science and Geospatial Institute | Ethiopia

Mr. Daniel Chekole specializes in atmospheric and space physics, with focused expertise in ionospheric modeling, space weather forecasting, and heliospheric studies. His research integrates ground-based and satellite data to investigate ionospheric dynamics, magnetospheric processes, and their coupling with solar-terrestrial interactions. His scientific contributions emphasize the development and validation of regional ionospheric and atmospheric models using advanced computational methods and machine learning algorithms. Daniel has played a leading role in projects such as the development of regional HF propagation and ionospheric models, prediction of solar energetic particle flux using artificial intelligence, and the establishment of monitoring systems like the Mini-Neutron Monitor. His scholarly work explores low-frequency plasma waves, magnetohydrodynamic instabilities, and the effects of rotation and self-gravity in plasma environments, contributing to the understanding of astrophysical and geophysical plasma systems. Through publications in reputed journals, he has analyzed the performance of ionospheric models such as NeQuick-2 and IRI-Plas over East Africa, evaluated solar and geomagnetic activity indices, and examined storm-time ionospheric irregularities. His technical proficiency spans MATLAB, Python, and MHD simulation tools, which he applies in the modeling and forecasting of space weather phenomena relevant to communication and navigation systems. Daniel’s participation in international workshops and collaborations with institutions such as NASA, UCAR/CPAESS, and DLR reflects a strong engagement in the global heliophysics and space science community. His ongoing work continues to contribute to regional and international initiatives aimed at enhancing predictive capabilities for solar-terrestrial disturbances and improving understanding of ionospheric variability over equatorial regions. Daniel Chekole’s research contributions are reflected in 17 citations, 5 documents, and an h-index of 2 (View h-index).

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Chekole, D. A., Giday, N. M., & Nigussie, M. (2019). Performance of NeQuick-2, IRI-Plas 2017 and GIM models over Ethiopia during varying solar activity periods. Journal of Atmospheric and Solar-Terrestrial Physics, 195, 105117. Cited by 14.

Moges, S. T., Giday, N. M., Chekole, D. A., Ulich, T., & Sherstyukov, R. O. (2022). Storm-time observations of traveling ionospheric disturbances and ionospheric irregularities in East Africa. Radio Science, e2022RS007426. Cited by 7.

Strauss, R. D., Giday, N. M., Seba, E. B., Chekole, D. A., Garuma, G. F., Kassa, B. H., & others. (2023). First results from the ENTOTO neutron monitor: Quantifying the waiting time distribution. Advances in Space Research, 72(3), 805–815. Cited by 5.

Garuma, G. F., Tessema, S. B., Tiky, A. Y., Addis, Z. W., Adde, Y. A., Giday, N. M., & others. (2022). First Ethiopian Remote Sensing Satellite (ETRSS-1): Mission information and overview. Authorea Preprints. Cited by 5.

Chekole, D. A., & Giday, N. M. (2020). Evaluation of ionospheric and solar proxy indices for IRI-Plas 2017 model over the East African equatorial region during solar cycle 24. Advances in Space Research, 66(3), 604–611. Cited by 3.

Yuan Yang | Data Visualization | Women Researcher Award

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Ms. Yuan Yang | Data Visualization | Women Researcher Award

Engineer | Shanxi university | China

Ms. Yuan Yang focuses on the interdisciplinary study of organic functional molecules and biomacromolecular systems, bridging chemistry and biology to explore molecular mechanisms and develop advanced functional materials with technological relevance. Yuan’s research aims to elucidate the structural and functional dynamics of biomacromolecules, contributing to a deeper understanding of how molecular architecture governs biological activity and material properties. Through investigations that integrate organic synthesis, biophysical characterization, and structural biology, Yuan seeks to design and engineer innovative molecular systems with applications in biotechnology, materials science, and medicinal chemistry. Yuan has contributed to multiple scientific publications indexed in SCI and Scopus, three patents under various stages of development, and several collaborative projects involving top research institutions such as Sun Yat-sen University and the Chengdu Institute of Biology of the Chinese Academy of Sciences. Yuan’s collaborative efforts also extend to industrial partners engaged in technological innovation, leading to recognition such as the First Prize of the Shanxi Provincial Technological Invention Award in 2023. With a strong foundation in molecular-level research, Yuan’s work enhances the understanding of structure–function relationships and facilitates the creation of new-generation bio-inspired materials. The research demonstrates a commitment to advancing sustainable and impactful scientific innovations that can drive progress in healthcare, environmental technology, and materials development. Yuan Yang continues to explore emerging directions in functional molecular design, aiming to contribute to the advancement of interdisciplinary science and technological transformation. This research reflects a vision that combines fundamental discovery with real-world applications to address complex challenges at the interface of chemistry, biology, and materials engineering.

Profiles: ORCID
Featured Publication

Yuan, Y., Zhang, B., & Zhang, J. (2025, November). Dynamic catalytic domain plasticity governs substrate specificity in industrial serine proteases: Structural and functional implications. International Journal of Biological Macromolecules.

Yuan, Y., Niu, L. J., & Zhang, B. X. (2025, October). Design, synthesis, biological evaluation, and molecular docking of heterocyclic aromatic thioether derivatives containing amides. ChemistrySelect.

Yuan, Y., Zhang, B., & Zhang, J. (2024). Probing the binding mode and interactions of proteinase K and glutathione: Molecular simulation and experiments. Soft Matter.

Yuan, Y., Niu, L., Yan, Z., Ye, S., & Zhang, B. (2024). Synthesis and antitumor activity of benzoheterocyclic aryl sulfide. Chinese Journal of Organic Chemistry.

Sirous Rafiei Asl | Computer Vision | Best Researcher Award

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Dr. Sirous Rafiei Asl | Computer Vision | Best Researcher Award

Medical Student | Ahvaz Jundishapur University of Medical Sciences | Iran

Dr. Safa Najafi’s research focuses on the intersection of medical education and parasitology, with particular attention to Leishmaniasis and other parasitic diseases prevalent in tropical and subtropical regions. Her work emphasizes evaluating medical students’ knowledge, awareness, and performance toward parasitic infections to identify gaps that hinder effective disease prevention and control. Through descriptive and analytical studies, she explores the relationship between demographic factors, clinical exposure, and academic performance in shaping medical students’ understanding of zoonotic diseases such as Leishmania infections. The findings of her research highlight that enhanced awareness and practical performance among future healthcare professionals play a critical role in public health preparedness and vector control strategies. Safa Najafi also investigates behavioral and environmental determinants of disease transmission and advocates for integrating targeted educational programs, including mobile-based learning and seminar-based interventions, into medical curricula to strengthen clinical competencies and promote early prevention. Her studies contribute to developing evidence-based strategies to reduce leishmaniasis transmission by bridging the gap between theoretical knowledge and field application. By analyzing key epidemiological factors, her research supports the design of culturally relevant training programs that empower medical students and healthcare providers to adopt preventive practices effectively. This work aligns with broader goals in global health to mitigate the burden of parasitic diseases through informed medical practice and community education. Overall, her research advances understanding of how educational approaches can shape health behavior and influence disease outcomes, reinforcing the significance of awareness, attitudes, and practices in sustainable disease management. Safa Najafi’s scholarly contributions are reflected in her academic record, with 2 Citations, 3 Documents, and an h-index of 1. View h-index.

Profiles: Google Scholar |  Scopus | ORCID
Featured Publication

Elahi, R. K., Asl, S., & Shahian, F. (2013). Study on the effects of various doses of Tribulus terrestris extract on epididymal sperm morphology and count in rat. Iranian Journal of Reproductive Medicine, 11(3), 207–212. Citations: 46

Mahdavinia, M., Alizadeh, S., Vanani, A. R., Dehghani, M. A., Shirani, M., et al. (2019). Effects of quercetin on bisphenol A-induced mitochondrial toxicity in rat liver. Iranian Journal of Basic Medical Sciences, 22(5), 499. Citations: 18

Moradi, M., Montazeri, E. A., Rafiei Asl, S., Pormohammad, A., Farshadzadeh, Z., et al. (2025). In vitro and in vivo antibacterial and antibiofilm activity of zinc sulfate (ZnSO₄) and carvacrol (CV) alone and in combination with antibiotics against Pseudomonas aeruginosa. Antibiotics, 14(4), 367. Citations: 5

Rafiei-Asl, S., Gh., K., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2021). Protective effects of bromelain against cadmium-induced pulmonary intoxication in rats: A histopathologic and cytologic study. Archives of Razi Institute, 76(5), 1427–1436. Citations: 3

Rafiei-Asl, S., Khadjeh, G., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2020). Investigating the protective effects of bromelain against inflammatory marker alterations induced by cadmium pulmonary intoxication in rat. Iranian Veterinary Journal, 16(2), 75–88. Citations: 3

Marco Capogni | Data Science | Best Researcher Award

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Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
Citations: 3

Shougui Zhang | High Performance Computing | Best Researcher Award

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Prof. Dr. Shougui Zhang | High Performance Computing | Best Researcher Award

Teacher |Chongqing Normal University| China

Prof. Dr. Shougui Zhang is a distinguished scholar whose academic contributions focus primarily on computational mathematics, particularly in the field of numerical analysis and applied mathematics. His extensive research explores the numerical solution of partial differential equations (PDEs), an area that forms the foundation of many scientific and engineering applications. Zhang has made notable progress in the development and refinement of boundary element methods, which are efficient numerical techniques for solving boundary value problems that arise in physics and engineering disciplines such as fluid dynamics, elasticity, and electromagnetism. His work emphasizes mathematical rigor combined with computational efficiency, aiming to provide stable and accurate algorithms for complex real-world systems. A major aspect of his research involves variational inequalities, where he investigates computational methods for handling inequality constraints that frequently appear in optimization, contact mechanics, and obstacle problems. Zhang’s studies in this area contribute to bridging theoretical mathematical formulations with practical computational tools, enabling more precise simulations and analyses of nonlinear and constrained physical systems. His contributions extend beyond methodological innovation, influencing the design of advanced algorithms that improve the performance of numerical solvers and support the development of scientific computing frameworks. Over the years, he has published numerous research papers in recognized journals, reflecting a strong engagement with the global mathematical community. His interdisciplinary approach, combining mathematical theory, numerical techniques, and computational experimentation, enhances the understanding and application of PDE-based models across diverse domains. Zhang’s ongoing investigations into computational variational inequalities mark an important direction in applied mathematics, where numerical precision and computational feasibility must coexist. His research continues to play a key role in advancing the field of computational mathematics, fostering collaborations and innovative applications in scientific and engineering contexts. He has achieved 362 citations, authored 33 documents, and holds an h-index of 11.

Profiles: Scopus | ORCID
Featured Publication

Author(s). (2025). A self-adaptive alternating direction multiplier method for variational inequality in 2 domains. Applied Mathematics and Mechanics.

Author(s). (2025). Analysis of a Crank–Nicolson fast element-free Galerkin method for the nonlinear complex Ginzburg–Landau equation. Journal of Computational and Applied Mathematics. 7 Citations.

Author(s). (2024). Self-adaptive alternating direction method of multiplier for a fourth order variational inequality. Journal of Inequalities and Applications.

Miao Cui | Artificial Intelligence | Best Researcher Award

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Prof. Miao Cui | Artificial Intelligence | Best Researcher Award

Professor |Dalian University of Technology| China

Professor Miao Cui focuses on the fields of digital transformation, innovation management, and data-driven business strategy, with extensive exploration in enterprise and community digitalization practices. Her research emphasizes how organizations orchestrate resources to adapt to digital economies, manage transformation, and foster innovation across various sectors, including state-owned enterprises, traditional manufacturing, high-tech firms, service industries, and non-profit community organizations. She has conducted in-depth case studies on more than 50 enterprises such as Haier, P&G, Inspur, and BBMW, as well as over 30 rural communities across China, providing valuable insights into digital capability development and data-oriented strategic renewal. Through her work, Miao Cui examines the interconnection between big data strategy and organizational growth, focusing on how data analysis informs decision-making, enhances resilience, and drives innovation in dynamic environments. Her studies extend to the role of information systems in enabling business transformation, ecosystem governance, and e-commerce-based social innovation, contributing significantly to both theory and practice in management sciences. Miao Cui’s research achievements include numerous high-impact publications in leading international journals such as the International Journal of Information Management, Information Systems Journal, and Journal of Strategic Information Systems, recognized as top-ranked in their field. Her scholarly contributions have been repeatedly highlighted through ESI highly cited and hot papers, reflecting the global relevance and influence of her work. Additionally, she has authored and edited multiple academic monographs, developed widely adopted management cases for Ivey Publishing, and received several awards for excellence in research and social science innovation. Her work has been cited extensively and applied in organizational and policy contexts, contributing to global discussions on digital transformation and innovation leadership. Miao Cui has 625 Citations, 26 Documents, and an h-index of 9. View h-index.

Profile: Scopus 
Featured Publication

Author(s) unknown. (2025). Collaborative innovation network embeddedness and a firm’s technological impact: Does prior networking experience matter? Journal of Technology Transfer. Cited by 1

Author(s) unknown. (2025). An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China. Journal of Environmental Management. Cited by 2

Farrukh Dekhkonov | Computational Theory | Best Researcher Award

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Assoc. Prof. Dr. Farrukh Dekhkonov | Computational Theory | Best Researcher Award

Associate Professor |Namangan State University | Uzbekistan

Associate Professor Farrukh Dekhkonov is a researcher in the field of mathematical physics, focusing on differential equations, partial differential equations, applied mathematics, integral equations, boundary value problems, control theory, and mathematical modeling. His work emphasizes the development and analysis of boundary and time-optimal control problems associated with parabolic and pseudo-parabolic equations, including multi-dimensional domains and systems with involution. By investigating the heating process of rods, plates, and other physical systems, Dekhkonov has contributed significantly to understanding the dynamics of thermal processes and optimal control strategies in various geometries and boundary conditions. His research integrates theoretical analysis with computational methods to design effective control protocols for complex systems, ensuring precise management of heat transfer and diffusion processes. Through rigorous mathematical modeling, he has addressed both classical and novel control challenges, offering solutions for one-dimensional, two-dimensional, and three-dimensional equations of parabolic and pseudo-parabolic types, including periodic and involution-influenced systems. Dekhkonov’s work encompasses time-optimal control, establishing frameworks that minimize the time required to achieve desired system states while adhering to boundary constraints, and extends to the analysis of higher-order parabolic equations. Collaborative efforts have expanded the scope of his research, applying advanced techniques to multi-dimensional and higher-order systems, ensuring the robustness of control strategies across diverse physical models. Contributions to journals such as Discrete and Continuous Dynamical Systems, Communications in Analysis and Mechanics, and Lobachevskii Journal of Mathematics highlight the combination of theoretical innovation and practical application in his research. His investigations provide a foundation for future studies in mathematical control theory, emphasizing efficiency, stability, and adaptability in complex dynamic systems. Farrukh Dekhkonov’s expertise bridges abstract mathematical concepts with applied problem-solving, facilitating advancements in the precise manipulation of thermal and diffusion processes. 126 Citations, 23 Documents, 9 h-index, View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Dekhkonov, F. N., & Kuchkorov, E. I. (2023). On the time-optimal control problem associated with the heating process of a thin rod. Lobachevskii Journal of Mathematics, 44(3), 1134–1144. Cited by 19

Dekhkonov, F. (2022). On a time-optimal control of thermal processes in a boundary value problem. Lobachevskii Journal of Mathematics, 43(1), 192–198. Cited by 17

Дехконов, Ф. Н. (2022). On the control problem associated with the heating process. Математические заметки СВФУ, 29(4), 62–71. Cited by 15

Dekhkonov, F. N. (2023). Boundary control problem for the heat transfer equation associated with heating process of a rod. Bulletin of the Karaganda University. Mathematics Series, 110(2), 63–71. Cited by 14

Dekhkonov, F. (2023). On a boundary control problem for a pseudo-parabolic equation. Communications in Analysis and Mechanics, 15(2), 289–299. Cited by14

Dekhkonov, F. N. (2024). On the control problem associated with a pseudo-parabolic type equation in an one-dimensional domain. International Journal of Applied Mathematics, 37(1), 109–118. Cited by 11