Samyar Sarraf | Engineering | Young Innovator Award

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Dr. Samyar Sarraf | Engineering | Young Innovator Award

Lab Assistant | Sharif University of Technology | Iran

Dr. Samyar Sarraf is a Ph.D. graduate in Mechanical Engineering from Sharif University of Technology, with recognized expertise spanning offshore and marine engineering, civil engineering, and applied hydrodynamics. His research focuses on experimental and numerical investigation of squat submarine hydrodynamic performance and ship–submarine interactions, with multiple peer-reviewed publications in the high-impact journal Ocean Engineering. He has authored several scholarly works, accumulating 16 citations and an h-index of 2, reflecting a growing research influence. In addition to marine hydrodynamics, his interdisciplinary contributions include innovative solutions in geotechnical and earthquake engineering, such as the development of an underground earthquake isolation system and advanced dynamic soil modeling methods. His work demonstrates strong experimental rigor, numerical proficiency, and collaborative engagement with multidisciplinary research teams, offering practical societal impact in maritime safety, offshore operations, and resilient infrastructure design at an international level.

Citation Metrics (Scopus)

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Shunming Li | Engineering | Research Excellence Award

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Mr. Shunming Li | Engineering | Research Excellence Award

Professor | Nantong Institute of Technology | China

Mr. Shunming Li is a senior researcher at Nantong University, China, specializing in intelligent fault diagnosis, vibration signal processing, rolling bearing health monitoring, and data-driven prognostics. He has published  323 peer-reviewed articles, 4,570 citations with an h-index of 34, demonstrating sustained scholarly impact. His research integrates deep learning, transfer learning, digital twins, and advanced signal denoising methods to address complex machinery health monitoring challenges under multi-operating and data-limited conditions. Dr. Li has developed interpretable and domain-adaptive diagnostic models that enhance fault detection accuracy and remaining useful life prediction. He maintains extensive national and international collaborations, having worked with over 250 co-authors across engineering disciplines. His contributions support safer, more reliable industrial systems and promote predictive maintenance strategies, delivering significant societal and economic benefits for smart manufacturing and infrastructure health management.

Citation Metrics (Scopus)

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Ali Bedii Candas | Engineering | Research Excellence Award

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Dr. Ali Bedii Candas | Engineering | Research Excellence Award

Head of Contracts | YDA Group | Turkey 

Dr. Candaş Ali Bedii is a researcher at the Middle East Technical University (METU), Ankara, Turkey, specializing in construction engineering, data analytics, and artificial intelligence. His work focuses on the application of machine learning and natural language processing to enhance automation and decision-making in construction contract management. He has authored 2 Scopus-indexed publications, which have collectively received 48 citations, with an h-index of 2, indicating the academic relevance and influence of his research. His scholarly contributions highlight the use of multilabel text classification to streamline coordination and review processes in complex construction projects. Through interdisciplinary collaboration, he integrates engineering knowledge with intelligent computational methods. His research supports more efficient, transparent, and reliable project delivery, contributing to reduced contractual disputes and improved risk management, thereby offering meaningful societal and industrial impact.

Citation Metrics (Scopus)

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Featured Publications


Automated Identification of Vagueness in the FIDIC Silver Book Conditions of Contract


– Journal of Construction Engineering and Management, 2022

Georgi Ivanov | Engineering | Innovative Research Award

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Dr. Georgi Ivanov | Engineering | Innovative Research Award

Technical University of Sofia | Bulgaria

Georgi Ivanov is a researcher at the Technical University of Sofia, Bulgaria, specializing in electrical engineering with a focus on power transformers, electromagnetic devices, and AI-assisted diagnostics. His research integrates neural networks, dissolved gas analysis (DGA), and advanced modeling techniques to assess insulation aging, operational reliability, and performance optimization of electrical equipment. Dr. Ivanov has authored 20 peer-reviewed publications, receiving 43 citations, with an h-index of 4, reflecting steady academic impact in applied power engineering research. His work demonstrates interdisciplinary collaboration between electrical engineering, materials science, and data-driven methods. Through contributions to transformer monitoring, cryogenic electromagnetic systems, and intelligent diagnostic frameworks, his research supports safer, more reliable, and energy-efficient power infrastructures, delivering tangible societal benefits in industrial reliability and sustainable energy systems.

Citation Metrics (Scopus)

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43

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Featured Publications


Modeling of fluid flow cooling of high-speed rotational electrical devices

– Proc. 16th Conf. on Electrical Machines, Drives and Power Systems (ELMA), 2019 Cited By: 10


Fluid flow modeling in 3D printed CO₂ absorption air contactor

– Proc. 22nd Int. Symposium on Electrical Apparatus and Technologies (SIELA), 2022 Cited By: 4


Vibration and noise analysis of a coaxial magnetic gear

– Proc. II Int. Conf. on High Technology for Sustainable Development, 2019 Cited By: 4


Seismic analysis of high voltage bushing

– Proc. 20th Int. Symposium on Electrical Apparatus and Technologies, 2018 Cited By: 4

Yao Ni | Engineering | Editorial Board Member

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Dr. Yao Ni | Engineering | Editorial Board Member

Researcher | Guangdong University of Technology | China

Dr. Ni Yao is a distinguished researcher at the Guangdong University of Technology, Guangzhou, China, widely recognized for his contributions to advanced materials, neuromorphic engineering, and intelligent sensing–processing systems. With an interdisciplinary focus spanning materials science, flexible electronics, artificial intelligence hardware, and intelligent control mechanisms, his research advances next-generation photonic synaptic transistors, in-sensor reservoir computing architectures, and flexible neuromorphic devices capable of multidimensional shape morphing. Dr. Yao has authored 65 peer-reviewed publications, achieved 1,679 citations, and maintains an h-index of 23, reflecting the depth, continuity, and global influence of his scholarly work. His recent high-impact contributions include crystallized conjugated polymer-based photonic synaptic transistors, paper-based perovskite artificial neuromorphic retinas, free shape-morphing neuromorphic devices published in Nature Communications, as well as novel methodologies for industrial control deadlock avoidance, frequency-aware transformers for pipeline leak detection, and symmetric optimization models for delivery duration forecasting. Engaging in collaborations with over 160 co-authors, Dr. Yao actively contributes to multidisciplinary research communities, promoting scientific advancement across materials innovation, industrial automation, computational sensing, and AI-driven systems engineering. His work delivers broad societal impact by enabling energy-efficient intelligent devices, enhancing autonomous perception capabilities, and driving innovations that support safer, more sustainable, and technologically advanced industrial ecosystems. Through continuous innovation, rigorous scholarship, and extensive international collaboration, Dr. Ni Yao remains at the forefront of shaping future directions in intelligent materials, neuromorphic computing, and integrated sensing technologies.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1. Wei, H., Yang, J., Fu, C., Li, Z., Ni, Y., Wang, B., He, B., Jiang, S., & He, G. (2025). ALD-driven ultra-thin ZnO channels for flexible electrolytic neuromorphic devices. IEEE Electron Device Letters.

2. Ni, Y., Zhang, Y., Lin, J., Liu, X., Yu, Y., Liu, L., Zhong, W., Chen, Y., Chen, R., Kwok, H. S., et al. (2025). Transistor-structured artificial dendrites for spatiotemporally correlated reservoir computing. IEEE Electron Device Letters.

3. Guan, X., Wu, W., & Ni, Y. (2025). A novel methodology to deadlock analysis and avoidance for automatic control systems based on Petri Net. Processes, 13(10).

4. Chen, M., Lu, Y., Wu, W., Ye, Y., Wei, B., & Ni, Y. (2025). Multi-scale frequency-aware transformer for pipeline leak detection using acoustic signals. Sensors, 25(20).

5. Ji, Z., Liu, J., He, Y., Yang, H., Zhang, L., Guan, S., Ni, Y., & Wu, T. (2025). Stretchable synaptic device with photonic–electric dual mode for sign language recognition. Advanced Materials Technologies

Farouk Zouari | Engineering | Editorial Board Member

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Assist. Prof. Dr. Farouk Zouari | Engineering | Editorial Board Member

Ecole Nationale d’Ingénieurs de Tunis | Tunisia

Dr. Farouk Zouari is a distinguished researcher at Université de Tunis El Manar, Tunisia, known for his significant contributions to control systems engineering and intelligent autonomous technologies. His research encompasses neural network–based adaptive control, nonlinear optimal control, MIMO systems, time-varying delay systems, and finite-time fuzzy synchronization of fractional-order and chaotic systems, with a strong focus on bridging theoretical advancements and real-world applications in automation, robotics, and intelligent transportation. Over his career, Dr. Zouari has produced 37 peer-reviewed publications and established a solid scholarly presence supported by 735 citations and an h-index of 17, indicating substantial impact and sustained relevance in his field. His recent works spanning robust adaptive output feedback control for time-delay MIMO systems, optimal control strategies for multi-axle autonomous vehicles, and chattering-free synchronization methods demonstrate his commitment to addressing emerging engineering challenges. With collaborations involving 30 co-authors, Dr. Zouari actively contributes to interdisciplinary progress, fostering innovation across global research communities. His work continues to support the advancement of intelligent control methodologies and their integration into next-generation dynamic and autonomous systems, contributing to both technological development and societal benefit.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

1. Zouari, F., Ibeas, A., Boulkroune, A., Cao, J., & Arefi, M. M. (2018). Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities. Neural Networks, 105, 256–276. Cited by: 82

2. Zouari, F., Ibeas, A., Boulkroune, A., Cao, J., & Arefi, M. M. (2021). Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying pseudo-state constraints. Chaos, Solitons & Fractals, 144, Article 110742. Cited by: 65

3. Boubellouta, A., Zouari, F., & Boulkroune, A. (2019). Intelligent fuzzy controller for chaos synchronization of uncertain fractional-order chaotic systems with input nonlinearities. International Journal of General Systems, 48(3), 211–234. Cited by: 65

4. Zouari, F., Boulkroune, A., & Ibeas, A. (2017). Neural adaptive quantized output-feedback control-based synchronization of uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. Neurocomputing, 237, 200–225. Cited by: 64

5. Zouari, F., Ibeas, A., Boulkroune, A., Cao, J., & Arefi, M. M. (2019). Neuro-adaptive tracking control of non-integer order systems with input nonlinearities and time-varying output constraints. Information Sciences, 485, 170–199. Cited by: 56

Yong Xu | Engineering | Research Excellence Award

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Mr. Yong Xu | Engineering | Research Excellence Award

Associate Researcher | Aerospace Technology Institute of CARDC | China

Dr. Yong Xu is a researcher specializing in intelligent sensing, autonomous systems, and advanced signal processing, with a particular focus on drone vision systems and radar-based environmental perception. His research integrates computer vision, machine learning, and adaptive signal normalization techniques to enhance the reliability, efficiency, and resilience of autonomous aerial and maritime systems in complex real-world environments. Dr. Xu has authored several high-impact publications, including An Air-to-Ground Visual Target Persistent Tracking Framework for Swarm Drones (Automation) and Adaptive Clustering-Based Marine Radar Sea Clutter Normalization (Journal of Sensors), showcasing his expertise in persistent target tracking, swarm coordination, and environmental noise reduction. These works demonstrate his ability to bridge theoretical innovation with practical engineering solutions, improving both sensor performance and system-level autonomy. Throughout his career, Dr. Xu has collaborated extensively with interdisciplinary teams, including researchers such as Shuai Guo, Hongtao Yan, An Wang, Tao Jia, Dong Cao, Pengyu Guo, Yue Ma, Tian Yao, and Jaime Lloret, highlighting his strong engagement in international and cross-institutional research. His contributions support real-world applications in autonomous drone navigation, maritime surveillance, environmental monitoring, and defense technologies, promoting safer and more efficient operational systems. By advancing methodologies for persistent tracking and adaptive radar signal processing, Dr. Xu’s research contributes significantly to the fields of robotics, unmanned systems, and intelligent sensing, offering societal benefits in areas such as public safety, disaster monitoring, and infrastructure protection, while reinforcing the development of next-generation autonomous technologies on a global scale.

Profile: ORCID

Featured Publications

1. Xu, Y., Guo, S., Yan, H., Wang, A., Ma, Y., Yao, T., & Song, H. (2025). An Air‑to‑Ground Visual Target Persistent Tracking Framework for Swarm Drones. Automation, 6(4), 81. https://doi.org/10.3390/automation6040081 MDPI

2. Xu, Y., Jia, T., Cao, D., Guo, P., Ma, Y., & Yan, H. (2021). Adaptive Clustering‑Based Marine Radar Sea Clutter Normalization. Journal of Sensors, 2021, Article 2938251 (11 pages). https://doi.org/10.1155/2021/2938251

Sümeyye Sınır | Engineering | Research Excellence Award

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Dr. Sümeyye Sınır | Engineering | Research Excellence Award

Lecturer | İzmir Katip Çelebi University | Turkey

Dr. Sümeyye Sınır is a researcher at İzmir Kâtip Çelebi University in Izmir, Turkey, specializing in applied mechanics, nonlinear systems, and fractional calculus, with a focus on developing innovative mathematical and computational methods for analyzing complex dynamical behaviors. She has authored 3 peer-reviewed publications, which have collectively received 63 citations, and holds an h-index of 2, reflecting her emerging influence in the field of applied mechanics and nonlinear dynamics. Among her notable contributions is the development of a general solution procedure for nonlinear single-degree-of-freedom systems incorporating fractional derivatives, providing critical insights for engineering applications, physics modeling, and mechanical system simulations. Dr. Sınır actively collaborates with colleagues across mathematics, engineering, and computational mechanics, demonstrating a commitment to interdisciplinary research and advancing methodologies that bridge theoretical developments with practical applications. Her work enhances the understanding and prediction of complex nonlinear behaviors, supporting innovations in structural engineering, robotics, energy systems, and other technologically relevant domains. Through her research, she contributes to improved simulation accuracy, efficient system design, and the development of tools that address real-world engineering challenges, translating theoretical insights into tangible societal benefits. Committed to scientific rigor, innovation, and collaboration, Dr. Sınır continues to expand her research portfolio, strengthen academic partnerships, and advance methodologies in nonlinear mechanics, promoting both the theoretical foundation and applied solutions in engineering and physics, while fostering technological progress and contributing to the broader scientific community through impactful research and interdisciplinary engagement.

Profiles: Google Scholar | Scopus | ResearchGate

Featured Publications

1. Sınır, S., Çevik, M., & Sınır, B. G. (2018). Nonlinear free and forced vibration analyses of axially functionally graded Euler-Bernoulli beams with non-uniform cross-section. Composites Part B: Engineering, 148, 123–131. (Cited by 76)

2. Sınır, S., Yıldız, B., & Sınır, B. G. (2021). Approximate solutions of nonlinear pendulum with fractional damping. In 5th International Students Science Congress Proceedings Book (p. 295). (Cited by 3)

3. Sınır, S., & Çevik, M. (2013). Taylor matrix solution of Euler-Bernoulli beam equation subjected to static loads. In Proceedings of the Fourth International Conference on Mathematical and …. (Cited by 3)

4. Sınır, S., Yıldız, B., & Sınır, B. G. (2025). A general solution procedure for nonlinear single degree of freedom systems including fractional derivatives. International Journal of Non-Linear Mechanics, 169, 104966. (Cited by 2)

5. Küzün, D., Yıldız, B., & Sınır, S. (2023). Euler-Bernoulli beam with fractional viscoelastic boundary conditions. 18. UBAK Kongresi. (Cited by 1)

Masoud Yaghini | Engineering | Best Researcher Award

Published on by Global Scholar Awards

Assoc Prof Dr Masoud Yaghini | Engineering | Best Researcher Award

Faculty Member at Iran University of Science and Technology, Iran

Dr. Masoud Yaghini is a distinguished faculty member in the Department of Rail Transportation at the Iran University of Science and Technology. Born on December 8, 1966, he holds an extensive academic and professional background in rail transportation planning and optimization techniques. With over two decades of experience, Dr. Yaghini has made substantial contributions to the fields of transportation logistics, network design, and data mining, particularly within the railway industry. His innovative approaches to complex rail transportation problems have earned him a reputation as a leading researcher in the field. Dr. Yaghini is widely published and continues to shape the future of transportation with cutting-edge research.

Professional Profile

Education

Dr. Yaghini received his Ph.D. in Rail Transportation Planning and Engineering from Northern Jiaotong University, Beijing, China, in 2003, with a focus on dynamic service network design. He also holds an MSc and BSc in Industrial Management from Islamic Azad University, Tehran. His master’s thesis on resource assignment optimization in preventive maintenance laid the foundation for his interest in large-scale optimization problems. Additionally, he furthered his knowledge with specialized training in Ergonomics and Human Factors for Railways from the University of Birmingham, UK, in 2005. This diverse educational background has equipped Dr. Yaghini with both theoretical and practical expertise in optimizing transportation systems.

Professional Experience

Dr. Yaghini has over 20 years of professional experience, primarily as a faculty member at the Iran University of Science and Technology. He teaches a wide range of courses, from advanced computer programming to railway operations management and data mining in transportation. His professional experience extends beyond academia into consultancy work in optimization and transportation planning. Dr. Yaghini has also conducted numerous short courses and workshops in data mining, information management, and metaheuristic algorithms for both academic institutions and private companies. His role as an educator and consultant has allowed him to bridge the gap between academic research and real-world transportation challenges.

Research Interests

Dr. Yaghini’s research primarily focuses on optimization problems in rail transportation, including train scheduling, fleet sizing, and locomotive scheduling. He has a strong interest in metaheuristics such as Genetic Algorithms, Tabu Search, and Ant Colony Optimization, as well as exact solution methods like Column Generation and Branch and Cut. His work also explores data mining techniques applied to railway systems, such as the prediction of train delays and analysis of accident data. His research is driven by the need to optimize and improve efficiency in transportation systems, particularly in large-scale rail networks. His work has significant practical implications for enhancing railway operations and minimizing costs.

Awards and Honors

Dr. Yaghini’s contributions to transportation research have earned him multiple accolades, though his recognition mainly stems from his published works in high-impact journals such as Applied Mathematical Modelling and Journal of Transportation Engineering. He has been recognized for his work on solving complex railway optimization problems through innovative algorithms like Ant Colony Optimization and Simulated Annealing. In addition to his publications, Dr. Yaghini has been invited to present his findings at numerous international conferences. While he has not widely publicized any specific awards, his ongoing research contributions have earned him a solid reputation in the global transportation research community, marking him as a key figure in rail transportation planning and optimization.

Conclusion

Dr. Masoud Yaghini’s research portfolio is impressive, with a strong emphasis on rail transportation and optimization problems. His consistent contributions to both academic knowledge and practical railway systems demonstrate his potential for recognition as a top researcher. By broadening his collaborative network and impact beyond academia, he could further strengthen his candidacy for prestigious awards like the Best Researcher Award.

Publication top noted

  1. Online prediction of arrival and departure times in each station for passenger trains using machine learning methods
    • Vafaei, S., Yaghini, M.
    • Transportation Engineering, 2024
    • 📖 0 citations
  2. Analysis of the relationship between geometric parameters of railway track and twist failure by using data mining techniques
    • Izadi Yazdan Abadi, E., Khadem Sameni, M., Yaghini, M.
    • Engineering Failure Analysis, 2023
    • 📖 2 citations
  3. A mathematical formulation and an LP-based neighborhood search matheuristic solution method for the integrated train blocking and shipment path problem
    • Yaghini, M., Mirghavami, M., Zare Andaryan, A.
    • Networks, 2021
    • 📖 5 citations
  4. Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times
    • Zarook, Y., Rezaeian, J., Mahdavi, I., Yaghini, M.
    • RAIRO – Operations Research, 2021
    • 📖 10 citations
  5. An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems
    • Hajiyan, H., Yaghini, M.
    • SN Applied Sciences, 2020
    • 📖 1 citation
  6. Optimization of embedded rail slab track with respect to environmental vibrations
    • Esmaeili, M., Yaghini, M., Moslemipour, S.
    • Scientia Iranica, 2020
    • 📖 0 citations
  7. An Effective Improvement to Main Non-periodic Train Scheduling Models by a New Headway Definition
    • Jafarian-Moghaddam, A.R., Yaghini, M.
    • Iranian Journal of Science and Technology – Transactions of Civil Engineering, 2019
    • 📖 2 citations
  8. Optimizing headways for urban rail transit services using adaptive particle swarm algorithms
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Public Transport, 2018
    • 📖 26 citations
  9. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Operational Research, 2017
    • 📖 48 citations
  10. Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals
  • Hassannayebi, E., Zegordi, S.H., Yaghini, M., Amin-Naseri, M.R.
  • Transportation Planning and Technology, 2017
  • 📖 35 citations