Yao Ni | Engineering | Editorial Board Member

Published on by Global Scholar Awards

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

Published on by Global Scholar Awards

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

Genfeng Liu | Engineering | Best Researcher Award

Published on by Global Scholar Awards

Dr. Genfeng Liu | Engineering | Best Researcher Award

Research Scholar at Henan University of Technology, China

Genfeng Liu is a highly qualified candidate for the Best Researcher Award, with a strong background in control science and engineering, specializing in data-driven control, adaptive control, and fault-tolerant systems. His research spans intelligent transportation, multiagent systems, and nonlinear systems, contributing to high-impact IEEE journals such as IEEE Transactions on Cybernetics (IF: 19.118) and IEEE Transactions on Neural Networks and Learning Systems (IF: 14.255). As a reviewer for leading journals, he holds strong academic credibility. His work on model-free adaptive control and cybersecurity applications demonstrates real-world relevance. To enhance his profile, he could expand international collaborations, increase industry applications, and lead large-scale research projects. While his contributions are highly significant, further engagement in technology transfer and interdisciplinary research would strengthen his impact. Overall, his extensive publication record and research influence make him a strong contender for the award, with potential for even greater contributions in the future.

Professional Profile

Education

Genfeng Liu received his Ph.D. in Control Science and Engineering from Beijing Jiaotong University, China, in 2021. His doctoral research focused on advanced control methodologies, including data-driven control, iterative learning control, and fault-tolerant control, which have significant applications in intelligent transportation and nonlinear systems. Throughout his academic journey, he developed expertise in adaptive control and multiagent systems, contributing to cutting-edge research in automation and cybernetics. His education provided a strong foundation in both theoretical and applied control engineering, enabling him to publish in prestigious IEEE journals. Additionally, his academic background equipped him with the analytical and problem-solving skills necessary to address complex challenges in system automation and intelligent control. His commitment to continuous learning and research excellence is evident in his contributions to high-impact scientific literature and his role as a reviewer for renowned international journals, solidifying his reputation as an expert in his field.

Professional Experience

Genfeng Liu is currently a Lecturer at the College of Electrical Engineering, Henan University of Technology, Zhengzhou, China. His professional experience revolves around advanced control engineering, with a focus on data-driven control, adaptive control, and fault-tolerant systems. As a researcher, he has made significant contributions to intelligent transportation systems, multiagent systems, and nonlinear control, publishing extensively in high-impact IEEE journals. Beyond his research, he actively participates in academic peer review for prestigious journals such as IEEE Transactions on Cybernetics and IEEE Transactions on Intelligent Vehicles, reinforcing his role as a respected scholar in the field. His expertise extends to supervising students and collaborating on interdisciplinary projects, bridging the gap between theoretical advancements and practical applications. His ongoing work in model-free adaptive control and cybersecurity-related control mechanisms further strengthens his impact in academia and industry, positioning him as a leader in modern control systems and intelligent automation research.

Research Interest

Genfeng Liu’s research interests lie in advanced control engineering, with a strong focus on data-driven control, adaptive control, and fault-tolerant control. His work explores iterative learning control and model-free adaptive control techniques, particularly in applications related to intelligent transportation systems, nonlinear systems, and multiagent systems. He is also interested in cybersecurity aspects of control systems, such as defense mechanisms against false data injection attacks. His research aims to enhance the efficiency, safety, and reliability of automation in modern transportation and industrial systems. By integrating artificial intelligence with control theory, he seeks to develop innovative solutions for complex, real-world engineering challenges. His studies have been published in top-tier journals, reflecting his commitment to advancing theoretical and applied knowledge in control science. Additionally, his expertise in intelligent transportation and system optimization continues to drive impactful contributions to the fields of automation, cybernetics, and industrial informatics.

Award and Honor

Genfeng Liu has received several accolades and recognition for his outstanding contributions to the field of control science and engineering. His research publications in prestigious IEEE journals, such as IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems, have earned him significant recognition within the academic community. As an active reviewer for renowned international journals, he has been acknowledged for his critical evaluations and contributions to the peer-review process. His innovative work in data-driven control, adaptive control, and fault-tolerant systems has positioned him as a leading researcher in intelligent transportation and nonlinear systems. Additionally, his participation in high-profile conferences and collaborations with esteemed researchers further highlight his impact in the field. While his research achievements are commendable, pursuing national and international research grants and awards would further enhance his recognition and establish him as a distinguished leader in control engineering and automation.

Research Skill

Genfeng Liu possesses strong research skills in advanced control engineering, specializing in data-driven control, adaptive control, and fault-tolerant control. He is proficient in developing and implementing iterative learning control and model-free adaptive control strategies for complex nonlinear and multiagent systems. His expertise extends to intelligent transportation systems, where he applies innovative control techniques to enhance automation and safety. He is highly skilled in mathematical modeling, algorithm development, and system optimization, enabling him to solve real-world engineering challenges effectively. His ability to conduct in-depth theoretical analysis and translate findings into practical applications is evident in his numerous high-impact publications in top-tier IEEE journals. Additionally, his experience as a reviewer for prestigious academic journals demonstrates his critical thinking and analytical skills. His research capabilities, combined with his ability to collaborate on interdisciplinary projects, make him a valuable contributor to the fields of cybernetics, automation, and industrial informatics.

Conclusion

Genfeng Liu is a highly suitable candidate for the Best Researcher Award due to his exceptional research output, high-impact publications, and contributions to control engineering and intelligent transportation systems. To further strengthen his candidacy, increasing international collaborations, practical industry applications, and leadership roles in large-scale projects would make his research even more impactful.

Publications Top Noted

  • Title: Improved Model-Free Adaptive Predictive Control for Nonlinear Systems with Quantization Under Denial of Service Attacks
    Authors: Genfeng Liu, Jinbao Zhu, Yule Wang, Yangyang Wang
    Year: 2025
    Citation: DOI: 10.3390/sym17030471

  • Title: Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2024
    Citation: DOI: 10.1109/TNNLS.2022.3185080

  • Title: Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2022
    Citation: DOI: 10.1109/TCYB.2020.2986006

  • Title: RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TSMC.2019.2957299

  • Title: Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TITS.2020.2970000

  • Title: A Model-Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude
    Authors: Gaoyang Jiang, Genfeng Liu, Hansong Yu
    Year: 2021
    Citation: DOI: 10.3390/sym13020347

  • Title: A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems
    Authors: Xian Yu, Shangtai Jin, Genfeng Liu, Ting Lei, Ye Ren
    Year: 2020
    Citation: DOI: 10.1109/ACCESS.2020.3038629

  • Title: Model-Free Adaptive Direct Torque Control for the Speed Regulation of Asynchronous Motors
    Authors: Ziwei Zhang, Shangtai Jin, Genfeng Liu, Zhongsheng Hou, Jianmin Zheng
    Year: 2020
    Citation: DOI: 10.3390/pr8030333

Xu Zhang | Engineering Award | Best Scholar Award

Published on by Global Scholar Awards

Dr. Xu Zhang | Engineering Award | Best Scholar Award

Associate professor at Hubei University of Technology, China

Xu Zhang is a distinguished scholar specializing in intelligent non-destructive testing (NDT) technologies. With a solid academic foundation in Acoustics, her expertise spans sensor design, guided wave testing, and the integration of artificial intelligence in NDT systems. Zhang has been the principal investigator on several prestigious projects, including National Natural Science Foundation of China and National Key Research and Development Plan projects. She has made significant contributions to the fields of electromagnetic acoustic transducers (EMATs), guided wave detection methods, and corrosion imaging. Her research is not only innovative but also highly relevant to critical industries such as aerospace, automotive, and infrastructure.

Professional Profile

Education

Xu Zhang’s academic journey began with a Bachelor’s degree in Acoustics from Nanjing University in 2010. She furthered her education with a Master’s and PhD in Acoustics from the prestigious Chinese Academy of Sciences, where she honed her research focus on non-destructive testing technologies. In 2016, she became an Associate Professor in the Department of Mechanical Engineering at Hubei University of Technology. Xu is currently a Visiting Fellow at the University of Bristol, where she collaborates with global experts on advanced NDT methods. Her academic background has equipped her with a deep understanding of the complexities in material testing, structural health monitoring, and the application of electromagnetic and ultrasonic technologies in engineering.

Experience

Xu Zhang has extensive experience in the field of non-destructive testing and advanced materials inspection. Since 2016, she has served as an Associate Professor at Hubei University of Technology, specializing in intelligent NDT technologies. Zhang has been the Principal Investigator (PI) in numerous high-profile national and provincial projects, focusing on ultrasonic and electromagnetic testing techniques for stress corrosion cracking and high-temperature creep materials. Notable projects she has led include the development of an ultrasonic phased array detection system for automotive steering parts and the creation of technology for pipeline corrosion imaging. Her expertise spans sensor design, guided wave testing, and the integration of artificial intelligence into NDT systems. Zhang is also a Senior Member of the Chinese Mechanical Engineering Society and an active participant in global research discussions on intelligent testing methodologies.

Research Focus

Xu Zhang’s research is primarily focused on intelligent non-destructive testing (NDT) technologies, with a specific emphasis on ultrasonic and electromagnetic guided wave techniques. She is dedicated to the development of advanced sensor systems and diagnostic tools that can detect flaws and assess material integrity in complex engineering structures. One of her key areas of research is the integration of artificial intelligence into NDT methodologies, enabling more efficient and accurate defect detection. Zhang’s work has applications in diverse industries, including automotive, aerospace, and infrastructure, particularly in stress corrosion cracking detection, high-temperature material assessment, and pipeline monitoring. Additionally, her research explores the enhancement of testing systems with electromagnetic transducers and phased array technologies, which improve detection sensitivity and system reliability. Her contributions to NDT technology continue to shape the future of materials testing and structural health monitoring.

Awards and Honors

Xu Zhang has been recognized for her pioneering work in non-destructive testing, particularly in the application of electromagnetic and ultrasonic guided wave technologies. As a Principal Investigator (PI), she has secured several prestigious grants and awards, including the National Key Research and Development Plan Project and the National Natural Science Foundation of China Project. Her research on stress corrosion cracking detection, material assessment, and corrosion imaging has earned her numerous accolades. Zhang has also been honored with key research project leadership positions from the Provincial Science and Technology Department, reflecting her influence in advancing the state of engineering diagnostics. She continues to contribute to the scientific community, and her work in non-destructive testing systems is frequently recognized for its practical applications in the fields of materials science and engineering.

Conclusion

Xu Zhang is a leading figure in the field of intelligent non-destructive testing, with an impressive array of research accomplishments and leadership in cutting-edge projects. Her scholarly work in the development of advanced testing systems and her commitment to pushing the boundaries of engineering innovation make her an outstanding candidate for the Best Scholar Award. With a strong foundation in both academic research and practical applications, Zhang’s continued contributions to the field hold the promise of significant advancements in industrial safety and technology.

Publications Top Noted

A novel amplitude enhancement method of EMAT for High-frequency Rayleigh-like waves in Circumferential propagation

Authors: Zhang, X., Li, B., Niu, X., Song, X., Wu, Q.

Citations: 0

Year: 2024

Journal: NDT and E International, 148, 103231

Investigation of an Active Focusing Planar Piezoelectric Ultrasonic Transducer

Authors: Wu, Q., You, B., Zhang, X., Tu, J.

Citations: 0

Year: 2024

Journal: Sensors, 24(13), 4082

Characterization of Small Delamination Defects by Multilayer Flexible EMAT

Authors: Chen, T., Liu, S., Lv, C., Wu, Q., Zhang, X.

Citations: 1

Year: 2024

Journal: IEEE Sensors Journal, 24(12), pp. 19210–19219

Unidirectional focusing Rayleigh waves EMAT for plate surface defect Inspection

Authors: Chen, T., Lou, T., Lv, C., Wu, Q., Zhang, X.

Citations: 0

Year: 2024

Journal: Nondestructive Testing and Evaluation (Article in Press)

Design and experimental study of electromagnetic ultrasonic single-mode guided wave transducer for small-diameter stainless steel tubes

Authors: Tu, J., Zhan, X., Sun, H., Zhang, X., Song, X.

Citations: 3

Year: 2024

Journal: Nondestructive Testing and Evaluation (Article in Press)

Internal and External Pipe Defect Characterization via High-Frequency Lamb Waves Generated by Unidirectional EMAT

Authors: Zhang, X., Li, B., Zhang, X., Yuan, J., Wu, Q.

Citations: 3

Year: 2023

Journal: Sensors (Basel, Switzerland), 23(21)

Bolt Axial Stress Measurement Based on the Dual-Mode Electromagnetic Acoustic Transducer

Authors: Zhang, X., Li, W., Wu, Q., Cai, C., Song, X.

Citations: 2

Year: 2023

Journal: IEEE Sensors Journal, 23(13), pp. 13978–13986

Energy Transfer Efficiency Based Nonlinear Ultrasonic Testing Technique for Debonding Defects of Aluminum Alloy Foam Sandwich Panels

Authors: Tu, J., Yao, N., Ling, Y., Zhang, X., Song, X.

Citations: 0

Year: 2023

Journal: Sensors, 23(6), 3008

Optimized Design of Torsional Guided Wave Magnetostrictive Patch Transducer Based on Reversed Wiedemann Effect

Authors: Li, C., Yang, R., Gu, J., Wang, S., Zhang, X.

Citations: 5

Year: 2023

Journal: Journal of Nondestructive Evaluation, 42(1), 26

Enhancing the Lift-Off Performance of EMATs by Applying an Fe3O4 Coating to a Test Specimen

Authors: Liang, B., Li, Z., Zhai, G., Zhang, X., Dixon, S.

Citations: 5

Year: 2023

Journal: IEEE Transactions on Instrumentation and Measurement, 72, 9502104