Zhiqi Wu | Engineering | Best Researcher Award

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Ms. Zhiqi Wu | Engineering | Best Researcher Award

Student at Anhui University of Science and Technology | China

Zhiqi Wu is a Master’s student in Electrical Engineering at Anhui University of Science and Technology, specializing in computer vision applications for intelligent coal gangue detection. His research emphasizes developing lightweight, high-performance models that improve automation and efficiency in mining operations. Zhiqi proposed a novel model integrating multispectral imaging with knowledge distillation, achieving a precision increase of up to 6% while reducing model size by 21.8%, enabling deployment on mobile and edge devices for real-time sorting. This approach advances intelligent coal industry practices by combining computational efficiency with practical industrial applicability. Zhiqi’s work exemplifies the translation of advanced AI techniques into actionable solutions for industrial automation, with a focus on scalability, accuracy, and sustainability. His contributions are recognized in the research community, with 1 citation for his published work, highlighting its impact and relevance. To date, he has contributed to 4 documents in his research portfolio, reflecting his engagement in ongoing innovation and knowledge dissemination. While his h-index currently stands at 1, it represents a promising start in a career dedicated to applied AI in mining technologies. Zhiqi continues to explore methods for optimizing computer vision systems in industrial contexts, advancing automation, operational safety, and resource efficiency. His work bridges theoretical research and practical deployment, positioning him as an emerging researcher making measurable contributions to intelligent mining technologies.

Profile: Scopus

Featured Publications

Yan, P. (2025). Transformer fault diagnosis based on LIF technology and COA-GRU algorithm. Engineering Research Express, 7(2), 25409.

Ashish Ranjan Dash | Engineering | Best Researcher Award

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Dr. Ashish Ranjan Dash | Engineering | Best Researcher Award

Associate Professor at Centurion University of Technology and Management | India

Dr. Ashish Ranjan Dash is a highly accomplished academic and researcher in the field of electrical engineering, specializing in power electronics, multilevel inverters, and power quality improvement. With a proven track record in research, teaching, and project leadership, he has significantly contributed to advancements in smart infrastructure, renewable energy systems, and IoT-enabled agricultural automation. He has also played a pivotal role in supervising doctoral students, developing innovative solutions for industrial applications, and leading consultancy projects for technology-driven agriculture and smart systems.

Publication Profile 

Scopus

Google Scholar

Educational Background 

Dr. Dash earned his Ph.D. in Power Electronics from the National Institute of Technology (NIT) Rourkela, focusing on cascaded multilevel inverter-based shunt active filters under varying grid voltage conditions. He also holds an M.Tech. in Power Control and Drives from NIT Rourkela, where his dissertation explored control strategies for grid-connected inverter systems during fault conditions. His academic foundation is further strengthened by a B.Tech. in Electrical Engineering and a Diploma in Electrical Engineering, complemented by a strong record of academic excellence throughout his studies.

Professional Experience 

With over a decade of academic and research experience, Dr. Dash serves as an Associate Professor at Centurion University of Technology and Management, Odisha. His prior roles include research and academic positions in engineering colleges and at the Council of Scientific and Industrial Research. He has held key administrative positions such as Dean and Associate Dean of the School of Engineering and CEO of the Smart Infrastructure Research Center, where he has led interdisciplinary projects integrating IoT, automation, and renewable energy systems.

Research Interests 

His research focuses on power electronics, multilevel inverter design, power quality enhancement, electric vehicle charging infrastructure, smart grid systems, and IoT-enabled automation. He also works extensively on agricultural automation, including polyhouse automation, speed breeding chambers, and plant phenotyping systems. Emerging interests include machine learning applications for plant disease detection, robotics, and smart farming technologies.

Awards and Honors 

Dr. Dash has received multiple accolades, including the Distinguished Achiever Award at the Provost Research Awards and recognition as a session chair at IEEE international conferences. He is the founder of a technology-driven startup and actively engages in professional communities such as the IEEE Power Electronics Society, IEEE Industry Applications Society, and IEEE SIGHT.

Research Skills 

He possesses strong expertise in the design, modeling, and implementation of cascaded multilevel inverters, power quality control algorithms, and renewable energy integration. His skills extend to IoT-based system design, automation technologies, electric vehicle charging systems, and cloud-based agricultural monitoring. He is also an experienced reviewer for several high-impact international journals in power electronics and smart grid applications.

Publications 

A unified control of grid-interactive off-board EV battery charger with improved power quality

Citations: 49

Year: 2022

Reactive power compensation using vehicle-to-grid enabled bidirectional off-board EV battery charger

Citations: 34

Year: 2021

Adaptive LMBP training‐based icosϕ control technique for DSTATCOM

Citations: 33

Year: 2020

Analysis of PI and PR controllers for distributed power generation system under unbalanced grid faults

Citations: 33

Year: 2011

Design and implementation of a cascaded transformer coupled multilevel inverter‐based shunt active filter under different grid voltage conditions

Citations: 24

Year: 2019

Conclusion 

Dr. Ashish Ranjan Dash is a forward-looking researcher and educator whose work bridges advanced power electronics with practical applications in smart infrastructure and agricultural automation. His multidisciplinary expertise, leadership in funded projects, and dedication to mentoring the next generation of engineers make him a valuable contributor to both academia and industry. His continued research promises innovative advancements in electric mobility, renewable energy integration, and intelligent automation systems.

Guojun Wu | Civil Engineering | Best Researcher Award

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Mr. Guojun Wu | Civil Engineering | Best Researcher Award

Professor at Chinese Academy of Sciences, China

Professor Guojun Wu is a distinguished researcher in geotechnical and underground engineering, serving at the Institute of Rock and Soil Mechanics, Chinese Academy of Sciences in Wuhan, China. With over two decades of impactful academic and industry contributions, he specializes in underground engineering disaster mechanisms and support control technologies. He has led 14 completed and 3 ongoing research projects, authored 78 peer-reviewed publications, published 4 books, and holds 37 patents (with 4 pending). He actively contributes to the advancement of tunnel safety and sustainable underground infrastructure development.

Publication Profile 

Scopus

Educational Background 🎓

  • Bachelor’s Degree (2001): Engineering Mechanics, Wuhan University, China

  • Master’s Degree (2003–2006): Geotechnical Engineering, Wuhan Institute of Geotechnical Mechanics, Chinese Academy of Sciences

  • Ph.D. Degree (2006–2009): Geotechnical Engineering, Wuhan Institute of Geotechnical Mechanics, Chinese Academy of Sciences

Professional Experience 💼

  • Current Position: Professor, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

  • Editorial Roles:

    • Editorial Board Member, International Journal of Mining Science and Technology

    • Guest Editor, Advances in Civil Engineering

  • International Collaboration:

    • Research on Boom Clay with Belgian Nuclear Research Center

    • Visiting Scholar at Lorrain University, France

  • Consultancy Projects: Led 6 industry projects

  • Professional Affiliations:

    • Director, Underground Engineering Branch, Chinese Society of Rock Mechanics and Engineering

    • Director, Hubei Underground Engineering Society

Research Interests 🔬

  • Geotechnical Engineering

  • Underground Engineering

  • Tunnel Safety and Deformation Control

  • Rock Mechanics

  • Dynamic Stress Testing

  • Support Control Technologies for Steeply Inclined and Squeezing Rock Tunnels

Awards and Honors🏆✨

  • Nominee for the Global Innovation Technologist Awards – Best Researcher Award

  • Recognized for proposing the dynamic stress testing method for soft rock and a buffer support design method for asymmetric deformation in tunnels, both adopted in deep coal mine projects.

Conclusion🌟

Professor Guojun Wu stands as a leading figure in the field of underground geotechnical engineering, with innovations that have significantly improved safety and design in high-risk environments. His extensive publication record, interdisciplinary collaborations, and patented technologies position him as a prime candidate for the Best Researcher Award. His work demonstrates practical impact, academic rigor, and global collaboration, making him an invaluable contributor to civil engineering and underground infrastructure sciences.

Publications 📚

🔬 Journal Article | 🆓 Open Access | 🗓️ 2025 | 🔢 0 citations
Li, Y., Wu, G., Chen, W., Yuan, J., & Huo, M. (2025). Laboratory model tests and unstable collapse analysis of SPB shield machine tunnelling in saturated sand. Tunnelling and Underground Space Technology.
📌 Focus: Collapse behavior during shield machine tunneling in saturated sand.

🔬 Journal Article | 🆓 Open Access | 🗓️ 2025 | 🔢 0 citations
Huo, M., Chen, W., Yuan, J., Li, Y., & Liu, Y. (2025). Experimental investigation and limit analysis of shield tunnel face failure mechanism in sand. Underground Space (China).
📌 Focus: Tunnel face failure mechanism under sandy conditions.

🔬 Journal Article | 🆓 Open Access | 🗓️ 2025 | 🔢 0 citations
Wen, C., Jia, S., Fu, X., & He, H. (2025). Semi-analytical assessment of dynamic sealing capacity of underground gas storage: A case of Songliao Basin, Northeastern China. Journal of Rock Mechanics and Geotechnical Engineering.
📌 Focus: Sealing effectiveness in underground gas reservoirs.

🔬 Journal Article | 🗓️ 2025 | 🔢 1 citation
Yu, J., Zhang, Z., Wu, G., Li, X., & Tian, H. (2025). Investigation on asymmetric dynamic response characteristics of anchored surrounding rock under blasting excavation of inclined layered rock tunnel. Canadian Geotechnical Journal.
📌 Focus: Response of surrounding rock under dynamic blasting loads.

🔬 Journal Article | 🗓️ 2024 | 🔢 1 citation
Shu, X., Chen, W., Qiu, X., Wu, G., & Tian, Y. (2024). Anchorage mechanism and parametric analysis of a novel interface-shear-stress-dispersing bolt. Tunnelling and Underground Space Technology.
📌 Focus: New bolt design for stress dispersion in tunneling.

🔬 Journal Article | 🗓️ 2024 | 🔢 2 citations
Li, Y., Wu, G., Chen, W., Huo, M., & Liu, Y. (2024). Laboratory experimental study of the forming and failure mechanisms of soil arching during EPBS tunnelling in sand. Engineering Failure Analysis.
📌 Focus: Soil arching effects during Earth Pressure Balance Shield (EPBS) tunneling.

🔬 Journal Article | 🗓️ 2024 | 🔢 1 citation
Li, Y., Wu, G., Chen, W., Yuan, J., & Huo, M. (2024). Influence of confined water on the limit support pressure of tunnel face in weakly water-rich strata. Journal of Central South University.
📌 Focus: Impact of water content on tunnel face stability.

Manas Ranjan Sethi | ECE | Best Researcher Award

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Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.

 

 

 

ABLA CHAOUNI BENABDELLAH | Engineering | Best Researcher Award

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Assist. Prof. Dr. ABLA CHAOUNI BENABDELLAH | Engineering | Best Researcher Award

BEST RESEARCHER at International University of rabat, Morocco

Abla Chaouni Benabdellah is an Assistant Professor of Supply Chain Management and Information Systems at Rabat Business School, International University of Rabat (UIR). She holds a Ph.D. in Industrial Engineering from Moulay Ismail University, Meknes, and a Master’s in Mathematics and Statistics from Mohamed V University, Rabat. With extensive teaching experience across various institutions including EUROMED University and Private University of Fez, she specializes in project management, risk management, and supply chain strategies.

Publication Profile : 

Scopus

🎓 Educational Background :

  • Ph.D. in Industrial Engineering (2016 – 2019), Moulay Ismail University, ENSAM, Meknes
  • Master in Mathematics and Statistics (2012 – 2014), Mohamed V University, Rabat
  • Bachelor in Applied Mathematics (2009 – 2012), Moulay Ismail University, Faculty of Science, Meknes
  • Baccalaureate in Mathematics (2008 – 2009), Moulay Ismail College, Meknes

💼 Professional Experience :

  • Assistant Professor of Supply Chain Management & Information Systems (Since 2022), Rabat Business School, International University of Rabat (UIR), Rabat
  • Human Resources Consultant (2021), Expert Human Capital (EHC), Casablanca
  • Professor (2020), School of Digital Engineering and Artificial Intelligence (EIDIA), EUROMED University, Fez
  • Professor (2020), Private University of Fez, Fez
  • Seminar Presenter (2020), “Holonic Multi-Agent Systems for Decision Making -Application to Knowledge Management-“, ENSAM, Meknès
  • Doctoral Course Instructor (2019), Statistical Modeling with R Software, ENSAM-Meknès
  • Coordinator (2018), Artificial Intelligence and Data Science Master, SUPMTI, Meknes
  • Professor (2016), Higher School of Management, Telecommunications and IT (SUPMTI), Meknes

📚 Research Interests : 

  • Supply Chain Management
  • Industrial Engineering
  • Digital Supply Chains
  • Blockchain Technology
  • Artificial Intelligence and Data Science
  • Statistical Modeling

📝 Publication Top Notes :

  1. Blockchain Technology in Supply Chains: Discusses blockchain’s role in enhancing digital supply chains and evaluates implementation barriers.
  2. Big Data Analytics in Supplier Selection: Explores a multi-agent system for supplier selection using big data analytics.
  3. Smart Product Design and Digital Agility: Develops an ontology for managing agility in digital product design.
  4. Blockchain and Smart Contracts in Automotive Supply Chains: Examines how blockchain and smart contracts can optimize automotive supply chains.
  5. Medical Waste Management Optimization: A multi-agent system approach for improving medical waste management.
  6. Sustainable Supplier Selection in Circular Economy: Uses an ontology-based model to improve supplier selection under a circular economy framework.
  7. Environmental Supply Chain Risk Management: Proposes a data mining framework for managing supply chain risks in Industry 4.0.
  8. Lean and Green Practices in Supply Chains: Integrates lean and green practices to enhance sustainable and digital supply chain performance.
  9. Digital Technologies and Circular Economy: Investigates how digital technologies support sustainable supply chain management post-COVID-19.
  10. Circular Digital Supply Chain Design: Focuses on sustainable design practices within digital supply chains.
  11. Supplier Selection Ontology: Develops an ontology for effective supplier selection in digital supply chains.
  12. Intersection of Design for X and Business Strategies: Analyzes the integration of design techniques and business strategies for product lifecycle management.
  13. Knowledge Discovery for Sustainability: Discusses methods for enhancing sustainability through knowledge discovery in design processes.