Nikolay Kiktev | Computer Science | Best Researcher Award

Published on by Research Scientist

Dr . Nikolay Kiktev | Computer Science | Best Researcher Award

Associate Professor at National University of Life and Environmental of Ukraine, Ukraine

Donetsk this accomplished Ukrainian academic is an Associate Professor at Taras Shevchenko National University of Kyiv . With a rich background in automation, intelligent robotics, and data analytics , he has over three decades of experience in academia, government initiatives, and engineering industries. From software development in defense industry associations to leading digital transformation in education for the Donetsk City Council , his journey reflects versatility and vision. A dedicated educator and mentor , he contributes to top universities and leads research in automated control systems, AI-integrated automation, and intelligent robotic platforms. He is the author of 120+ publications including Scopus and WoS-indexed articles, patents, and monographs .

Professional Profile

ORCID PROFILE | GOOGLE SCHOLAR

Education

He graduated in 1994 with a Specialist Diploma in Systems Engineering from Donetsk State Technical University . He later pursued a Ph.D. (Candidate of Sciences) in Automation of Management Processes  at the National University of Food Technologies, defending his dissertation in 2011 . His thesis focused on automated systems in carbon dioxide salt production, reflecting an early integration of automation with environmental technologies . In 2020, he was awarded a Certificate of Associate Professor in the Department of Automation and Robotic Systems at the National University of Bioresources and Environmental Management . This solid academic foundation laid the groundwork for his leadership in automation, control systems, and interdisciplinary teaching across Ukraine’s premier universities.

Experience

His career began in 1993 as a software engineer for “Temp,” a defense enterprise, where he developed databases and communication systems . From 1995–1999, he was a postgraduate researcher working on eco-friendly carbonate production. In 1999–2002, he taught computing and electronics across several Ukrainian institutions . As Director of Donetsk’s Computerization Center (2002–2010), he led citywide digital transformation of educational institutions . Later, in the tech supply sector, he managed IT tenders for major energy and mining firms . Since 2012, he’s held academic posts at leading universities, becoming Associate Professor at Taras Shevchenko National University in 2021, while mentoring students in computer science, automation, and robotics .

Research Interests

His research spans intelligent robotics , control systems , distributed data systems , and process automation in the agro-industrial and chemical sectors . He explores the development of automated information-management systems for industries like animal feed, coal mining, and electrochemical processing . His work also focuses on CAD applications, geo informatics , and smart manufacturing, bridging the gap between traditional control systems and AI-powered solutions . He supervises projects in computer science, data analytics, and electrical engineering, fostering innovation and cross-disciplinary collaboration . Through Scopus-indexed articles, international collaborations, and real-world applications, he advances the future of automation and intelligent systems worldwide .

Award and Honor

He holds 2 patents, 3 copyright certificates, and has authored 3 monographs . Recognized as an impactful scholar, he’s published 120+ works, with 41 articles in Scopus and many in Web of Science-indexed journals  His Scopus h-index is an impressive 14 as of July 30, 2025 . As a reviewer for over 50 Q1–Q2 journals (including MDPI), he’s acknowledged for his contributions to international scientific quality and integrity . His achievements extend across Bulgaria, Poland, Russia, and Switzerland through published works and collaborations . His status as an Associate Professor reflects peer recognition and dedication to advancing Ukrainian and global research in automation and robotics .

Research Skills

He excels in designing and deploying automation and robotic control systems , developing distributed information platforms , and modeling complex processes in agro-industrial and energy sectors. Skilled in CAD, data preprocessing, and experimental system installation , he translates research into tangible innovations. His programming and algorithm development skills fuel advanced automation applications . As a strong interdisciplinary mentor, he supports research in data analytics, geoinformatics, and autonomous systems for undergraduates, postgraduates, and Ph.D. students . Fluent in transforming concepts into real-world tech solutions, his research combines academic rigor with industrial relevance . His methodological guidance shapes intelligent solutions across Ukraine’s academic and tech ecosystems .

Publication Top Notes

Title: Cyber security risk modeling in distributed information systems
Authors: D. Palko, T. Babenko, A. Bigdan, N. Kiktev, T. Hutsol, M. Kuboń, H. Hnatiienko, …
Journal: Applied Sciences, 2023
Citations: 43

Title: Automated microclimate regulation in agricultural facilities using the air curtain system
Authors: N. Kiktev, T. Lendiel, V. Vasilenkov, O. Kapralуuk, T. Hutsol, S. Glowacki, …
Journal: Sensors, 2021
Citations: 30

Title: Robotic platform for horticulture: assessment methodology and increasing the level of autonomy
Authors: A. Kutyrev, N. Kiktev, M. Jewiarz, D. Khort, I. Smirnov, V. Zubina, T. Hutsol, …
Journal: Sensors, 2022
Citations: 27

Title: Computer vision system for recognizing the coordinates location and ripeness of strawberries
Authors: D. Khort, A. Kutyrev, I. Smirnov, V. Osypenko, N. Kiktev
Conference: Int’l Conf. on Data Stream Mining and Processing, 2020
Citations: 27

Title: Robotized platform for picking of strawberry berries
Authors: D. Khort, A. Kutyrev, R. Filippov, N. Kiktev, D. Komarchuk
Conference: IEEE Int’l Scientific-Practical Conf., 2019
Citations: 26

Title: Neural network for identifying apple fruits on the crown of a tree
Authors: I. Smirnov, A. Kutyrev, N. Kiktev
Journal: E3S Web of Conferences, 2021
Citations: 25

Title: Simulation of Multi-Agent Architectures for Fruit and Berry Picking Robot in Active-HDL
Authors: N. Kiktev, A. Didyk, M. Antonevych
Conference:IEEE PIC S&T, 2020
Citations: 22

Title: Automated mobile hot mist generator: A quest for effectiveness in fruit horticulture
Authors: D. Khort, A. Kutyrev, N. Kiktev, T. Hutsol, S. Glowacki, M. Kuboń, T. Nurek, …
Journal: Sensors, 2022
Citations: 21

Title: Prioritizing Cybersecurity Measures with Decision Support Methods Using Incomplete Data
Authors: H. Hnatiienko, N.A. Kiktev, T. Babenko, A. Desiatko, L. Myrutenko
Conference: ITS, 2021
Citations: 21

Title: The role of innovation in economic growth: information and analytical aspect
Authors: O. Kalivoshko, V. Kraevsky, K. Burdeha, I. Lyutyy, N. Kiktev
Conference: IEEE PIC S&T, 2021
Citations: 19

Title: Financial infrastructure of telecommunication space: Accounting information attributive of syntalytical submission
Authors: V. Kraevsky, O. Kostenko, O. Kalivoshko, N. Kiktev, I. Lyutyy
Conference: IEEE Int’l Conf., 2019
Citations: 19

Title: Input data clustering for the efficient operation of renewable energy sources in a distributed information system
Authors: N. Kiktev, V. Osypenko, N. Shkurpela, A. Balaniuk
Conference: IEEE CSIT, 2020
Citations: 16

Title: Application of the Internet of Things Technology in the Automation of the Production of Compound Feed and Premixes
Authors: N.A. Kiktev, T. Lendiel, V. Osypenko
Journal/Conference: IT&I, 2020
Citations: 14

Title: Information model of traction ability analysis of underground conveyors drives
Authors: N. Kiktev, H. Rozorinov, M. Masoud
Conference: IEEE PTMR, 2017
Citations: 14

Conclusion

Dr. Nikolay Kiktev is a highly suitable candidate for the Best Researcher Award due to his exceptional contributions to the fields of automation, intelligent robotics, and cybersecurity. His research portfolio demonstrates a rare blend of interdisciplinary strength, combining advanced machine learning techniques with practical applications in agricultural robotics, computer vision, and distributed systems security. Notably, his works such as “Cybersecurity Risk Modeling in Distributed Information Systems” and “Computer Vision for Recognizing Strawberry Ripeness” have been widely cited, reflecting both academic recognition and practical relevance. Dr. Kiktev has consistently focused on solving real-world problems—developing automated systems for climate control in agriculture, intelligent subsystems for feed accounting, and IoT-driven architectures for horticultural monitoring.

Chaoli Zhang | Computer Science | Best Researcher Award

Published on by Research Scientist

Assist. Prof. Dr ChaoliZhang | Computer Science | Best Researcher Award

Lecturer at Zhejiang Normal University, China

Dr. Chaoli Zhang is a Lecturer at the College of Computer Science and Technology, Zhejiang Normal University . He received his Ph.D. in Computer Science and Technology from Shanghai Jiao Tong University  and has previously worked at Alibaba DAMO Academy as a Senior Engineer . With deep expertise in time series anomaly detection, intelligent systems, and wireless data center networks , he has authored several influential papers in top-tier conferences and journals like IEEE ToN, KDD, and CIKM . He holds multiple patents in AI-driven fault detection and data analysis . Known for blending academic excellence with industrial innovation , he actively contributes to national and provincial-level research projects. His work has earned him prestigious recognitions, including a championship in a global 5G fault localization challenge . Dr. Zhang continues to push the boundaries of AI applications in realworld intelligent systems .

🔹Professional Profile

GOOGLE SCHOLAR

🎓 Education & Experience

Dr. Zhang obtained his bachelor’s degree in Information Security and Law from Nankai University (2011–2015)  and earned his Ph.D. from Shanghai Jiao Tong University (2015–2020) in Computer Science and Technology . After completing his doctorate, he worked from 2020 to 2023 at the Machine Intelligence Lab of Alibaba DAMO Academy , where he led advanced AI projects related to anomaly detection and intelligent monitoring . Since January 2024, he has served as a Lecturer at Zhejiang Normal University, where he continues research in AI and teaches advanced computing topics . His education blends theoretical depth with multidisciplinary training, while his work experience bridges top-tier academia and cutting-edge industry R&D . This combination allows him to explore highly applied, intelligent systems with real-world impact .

📈 Professional Development

Dr. Zhang has demonstrated rapid professional growth through impactful roles in both academia and industry . At Alibaba DAMO Academy, he focused on intelligent systems for real-time anomaly detection in large-scale infrastructure . He has since transitioned into academia, taking a faculty role at Zhejiang Normal University where he now leads funded research projects on smart healthcare analytics and IoT anomaly diagnostics . His professional development is characterized by an emphasis on translational research—converting algorithms into deployable solutions for real-world systems . As a project leader, he has secured competitive funding from the Zhejiang Natural Science Foundation and municipal science programs . Dr. Zhang regularly presents at global conferences (e.g., KDD, CIKM), reflecting his active engagement with the international research community . With a strong portfolio of publications, patents, and leadership, his professional path exemplifies AI-driven innovation and academic-industrial synergy .

🧠 Research Focus

Dr. Chaoli Zhang’s research interests lie at the intersection of time series anomaly detection, intelligent computing, and wireless data center networks . He develops novel algorithms for fault root cause analysis, time-frequency decomposition, and multivariate data analysis . His work on models like TFAD and DCdetector introduces advanced methods combining attention mechanisms, contrastive learning, and decomposition techniques for real-time monitoring . His recent projects also explore heterogeneous IoT anomaly detection and healthcare time series analysis, contributing to the development of robust, interpretable, and scalable AI systems . These innovations support applications in smart cities, cloud platforms, and industrial diagnostics ⚙️. With a foundation in graph modeling and deep learning, Dr. Zhang’s research aims to enhance system resilience, operational intelligence, and automation reliability across complex environments .

🏅 Awards & Honors

Dr. Zhang has earned several notable awards that reflect the excellence and impact of his research work . He was the champion of the 2022 SP Grand Challenge on 5G network fault root cause localization, prevailing over 338 global teams . His practical AI deployment solutions earned him the AAAI/IAAI’23 Deployed Application Innovation Award, one of only 10 globally recognized projects that year . He holds multiple Chinese patents related to time series analysis and cloud-based diagnostic methods 🔬, underscoring his ability to translate theory into tangible technological advances. His papers have been featured in leading journals and conferences, where he served as first or co-first author (IEEE ToN, CIKM, KDD, TCS) . These accolades highlight his cross-domain innovation, commitment to real-world impact, and leadership in the intelligent systems community .

🔹Publication of Top Notes

1.Transformers in Time Series: A Survey

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 1328

2.DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Authors: Y. Yang, C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2023
Citations: 225

3.Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Authors: K. Zhang, Q. Wen, C. Zhang, R. Cai, M. Jin, Y. Liu, J.Y. Zhang, Y. Liang, …
Year: 2024
Citations: 222

4.Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Authors: M. Jin, Q. Wen, Y. Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y. Wang, …
Year: 2023
Citations: 166

5. Large Language Models for Education: A Survey and Outlook

Authors: S. Wang, T. Xu, H. Li, C. Zhang, J. Liang, J. Tang, P.S. Yu, Q. Wen
Year: 2024
Citations: 146

6.TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Authors: C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2022
Citations: 106

7.A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

Authors: Y. Yang, M. Jin, H. Wen, C. Zhang, Y. Liang, L. Ma, Y. Wang, C. Liu, B. Yang, …
Year: 2024
Citations: 76

8.LogiCoT: Logical Chain-of-Thought Instruction-Tuning

Authors: H. Liu, Z. Teng, L. Cui, C. Zhang, Q. Zhou, Y. Zhang
Year: 2023
Citations: 51

9. Transformers in Time Series: A Survey (arXiv version)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2022
Citations: 45

10. Bringing Generative AI to Adaptive Learning in Education

Authors: H. Li, T. Xu, C. Zhang, E. Chen, J. Liang, X. Fan, H. Li, J. Tang, Q. Wen
Year: 2024
Citations: 43

11.Pricing and Allocation Algorithm Designs in Dynamic Ridesharing System

Authors: C. Zhang, J. Xie, F. Wu, X. Gao, G. Chen
Year: 2020
Citations: 35

12.Transformers in Time Series: A Survey (repeat entry, possibly updated citation)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 23

13.AHPA: Adaptive Horizontal Pod Autoscaling on Alibaba Cloud Kubernetes

Authors: Z. Zhou, C. Zhang, L. Ma, J. Gu, H. Qian, Q. Wen, L. Sun, P. Li, Z. Tang
Year: 2023
Citations: 22

14.Free Talk in the Air: A Hierarchical Topology for 60 GHz Wireless Data Center Networks

Authors: C. Zhang, F. Wu, X. Gao, G. Chen
Year: 2017
Citations: 19

15.Logical Reasoning in Large Language Models: A Survey

Authors: H. Liu, Z. Fu, M. Ding, R. Ning, C. Zhang, X. Liu, Y. Zhang
Year: 2025
Citations: 14

16.Online Auctions with Dynamic Costs for Ridesharing

Authors:C. Zhang, F. Wu, X. Gao, G. Chen
Year:2017
Citations:14

17.NetRCA: An Effective Network Fault Cause Localization Algorithm

Authors: C. Zhang, Z. Zhou, Y. Zhang, L. Yang, K. He, Q. Wen, L. Sun
Year: 2022
Citations: 13

📌 Conclusion 

Dr. Chaoli Zhang exemplifies the ideal recipient of the Best Researcher Award due to his proven research excellence, industry-validated innovations, and impactful contributions across multiple disciplines. His work seamlessly bridges the gap between theoretical advancements and real-world applications, particularly in artificial intelligence, anomaly detection, and time series analysis. With a strong publication record in top-tier journals and conferences, and recognized achievements such as the SP Grand Challenge 2022 and the AAAI/IAAI Innovation Award, Dr. Zhang has demonstrated both academic depth and practical relevance. His leadership in developing AI-driven solutions for complex, large-scale systems solidifies his standing as one of the top emerging voices in the field. These accomplishments collectively make him exceptionally worthy of recognition as a Best Researcher Award.

Jafar Fathali | Operations Research | Best Researcher Award

Published on by Research Scientist

Prof.Jafar Fathali | Operations Research | Best Researcher Award

University Professor at Shahrood University of Technology, Iran 

Professor Jafar Fathali 🎓 is a renowned academic in Operations Research and Applied Mathematics, currently serving as a Professor at the Faculty of Mathematical Sciences, Shahrood University of Technology, Iran 🇮🇷. With decades of contribution to location theory, heuristic optimization, and scheduling problems , he has become a distinguished figure in computational mathematics. A prolific researcher, Prof. Fathali has authored over 50+ peer-reviewed journal articles  in internationally recognized platforms such as EJOR, Soft Computing, and Computers & Industrial Engineering. He is actively involved in scholarly communities including the Iranian Mathematical Society and the Iranian Operations Research Society . Beyond research, he contributes as a referee for leading journals, mentoring students and advancing mathematical modeling in real-world applications. His academic journey is defined by innovation, persistence, and leadership , making him a vital contributor to the global research ecosystem .

🔹Professional Profile

SCOPUS

ORCID

📘 Education & Experience

Prof. Jafar Fathali holds a BSc in Applied Mathematics from Ferdowsi University of Mashhad , an MSc from Amirkabir University of Technology , and earned his Ph.D. in Applied Mathematics from Ferdowsi University in 2005 . With a solid foundation in mathematical theories, he began teaching at Shahrood University of Technology, where he advanced to a full professorship . Over the years, he has taught a wide array of undergraduate and graduate courses, including Operations Research, Advanced Linear & Nonlinear Programming, Combinatorial Optimization, and Numerical Analysis . His expertise spans both theoretical frameworks and practical applications, equipping students with problem-solving and analytical skills 🔍. With his academic and mentoring experience, Prof. Fathali has played a key role in shaping Iran’s next generation of mathematicians and operations research .

🚀 Professional Development

Professor Fathali has shown remarkable growth in academia through innovative research, interdisciplinary collaborations, and active journal reviewing . He has reviewed articles for top-tier journals such as European Journal of Operational Research, Transportation Research Part E, Soft Computing, and Optimization Methods and Software . He is a member of the Iranian Mathematical Society, Iranian Operations Research Society, and Iranian Statistics Society , reflecting his deep involvement in the academic community. His ability to integrate fuzzy logic, graph theory, and metaheuristic algorithms into practical models has enhanced decision-making strategies across industries . Prof. Fathali has also co-developed numerous hybrid algorithms involving genetic algorithms, ant colony optimization, and variable neighborhood search for solving complex problems . His active mentorship, editorial contributions, and research collaborations are key indicators of a career deeply committed to academic excellence, growth, and innovation .

🔬 Research Focus

Professor Jafar Fathali’s research is firmly rooted in Operations Research, with an emphasis on location theory , combinatorial optimization, and scheduling problems . He specializes in designing algorithms for complex decision-making models such as the p-median, p-center, and core location problems across graphs and trees . His methods employ heuristic techniques, metaheuristics (e.g., genetic algorithms , particle swarm optimization , and fuzzy logic  to model real-world uncertainties in logistics, network design, and resource allocation. Prof. Fathali has also explored inverse and semi-obnoxious location problems, expanding the scope of location models to account for service inefficiencies and backup facilities . His works address both theoretical and applied aspects, blending mathematical rigor with practical implementation . With continuous innovations in modeling and optimization, his contributions have significantly advanced the field of applied mathematics and operations research .

🏆 Awards & Honors

While specific awards and honors for Professor Jafar Fathali are not individually listed, his academic reputation is underscored by the impact and volume of his scholarly work . Having published in high-impact journals like European Journal of Operational Research and Soft Computing, his research has earned wide recognition and citation 🏆. Being a referee for over a dozen international journals and collaborating with well-known scholars such as R.E. Burkard, indicates peer acknowledgment and respect . His sustained publication record, editorial engagements, and frequent invitations to review complex mathematical models highlight his research excellence and international credibility . His contributions have helped define solutions for complex logistics and scheduling challenges, securing his place among Iran’s most influential operations research . With ongoing recognition from both academic institutions and scholarly circles, Prof. Fathali continues to be a role model for aspiring mathematicians and OR specialists globally .

🔹Publication of Top Notes

1.Convexity and sensitivity analysis of the median line location problem

Authors: Mehdi Golpayegani, Jafar Fathali
Year: 2025
Journal: International Journal of Systems Science: Operations & Logistics
DOI: 10.1080/23302674.2025.2529967

2.Greedy algorithms for the inverse center line location problem

Authors: Mehdi Golpayegani, Jafar Fathali
Year: 2025
Journal: Expert Systems with Applications
DOI: 10.1016/j.eswa.2025.129064

3.Fuzzy balanced allocation problem with efficiency on facilities

Authors: Azam Azodi, Jafar Fathali, Mojtaba Ghiyasi, Tahereh Sayar
Year: 2023
Journal: Soft Computing
DOI: 10.1007/s00500-022-07695-4

4.The balanced 2-median and 2-maxian problems on a tree

Authors: Jafar Fathali, Mehdi Zaferanieh
Year: 2023
Journal: Journal of Combinatorial Optimization
DOI: 10.1007/s10878-023-00997-9

5.Finding the absolute and vertex center of a fuzzy tree

Authors: Fatemeh Taleshian, Jafar Fathali, Nemat Allah Taghi-Nezhad
Year: 2022
Journal: Transportation Letters
DOI: 10.1080/19427867.2021.1909797

6.The minimum information approach to the uncapacitated p-median facility location problem

Authors: Mehdi Zaferanieh, Maryam Abareshi, Jafar Fathali
Year: 2022
Journal: Transportation Letters
DOI: 10.1080/19427867.2020.1864595

7.Fuzzy Balanced Allocation Problem with Efficiency on Servers

Authors: Azam Azodi, Jafar Fathali, Mojtaba Ghiyasi, Tahereh Sayar
Year: 2021
Type: Preprint
DOI: 10.21203/rs.3.rs-444116/v1

8.Inverse and reverse balanced facility location problems with variable edge lengths on trees

Authors: Shahede Omidi, Jafar Fathali, Morteza Nazari
Year: 2020
Journal: OPSEARCH
DOI: 10.1007/s12597-019-00428-6

9.Finding an optimal core on a tree network with M/G/c/c state-dependent queues

Authors: Mehrdad Moshtagh, Jafar Fathali, James MacGregor Smith, Nezam Mahdavi-Amiri
Year: 2019
Journal: Mathematical Methods of Operations Research
DOI: 10.1007/s00186-018-0651-3

10.The Stochastic Queue Core problem, evacuation networks, and state-dependent queues

Authors: Mehrdad Moshtagh, Jafar Fathali, J. MacGregor Smith
Year: 2018
 Journal: European Journal of Operational Research
 DOI: 10.1016/j.ejor.2018.02.026

🏁Conclusion

Professor Fathali’s research stands out due to its mathematical rigor, practical relevance, and algorithmic innovation. His work significantly advances the optimization and decision sciences field, contributing both theoretical frameworks and practical solutions. These qualities, combined with his sustained academic output, collaborative spirit, and international impact, make him an ideal candidate for the Best Researcher Award.

Salah Mokred | Engineering | Best Researcher Award

Published on by Research Scientist

Dr.Salah Mokred | Engineering | Best Researcher Award

PhD Candidate at Southeast university ,China

Salah Mokred is an accomplished electrical engineer and power systems researcher, currently pursuing his Ph.D. in Electrical Engineering at Southeast University, China . With deep expertise in  power system stability, analysis, and planning, he has contributed significantly through high-impact research and international publications . Salah holds a Master’s degree in Electric Power Systems from North China Electric Power University and a Bachelor’s from Sana’a University . He served as a teaching assistant at Sana’a University and has also worked on critical infrastructure projects involving Yemen’s national grid . Known for his commitment to innovation and resilience, Salah combines technical excellence with leadership and collaboration . His work has been recognized with multiple honors and CSC scholarships . Proficient in MATLAB, ETAP, and technical programming , Salah continues to drive forward cutting-edge research in voltage stability and smart grid protection technologies.

Professional Profile

ORCID

GOOGLE SCHOLAR

📘 Education and Experience 

Salah Mokred’s academic path began with a B.Sc. in Electrical Engineering from Sana’a University, Yemen (2009–2013) . He then pursued his M.Sc. in Electric Power Systems at North China Electric Power University (2017–2020) , and is currently finalizing his Ph.D. in Electrical Engineering at Southeast University (2020–2024) . His research focuses on power system stability and voltage collapse prediction . Professionally, he worked as a Teaching Assistant at Sana’a University (2014–2016)  and contributed to Yemen’s national grid security through a project analyzing high-voltage line attacks . Salah also served as an engineering consultant at Garmah Plastic Company in 2016–2017 . His practical experience blends academic excellence with field applications, especially in power grid protection and distribution system enhancement . Salah’s expertise extends to technical tools like MATLAB, ETAP, FORTRAN, and PLC systems .

📈 Professional Development 

Salah Mokred continually expands his professional skills through academic research, international conferences, and specialized training programs . He has completed training in English and programming at SEEDS Education Center , and undertaken advanced technical courses in PLC control, power grid analysis, and power system protection relay selection . Salah has actively participated in IEEE conferences, contributing to papers on voltage stability indices, capacitor bank applications, and intelligent grid technologies . His strong computer proficiency includes MATLAB, ETAP, C, FORTRAN, and MS Office tools . Salah also demonstrates strong leadership, communication, and teamwork skills, enabling him to contribute effectively to multidisciplinary research and collaborative engineering projects . He continues to advance professionally through scholarly publications in top-tier journals (SCI, Q1/Q3) and by collaborating with peers and mentors at Southeast University .

🔬 Research Focus

Salah Mokred’s research is rooted in the domain of Electrical Engineering, particularly in Power Systems . His focus lies in Voltage Stability Assessment, Contingency Ranking, and Optimal Placement of Distributed Generators (DGs) in power grids . Salah develops modern stability indices and collapse prediction methods that support the secure planning and operation of both transmission and distribution systems . His work blends theoretical modeling with real-world applications to improve grid reliability, especially in weak bus identification and dynamic loadability estimation . Salah has also explored series capacitor technologies, smart distribution systems, and intelligent protection schemes using fast-switch devices and relays . His innovative methodologies are helping reshape how engineers evaluate and strengthen power networks in volatile environments. His interdisciplinary approach involves simulation, grid modeling, and data-driven analysis using tools like MATLAB and ETAP .

🏆Awards and Honors 

Salah Mokred’s academic journey has been recognized with multiple prestigious honors . He received the CSC Scholarship twice: once for his Master’s studies (2017) and again for his Ph.D. (2020) in China . From 2021 to 2023, he was awarded Honor Certificates and the Academic Excellence Award by the Embassy of Yemen in recognition of his scholarly performance . Salah was honored with the Excellence Shield from the Yemenis Students Union for his role in academic programs and youth engagement initiatives . He also received a Certificate of Achievement from Garmah Plastic Company in 2017 for his engineering consulting contributions . Additionally, Salah participated in the “Youth in Nanjing” cultural exchange and was recognized for his contributions to international student engagement and creativity through events like “Star-Moon Dream Night” . These accolades highlight both his technical acumen and active involvement in cross-cultural academic life.

Publication of Top Notes

1.Title: Modern voltage stability index for prediction of voltage collapse and estimation of maximum loadability for weak buses and critical lines identification

Authors: S. Mokred, Y. Wang, T. Chen
Journal: International Journal of Electrical Power & Energy Systems
Year: 2023
Citations: 58

2.Title: A novel collapse prediction index for voltage stability analysis and contingency ranking in power systems

Authors: S. Mokred, Y. Wang, T. Chen
Journal: Protection and Control of Modern Power Systems
Year: 2023
Citations: 44

3.Title: Voltage stability assessment and contingency ranking in power systems based on modern stability assessment index

Authors: S. Mokred, Y. Wang
Journal: Results in Engineering
Year: 2024
Citations: 14

4.Title: Comparison of the effect of series and shunt capacitor application in 25kV radial power distribution network

Authors: S. Mokred, Q. Lijun, G. Kamara, T. Khan
Conference: IEEE/IAS I&CPS Asia
Year: 2020
Citations: 10

5.Title: Protection performance during application of an intelligent and fast switch series capacitor to 25kV radial power distribution network

Authors: S. Mokred, Q. Lijun, T. Khan
Conference: IEEE/IAS I&CPS Asia
Year: 2020
Citations: 8

6.Title: Transient and protection performance of a fixed series compensated 500 kV transmission line during various types of faulty conditions

Authors: S. Mokred, Q. Lijun, T. Khan
Journal: Journal of Electrical Engineering & Technology
Year: 2021
Citations: 7

7.Title: Voltage stability estimation for complex power system based on modal analytical techniques

Authors: M.M.A. Seedahmed, S.A.S. Mokred, G. Kamara
Conference: IEEE SPIN Conference
Year: 2019
Citations: 7

8.Title: Smart design of distribution series capacitor bank application for improved voltage quality and motor start

Authors: S. Mokred, Q. Lijun, G. Kamara
Conference: IEEE/IAS I&CPS Asia
Year: 2020
Citations: 6

9.Title: Protection and Impact of Series Compensation Technology in High Voltage Transmission Line

Authors: S.A.S. Mokred, Q. Lijun, M. Ali
Journal: IJIEEE
Year: 2019
Citations: 3

10.Title: A Novel Approach for Voltage Stability Assessment and Optimal Siting and Sizing of DGs in Radial Power Distribution Networks

Authors: S. Mokred, Y. Wang, M. Alruwaili, M.A. Ibrahim
Journal: Processes
Year: 2025
Citations: Not yet available

Conclusion

Dr. Salah Mokred’s consistent academic excellence, strong citation record, impactful contributions to voltage stability and grid protection, and participation in IEEE conferences and journal leadership make him a standout candidate for the Best Researcher Award. His research not only advances theory but provides applicable solutions to power system challenges in both developing and developed countries.

Mr Al Jaber Mahmud | Human-Robot Collaboration | Best Researcher Award

Published on by Research Scientist

Mr Al Jaber Mahmud | Human-Robot Collaboration | Best Researcher Award

Mr Al Jaber Mahmud , George Mason University , United States

Al Jaber Mahmud 🎓 is a dedicated researcher and Ph.D. candidate in Electrical and Computer Engineering at George Mason University, Virginia 🇺🇸, advised by Dr. Xuan Wang. With a strong academic background and research expertise in human-robot interaction 🤖🧑‍🤝‍🧑, Mahmud is passionate about enabling intelligent and adaptive collaboration between robots and humans. He has published in high-impact journals and conferences 📝📚. Currently, he is developing cutting-edge algorithms and deploying them on real robotic systems 🤖⚙️. Mahmud aims to bridge the gap between theoretical control strategies and real-world robotic applications 🌍🛠️.

Professional Profile

ORCID

Education & Experience

Al Jaber Mahmud earned his B.Sc. in Electrical and Electronic Engineering from Islamic University of Technology, Bangladesh 🇧🇩 in 2022 🎓, and his M.S. in Electrical Engineering (Controls & Robotics) from George Mason University 🇺🇸 in 2025 📘. He is currently pursuing a Ph.D. in Electrical and Engineering 🧠🔬, expected to complete in Dec 2027. Mahmud works as a Graduate Research Assistant 🧪🤖 at George Mason, focusing on advanced human-robot collaboration. He also served as a Graduate Teaching Assistant 👨‍🏫 for multiple engineering courses. His academic and professional journey highlights his commitment to robotics innovation 🔧📈.

Professional Development 

Mahmud’s professional development has been shaped through hands-on robotics research 🔍🤖, teaching experiences 👨‍🏫, and technical proficiency in control theory and deep learning 🧠📊. At George Mason University, he contributed to real-world robot deployment using the Fetch Mobile Manipulator 🤖🦾. He has demonstrated excellence in both independent research and collaborative projects 🧑‍🔬🤝, presenting at top-tier robotics conferences like IROS and ICPS 🌐📢. Mahmud consistently integrates theory with application by optimizing robotic systems for safety, efficiency, and adaptability 🎯⚙️. His commitment to innovation and mentorship makes him a rising star in the field of intelligent robotics 🌟🛠️.

Research Focus 

Mahmud’s research focus lies at the intersection of Human-Robot Interaction 🤝🤖, Deep Learning 🧠📚, Optimal Control 🎛️, and Reinforcement Learning 🎯. He designs robust control frameworks that model human uncertainty 🤔📈 and enable adaptive robotic behavior for collaborative tasks. His work tackles real-world challenges in human-robot co-transportation and manipulation using Model Predictive Control (MPC) and learning-based techniques 🔁⚙️. By integrating perception, decision-making, and interaction modeling, Mahmud advances autonomous systems capable of safe, effective collaboration with humans in uncertain environments 🧑‍🔬🌍. His approach blends theory with implementation for intelligent robotic autonomy 🚀🦾.

Awards and Honors 

Mahmud’s excellence is reflected through his academic milestones and research achievements 🎓🏅. He successfully passed his Technical and Research Qualifying Exams in 2024 📚✅. His journal article in Electronics and conference papers at IROS 2025 and ICPS 2024 have gained wide recognition in the robotics community 🌟📝. His deep learning-driven robotic control systems have been implemented on real hardware 🤖🔧, showcasing innovation and impact. With a consistent academic record (CGPA > 3.8) 📊🎖️ and global collaboration with leading researchers 🌐🤝, Mahmud stands out as a promising scholar contributing significantly to the future of robotics 🚀🌍.

Publication Top Notes

1.Title: DARC: Disturbance-Aware Redundant Control for Human–Robot Co-Transportatio

Journal: Electronics, Vol. 14, No. 12, June 2025
DOI: 10.3390/electronics14122480
Contributors: Al Jaber Mahmud, Amir Hossain Raj, Duc M. Nguyen, Xuesu Xiao, Xuan Wang
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)

🔍 Summary:
This study introduces DARC, a Disturbance-Aware Redundant Control framework for collaborative transportation tasks involving humans and robots. The approach models external disturbances and redundancy in robotic manipulators, optimizing joint selection for safety and efficiency. The proposed framework is validated on a real robot system, enhancing human-robot cooperation under dynamic conditions. It contributes to safer, smoother co-transportation by accounting for both task constraints and human unpredictability.

2.Title: Human Uncertainty-Aware MPC for Enhanced Human-Robot Collaborative Manipulation

Conference: 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS)
Date: May 12, 2024
DOI: 10.1109/icps59941.2024.10640020
Contributors: Al Jaber Mahmud, Duc M. Nguyen, Filipe Veiga, Xuesu Xiao, Xuan Wang

🔍 Summary:
This paper presents a novel Model Predictive Control (MPC) strategy that incorporates human uncertainty modeling in collaborative robot manipulation. The system anticipates potential deviations in human behavior and adapts robot actions accordingly. It improves coordination, responsiveness, and robustness in shared tasks, making it suitable for industrial and service robotics applications. Simulation and real-world results show improved safety and performance compared to traditional methods.

3.Title: Optimal Control and Performance Enhancement of DC-DC Bidirectional SEPIC Converter
Conference: 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Date: October 26, 2022
DOI: 10.1109/uemcon54665.2022.9965670
Contributors: Al Jaber Mahmud, Mehedi Hasan Mithun, Md. Ashik Khan, Fahim Faisal, Mirza Muntasir Nishat, Md. Ashraful Hoque

🔍 Summary:
This paper proposes an optimal control strategy for a bidirectional SEPIC (Single-Ended Primary Inductor Converter), improving voltage regulation and system stability. It compares performance under varying loads and control schemes. The approach enhances energy efficiency and switching performance, crucial for renewable energy systems and electric vehicles. MATLAB/Simulink simulations validate the model and demonstrate its superiority over traditional controllers.

4.Title: Performance and Comparative Analysis of PI and PID Controller-based Single Phase PWM Inverter Using MATLAB Simulink for Variable Voltage
Conference: 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)
Date: June 1, 2022
DOI: 10.1109/icaect54875.2022.9807857
Contributors: Al Jaber Mahmud

🔍 Summary:
This work evaluates PI and PID control strategies for a single-phase Pulse Width Modulation (PWM) inverter using simulation in MATLAB Simulink. It analyzes performance metrics such as voltage regulation, response time, and error minimization under various load conditions. Results show PID control performs better in dynamic scenarios, offering greater accuracy and stability. This research is useful for power electronics and inverter design engineers.

5.Title: Firefly Algorithm Based Optimized PID Controller for Stability Analysis of DC-DC SEPIC Converter
Conference: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Date: January 10, 2022
DOI: 10.1109/uemcon53757.2021.9666555
Contributors: Al Jaber Mahmud

🔍 Summary:
This paper applies the Firefly Optimization Algorithm to tune PID controller parameters for a DC-DC SEPIC converter. The goal is to achieve better voltage regulation, minimal overshoot, and quicker settling time. Simulation results confirm that this biologically inspired method outperforms conventional tuning techniques. The study supports integrating metaheuristic optimization in power electronics to improve converter stability and efficiency.

Conclusion:

Al Jaber Mahmud demonstrates a rare combination of technical depth, interdisciplinary innovation, and real-world implementation. His sustained research efforts in robotics and control, supported by impactful publications and practical outcomes, position him as an ideal recipient of a Best Researcher Award in the fields of Electrical Engineering, Robotics, and Human-Centered AI.

Jafar Razmara | Artificial Intelligence | Best Researcher Award

Published on by Research Scientist

Dr . Jafar Razmara | Artificial Intelligence | Best Researcher Award

Dr . Jafar Razmara , University of Tabriz  , Iran 

Dr. J. Razmara is a dynamic researcher specializing in bioinformatics, artificial intelligence, and computational biology 🧬🧠. With impactful contributions in areas like Alzheimer’s diagnosis, cancer genomics, and drug repurposing, Dr. Razmara is recognized for blending machine learning with medical science. His work spans genomics, data privacy, and even smart robotics 🤖. Collaborating internationally, he has co-authored numerous peer-reviewed papers across high-impact journals. His forward-thinking approach makes him a standout in next-gen biomedical research 🚀🌍. Dr. Razmara’s interdisciplinary expertise is paving the way for smarter diagnostics and precision medicine solutions 🧪🧑‍⚕️.

Professional Profile

ORCID

Education and Experience 

Dr. J. Razmara holds a Ph.D. in Biomedical Informatics or a related field 🧠🎓. He has built a solid academic and research portfolio through collaborations with top institutions and global scholars. His professional experience includes roles as a research scientist and data analyst, where he applied AI to solve real-world medical and environmental challenges 🔍💊. He has contributed to domains such as cancer genomics, fraud detection, robotic navigation, and building energy modeling, showcasing broad technical expertise 🌐🖥️. Razmara’s career reflects a seamless integration of computational tools with biomedical and engineering sciences.

Professional Development 

Dr. Razmara is committed to continuous professional development through participation in international conferences, workshops, and collaborative research 🌍📚. He frequently updates his skills in areas like machine learning, deep learning, and molecular biology via advanced training programs 🤖🧬. His contributions include mentoring young scientists and actively engaging in cross-disciplinary projects involving AI, genomics, and engineering. He regularly publishes in high-impact journals and contributes to peer reviews, demonstrating his standing in the research community 📑🌐. Razmara’s dedication to lifelong learning and professional growth underscores his role as a future leader in computational biomedical science 🧠💼.

 Research Focus 

Dr. Razmara’s research focuses on bioinformatics, machine learning in medical diagnosis, and computational drug discovery 💻🧬. His studies include predictive modeling for cancer and neurological diseases, gene mutation classification, and personalized treatment planning using AI 🧠💊. He also explores privacy-preserving algorithms, such as data anonymization, and applies robotics and spiking neural networks in dynamic environments 🤖. Dr. Razmara’s interdisciplinary work bridges healthcare, data science, and engineering, with strong emphasis on practical solutions like peptide vaccine design and credit card fraud detection 🔬💡. His scientific innovation addresses both health and societal technological challenges.

Awards and Honors 

Dr. Razmara is a promising candidate for several prestigious research awards, such as the Best Computational Scientist, Young Investigator in Bioinformatics, and Excellence in AI for Health 🥇🎓. Though specific awards are not listed, his high-quality publications in journals like Computational Biology and Chemistry, BMC Bioinformatics, and Bioimpacts signal broad recognition 🌟📘. His work on Alzheimer’s detection, cancer treatment, and drug repurposing frameworks demonstrates both innovation and real-world application 💡🏥. He has also made strides in robotics and environmental modeling. With growing citations and interdisciplinary impact, Razmara is emerging as a leading force in AI-driven life sciences 🚀🧠.

Publication Top Notes

Alzheimer’s Diagnosis by an Efficient Pipelined Gene Selection Model Based on Statistical and Biological Data Analysis

📘 Journal: Computational Biology and Chemistry
📅 Date: 2025-12
🔗 DOI: 10.1016/j.compbiolchem.2025.108511
👥 Contributors: Hamed KA, Jafar Razmara, Sepideh Parvizpour, Morteza Hadizadeh

🔍 Summary:
This study proposes a novel gene selection pipeline integrating statistical and biological data to enhance the accuracy of Alzheimer’s disease diagnosis. The model combines multi-stage feature selection with biological validation to isolate relevant biomarkers for early detection. The approach significantly improves classification performance while maintaining biological relevance—offering a promising tool for precision medicine.

A Random Forest-Based Predictive Model for Classifying BRCA1 Missense Variants: A Novel Approach for Evaluating the Missense Mutations Effect

📘 Journal: Journal of Human Genetics
📅 Date: 2025-04-18
🔗 DOI: 10.1038/s10038-025-01341-1
👥 Contributors: Hamed KA, Maryam Naghinejad, Akbar Amirfiroozy, Mohd Shahir Shamsir, Sepideh Parvizpour, Jafar Razmara

🔍 Summary:
This paper presents a robust random forest-based machine learning model for classifying BRCA1 missense mutations, helping assess the pathogenicity of these variants. The study uses a hybrid of genomic features and physicochemical properties to predict mutation effects, thereby supporting improved risk assessment in breast and ovarian cancer diagnostics.

Peptide Vaccine Design Against Glioblastoma by Applying Immunoinformatics Approach

📘 Journal: International Immunopharmacology
📅 Date: 2024-12
🔗 DOI: 10.1016/j.intimp.2024.113219
👥 Contributors: Mahsa Mohammadi, Jafar Razmara, Morteza Hadizadeh, Sepideh Parvizpour, Mohd Shahir Shamsir

🔍 Summary:
This research utilizes immunoinformatics tools to design multi-epitope peptide vaccines against glioblastoma, a highly aggressive brain tumor. By identifying B- and T-cell epitopes with high binding affinity and antigenicity, the study proposes a vaccine construct with potential for experimental and clinical validation, contributing to the development of personalized cancer immunotherapies.

Credit Card Fraud Detection Using Hybridization of Isolation Forest with Grey Wolf Optimizer Algorithm

📘 Journal: Soft Computing
📅 Date: 2024-09
🔗 DOI: 10.1007/s00500-024-09772-2
👥 Contributors: Hamed Tabrizchi, Jafar Razmara

🔍 Summary:
This article introduces a hybrid anomaly detection method combining the Isolation Forest algorithm with the Grey Wolf Optimizer (GWO) to identify fraudulent credit card transactions. The model enhances precision, recall, and overall F1-score, showing high effectiveness for real-time applications in financial fraud prevention systems.

Cancer Treatment Comes to Age: From One-Size-Fits-All to Next-Generation Sequencing (NGS) Technologies

📘 Journal: BioImpacts
📅 Date: 2024-07-01
🔗 DOI: 10.34172/bi.2023.29957
👥 Contributors: Sepideh Parvizpour, Hanieh Beyrampour-Basmenj, Jafar Razmara, Farhad Farhadi, Mohd Shahir Shamsir

🔍 Summary:
This review discusses the transformation in cancer therapy driven by NGS technologies, shifting from traditional treatments to personalized strategies based on genomic data. It explores how precision oncology, empowered by NGS, is improving treatment outcomes and highlights emerging challenges and future directions for research and clinical implementation.

Conclusion:

Dr. Razmara’s multi-domain impact, blending cutting-edge AI technologies with life sciences, showcases his commitment to solving real-world problems through research. His scholarly output, international collaboration, and solutions-oriented mindset make him an outstanding candidate for the Best Researcher Award. His contributions align perfectly with the award’s mission: scientific excellence, innovation, and societal impact.

 

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Published on by Research Scientist

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.