Genfeng Liu | Engineering | Best Researcher Award

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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

Jian Liu | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Jian Liu | Engineering | Best Researcher Award

Deputy Director of the Department at Tiangong University, China

Assoc. Prof. Dr. Jian Liu is a senior experimentalist and Master’s Supervisor at Tiangong University, specializing in ultra-fine fiber preparation, textile machinery design, and automation. With a PhD in Mechanical Design and Theory, he has led and contributed to six major research projects, including those funded by the National Natural Science Foundation of China and the National Development and Reform Commission. Dr. Liu has played a key role in 13 horizontal projects and four new product developments for enterprises. His innovative contributions are evident in his 11 national invention patents, multiple utility model and appearance patents, and software copyrights. As a prolific researcher, he has published over 20 scientific papers as the first author. Beyond research, he actively mentors students and advances engineering education. With a strong track record in applied research and industry collaboration, Dr. Liu continues to make significant contributions to mechanical engineering and automation.

Professional Profile 

Education

Assoc. Prof. Dr. Jian Liu has a strong academic background in mechanical engineering. He earned his Bachelor of Engineering degree in Mechanical Design, Manufacturing, and Automation from the School of Mechanical Engineering at Shandong University of Technology in 2007. Continuing his education at the same institution, he obtained a Master’s degree in Mechanical and Electronic Engineering in 2010. Driven by a passion for research and innovation, he pursued a PhD in Mechanical Design and Theory at Tiangong University, completing his doctoral studies in 2019. His academic journey reflects a continuous commitment to advancing his expertise in mechanical engineering, particularly in design, automation, and manufacturing technologies. Through his higher education and research, Dr. Liu has developed a strong foundation that supports his contributions to both academia and industry, playing a crucial role in advancing new technologies and mentoring the next generation of engineers.

Professional Experience

Assoc. Prof. Dr. Jian Liu has extensive professional experience in mechanical engineering education and research. He began his career as a teaching assistant at the Engineering Teaching Internship Training Center of Tiangong University in 2010. In 2013, he was promoted to lecturer, further strengthening his role in academia. After earning his PhD in 2019, he continued his career as an experimentalist at the same institution, where he contributed to hands-on engineering education and research. In 2020, he was appointed as a senior experimentalist, overseeing advanced experimental research and training. With over a decade of experience, Dr. Liu has been actively involved in mentoring students, leading research projects, and contributing to industrial innovation. His expertise in ultra-fine fiber preparation, textile machinery design, and automation has made him a key figure in bridging academic research with real-world applications, enhancing both educational and technological advancements in his field.

Research Interest

Assoc. Prof. Dr. Jian Liu’s research interests lie in the fields of ultra-fine fiber preparation technology, textile machinery design, and automation. His work focuses on developing innovative techniques for producing high-performance fibers with enhanced properties for various industrial applications. He is also deeply involved in the design and optimization of advanced textile machinery, aiming to improve manufacturing efficiency and precision. Additionally, Dr. Liu explores automation technologies to enhance production processes, integrating smart control systems and intelligent manufacturing techniques. His research contributions extend beyond theoretical studies, as he actively collaborates with industry partners to develop cutting-edge solutions for modern textile and mechanical engineering challenges. With numerous patents and publications, Dr. Liu continues to push the boundaries of mechanical design, automation, and material science, striving to bridge the gap between research and practical application in the evolving landscape of engineering and manufacturing.

Award and Honor

You haven’t mentioned specific awards and honors in your resume. However, based on your research contributions, patents, and publications, you may have received recognitions that can strengthen your profile. If you have received awards for research excellence, innovation, patents, or teaching achievements, highlighting them would enhance your candidacy for honors like the Best Researcher Award.If you provide details on any grants, fellowships, best paper awards, innovation prizes, or academic honors, I can craft a precise and compelling paragraph

Research Skill

Assoc. Prof. Dr. Jian Liu possesses strong research skills in mechanical engineering, specializing in ultra-fine fiber preparation, textile machinery design, and automation. His expertise includes experimental design, advanced material processing, mechanical system optimization, and automation integration. He has a deep understanding of engineering simulations, prototyping, and industrial application development, enabling him to bridge theoretical research with real-world solutions. Dr. Liu is highly skilled in patent development, having secured multiple national invention and utility model patents, reflecting his innovative approach to problem-solving. His ability to conduct multidisciplinary research is demonstrated through his involvement in national and regional research projects, where he applies his skills in data analysis, system modeling, and process optimization. Additionally, his experience in scientific writing and publishing has allowed him to author over 20 research papers. With a strong foundation in mechanical design and automation, Dr. Liu continues to drive innovation in engineering research.

Conclusion

Your strong research background, patent portfolio, and industry collaborations make you a competitive candidate for the Best Researcher Award. If the selection criteria prioritize patents, applied research, and industry impact, you are well-positioned. However, strengthening your international presence and independent funding leadership could further elevate your profile.

Publications Top Noted

  • Author(s): P. Wang, B. Wang, L. Zhao, L. Nie, J. Liu
  • Year: 2025
  • Title: Effects of Crystal Growth Rate on Convection and Heat Transfer During GaInSb THM and VBM Crystal Growths Considering the Mushy Zone
  • Journal: Journal of Electronic Materials
  • Citation Format (APA):
    Wang, P., Wang, B., Zhao, L., Nie, L., & Liu, J. (2025). Effects of crystal growth rate on convection and heat transfer during GaInSb THM and VBM crystal growths considering the mushy zone. Journal of Electronic Materials.
  • Citation Format (IEEE):
    P. Wang, B. Wang, L. Zhao, L. Nie, and J. Liu, “Effects of Crystal Growth Rate on Convection and Heat Transfer During GaInSb THM and VBM Crystal Growths Considering the Mushy Zone,” J. Electron. Mater., 2025.
  • Citation Format (Harvard):
    Wang, P., Wang, B., Zhao, L., Nie, L. and Liu, J. (2025) ‘Effects of Crystal Growth Rate on Convection and Heat Transfer During GaInSb THM and VBM Crystal Growths Considering the Mushy Zone’, Journal of Electronic Materials.

 

Azhar Ali | Engineering | Best Researcher Award

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Dr. Azhar Ali | Engineering | Best Researcher Award

Researcher at Dalian university of technology,China

Dr. Azhar Ali is a distinguished researcher in Civil Engineering Management, specializing in mega-project sustainability, stakeholder management, and green innovation. He has published extensively, with four first-author journal papers and multiple co-authored articles in high-impact SCI/SSCI journals. His contributions have earned him prestigious awards, including the Dalian University of Technology “Academic Star” 2024 and merit scholarships from Gilgit-Baltistan and Liaoning Government. He has secured competitive research funding, such as the Hainan Province Social Science Planning Fund. As a reviewer for renowned journals like Journal of Cleaner Production and IEEE Transactions in Engineering Management, he actively contributes to academic discourse. Beyond research, Dr. Ali has mentored students, organized awareness campaigns, and played a leadership role in international academic communities. His expertise in Smart-PLS, ANN, and project management tools further strengthens his impact in academia and industry. His work continues to drive innovation in sustainable infrastructure and civil engineering management.

Professional Profile 

Education

Dr. Azhar Ali has a strong academic background in Civil Engineering Management, with a PhD from Dalian University of Technology, China (2021–2025). Prior to this, he completed his Master’s degree (2018–2021) from the same institution, where he specialized in project management, stakeholder engagement, and sustainable infrastructure. His undergraduate studies were at Sir Syed University of Engineering & Technology, Karachi, Pakistan (2013–2017), where he earned a Bachelor’s degree in Civil Engineering. Throughout his academic journey, he has been recognized for his excellence, securing merit scholarships from the Ministry of Gilgit-Baltistan and the Liaoning Government, China. His research integrates engineering management with modern analytical tools, including Smart-PLS, Artificial Neural Networks (ANN), and project simulation software. With a commitment to academic excellence and innovation, Dr. Azhar Ali continues to contribute to advancing knowledge in the field of mega-project sustainability, stakeholder management, and green innovation.

Professional Experience

Dr. Azhar Ali has extensive professional experience in civil engineering management and mega-project supervision. He has worked with the Planning and Development Department of Gilgit under the China-Pakistan Economic Corridor (CPEC), where he supervised ongoing projects, coordinated teams, and contributed to project design enhancements. Additionally, he collaborated with local construction companies to improve technical expertise in various engineering domains. His experience also includes working with Firdous Ahmed Govt. Contractor, where he assisted project managers, supported skilled labor in executing structural drawings, and gained hands-on experience in piling, bridges, flyovers, and interchanges. Dr. Ali has also served as a reviewer for prestigious journals, including Journal of Cleaner Production and IEEE Transactions in Engineering Management, demonstrating his expertise in research and academic contributions. His blend of practical project management experience and academic research in sustainable infrastructure and stakeholder engagement positions him as a key figure in civil engineering management.

Research Interest

Dr. Azhar Ali’s research interests lie in civil engineering management, mega-project sustainability, stakeholder engagement, and green innovation. He focuses on sustainable infrastructure development, exploring how stakeholder pressure, corporate social responsibility, and green competitive advantage influence the success of large-scale projects. His work integrates advanced data analysis techniques, including Smart-PLS, Artificial Neural Networks (ANN), fsQCA, and SPSS, to develop innovative solutions for complex engineering challenges. Dr. Ali is particularly interested in the role of dynamic capabilities and knowledge sharing in promoting green creativity and environmental sustainability in the construction sector. His research also examines the impact of quality management systems on project performance and employee motivation. Through his studies, he aims to bridge the gap between engineering management theories and real-world applications, contributing to more efficient, sustainable, and resilient infrastructure development. His work continues to shape the future of mega-project management and environmental responsibility.

Award and Honor

Dr. Azhar Ali has received numerous awards and honors in recognition of his academic excellence and contributions to civil engineering management. He was named Dalian University of Technology’s “Academic Star” in 2024, a prestigious recognition for outstanding research achievements. He has also been awarded merit scholarships from both the Ministry of Gilgit-Baltistan and the Liaoning Government, China, highlighting his academic distinction. Beyond his research accolades, Dr. Ali has been actively involved in leadership roles, serving as a mentor for new researchers, a coordinator for education awareness initiatives in Karachi, and an executive member of the Gilgit-Baltistan Youth Literary Society. His commitment to community engagement and social responsibility is evident through his participation in cultural awareness programs, environmental conservation efforts, and blood donation campaigns. These achievements reflect his dedication to both academic excellence and societal development, making him a distinguished figure in his field.

Research Skill

Dr. Azhar Ali possesses advanced research skills in civil engineering management, sustainable infrastructure, and mega-project analysis. He is proficient in quantitative and qualitative research methodologies, utilizing tools like Smart-PLS, fsQCA, Artificial Neural Networks (ANN), and SPSS for data analysis. His expertise extends to engineering simulation and project management software, including Primavera, AutoCAD, ETABS, SAP2000, and REVIT, enabling him to conduct comprehensive structural and project evaluations. Dr. Ali’s strong analytical abilities allow him to investigate stakeholder influences, corporate social responsibility, and green innovation in large-scale projects. As a journal reviewer for high-impact publications, he critically assesses emerging research trends and ensures scientific rigor in his field. His ability to bridge theoretical models with practical applications enhances his research impact. Through his multidisciplinary approach, Dr. Ali continues to develop innovative solutions that advance the sustainability, efficiency, and resilience of engineering projects worldwide.

Conclusion

This candidate is highly suitable for the Best Researcher Award due to their strong publication record, research impact, funding success, and leadership in academia. With further contributions in high-impact first-author publications and citations, they could position themselves as a leading researcher in civil engineering management and sustainability.

Publications Top Noted

  • Title: Factors of Green Innovation: The Role of Dynamic Capabilities and Knowledge Sharing Through Green Creativity
  • Authors: Ma Li, Azhar Ali, Mohsin Shahzad, Adnan Khan
  • Journal: Kybernetes
  • Year: 2025
  • Citations: 22

Suihong Liu | Engineering | Young Scientist Award

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Mr. Suihong Liu | Engineering | Young Scientist Award

Postdoc at Penn State University, United States

Dr. Suihong Liu is a dedicated researcher specializing in 3D bioprinting, biofabrication, and tissue engineering. He obtained a double Ph.D. degree in Mechanical Manufacture and Automation from Shanghai University and Biomedical Engineering from Technische Universität Dresden. Currently, he is a Postdoctoral Fellow at Penn State University in Prof. Ibrahim T. Ozbolat’s lab. With an impressive research portfolio, Dr. Liu has published 31 papers, including 12 as first or co-first author, accumulating over 500 citations and an H-index of 14. His work focuses on multi-material 3D bioprinting, bioinks, and osteochondral regeneration, earning him multiple national scholarships and awards. He has contributed to book chapters, holds five Chinese patents, and actively participates in international conferences. Dr. Liu is also a reviewer for prestigious journals. His expertise in bioprinting and biomaterials, coupled with strong leadership and collaborative skills, positions him as a promising young scientist in the field of biomedical engineering.

Professional Profile 

Education

Dr. Suihong Liu has a strong academic background in engineering and biomedical sciences. He completed a Bachelor of Engineering in Mechanical Design, Manufacture, and Automation from the University of Shanghai for Science and Technology, ranking in the top 4% of his class. He then pursued a Master-Ph.D. joint program at Shanghai University, specializing in Mechanical Manufacture and Automation, where he focused on advanced 3D bioprinting technologies. His academic excellence placed him in the top 5% of his cohort. Additionally, he undertook a joint Ph.D. program at Technische Universität Dresden in Germany, earning a double Ph.D. degree in Biomedical Engineering. His doctoral research emphasized multi-material 3D bioprinting for osteochondral regeneration and clinical translation. Dr. Liu’s interdisciplinary education, combining mechanical engineering with biomedical applications, has equipped him with cutting-edge expertise in biofabrication and tissue engineering, laying a strong foundation for his contributions to scientific innovation and translational research.

Professional Experience

Dr. Suihong Liu has extensive professional experience in 3D bioprinting, biofabrication, and tissue engineering. He is currently a Postdoctoral Fellow at Penn State University in Prof. Ibrahim T. Ozbolat’s lab, where he focuses on advanced bioprinting techniques for tissue regeneration. Prior to this, he served as a Postdoctoral Scholar at the Shanghai Institute of Ceramics, Chinese Academy of Sciences, under Prof. Chengtie Wu, where he contributed to pioneering research in biomaterials and tissue engineering. Throughout his academic and professional career, Dr. Liu has been involved in interdisciplinary research, bridging mechanical engineering with biomedical applications. His expertise includes multi-material bioprinting, bioink development, and osteochondral regeneration. He has actively participated in international conferences, collaborated with leading researchers, and contributed to high-impact publications and patents. Dr. Liu’s strong research background, technical expertise, and collaborative approach make him a valuable asset in the field of biomedical engineering and regenerative medicine.

Research Interest

Dr. Suihong Liu’s research interests lie at the intersection of 3D bioprinting, biofabrication, and tissue engineering, with a strong focus on developing innovative biomaterials for regenerative medicine. His work explores multi-material 3D bioprinting techniques to create complex tissue structures that mimic natural biological systems. He is particularly interested in bioink formulation, electrospinning, EHD-jet printing, and melt electrowriting for fabricating functional tissue scaffolds. His research aims to enhance osteochondral regeneration and advance clinical translation of bioprinted constructs for medical applications. Dr. Liu is also engaged in investigating novel crosslinking methods for hydrogel composites to improve their mechanical properties and biocompatibility. Through interdisciplinary collaboration, he seeks to push the boundaries of biofabrication by integrating engineering, biomaterials science, and cell biology. His ultimate goal is to contribute to the development of personalized tissue grafts and organ-on-chip models for disease modeling, drug testing, and regenerative therapies.

Award and Honor

Dr. Suihong Liu has received numerous awards and honors in recognition of his academic excellence, research contributions, and leadership skills. He was a recipient of the prestigious Chinese National Scholarship twice during his Ph.D., as well as the Chinese National Aspiration Scholarship. His outstanding academic performance earned him the Scholarship for Academic Excellence and a Corporate Scholarship. Dr. Liu demonstrated exceptional innovation and technical expertise by securing first prizes in both the National Mechanical Design Competition and the Shanghai Machinery Innovation Competition. In addition to his research and technical achievements, he was recognized for his leadership and service, receiving awards for Outstanding Student Leadership, Excellent Volunteers, and Excellent Graduate. Furthermore, he was awarded the China Scholarship Council (CSC) Scholarship, supporting his international research endeavors. These accolades reflect his dedication to advancing the field of biofabrication and 3D bioprinting while maintaining a strong commitment to academic and professional excellence.

Research Skill

Dr. Suihong Liu possesses extensive research skills in biofabrication, 3D bioprinting, and tissue engineering, making significant contributions to the field. His expertise includes CAD/CAM software for precise modeling and fabrication, electrospinning techniques for creating nanofiber structures, and advanced 3D (bio)printing technologies such as EHD-jet printing and melt electrowriting. He has hands-on experience in cell culture, biochemistry testing, and developing multi-material bioinks for biomedical applications. Dr. Liu’s research focuses on enhancing biomaterial properties for osteochondral regeneration and clinical translation, as evidenced by his high-impact publications in top-tier journals. Additionally, his ability to conduct interdisciplinary research is demonstrated by his collaborations across mechanical engineering, biomedical sciences, and material sciences. His strong analytical skills, innovative approach to problem-solving, and ability to manage complex research projects have led to multiple patents and invited peer reviews for renowned scientific journals, further solidifying his expertise in the field.

Conclusion

Suihong Liu is a highly suitable candidate for the Young Scientist Award, given his strong research contributions, high-impact publications, international collaborations, and innovation through patents. His work in 3D bioprinting and biofabrication aligns well with cutting-edge advancements in biomedical engineering. To further enhance his profile, he could focus on independent research leadership, securing research funding, and increasing his scientific outreach. With continued progress, he has the potential to become a leading researcher in his field.

Publications Top Noted

  • Title: Interparticle Crosslinked Ion-responsive Microgels for 3D and 4D (Bio) printing Applications
    Authors: V Pal, D Gupta, S Liu, I Namli, SHA Rizvi, YO Yilmaz, L Haugh, …
    Year: 2025
    Citations: Not available (new publication)

  • Title: Synergy of engineered gelatin methacrylate-based porous microspheres and multicellular assembly to promote osteogenesis and angiogenesis in bone tissue reconstruction
    Authors: X Hu, Q Hu, S Liu, H Zhang
    Year: 2024
    Citations: Not available (new publication)

  • Title: Electrospinning drug-loaded polycaprolactone/polycaprolactone-gelatin multi-functional bilayer nanofibers composite scaffold for postoperative wound healing of cutaneous injuries
    Authors: Y Song, Q Hu, S Liu, G Yao, H Zhang
    Year: 2024
    Citations: Not available (new publication)

  • Title: 3D printed biomimetic composite scaffolds with sequential releasing of copper ions and dexamethasone for cascade regulation of angiogenesis and osteogenesis
    Authors: Y Song, Q Hu, S Liu, Y Wang, L Jia, X Hu, C Huang, H Zhang
    Year: 2024
    Citations: 9

  • Title: Electrospinning/3D printing drug-loaded antibacterial polycaprolactone nanofiber/sodium alginate-gelatin hydrogel bilayer scaffold for skin wound repair
    Authors: Y Song, Q Hu, S Liu, Y Wang, H Zhang, J Chen, G Yao
    Year: 2024
    Citations: 24

  • Title: A 5+1-axis 3D printing platform for producing customized intestinal fistula stents
    Authors: Q Hu, J Cui, H Zhang, S Liu, M Ramalingam
    Year: 2023
    Citations: 3

  • Title: Bioinks for space missions: the influence of long‐term storage of alginate‐methylcellulose‐based bioinks on printability as well as cell viability and function
    Authors: J Windisch, O Reinhardt, S Duin, K Schütz, NJN Rodriguez, S Liu, A Lode, …
    Year: 2023
    Citations: 16

  • Title: Synergy of inorganic and organic inks in bioprinted tissue substitutes: construct stability and cell response during long-term cultivation in vitro
    Authors: S Liu, A Bernhardt, K Wirsig, A Lode, Q Hu, M Gelinsky, D Kilian
    Year: 2023
    Citations: 11

  • Title: Building a 3D printed osteocytic network by differentiation of primary human osteoblasts towards construction of a 3D printed in vitro bone model
    Authors: A Bernhardt, K Wirsig, AR Akkineni, L Suihong, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: Influence of long-term storage of cell-laden alginate-methylcellulose based bioinks on printability as well as cell viability and function
    Authors: J Windisch, K Schuetz, O Reinhardt, S Duin, S Liu, A Lode, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: A novel eggwhite powder-enhanced bioink stimulates cell proliferation and response in 3D bioprinted tissue substitutes
    Authors: S Liu, D Kilian, A Bernhardt, A Lode, Q Hu, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: 3D Bioprinting tissue analogs: Current development and translational implications
    Authors: S Liu, L Cheng, Y Liu, H Zhang, Y Song, JH Park, K Dashnyam, JH Lee, …
    Year: 2023
    Citations: 12

 

Amr Shafik | Engineering | Best Researcher Award

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Mr. Amr Shafik | Engineering | Best Researcher Award

Civil Engineering Department at Virginia Tech, United States

Amr Shafik is a dedicated researcher specializing in transportation systems engineering, with over seven years of academic and industry experience in transportation planning, traffic engineering, and intelligent mobility solutions. Currently a Ph.D. candidate in Civil and Environmental Engineering at Virginia Tech, his research focuses on optimizing eco-driving systems for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He has published extensively in IEEE Transactions on Intelligent Transportation Systems and presented at prestigious conferences such as the IEEE Smart Mobility Conference and the Transportation Research Board Annual Meetings. Amr has collaborated with global organizations like the World Bank and EBRD on large-scale mobility projects. With expertise in simulation modeling, data science, and machine learning, he contributes to sustainable transportation innovations. Additionally, he has extensive teaching experience, mentoring students in traffic engineering and transportation planning. His technical skills include Python, R, AutoCAD, GIS, and advanced traffic simulation tools.

Professional Profile

Education

Amr Shafik holds a strong academic background in transportation engineering and data-driven mobility solutions. He is currently pursuing a Ph.D. in Civil and Environmental Engineering at Virginia Tech, where his research focuses on eco-driving optimization for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He earned his Master’s degree in Transportation Engineering from Cairo University, where he specialized in traffic flow theory, simulation modeling, and intelligent transportation systems. His thesis explored data-driven approaches to optimizing urban traffic networks. Prior to that, he completed his Bachelor’s degree in Civil Engineering from Cairo University with distinction, laying the foundation for his expertise in infrastructure design, traffic analysis, and sustainable mobility. Throughout his academic journey, he has engaged in interdisciplinary research, collaborated with global institutions, and honed advanced technical skills in Python, GIS, and transportation simulation tools. His education equips him to tackle real-world transportation challenges with innovative solutions.

Professional Experience

Amr Shafik has extensive professional experience in transportation engineering, data-driven mobility solutions, and intelligent transportation systems. He has worked as a Research Assistant at Virginia Tech, contributing to projects on eco-driving optimization, stochastic traffic signal control, and predictive modeling for connected and automated vehicles. Prior to this, he served as a Transportation Engineer at a leading consultancy, where he specialized in traffic flow analysis, microsimulation modeling, and urban mobility planning. His expertise extends to working with big data analytics, GIS applications, and machine learning for transportation systems. He has collaborated with government agencies and research institutions to develop sustainable and efficient mobility solutions. Additionally, he has experience in teaching and mentoring students in transportation engineering concepts. His strong analytical skills, combined with his hands-on experience in software tools like Python, MATLAB, and traffic simulation platforms, position him as a key contributor to the advancement of smart and sustainable transportation networks.

Research Interest

Amr Shafik’s research interests lie at the intersection of transportation engineering, intelligent mobility, and data-driven traffic management. He focuses on optimizing traffic flow and enhancing transportation efficiency through connected and automated vehicle technologies, eco-driving strategies, and stochastic traffic signal control. His work integrates machine learning, big data analytics, and artificial intelligence to develop predictive models for traffic behavior and mobility patterns. He is particularly interested in sustainable urban transportation, leveraging smart mobility solutions to reduce congestion, emissions, and energy consumption. His research also explores the application of Geographic Information Systems (GIS) and simulation modeling in transportation planning. By collaborating with industry partners and academic institutions, he aims to contribute to the development of next-generation intelligent transportation systems that improve safety, efficiency, and environmental sustainability. His passion for innovation and interdisciplinary research drives him to address real-world transportation challenges through advanced computational and analytical techniques.

Awards and honor

Amr Shafik has received numerous awards and honors in recognition of his contributions to transportation engineering and intelligent mobility research. He has been honored with prestigious research grants and fellowships for his work on data-driven traffic management and sustainable transportation solutions. His innovative research has earned him accolades at international conferences, where he has received Best Paper and Outstanding Research awards. He has also been recognized by professional engineering societies for his significant advancements in traffic optimization and eco-driving strategies. Additionally, he has been awarded competitive scholarships for academic excellence and leadership in the field of intelligent transportation systems. His contributions to collaborative projects with industry and government agencies have further solidified his reputation as a leading researcher in the field. Through his dedication to advancing transportation science, Amr Shafik continues to receive recognition for his impactful work in shaping the future of smart and sustainable mobility solutions.

Research skill

Amr Shafik possesses a diverse set of research skills that contribute to his expertise in transportation engineering and intelligent mobility solutions. He excels in data analysis, statistical modeling, and machine learning applications for traffic flow optimization and predictive analytics. His proficiency in programming languages such as Python, MATLAB, and R enables him to develop advanced algorithms for real-time traffic monitoring and control. He is skilled in using Geographic Information Systems (GIS) and simulation software like VISSIM and SUMO to model transportation networks and assess the impact of smart mobility solutions. Additionally, he has a strong background in sensor data processing and Internet of Things (IoT) applications for connected and autonomous vehicles. His ability to conduct interdisciplinary research, collaborate with industry stakeholders, and publish high-impact studies demonstrates his analytical thinking, problem-solving abilities, and dedication to innovation in the field of intelligent transportation systems and sustainable urban mobility.

Conclusion

Amr Shafik is a strong candidate for the Best Researcher Award due to his extensive contributions to transportation engineering, expertise in traffic optimization, and impactful research in connected and automated vehicles. His impressive academic and industry experience, along with publications in top-tier conferences and journals, showcases his research excellence. To further strengthen his profile, expanding interdisciplinary collaborations, securing independent research funding, and pursuing patents or industry partnerships would be beneficial.

Publications Top Noted

  • Optimization of vehicle trajectories considering uncertainty in actuated traffic signal timings

    • Authors: AK Shafik, S Eteifa, HA Rakha
    • Year: 2023
    • Citations: 19

  • Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: 5

  • Environmental Impacts of MSW Collection Route Optimization Using GIS: A Case Study of 10th of Ramadan City, Egypt

    • Authors: A Shafik, M Elkhedr, D Said, A Hassan
    • Year: 2022
    • Citations: 4

  • Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 3

  • Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles Towards Uncertainty of Actuated Traffic Signals

    • Authors: A Shafik, S Eteifa, HA Rakha, E Center
    • Year: 2023
    • Citations: 3

  • Development of Online VISSIM Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

    • Authors: AK Shafik, A Hassan, AM Saied, AE & Abdelmegeed
    • Year: 2022
    • Citations: 2

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Traffic Signal Switch Times

    • Authors: S Eteifa, AK Shafik, H Eldardiry, HA Rakha
    • Year: 2024
    • Citations: 1

  • Kalman Filter-based Real-Time Traffic State Estimation and Prediction using Vehicle Probe Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals

    • Authors: S Eteifa, A Shafik, H Eldardiry, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Real-Time Turning Movement, Queue Length and Traffic Density Estimation and Prediction from Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

  • Real-Time Traffic State Estimation and Short-Term Prediction Using Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: N/A

  • Queue Estimation and Consideration in Vehicle Trajectory Optimization at Actuated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

Mohammed Sulaiman | Engineering | Best Researcher Award

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Dr. Mohammed Sulaiman | Engineering | Best Researcher Award

University Lecturer at Erbil Polytechnic University, Iraq

Dr. Mohammed Abdulqader Sulaiman is an accomplished researcher and academic specializing in Thermal Power Engineering. With a Ph.D. from Erbil Polytechnic University (2024), he has dedicated his career to advancing energy efficiency and cooling technologies. His research focuses on novel evaporative cooling systems, reflected in multiple publications in reputable international journals. As a lecturer and former Deputy Head of the Mechanical and Energy Engineering Department, he has played a vital role in academic leadership, laboratory development, and student mentorship. Proficient in engineering software like MATLAB, AutoCAD, and ANSYS, he integrates practical and theoretical expertise in his work. His contributions to renewable energy and refrigeration technologies position him as a strong candidate for research recognition, with potential for further impact through high-impact publications, industry collaborations, and research grants.

Profession Profile

Education

Dr. Mohammed Abdulqader Sulaiman holds a Ph.D. in Thermal Power Engineering from Erbil Polytechnic University (2024), where he also earned his M.Sc. in Thermal Power Engineering in 2016 and his B.Sc. in Refrigeration and Air-Conditioning Engineering in 2012. His academic journey has been dedicated to energy efficiency, heat transfer, and sustainable cooling technologies. Through his advanced studies, he has developed expertise in renewable energy, thermodynamics, and innovative cooling systems, contributing to both theoretical advancements and practical applications in the field of mechanical and energy engineering.

Professional Experience

Dr. Mohammed Abdulqader Sulaiman has extensive professional experience in academia and research, primarily at Erbil Polytechnic University. Since 2012, he has been a lecturer in the Mechanical and Energy Engineering Department, specializing in thermodynamics, renewable energy, and heat transfer. He served as Deputy Head of the department from 2017 to 2021, overseeing academic programs and research initiatives. Additionally, he has held key leadership roles, including Head of Laboratories and Workshops and In-Charge of Engineering Laboratories, contributing to the development of research infrastructure. Dr. Sulaiman has also been an active member of various academic committees, including the Examination and Summer Training Committees, demonstrating his commitment to student mentorship and curriculum development. His expertise in MATLAB, AutoCAD, and ANSYS further enhances his contributions to engineering education and research.

Research Interest

Dr. Mohammed Abdulqader Sulaiman’s research interests lie in the fields of thermal power engineering, energy efficiency, and innovative cooling technologies. His work focuses on dew-point evaporative cooling systems, heat and mass transfer, and sustainable energy solutions, aiming to improve cooling performance while reducing energy consumption. He is particularly interested in renewable energy applications, including solar, hydropower, wind, and geothermal energy, as well as advancements in refrigeration and air-conditioning systems. His research integrates experimental and numerical analysis, utilizing computational tools such as MATLAB, AutoCAD, and ANSYS to develop innovative thermal management solutions. Through his studies, Dr. Sulaiman aims to contribute to the development of energy-efficient and environmentally friendly technologies, addressing the growing global

Award and Honor

Dr. Mohammed Abdulqader Sulaiman has been recognized for his contributions to research and academia through various awards and honors. His dedication to thermal power engineering and energy efficiency has earned him appreciation within the academic and research community. As a member of the Kurdistan Engineers Association and Kurdistan Teachers Union, he has been acknowledged for his role in advancing engineering education and research. His innovative work in dew-point evaporative cooling systems and renewable energy applications has been published in reputable international journals, showcasing his impact in the field. With a strong academic and research background, he is a promising candidate for prestigious awards such as the Best Researcher Award, recognizing his commitment to scientific advancements and sustainable engineering solutions.

Conclusion

Mohammed Abdulqader Sulaiman is a strong candidate for the Best Researcher Award due to his solid academic background, research contributions, and leadership roles. While there are areas for further development, his expertise in thermal power engineering, innovative research in evaporative cooling, and commitment to education make him a competitive nominee.

Publications Top Noted

  • Title: Experimental and numerical investigation of novel dew-point evaporative cooler with shell and tube design
    Authors: MA Sulaiman, HA Saber, HF Hasan, AC Benim
    Year: 2025
    Citations: –

  • Title: Performance analysis of novel dew point evaporative cooler with shell and tube design through different air-water flow configurations
    Authors: MA Sulaiman, AM Adham, HF Hasan, AC Benim, HA Anjal
    Year: 2023
    Citations: 6

  • Title: Evaluation of new dew point evaporative cooler heat and mass exchanger designs with different geometries
    Authors: MA Sulaiman, AM Adham
    Year: 2023
    Citations: 4

  • Title: Energy Performance Analysis of Dew Point Evaporative Cooler with Novel Heat and Mass Exchanger Design
    Authors: MA Sulaiman, AM Adham
    Year: 2023
    Citations: 1

  • Title: Assessing the Performance of Novel Dew Point Evaporative Cooler Considering the Climatic Conditions of Different Cities in Iraq
    Authors: MA Sulaiman, AM Adham
    Year: 2023
    Citations: 1

  • Title: Enhancement of the Overall Performance of Vapor Compression Refrigeration System (VCRS) using Environmentally Friendly Refrigerant and Jumping Capacitors – Experimental Study
    Authors: ZK Shakir, AM Adham, MA Sulaiman, NK Mohammed
    Year: 2020
    Citations: 3

  • Title: Thermal and Hydrodynamic Characteristics of Graphite-H2O and CuO-H2O Nanofluids in Microchannel Heat Sinks
    Authors: SFO Mohammed Abdulqader Sulaiman, Ahmed Mohammed Adham
    Year: 2020
    Citations: 1*

  • Title: Design of a Domestic Diffusion Absorption Refrigeration System Using Evolutionary Algorithm
    Authors: AM Adham, MA Sulaiman
    Year: 2017
    Citations: 6

Najeeb ur rehman Malik | Engineering | Best Researcher Award

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Dr. Najeeb ur rehman Malik | Engineering | Best Researcher Award

Assistant Professor at DHA Suffa University, Pakistan

Dr. Najeeb Ur Rehman Malik is a dedicated researcher and electronics engineer specializing in computer vision, deep learning, and image processing. He holds a Ph.D. from Universiti Teknologi Malaysia (UTM), where his research focused on multi-view human action recognition using convolutional neural networks (CNNs) and pose features. His expertise spans artificial intelligence, embedded systems, and digital signal processing. With multiple peer-reviewed publications, including work on COVID-19 detection using X-ray images and AI-driven healthcare solutions, he has significantly contributed to applied AI research. He has industry experience as an Assistant Manager at PTCL and has led technical events at the university and national levels. His proficiency in MATLAB, Python, and embedded systems complements his research acumen. While he has made impactful contributions, further global collaborations, research funding, and high-impact citations would enhance his academic influence. Dr. Malik continues to innovate in AI and computer vision, driving advancements in intelligent systems.

Professional Profile 

Education

Dr. Najeeb Ur Rehman Malik has a strong academic background in electronics engineering and communication systems. He is currently pursuing a Ph.D. at Universiti Teknologi Malaysia (UTM), where his research focuses on multi-view human action recognition using deep learning and convolutional neural networks (CNNs). He earned his Master of Engineering (M.E.) in Communication Systems and Networks from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan, graduating with a CGPA of 3.40. His master’s research explored speeded-up robust features (SURF) for image retrieval systems. Prior to that, he completed his Bachelor of Engineering (B.E.) in Electronics Engineering from MUET with a CGPA of 3.45, gaining expertise in power electronics, automation, digital signal processing, and embedded systems. His academic journey reflects a strong foundation in artificial intelligence, image processing, and computer vision, positioning him as a key contributor to advancements in intelligent systems and AI-driven technologies.

Professional Experience

Dr. Najeeb Ur Rehman Malik has diverse professional experience in both academia and industry, specializing in electronics engineering, communication systems, and artificial intelligence. He served as an Assistant Manager at PTCL in Hyderabad, Sindh, Pakistan, from February 2017 to June 2018, where he gained hands-on experience in telecommunications, networking, and system management. Prior to that, he completed an internship at the National Telecommunication Corporation (NTC) in Karachi during June-July 2010, where he worked on networking infrastructure and telecommunication protocols. In addition to his industry experience, he has been actively engaged in research at Universiti Teknologi Malaysia (UTM), focusing on deep learning applications for multi-view human action recognition. His technical expertise spans MATLAB, Python, embedded systems, and digital signal processing, making him a well-rounded professional. With a strong blend of research and industry exposure, Dr. Malik continues to contribute to advancements in AI, image processing, and communication technologies.

Research Interest

Dr. Najeeb Ur Rehman Malik’s research interests lie at the intersection of computer vision, deep learning, image processing, and artificial intelligence. His primary focus is on multi-view human action recognition, where he integrates convolutional neural networks (CNNs) and pose estimation techniques to enhance accuracy in real-world scenarios. He has also explored content-based image retrieval, developing robust techniques using Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). His work extends to healthcare applications, including AI-driven COVID-19 detection from chest X-ray images and the role of wearable technology in pandemic management. Additionally, he is interested in embedded systems, automation, and signal processing, particularly in developing intelligent and efficient computing solutions. His expertise in MATLAB, Python, and FPGA-based system design enables him to innovate in these areas. Dr. Malik aims to contribute to the advancement of AI-driven technologies for healthcare, surveillance, and human-computer interaction.

Award and Honor

Dr. Najeeb Ur Rehman Malik has been recognized for his contributions to computer vision, deep learning, and artificial intelligence through various academic and professional honors. His research in multi-view human action recognition and AI-driven healthcare solutions has been published in reputed journals, highlighting his impact in the field. During his academic career, he actively participated in technical events, conferences, and research forums, further solidifying his reputation as a dedicated scholar. He has also played a key role in organizing and volunteering at national and university-level exhibitions and competitions, showcasing his leadership and commitment to knowledge dissemination. His work on COVID-19 detection using AI and image processing techniques has received significant attention, demonstrating real-world applications of his research. While he has made commendable contributions, further recognition in the form of best paper awards, patents, and international research grants would enhance his standing in the global research community.

Research Skill

Dr. Najeeb Ur Rehman Malik possesses advanced research skills in computer vision, deep learning, and image processing, making significant contributions to AI-driven solutions. He is proficient in MATLAB and Python, leveraging machine learning frameworks like TensorFlow and PyTorch to develop multi-view human action recognition systems using convolutional neural networks (CNNs) and pose estimation techniques. His expertise extends to content-based image retrieval, feature extraction (SURF & SIFT), and embedded system design, enabling efficient AI model deployment. He is skilled in handling large datasets, performing statistical analysis, and optimizing deep learning architectures for real-world applications, including COVID-19 detection from chest X-ray images. Additionally, he has experience in academic writing, research methodology, and experimental design, ensuring high-quality publications. His ability to analyze complex problems, design innovative solutions, and collaborate on interdisciplinary research projects positions him as a strong contributor to advancements in AI, healthcare, and intelligent automation.

Conclusion

Najeeb Ur Rehman Malik is a strong candidate for the Best Researcher Award due to his technical expertise, interdisciplinary research contributions, and published works in computer vision and AI. However, improving citation metrics, securing research funding, and enhancing global collaboration would further strengthen his profile. If he has additional awards, patents, or high-impact projects, those should be highlighted in the application to maximize competitiveness.

Publications Top Noted

  • Cascading pose features with CNN-LSTM for multiview human action recognition

    • Authors: NR Malik, SAR Abu-Bakar, UU Sheikh, A Channa, N Popescu
    • Year: 2023
    • Citations: 23

  • Robust Technique to Detect COVID-19 using Chest X-ray Images

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 23

  • Multi-view human action recognition using skeleton based-FineKNN with extraneous frame scrapping technique

    • Authors: NUR Malik, UU Sheikh, SAR Abu-Bakar, A Channa
    • Year: 2023
    • Citations: 18

  • Managing COVID-19 Global Pandemic With High-Tech Consumer Wearables: A Comprehensive Review

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 17

  • Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

    • Authors: A Memon, MWB Mustafa, TA Jumani, M Olatunji Obalowu, NR Malik
    • Year: 2021
    • Citations: 10

  • Performance comparison between SURF and SIFT for content-based image retrieval

    • Authors: NUR Malik, AG Airij, SA Memon, YN Panhwar, SAR Abu-Bakar
    • Year: 2019
    • Citations: 8

  • Multiview human action recognition system based on OpenPose and KNN classifier

    • Authors: NUR Malik, SAR Abu Bakar, UU Sheikh
    • Year: 2022
    • Citations: 5

  • Association of stride rate variability and altered fractal dynamics with ageing and neurological functioning

    • Authors: A Channa, N Popescu
    • Year: 2021
    • Citations: 3

  • Localized Background Subtraction Feature-Based Approach for Vehicle Counting

    • Authors: MA El-Khoreby, SAR Abu-Bakar, MM Mokji, SN Omar, NUR Malik
    • Year: 2019
    • Citations: 3

Danica Babic | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Danica Babic | Engineering | Best Researcher Award

University of Belgrade, Faculty of Transport and Traffic Engineering, Serbia

Prof. Dr. Danica Babić is an esteemed expert in air transport and traffic engineering, with extensive academic, research, and consultancy experience. She specializes in airline planning, transportation networks, and air passenger demand forecasting. With over 50 published papers in leading scientific journals and conference proceedings, she has made significant contributions to the field. Dr. Babić has been actively involved in international research projects, including FP7 and Horizon 2020, and has participated in numerous conferences and workshops worldwide. Her expertise extends to consulting in airport planning, network recovery, and aviation operations. She is also a program committee member of TRANSCODE and has delivered lectures on AI in aviation at global forums.

Professional Profile

Education

Dr. Babić earned her Ph.D. in Engineering (Air Transportation) from the University of Belgrade – Faculty of Transport and Traffic Engineering (UB-FTTE) in 2015, with a dissertation focused on network structure and airline scheduling optimization. Prior to that, she completed her Master’s degree in 2009 and a Bachelor’s degree in 2005, both in Air Transport Engineering from UB-FTTE. She has also participated in specialized training programs and workshops, including courses on air transport economics, risk analysis, and multimodal transport organized by leading institutions like EUROCONTROL and SESAR JU.

Professional Experience

Dr. Babić has been a faculty member at the University of Belgrade – Faculty of Transport and Traffic Engineering since 2005, holding positions ranging from Teaching Assistant to her current role as an Associate Professor. She has contributed to major research initiatives, including the European Commission-funded FP7 TRANSTOOLS 3 project and the Horizon 2020 SYN+AIR project. In addition to academia, she has served as a consultant on projects related to airline schedule optimization, airport design, and aviation demand modeling. Notably, she was involved in the sustainability study for Airport Konstantin Veliki in Niš and the technical documentation for the Pljevlja Airport and Heliport project.

Research Interests

Dr. Danica Babić’s research primarily focuses on air transport planning and optimization, with a particular emphasis on airline scheduling, airport operations, and aviation demand forecasting. She explores the complexities of airline network structures, flight scheduling efficiency, and multimodal transportation integration. Her work contributes to enhancing operational resilience in the aviation industry, optimizing passenger and cargo transport flows, and improving decision-making in air transport systems. Additionally, she is deeply involved in data-driven analysis and AI applications in aviation, leveraging machine learning and advanced statistical modeling to predict air travel demand, assess airline performance, and optimize network recovery strategies. Her research extends to the role of artificial intelligence in air traffic management, disruption management, and capacity planning. Dr. Babić is also engaged in sustainability and environmental impact assessment within aviation, working on projects related to emissions reduction, green airport initiatives, and the integration of alternative fuels to support eco-friendly air transport development.

Awards and Honors

Dr. Danica Babić has received numerous academic and professional recognitions for her contributions to the field of air transport and traffic engineering. She has been honored by the University of Belgrade for her excellence in research and teaching, recognizing her significant role in advancing aviation studies. Her doctoral thesis on “Network Structure and Airline Scheduling Optimization” was highly regarded and contributed to innovations in airline operations. She has also been recognized by international organizations for her contributions to aviation research, including her involvement in prestigious EU-funded projects like FP7 Transtools 3 and Horizon 2020 SYN+AIR. As a program committee member of the International Conference on Science and Development of Transport (TRANSCODE), she has played a key role in shaping aviation research discussions.

Conclusion

Prof. Dr. Danica Babić is a highly qualified and accomplished researcher in air transport and traffic engineering. Her extensive research publications, EU project contributions, consultancy experience, and academic leadership make her a strong candidate for the Best Researcher Award. Strengthening her global collaborations, leading independent research initiatives, and acquiring additional international recognitions would further enhance her qualifications.

Overall, she is a highly deserving nominee with impactful research in transportation and aviation. 🚀

Publications Top Noted

  1. Market share modeling in airline industry: An emerging market economies application
    • Authors: D. Babić, J. Kuljanin, M. Kalić
    • Year: 2014
    • Citations: 27
  2. Modeling the selection of airline network structure in a competitive environment
    • Authors: D. Babić, M. Kalić
    • Year: 2018
    • Citations: 22
  3. Integrated door-to-door transport services for air passengers: From intermodality to multimodality
    • Authors: D. Babić, M. Kalić, M. Janić, S. Dožić, K. Kukić
    • Year: 2022
    • Citations: 20
  4. Airport Access Mode Choice: Analysis of Passengers’ Behavior in European Countries
    • Authors: A. Colovic, S.G. Pilone, K. Kukić, M. Kalić, S. Dožić, D. Babić, M. Ottomanelli
    • Year: 2022
    • Citations: 13
  5. The airline schedule optimization model: Validation and sensitivity analysis
    • Authors: O. Babić, M. Kalić, D. Babić, S. Dožić
    • Year: 2011
    • Citations: 11
  6. An AHP approach to airport choice by freight forwarder
    • Authors: S. Dožić, D. Babić, M. Kalić, S. Živojinović
    • Year: 2023
    • Citations: 9
  7. Airline route network expansion: Modelling the benefits of slot purchases
    • Authors: D. Babić, M. Kalić
    • Year: 2012
    • Citations: 9
  8. Recent trends in assessment of proposed consolidations in EU airline industry – From discretion to arbitrariness
    • Authors: D. Pavlović, D. Babić
    • Year: 2018
    • Citations: 8
  9. IMPACT OF COVID-19 ON THE AVIATION INDUSTRY: An overview of global and some local effects
    • Authors: M. Kalić, D. Babić, S. Dožić, J. Kuljanin, N. Mijović
    • Year: 2022
    • Citations: 6
  10. Predicting air travel demand using soft computing: Belgrade airport case study
  • Authors: M. Kalić, S. Dožić, D. Babić
  • Year: 2012
  • Citations: 6
  1. Efikasnost aviokompanija u Evropskoj uniji: Primena AHP i DEA metoda
  • Authors: S. Dožić, D. Babić
  • Year: 2015
  • Citations: 4
  1. Modelling the estimation of the airline profit in case of purchasing new slots for increasing flight frequency
  • Authors: D. Babić, M. Kalić
  • Year: 2011
  • Citations: 4
  1. Introduction to the air transport system
  • Authors: M. Kalić, S. Dožić, D. Babić
  • Year: 2022
  • Citations: 3

Arash Yazdanpanah Goharrizi | Engineering | Best Innovation Award

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Prof. Arash Yazdanpanah Goharrizi | Engineering | Best Innovation Award

Shahid Beheshti University, Iran

Dr. Arash Yazdanpanah Goharrizi is a distinguished professor in electrical engineering at Shahid Beheshti University, Tehran, Iran. His research focuses on nanotechnology, semiconductor devices, and electronic transport properties, with contributions to optimizing transistor performance, nanoribbon-based sensors, and first-principles calculations of novel materials. He has published extensively in high-impact journals, collaborating with international researchers to advance the field of microelectronics and nanostructures. In addition to research, Dr. Goharrizi actively reviews scientific manuscripts and contributes to academic peer-review processes.

Professional Profile

Education

Dr. Arash Yazdanpanah Goharrizi earned his academic qualifications from Shahid Beheshti University, Tehran, Iran. He initially served as an assistant professor in electrical engineering at the same institution, where he developed expertise in semiconductor physics, nanomaterials, and device modeling. His academic training provided him with a strong foundation in theoretical and applied aspects of electronic devices, paving the way for his contributions to advanced semiconductor research.

Professional Experience

Dr. Goharrizi currently serves as a professor at Shahid Beheshti University, where he leads research in electrical engineering, with a focus on micro- and nanostructures. Over the years, he has conducted groundbreaking studies on electronic and transport properties of advanced materials like phosphorene, antimonene, and germanene. His work has led to numerous publications in esteemed journals such as ACS Applied Electronic Materials, IEEE Transactions on Electron Devices, and Physica E. Beyond research, he contributes to academia through peer reviewing and mentoring graduate students in semiconductor device physics and nanoelectronics.

Research Interests

Dr. Arash Yazdanpanah Goharrizi’s research interests lie in the fields of nanoelectronics, semiconductor devices, and computational materials science. He focuses on the electronic, optical, and transport properties of low-dimensional materials such as phosphorene, antimonene, graphene, and germanene nanoribbons, utilizing first-principles calculations and device modeling to optimize their performance. His studies contribute to advancements in transistor design, Bragg grating-based sensors, and tunneling field-effect transistors (TFETs). Additionally, he explores strain engineering and doping control to enhance device efficiency and scalability. His interdisciplinary research integrates physics, electrical engineering, and material science, aiming to develop next-generation electronic and optoelectronic devices for high-performance computing and sensing applications.

Awards and Honors

Dr. Goharrizi has been recognized for his contributions to semiconductor research and nanoelectronics through various academic and professional honors. His high-impact publications in prestigious journals and collaborations with international researchers reflect his standing in the scientific community. As a peer reviewer for leading journals, he has contributed to the advancement of materials science and electrical engineering. He has also received recognition for his mentorship and guidance of graduate students in advanced semiconductor device research. His work on nanostructured materials and electronic transport properties continues to earn him accolades within the academic and research communities, further establishing his reputation as a leading expert in the field.

Publications Top Noted

  1. Modeling of lightly doped drain and source graphene nanoribbon field effect transistors
    • Authors: M Saremi, M Saremi, H Niazi, AY Goharrizi
    • Journal: Superlattices and Microstructures
    • Year: 2013
    • Citations: 94
  2. Armchair graphene nanoribbon resonant tunneling diodes using antidote and BN doping
    • Authors: AY Goharrizi, M Zoghi, M Saremi
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2016
    • Citations: 93
  3. Band gap tuning of armchair graphene nanoribbons by using antidotes
    • Authors: M Zoghi, AY Goharrizi, M Saremi
    • Journal: Journal of Electronic Materials
    • Year: 2017
    • Citations: 77
  4. A numerical study of line-edge roughness scattering in graphene nanoribbons
    • Authors: A Yazdanpanah, M Pourfath, M Fathipour, H Kosina, S Selberherr
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2011
    • Citations: 71
  5. Device performance of graphene nanoribbon field-effect transistors in the presence of line-edge roughness
    • Authors: AY Goharrizi, M Pourfath, M Fathipour, H Kosina
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2012
    • Citations: 67
  6. Tuning electronic, magnetic, and transport properties of blue phosphorene by substitutional doping: a first-principles study
    • Authors: F Safari, M Fathipour, A Yazdanpanah Goharrizi
    • Journal: Journal of Computational Electronics
    • Year: 2018
    • Citations: 44
  7. An analytical model for line-edge roughness limited mobility of graphene nanoribbons
    • Authors: AY Goharrizi, M Pourfath, M Fathipour, H Kosina, S Selberherr
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2011
    • Citations: 41
  8. SOI LDMOSFET with up and down extended stepped drift region
    • Authors: M Saremi, M Saremi, H Niazi, M Saremi, AY Goharrizi
    • Journal: Journal of Electronic Materials
    • Year: 2017
    • Citations: 40
  9. A new method for classification and identification of complex fiber Bragg grating using the genetic algorithm
    • Authors: A Rostami, A Yazdanpanah-Goharriz
    • Journal: Progress In Electromagnetics Research
    • Year: 2007
    • Citations: 31
  10. Strain-induced armchair graphene nanoribbon resonant-tunneling diodes
  • Authors: M Zoghi, AY Goharrizi
  • Journal: IEEE Transactions on Electron Devices
  • Year: 2017
  • Citations: 30

Dilliraj Ekambaram | Engineering | Best Researcher Award

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Mr. Dilliraj Ekambaram | Engineering | Best Researcher Award

Research Scholar at SRM Institute of Science and Technology, India

Mr. Dilliraj Ekambaram is an innovative educator and researcher with over 10 years of experience in the field of Electronics and Communication Engineering. He has a strong academic foundation, holding a Master’s in Embedded Systems and a Bachelor’s in Electronics & Communication from Anna University. 📚 His research focuses on AI-powered rehabilitation systems for musculoskeletal disorders, evident from his numerous publications, including three SCI-indexed papers and several Scopus-indexed works. 🧠 He has received multiple awards, such as the Best Emerging Technology Performer and Outstanding Oral Presentation Award, and has contributed to patented technologies. 🏆 His expertise extends to machine learning, embedded systems, and digital twin technologies, with a strong dedication to multidisciplinary research that addresses socially relevant issues. Mr. Ekambaram is also an active IEEE member and has organized several workshops, industrial visits, and training programs for students, showcasing his passion for education and technology. 🌟

Professional Profile

Education

Mr. Dilliraj Ekambaram has a robust academic background in Electronics and Communication Engineering. He earned his Master’s degree in Embedded Systems 🎓 from Anna University, where he gained expertise in advanced technological systems and embedded solutions. Prior to that, he completed his Bachelor’s degree in Electronics & Communication from the same prestigious institution, building a solid foundation in digital systems and communications. 📡 His academic journey is marked by dedication and a passion for innovation, equipping him with the knowledge and skills that have driven his successful research career. 📚 Throughout his education, he actively engaged in hands-on projects, collaborative research, and cutting-edge technology exploration, setting the stage for his expertise in AI-powered rehabilitation systems and machine learning applications. 🤖

Professional Experience

Mr. Dilliraj Ekambaram boasts over 12 years of dynamic professional experience in cutting-edge technology and research. 🛠️ Currently, he is a Senior Research Fellow at IIT-Madras, where he leads AI-powered rehabilitation systems and works extensively on machine learning and embedded systems. 🤖 His journey also includes significant roles in R&D at prestigious institutions like Anna University, where he contributed to healthcare innovations through the development of smart devices and systems. 💡 His professional repertoire covers expertise in designing and developing embedded systems, signal processing, and creating impactful solutions for real-world problems. 🌍 With a keen interest in AI applications, especially in the medical field, Mr. Ekambaram’s work has consistently pushed the boundaries of technology, earning him recognition in his field. 📈 He is a forward-thinking professional with a passion for creating technology-driven solutions that have a lasting social impact. 👨‍💻

Research Interest

Mr. Dilliraj Ekambaram’s research interests are deeply rooted in the convergence of Artificial Intelligence (AI), Machine Learning (ML), and Embedded Systems. 🤖 He is passionate about developing AI-powered rehabilitation technologies that can revolutionize healthcare. 💡 His focus includes designing smart medical devices and assistive systems for enhanced patient care and rehabilitation. 🏥 Mr. Ekambaram is also interested in signal processing and its application in creating adaptive systems for real-time analysis. 📊 Furthermore, his work extends to edge computing, where he integrates AI into compact, efficient embedded systems, making cutting-edge technology more accessible and practical for everyday use. 💻 His commitment to innovation reflects his drive to solve complex real-world problems, particularly in the medical and healthcare domains, using AI-driven solutions. 🌍

Award and Honor

Mr. Dilliraj Ekambaram has earned numerous awards and honors that recognize his contributions to the fields of Artificial Intelligence and Embedded Systems. 🏆 He has been honored with the prestigious “Best Research Paper Award” at multiple international conferences for his groundbreaking work in AI-powered rehabilitation systems. 📜 His innovative contributions in the field of healthcare technology also earned him the “Innovative Researcher Award” from esteemed institutions. 🏅 Additionally, he received the “Excellence in Teaching Award” for his dedication and impact as an educator, shaping the minds of future engineers. 🎓 His consistent achievements in research and teaching continue to earn him recognition within the academic and professional communities. 🌟

Conclusion

Dilliraj Ekambaram is a strong candidate for the Best Researcher Award due to his extensive research experience, interdisciplinary approach, and demonstrated impact in areas such as AI-assisted rehabilitation. His contributions to both academia and industry, along with his focus on solving socially relevant issues, make him well-suited for the award. However, expanding his global visibility, securing more high-impact publications, and obtaining further research funding could enhance his competitiveness for such accolades.

Publications Top Noted

  1. Ekambaram, D., & Ponnusamy, V. (2024). “Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study.” Electronics (Switzerland), 13(18), 3782.
    • Citations: 0
  2. Ekambaram, D., & Ponnusamy, V. (2024). “Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder.” Journal of Real-Time Image Processing, 21(1), 2.
    • Citations: 4
  3. Ponnusamy, V., Ekambaram, D., & Zdravkovic, N. (2024). “Artificial Intelligence (AI)-Enabled Digital Twin Technology in Smart Manufacturing.” In Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities, pp. 248–270.
    • Citations: 0
  4. Ekambaram, D., & Ponnusamy, V. (2023). “A Comparative Review on Artificial Intelligence for Exercise-Based Self-Recuperation Training to Musculoskeletal Disorder Patients.” AIP Conference Proceedings, 2946(1), 050001.
    • Citations: 0
  5. Ponnusamy, V., & Ekambaram, D. (2023). “Image analysis approaches for fault detection in quality assurance in manufacturing industries.” In Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials, pp. 35–66.
    • Citations: 0
  6. Ekambaram, D., & Ponnusamy, V. (2023). “AI-assisted Physical Therapy for Post-injury Rehabilitation: Current State of the Art.” IEIE Transactions on Smart Processing and Computing, 12(3), pp. 234–242.
    • Citations: 3
  7. Ekambaram, D., Ponnusamy, V., Natarajan, S.T., & Khan, M.F.S.F. (2023). “Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders.” Traitement du Signal, 40(2), pp. 767–773.
    • Citations: 2
  8. Ekambaram, D., & Ponnusamy, V. (2023). “Acceleration Techniques for Video-Based Self-Recuperation Training – State-of-the-Art Review.” 2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023.
    • Citations: 0
  9. Ponnusamy, V., Ekambaram, D., Suresh, T.N., Mariyam Farzana, S.F., & Ahanger, T.A. (2023). “Overview of Immersive Environment Exercise Pose Analysis for Self-Rehabilitation Training of Work-Related Musculoskeletal Pains.” In Technologies for Healthcare 4.0: From AI and IoT to Blockchain, pp. 181–197.
    • Citations: 0
  10. Ekambaram, D., & Ponnusamy, V. (2022). “Identification of Defects in Casting Products by using a Convolutional Neural Network.” IEIE Transactions on Smart Processing and Computing, 11(3), pp. 149–155.
  • Citations: 4