Maria de Lurdes Dinis | Engineering | Best Researcher Award – 1999

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Prof. Maria de Lurdes Dinis | Engineering | Best Researcher Award 

Full Professor at University of Porto | Portugal 

Prof. Maria de Lurdes Dinis is a highly accomplished academic at the University of Porto, widely recognized for her pioneering contributions in the field of Engineering. With a strong educational background culminating in a Ph.D. in Civil Engineering from the University of Porto, her doctoral research centered on advanced computational modeling, structural optimization, and sustainable design, laying the foundation for her long-standing research excellence. Over the course of her career, she has gained extensive professional experience, leading and collaborating on national and international research projects, with a focus on sustainable infrastructure, energy-efficient solutions, and computational approaches to structural mechanics. Her research interests span computational engineering, applied mechanics, sustainable construction, and innovative materials, where she consistently integrates theory with real-world engineering applications. Prof. Maria de Lurdes Dinis has demonstrated a broad set of research skills, including advanced simulation techniques, multidisciplinary project management, mentoring of Ph.D. students, and the ability to build cross-institutional collaborations. She has published 63 scholarly documents indexed in Scopus, which collectively have received 827 citations across 686 documents, reflecting her strong academic influence, with an h-index of 13 showcasing the impact of her research contributions. Her work appears in reputed international journals and IEEE/Scopus-indexed conferences, and she has actively participated in collaborative European consortia advancing engineering solutions. In recognition of her achievements, she has received awards and honors for both her scholarly excellence and her commitment to advancing engineering education, while also contributing to professional associations and volunteer platforms supporting student engagement and knowledge dissemination. In conclusion, Prof. Maria de Lurdes Dinis stands out as a leading researcher whose expertise, impactful publications, international collaborations, and dedication to academic leadership make her highly deserving of recognition. Her future research potential lies in further advancing sustainable engineering, expanding global collaborations, and continuing to shape the next generation of engineers through mentorship and innovation.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

  • Dinis, M. L., & Camotim, D. (2014). A numerical investigation of the post-buckling behavior of cold-formed steel columns. Thin-Walled Structures, 83(1), 121–133.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2012). Local-global interaction in cold-formed steel lipped channel columns: Numerical investigation. Journal of Constructional Steel Research, 68(1), 1–13.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2011). FEM-based analysis of cold-formed steel columns with distortional buckling. Thin-Walled Structures, 49(5), 614–631.

  • Dinis, M. L., & Camotim, D. (2009). Post-buckling behavior and strength of thin-walled lipped channel columns experiencing local–distortional interaction. International Journal of Structural Stability and Dynamics, 9(4), 691–714.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2008). On the mechanics of local-distortional interaction in cold-formed steel lipped channel columns. Thin-Walled Structures, 46(4), 401–420.

  • Dinis, M. L., Silvestre, N., & Camotim, D. (2007). Numerical investigation of the local–global interaction in lipped channel columns. Computers & Structures, 85(19–20), 1461–1474.

  • Dinis, M. L., Camotim, D., & Silvestre, N. (2006). FEM-based analysis of cold-formed steel members: Local–distortional interaction. Computers & Structures, 84(17–18), 1208–1227.

Yuezhao Pang | Engineering | Best Researcher Award

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Dr. Yuezhao Pang | Engineering | Best Researcher Award 

Engineer at Marine Design and Research Institute of China | China

Dr. Yuezhao Pang is a highly accomplished structural engineer at the Marine Design and Research Institute of China with a Ph.D. in Mechanics, whose expertise centers on impact dynamics, composite materials, and the development of advanced metal and non-metallic sandwich structures. His academic foundation and research journey reflect a commitment to understanding mechanical responses, energy absorption, and failure mechanisms under impact loading, combining both multi-scale experimentation and numerical simulations to address complex engineering problems. Professionally, he has completed five major research projects, engaged in three consultancy and industry-linked initiatives, and contributed significantly to the field through innovative solutions aimed at structural protection and crashworthiness, with applications in aerospace, transportation, and industrial safety. His research interests extend to dynamic and static compression of closed-cell PVC foams, exploring material properties under varying strain rates to design lightweight protective structures with improved resilience. Dr. Yuezhao Pang has produced a notable body of work with 17 publications indexed in reputed databases, amassing 139 citations by 136 documents with an h-index of 7, reflecting the quality and relevance of his research contributions. In addition, he has secured five patents that bridge the gap between theoretical advancements and practical applications, underscoring his strength in innovation-driven engineering. His research skills encompass advanced materials testing, computational modeling, mechanical characterization, and cross-disciplinary collaborations, making him a versatile and impactful researcher. While he has not yet accumulated extensive professional memberships, his strong collaborations and project outputs demonstrate leadership potential and dedication to advancing the field. Recognized for his significant contributions, Dr. Yuezhao Pang stands as a deserving recipient of research honors, and his future trajectory indicates immense promise in expanding global collaborations, enhancing high-impact publications, and shaping protective engineering solutions that benefit both academia and industry.

Profile: Scopus

Fuetured Publications:

  • Pang, Y., Wang, C., Zhao, Y., & Wang, X. (2025). Strain‐Rate Effects on the Mechanical Behavior of Basalt-Fiber-Reinforced Polymer Composites: Experimental Investigation and Numerical Validation. Materials, 18(15).

  • Pang, Y. (2022). Experimental study of basalt fiber/steel hybrid laminates: Low‐velocity impact characteristics with different lay-up structures. International Journal of Impact Engineering.

Jing Xu | Engineering | Best Scholar Award

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Dr. Jing Xu | Engineering | Best Scholar Award 

Lecturer at Shenyang University of Technology | China

Dr. Jing Xu is a distinguished academic and researcher serving as a Lecturer at the School of Mechanical Engineering, Shenyang University of Technology, while also contributing as a Research Assistant at the Key Laboratory of Intelligent Manufacturing and Industrial Robots of Liaoning. With a solid academic foundation in mechanical engineering, he has built a career centered on robotics, automation, and intelligent systems. His dedication to advancing robotics and industrial automation has been demonstrated through impactful research and innovative contributions in motion planning, kinematics, and computer vision. Dr. Jing Xu’s career reflects his commitment to both teaching and pioneering scientific inquiry.

Profile:

Orcid | Google Scholar

Education:

Dr. Jing Xu pursued his Bachelor’s and Master’s studies in Mechanical Engineering at Liaoning Petrochemical University, where he laid a strong foundation in engineering principles, robotics, and automation systems. He further advanced his academic journey by earning a Ph.D. in Mechanical Engineering and Automation from Northeastern University in Shenyang. His doctoral studies deepened his expertise in robotics, particularly focusing on robot kinematics, motion planning, and computer vision. These academic experiences shaped his research trajectory and provided the skills necessary for innovative problem-solving, enabling him to contribute significantly to both theoretical and applied aspects of robotics engineering.

Experience:

Dr. Jing Xu’s professional journey is characterized by a strong integration of teaching, research, and applied innovation. As a Lecturer at Shenyang University of Technology, he imparts knowledge in mechanical engineering and robotics, nurturing the next generation of engineers. Alongside, his role as a Research Assistant at the Key Laboratory of Intelligent Manufacturing and Industrial Robots has allowed him to contribute to high-level projects in intelligent robotics and automation. His research and professional activities bridge theory and practice, enhancing both academic excellence and industrial applications. Dr. Jing Xu’s career reflects his ability to blend research with practical engineering advancements.

Research Interests:

Dr. Jing Xu’s research interests lie at the intersection of robotics, automation, and intelligent systems. His primary focus areas include robot kinematics, motion planning, and computer vision. Within these domains, he has developed advanced methodologies for solving complex robotic challenges such as optimal path planning in high-dimensional and cluttered environments. His contributions also extend to developing efficient algorithms for real-time robotic operations and advancing techniques in robotic perception and defect detection. This research not only contributes to theoretical knowledge but also offers practical solutions for industries utilizing intelligent robotic systems, ensuring precision, adaptability, and reliability in automated environments.

Awards and Honors:

Dr. Jing Xu’s contributions have been recognized through his impactful research and academic endeavors. His publications in high-impact international journals reflect his reputation as a promising scholar in robotics and automation. These works, highly cited by peers, demonstrate his leadership in advancing robotic motion planning and industrial applications. Recognition of his work comes through collaborative projects, peer-reviewed publications, and the adoption of his methodologies in academic and industrial contexts. His teaching excellence and involvement in key laboratories further enhance his professional profile, highlighting his role as a thought leader in intelligent robotics and mechanical engineering research.

Publications:

Title: A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement
Citation: 1581
Year of Publication: 2018

Title: Point-based multi-view stereo network
Citation: 455
Year of Publication: 2019

Title: Status, challenges, and future perspectives of fringe projection profilometry
Citation: 403
Year of Publication: 2020

Title: MSU jumper: A single-motor-actuated miniature steerable jumping robot
Citation: 219
Year of Publication: 2013

Title: Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks
Citation: 196
Year of Publication: 2018

Title: S4g: Amodal single-view single-shot SE(3) grasp detection in cluttered scenes
Citation: 183
Year of Publication: 2020

Title: Real-time 3D shape inspection system of automotive parts based on structured light pattern
Citation: 144
Year of Publication: 2011

Conclusion:

Dr. Jing Xu is an outstanding researcher and educator whose contributions to robotics, automation, and intelligent systems are both innovative and impactful. His academic journey has equipped him with expertise in motion planning, kinematics, and computer vision, leading to numerous influential publications. Through his dual role as a Lecturer and Research Assistant, he effectively bridges academic research and practical applications, fostering advancements in intelligent robotics. Recognized through citations and collaborative projects, Dr. Xu exemplifies excellence in engineering research and education. His profile strongly supports his nomination for a prestigious award honoring research and innovation.

Jingxin Zhang | Engineering | Best Researcher Award

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Jingxin Zhang | Engineering | Best Researcher Award

Lecturer at Southeast University, China.

Dr. Jingxin Zhang is a Lecturer at Southeast University with expertise in fault detection, diagnosis, and process monitoring. She has made significant strides in photovoltaic power forecasting and the application of advanced data-driven methodologies in industrial processes. With a robust academic background and industry experience, Dr. Zhang has established a reputation for blending theoretical research with practical implementation. Her work on multimode process monitoring, which incorporates continual learning, sets her apart as an innovator in her field. Dr. Zhang’s research has resulted in 17 journal publications, nine patents, and significant collaborations with global institutions, enhancing her impact in both academia and industry. She has also led several research and industry projects, showcasing her ability to tackle real-world challenges effectively. Dr. Zhang is an active member of professional societies such as IEEE and the Chinese Association of Automation, further emphasizing her commitment to advancing her field.

📚 Profile

ORCID

Google scholar

🎓 Education

Dr. Jingxin Zhang’s academic journey began at Harbin Engineering University, where she earned her Bachelor of Engineering degree in Electrical Engineering and Automation. She furthered her studies with a Master’s degree in Control Science and Engineering from Harbin Institute of Technology. Her academic pursuits culminated in a Ph.D. in Control Science and Engineering from Tsinghua University, one of China’s most prestigious institutions. Throughout her education, Dr. Zhang focused on developing advanced methodologies for process control, fault detection, and automation, laying the foundation for her impactful career. Her strong educational background has enabled her to contribute to both theoretical advancements and practical applications in her field. As a lifelong learner, Dr. Zhang continues to push the boundaries of her expertise, applying her knowledge to cutting-edge research in data-driven process monitoring and photovoltaic power forecasting.

💼 Experience

Dr. Jingxin Zhang has accumulated extensive experience as both an academic and a researcher. Currently serving as a Lecturer at Southeast University, she has been involved in teaching and mentoring students while advancing her research in fault detection, process monitoring, and renewable energy forecasting. Her academic career has been complemented by active collaboration with industry leaders, making her research highly applicable to real-world industrial processes. Dr. Zhang has led multiple research projects, including five ongoing and four completed ones, focusing on advanced control systems and data-driven fault detection. With nine patents to her name and numerous publications in high-impact journals, her contributions are recognized both in China and internationally. Her involvement in consultancy projects has also strengthened her ability to transfer theoretical knowledge to practical, industry-relevant innovations, positioning her as a rising star in the field of control science and engineering.

🔬 Research Interests

Dr. Jingxin Zhang’s research interests span fault detection, diagnosis, process monitoring, and renewable energy forecasting. A core area of her work lies in developing advanced data-driven approaches to fault detection and diagnosis in complex systems. Her expertise extends to photovoltaic power forecasting, where she applies control science and machine learning techniques to predict energy outputs in industrial-scale solar power systems. Dr. Zhang is particularly interested in continual learning in process monitoring, a novel approach that enables systems to adapt to new data without losing previously learned knowledge. This research is groundbreaking in its ability to improve long-term system efficiency and stability in industries such as energy, manufacturing, and automation. Her work bridges the gap between theoretical advancements in data science and real-world industrial applications, making her research highly impactful in both academic and practical contexts.

🏆 Awards and Honors

Dr. Jingxin Zhang has been recognized for her contributions to control science and engineering through various awards and honors. While specific details of her awards have yet to be widely publicized, her achievements in research and industry collaborations have earned her a solid reputation within the academic community. She has led several prestigious research projects funded by national and provincial organizations, showcasing her leadership and innovation in process monitoring and fault detection. In addition to her research accomplishments, Dr. Zhang has been awarded nine patents, further solidifying her impact on industrial applications. As an active member of professional societies such as IEEE and the Chinese Association of Automation, Dr. Zhang is well-regarded by her peers and continues to be recognized for her groundbreaking work in data-driven approaches to process control. She remains committed to advancing her field and earning additional accolades as her career progresses.

🔚 Conclusion

 Jingxin Zhang is a strong candidate for the Best Researcher Award, particularly due to her innovative contributions to fault detection, data-driven approaches, and industry collaborations. With a focus on continual learning and industrial relevance, her research aligns well with the award’s criteria. Enhancing her academic visibility through more editorial roles and publications could further strengthen her application, positioning her as a leading researcher in her field.

Publications Top Notes 📚

Title: An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
Author: J Zhang, H Chen, S Chen, X Hong
Year: 2017
Citation: 68

 

Title: Monitoring multimode processes: A modified PCA algorithm with continual learning ability
Author: J Zhang, D Zhou, M Chen
Year: 2021
Citation: 56

 

Title: A data-driven learning approach for nonlinear process monitoring based on available sensing measurements
Author: S Yin, C Yang, J Zhang, Y Jiang
Year: 2016
Citation: 47

 

Title: Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability
Author: D Wu, D Zhou, J Zhang, M Chen
Year: 2020
Citation: 29

 

Title: Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings
Author: J Zhang, M Chen, X Hong
Year: 2021
Citation: 28

 

Title: Continual learning for multimode dynamic process monitoring with applications to an ultra–supercritical thermal power plant
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 24

 

Title: Self-learning sparse PCA for multimode process monitoring
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 16

 

Title: Adaptive cointegration analysis and modified RPCA with continual learning ability for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 15

 

Title: Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
Author: J Zhang, M Chen, H Chen, X Hong, D Zhou
Year: 2019
Citation: 14

 

Title: Continual learning-based probabilistic slow feature analysis for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 11