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

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.

Jing Xu | Engineering | Best Scholar Award

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.

Shuai Li | Engineering | Best Researcher Award

Dr. Shuai Li | Engineering | Best Researcher Award

Lecturer at Henan University of Urban Construction, China

Dr. Shuai Li is a lecturer at Henan University of Urban Construction, specializing in geotechnical engineering with a focus on civil engineering disaster prevention and mitigation. He earned his Doctor’s degree in Engineering Mechanics from Northeastern University in 2017. Dr. Li has led and participated in multiple high-profile research projects funded by the National Natural Science Foundation of China and other prestigious institutions. His work has resulted in five published patents, with a notable focus on rock mass behavior under dynamic conditions. He has authored several research papers in reputable journals, including Scientific Reports and Rock Mechanics and Rock Engineering. Dr. Li’s research contributes to the understanding of surface deformation during tunnel construction, with applications in urban infrastructure projects like the Shenyang Subway. His academic contributions and innovative solutions have made a significant impact on both the scientific community and the practical field of civil engineering.

Professional Profile : 

Google Scholar

Education: 

Dr. Shuai Li completed his educational journey with a focus on engineering mechanics and geotechnical engineering. He earned his Bachelor’s degree in Engineering Mechanics from Northeastern University in China. Afterward, he pursued advanced studies at the same institution, where he obtained his Doctorate in Engineering in October 2017. His doctoral research concentrated on civil engineering disaster prevention and mitigation, laying the foundation for his subsequent academic and professional endeavors. During his academic tenure, Dr. Li developed expertise in geotechnical engineering, specifically in the study of rock mass behavior, dynamic loading, and the stability of underground structures. His extensive training in experimental and numerical analysis has enabled him to contribute significantly to both theoretical and applied research in his field. Through his education, Dr. Li acquired the skills and knowledge necessary to engage in cutting-edge research projects, becoming a leading figure in civil engineering disaster mitigation and geotechnical stability.

Professional Experience :

Dr. Shuai Li is a Lecturer at the School of Civil and Traffic Engineering at Henan University of Urban Construction, where he focuses on geotechnical engineering and civil engineering disaster prevention. He completed his Ph.D. in Engineering Mechanics at Northeastern University in 2017. Over the years, Dr. Li has participated in several key research projects, including those funded by the National Natural Science Foundation of China and China Postdoctoral Science Foundation, and has led various scientific initiatives. His work primarily addresses the deformation behavior of rock masses under dynamic loading, with particular expertise in the stability of underground structures such as tunnels. Dr. Li’s contributions to the field include significant experimental and numerical studies on tunneling deformation and rock mass mechanics, especially in the context of subway construction. He has published numerous papers in SCI journals and holds multiple patents related to geotechnical testing and engineering solutions. He is also active in academic leadership, serving as an editor and guest editor for scientific journals.

Research Interest :

Dr. Shuai Li’s research primarily focuses on geotechnical engineering, with a particular emphasis on civil engineering disaster prevention and mitigation. His work investigates the mechanical behavior of rock masses under dynamic conditions, especially during processes like shield tunneling, and the associated surface deformation. Dr. Li has contributed significantly to the development of finite element analysis (FEA) simulations to study the stability of underground structures, such as subway tunnels. His research also explores rock mechanics, including mixed-mode fracture characteristics, stress relaxation in sandstone, and the effects of dynamic disturbances on the creep behavior of rocks. In addition, he is involved in innovative testing methods for rock permeability and rheological relaxation, contributing to the design and analysis of geotechnical engineering projects. Dr. Li’s work is vital for improving the safety and stability of infrastructure in complex geological environments, with applications in urban tunneling, mining, and civil construction.

Award and Honor :

Dr. Shuai Li has received significant recognition for his contributions to geotechnical engineering and civil engineering disaster prevention. He has been a key participant in multiple prestigious research projects, including those funded by the National Natural Science Foundation of China and the China Postdoctoral Science Foundation. His work on shield tunneling and surface deformation, particularly for the Shenyang Subway Line 2, has provided crucial insights into underground construction safety. Dr. Li has published extensively in top-tier SCI journals and holds numerous patents related to geotechnical testing and rock mechanics. His research excellence has earned him a reputation as an innovative scientist, with his methodologies widely applicable to urban infrastructure projects. Additionally, Dr. Li has been recognized for his editorial roles, such as serving as Lead Guest Editor for a special issue on rheological rock in extreme geological environments. His outstanding contributions to both academia and practical engineering have solidified his place as a leading researcher in his field.

Conclusion :

Dr. Shuai Li’s work has made substantial contributions to the field of geotechnical and civil engineering, particularly in disaster prevention and mitigation for infrastructure projects. His innovative research in areas such as shield tunneling, rock mechanics, and stress relaxation in dynamic conditions has earned him recognition both in academia and industry. Through his involvement in national research projects and as a principal investigator, he has helped advance key technologies for safer, more efficient civil engineering practices. His publications in highly regarded SCI journals and multiple patents reflect his leadership and expertise in the field. Furthermore, Dr. Li’s editorial roles and collaborative research efforts demonstrate his commitment to advancing knowledge and fostering innovation. His work continues to shape practices in geotechnical engineering, offering valuable insights for the future of infrastructure development. Dr. Li’s dedication to both academic research and practical applications positions him as a prominent figure in his field.

Publications Top Noted :

  • Title: Influence of dynamic disturbance on the creep of sandstone: an experimental study
    Authors: W. Zhu, S. Li, S. Li, L. Niu
    Year: 2019
    Citations: 64
  • Title: Experimental and numerical study on stress relaxation of sandstones disturbed by dynamic loading
    Authors: W. Zhu, S. Li, L. Niu, K. Liu, T. Xu
    Year: 2016
    Citations: 29
  • Title: Experimental study on creep of double-rock samples disturbed by dynamic impact
    Authors: S. Li, W. Zhu, L. Niu, K. Guan, T. Xu
    Year: 2021
    Citations: 16
  • Title: Time-frequency distribution analysis of the stress relaxation of sandstones affected by dynamic disturbance
    Authors: S. Li, W.C. Zhu, T. Xu, R.X. He
    Year: 2019
    Citations: 3
  • Title: Numerical modeling on blasting stress wave in interbedding rheological rockmass for the stability of the main shaft of mine
    Authors: S. Li, C. Zheng, Y. Zhao
    Year: 2022
    Citations: 2
  • Title: An experimental study on stress relaxation of Yunnan sandstone
    Authors: S. Li, C. Zheng, P. Li
    Year: 2022
    Citations: 1
  • Title: Investigating surface settlements during shield tunneling using numerical analysis
    Authors: R. He, Z. Zhou, S. Li, S. Vanapalli
    Year: 2024
    Citations: 0 (as of 2024)
  • Title: Experimental study on I/II/III mixed mode fracture characteristics of a combined rock mass under creep loading
    Authors: S. Li, C. Zheng, P. Li, S. Zhang
    Year: 2024
    Citations: 0 (as of 2024)

Rajeevan Arunthavanathan | Engineering | Best Researcher Award

Dr. Rajeevan Arunthavanathan | Engineering | Best Researcher Award

Postdoctoral Researcher at Texas A&M University, United States.

🌍Dr. Rajeevan Arunthavanathan is a distinguished researcher and educator specializing in AI safety, process safety, and ICS cybersecurity. With a Ph.D. in Process Engineering and over a decade of academic and industrial experience, he has developed groundbreaking methods for risk evaluation and safety in critical infrastructures. His prolific publication record includes high-impact journals and book chapters on AI-human conflict, machine learning applications, and process fault diagnosis. Dr. Arunthavanathan has contributed significantly to curriculum development, student mentorship, and project management in academia and industry, positioning himself as a leader in the intersection of AI and process safety.

Profile👤

Education 🎓

🎓Dr. Arunthavanathan completed his Ph.D. in Process Engineering at Memorial University, Canada, in 2022, focusing on AI-driven fault diagnosis in process systems. He earned his MSc in Microelectronics and Communication from Northumbria University, UK, in 2010, graduating with distinction, and a B.Eng. in Electrical and Electronics Engineering from the same institution in 2007. His academic mentors included renowned professors, under whom he honed expertise in AI, control systems, and microelectronics. Throughout his education, he demonstrated excellence through research on AI-human interaction and advanced microelectronics, laying the foundation for his impactful career.🧬🎓

Experience💼

🩺Dr. Arunthavanathan has extensive experience in academia and industry. At Texas A&M University, he researches AI safety and mentors graduate students. Previously, at C-CORE, Canada, he developed ML models for data noise cleaning and smart ice management. He served as a senior lecturer at SLIIT, Sri Lanka, revising engineering curricula to meet international accreditation standards. His industrial experience includes work as a trainee engineer at Perry Slingsby Systems, UK, where he contributed to advanced underwater surveillance systems. His teaching spans multiple institutions, offering courses in process safety, microelectronics, and programming, blending theory with practical applications.👨‍🔬🌍

Research Interests 🔬

🔬Dr. Arunthavanathan’s research lies at the nexus of AI safety, process safety, and industrial control systems (ICS) cybersecurity. He develops innovative models to evaluate AI efficiency and mitigate risks in human-AI collaboration. His work on fault diagnosis, risk assessment, and operational technology cybersecurity addresses pressing challenges in critical infrastructure. His focus extends to integrating machine learning for noise cleaning in data systems and applying AI in Industry 4.0 technologies. With a commitment to enhancing process safety and addressing cyber threats, his research bridges theoretical advancements with practical applications for safer industrial operations. 🌿🧪

Awards and Honors 🏆

🏆Dr. Arunthavanathan has received numerous accolades, including being named a Fellow of the School of Graduate Studies at Memorial University (2022). His MSc degree was conferred with distinction by Northumbria University (2010). He serves as an editor for leading journals like Sensors and AI and reviews manuscripts for high-impact publications, including IEEE Access. His professional memberships with IEEE and AIChE reflect his standing in the academic community. These achievements underscore his dedication to advancing AI, process safety, and engineering education through impactful research and professional service. 🏆🎉

Conclusion 🔚 

Dr. Rajeevan Arunthavanathan is a strong contender for the Best Researcher Award, given his impactful contributions to AI safety, process fault diagnosis, and industrial control systems. His expertise, combined with a commitment to education and industry applications, exemplifies the qualities of an outstanding researcher. Recognizing his achievements will inspire further advancements in safety and AI-driven solutions for critical infrastructure.

Publications Top Notes 📚

An analysis of process fault diagnosis methods from safety perspectives

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 126

Year: 2021

A deep learning model for process fault prognosis

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 120

Year: 2021

Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz, R. Rusli

Citations: 76

Year: 2020

Autonomous fault diagnosis and root cause analysis for the processing system using one-class SVM and NN permutation algorithm

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 52

Year: 2022

Industry 4.0-based process data analytics platform

Authors: T.R. Wanasinghe, M.G. Don, R. Arunthavanathan, R.G. Gosine

Citations: 10

Year: 2022

Machine Learning for Process Fault Detection and Diagnosis

Authors: R. Arunthavanathan, S. Ahmed, F. Khan, S. Imtiaz

Citations: 9

Year: 2022

Vehicle monitoring controlling and tracking system by using Android application

Authors: A. Rajeevan, N.K. Payagala

Citations: 8

Year: 2016

Artificial intelligence–Human intelligence conflict and its impact on process system safety

Authors: R. Arunthavanathan, Z. Sajid, F. Khan, E. Pistikopoulos

Citations: 7

Year: 2024

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

Authors: R. Arunthavanathan, Z. Sajid, M.T. Amin, Y. Tian, F. Khan, E. Pistikopoulos

Citations: 7

Year: 2024

Statistical approaches and artificial neural networks for process monitoring

Authors: M. Alauddin, R. Arunthavanathan, M.T. Amin, F. Khan

Citations: 6

Year: 2022