Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award

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Dr. Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award 

Research Consultant at C.V. Raman Global University | India

Dr. Abhijeet Das is a distinguished researcher in Civil and Water Resources Engineering, known for his impactful contributions to hydrology, water quality, and environmental sustainability. With a career spanning academic teaching, consultancy, and international collaborations, he has demonstrated excellence in advancing methods of water quality assessment, climate change analysis, and sustainable water management practices. His interdisciplinary approach integrates hydrological modeling, GIS, and machine learning, creating solutions for both local and global water challenges. Dr. Abhijeet Das has authored books, research articles, and innovative patents, reflecting his vision of applying science and technology to address pressing environmental and societal needs.

Profile:

Orcid 

Education:

Dr. Abhijeet Das holds a Ph.D. in Water Resources Engineering from C.V. Raman Global University, where he specialized in hydrological modeling, water quality management, and GIS-based analysis. He earned his M.Tech in Water Resources Engineering from Biju Patnaik University of Technology, following his B.Tech in Civil Engineering from the same institution. His education built a strong foundation in watershed hydrology, climate change impact assessment, and environmental sustainability. Dr. Abhijeet Das’s academic achievements were marked by high distinctions, and he consistently pursued excellence in research-oriented projects. His progression from undergraduate to doctoral studies reflects a clear dedication to solving water resource challenges.

Experience:

Dr. Abhijeet Das has accumulated valuable experience as a researcher, consultant, and educator in civil and water resources engineering. He has taught undergraduate and postgraduate students at premier engineering institutions, nurturing the next generation of engineers and researchers. His consultancy roles allowed him to lead projects involving hydrological assessments, water resource management, and GIS-based solutions for river basin studies. Beyond national engagements, he has collaborated with international universities, working on projects related to wastewater management, climate impact analysis, and geoinformatics. Dr. Abhijeet Das’s combined academic and professional experience reflects his ability to bridge research, teaching, and practical applications.

Research Interests:

Dr. Abhijeet Das’s research interests encompass watershed hydrology, water resources engineering, and hydrological extremes such as droughts and floods. He focuses on climate change impact assessment and its influence on water security, emphasizing sustainable solutions. His expertise extends to the Food-Energy-Water nexus, applying machine learning, GIS, and remote sensing to optimize water management strategies. Dr. Abhijeet Das also contributes to simulation-optimization modeling, neural networks, and fuzzy logic applications for water quality control. His work highlights environmental impact assessment and sustainable management approaches, ensuring that his research directly supports global goals of resilience, sustainability, and resource conservation.

Awards and Honors:

Dr. Abhijeet Das has been honored with multiple awards for his innovative research and outstanding academic contributions. His papers on water quality assessment and GIS-based modeling have received recognition at prestigious national and international conferences. He has won best paper awards for advancing novel methodologies in multivariate statistical analysis and decision-making approaches for water quality management. Additionally, his poster presentations have earned accolades for their clarity, innovation, and societal relevance. Dr. Abhijeet Das’s recognition extends to his role as a valued reviewer and editor for renowned international journals, reflecting his leadership and credibility within the scientific and engineering community.

Publications:

Title: An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance
Year of Publication: 2025

Title: A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India
Citation: 2
Year of Publication: 2025

Title: Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques
Citation: 1
Year of Publication: 2025

Title: Bioplastics: a sustainable alternative or a hidden microplastic threat
Year of Publication: 2025

Title: Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis
Year of Publication: 2025

Title: Geographical Information System–driven intelligent surface water quality assessment for enhanced drinking and irrigation purposes in Brahmani River, Odisha (India)
Citation: 6
Year of Publication: 2025

Title: Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India
Year of Publication: 2025

Conclusion:

Dr. Abhijeet Das has established himself as a dedicated scholar and innovator in water resources and environmental engineering. His research advances the understanding of hydrological systems, water quality monitoring, and climate change adaptation strategies. By integrating modern tools like GIS, machine learning, and multivariate statistical methods, he has proposed solutions with practical implications for sustainable development. Recognized with awards and widely published, Dr. Abhijeet Das represents a new generation of engineers combining academic rigor with societal impact. His body of work exemplifies excellence in research, making him a highly deserving candidate for recognition through this award nomination.

Xize Dai | Engineering | Best Academic Researcher Award

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Dr. Xize Dai | Engineering | Best Academic Researcher Award 

Postdoctoral Research Fellow at Unversity of Queensland | Australia

Dr. Xize Dai is a distinguished Postdoctoral Research Fellow at the University of Queensland, Australia, specializing in high-voltage insulation and dielectric physics. His work has centered on advancing the reliability of polymer insulation systems, particularly within renewable energy and power electronics applications. Through extensive research into degradation mechanisms and advanced diagnostic techniques, he has built a strong international reputation in insulation science. Recognized for his academic excellence and technical expertise, he has actively contributed to both experimental studies and theoretical modeling, bridging the gap between material behavior and system-level reliability in modern energy applications.

Profile:

Google Scholar

Education:

Dr. Xize Dai earned his Ph.D. in Energy from Aalborg University, Denmark, where his doctoral research focused on dielectric dynamics and equivalent circuit modeling of polymer insulation under multifrequency stress conditions. He also pursued advanced studies as a visiting researcher at the University of Bologna in Italy, where he refined his expertise in high-field dielectric spectroscopy and partial discharge characterization. Prior to this, he obtained his Master’s degree in Electrical Engineering at Chongqing University, China, with a thesis on thermal degradation of submarine cable insulation, and a Bachelor’s degree in Smart Grid and Information Engineering at Liaoning Technical University.

Experience:

Dr. Xize Dai’s professional experience spans leading research institutions and industry collaborations. He has worked on projects addressing degradation mechanisms, condition monitoring, and modeling of insulation materials for renewable energy applications. His tenure as a visiting researcher at Khalifa University provided exposure to photovoltaic system diagnostics and advanced insulation methodologies. Collaborations with globally recognized experts at Bologna, Oxford, and Khalifa University enriched his expertise in multiphysics modeling and advanced dielectric testing. Additionally, his engagement with IEEE as an active member of the Dielectrics and Electrical Insulation Society reflects his dedication to international research exchange and technical community service.

Research Interests:

Dr. Xize Dai’s research lies at the intersection of dielectric physics, material science, and renewable energy. His interests include investigating high-performance insulation materials for power systems, with a focus on polymeric and heterogeneous composites. He explores aging mechanisms and degradation processes under combined electrical, thermal, mechanical, and environmental stresses. His expertise extends to dielectric and impedance spectroscopy, partial discharge analysis, and multiphysics simulations using finite element methods. By developing advanced equivalent circuit models and health monitoring frameworks, he aims to enhance predictive maintenance and digital twin applications, ensuring greater efficiency, safety, and sustainability in high-voltage energy infrastructure.

Awards and Honors:

Dr. Xize Dai has been recognized with numerous academic honors for his exceptional contributions to high-voltage engineering and insulation research. His work has earned prestigious national scholarships and merit-based academic awards during his Bachelor’s and Master’s studies, reflecting his consistent academic excellence. His Master’s thesis was recognized with an award for outstanding research on insulation aging behavior. He has also received international research funding to support overseas collaborations and has been invited as a session chair, technical committee member, and keynote speaker at international conferences. These honors reflect his global recognition as an emerging leader in his field.

Publications:

Title: Multi-dimensional analysis and correlation mechanism of thermal degradation characteristics of XLPE insulation for extra high voltage submarine cable
Citation: 49
Year of Publication: 2021

Title: Synergistic enhancement effect of moisture and aging on frequency dielectric response of oil-immersed cellulose insulation and its degree of polymerization evaluation using …
Citation: 43
Year of Publication: 2021

Title: Physical mechanism analysis of conductivity and relaxation polarization behavior of oil-paper insulation based on broadband frequency domain spectroscopy
Citation: 36
Year of Publication: 2021

Title: Ageing state identification and analysis of AC 500 kV XLPE submarine cable based on high-voltage frequency dielectric response
Citation: 32
Year of Publication: 2020

Title: High-voltage frequency domain spectroscopy analysis of a thermally aged XLPE submarine cable under continuous and cyclic voltage based on carrier transport and polarisation …
Citation: 18
Year of Publication: 2022

Title: Influence of thermal ageing on high-field polarisation characteristics and conductivity behaviour of submarine polymeric cables insulation
Citation: 17
Year of Publication: 2023

Title: Unraveling High Temperature-Induced Glass Transition Effect on Underlying Multitimescales Dynamic Mechanisms of Epoxy Resin Insulation in Power Electronic Applications
Citation: 3
Year of Publication: 2024

Conclusion:

Dr. Xize Dai has established himself as a highly accomplished researcher at the forefront of electrical insulation and renewable energy studies. Through innovative modeling approaches, advanced diagnostic methods, and impactful collaborations, he has significantly contributed to enhancing the reliability of high-voltage systems. His work directly supports the integration of renewable energy technologies with safer and more efficient insulation materials. Recognized by leading international scholars and organizations, Dr. Xize Dai continues to push the boundaries of dielectric physics and insulation science. His academic rigor, professional service, and global collaborations make him a strong candidate for this award.

Jinzhu Shen | Engineering | Best Researcher Award

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Dr. Jinzhu Shen | Engineering | Best Researcher Award 

Doctor at Donghua University | China

Dr. Jinzhu Shen is a dedicated researcher and innovator in fashion design and intelligent manufacturing, focusing on integrating soft robotics and machine vision into textile automation. As a Ph.D. candidate at Donghua University and a visiting researcher at Universidad Politécnica de Madrid, she has established herself as a leading young scholar bridging technology with traditional apparel industries. Her professional experience as an R&D engineer at Rouchu Robotics reflects her applied expertise in developing robotic grippers and autonomous sewing systems. With patents, international conference presentations, and impactful journal publications, she contributes significantly to advancing automation in the fashion and textile field.

Profile:

Orcid

Education:

Dr. Jinzhu Shen’s academic journey is distinguished by progressive specialization in fashion design and technology. She began with a strong foundation at Jiangnan University, completing her bachelor’s and master’s degrees in fashion design, where she engaged in research on robotic applications in garment manufacturing. Currently pursuing her doctoral studies in fashion design at Donghua University, her academic path emphasizes intelligent textile production. She further expanded her global academic engagement as a visiting researcher at Universidad Politécnica de Madrid. This comprehensive educational trajectory demonstrates her commitment to interdisciplinary learning and applying scientific principles to transform fashion and textile industries.

Experience:

Dr. Jinzhu Shen’s professional experience combines academic research with industrial innovation. At Rouchu Robotics, she contributed to developing robotic arms and intelligent sewing systems, pioneering the use of soft robotic fingers for fabric handling. Her work included simulations of gripping forces, experimental validation, and vision-based automation for garment assembly. She has also engaged in significant funded research projects, collaborating with universities and industry partners to design intelligent quilting systems, autonomous sewing solutions, and advanced textile automation methods. Through her roles, she blends theory with practice, producing tangible results in industrial applications and enriching her doctoral research with practical expertise.

Research Interest:

Dr. Jinzhu Shen’s research interests center on digitizing and automating garment production using cutting-edge technologies. Specifically, she explores the integration of soft robotics, artificial intelligence, and machine vision to optimize fabric manipulation, alignment, and sewing. Her studies address challenges in fabric variability, grasping precision, and real-time control within textile processes. By applying intelligent algorithms and robotic systems, Shen seeks to eliminate manual limitations and enhance automation efficiency. Her work not only advances garment manufacturing but also contributes broadly to the fields of intelligent robotics and industrial engineering, offering sustainable and innovative solutions for next-generation smart manufacturing systems.

Awards and Honors:

Dr. Jinzhu Shen’s academic and professional journey, Shen has been recognized with multiple scholarships, merit awards, and research prizes. She received honors for academic excellence during her undergraduate and master’s studies, reflecting her consistent dedication to learning. Her doctoral research has been supported by prestigious scholarships, highlighting her innovation in textile automation. Additionally, she earned recognition for presenting outstanding research at national apparel science and technology conferences, where her work on soft robotics and garment automation was distinguished. These accolades underline her strong scholarly potential, research excellence, and contributions to advancing both theory and industrial practice.

Publications:

Title: Research progress of automatic grasping methods for garment fabrics
Citation: 6
Year of Publications: 2023

Title: Automatic grasping technology and arrangement method for garment cutting pieces
Citation: 2
Year of Publications: 2023

Title: Arrangement of soft fingers for automatic grasping of fabric pieces of garment
Citation: 7
Year of Publications: 2022

Title: Grasping model of fabric cut pieces for robotic soft fingers
Citation: 17
Year of Publications: 2021

Title: Design and ergonomic evaluation of flexible rehabilitation gloves
Citation: 18
Year of Publications: 2020

Conclusion:

Dr. Jinzhu Shen represents an emerging leader in intelligent garment manufacturing and textile automation. Her academic excellence, industrial experience, and global collaborations highlight her as a strong candidate for recognition. Through her pioneering work in soft robotics and intelligent vision systems, she has contributed to bridging traditional craftsmanship with modern digital technologies. Her innovative publications, patents, and awards reflect both scientific merit and real-world impact. Shen’s profile embodies the qualities of a future-oriented researcher who is shaping the transformation of textile engineering and ensuring its sustainable evolution in the age of smart manufacturing.

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