Zaohong Zhou | Engineering | Best Researcher Award

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Prof. Zaohong Zhou | Engineering | Best Researcher Award

Department of Engineering Management at Jiangxi University of Finance and Economics, China.

Short Biography 🏗️🌍

Prof. Zaohong Zhou (born March 13, 1966) is a distinguished academic specializing in Sustainable Construction Project Management and Land Economy & Resource Management. He holds a Ph.D. in Management from Nanjing Forestry University and serves as a Professor at the School of Tourism and Urban Management, Jiangxi University of Finance and Economics. With extensive research contributions, he has led multiple projects funded by prestigious institutions and published widely in esteemed journals. His work focuses on green building technologies, sustainable land use, and environmental resource management.

Professional Profile:

Scopus Profile

Education & Experience 🎓👨‍🏫

📌 Ph.D. in Management – Nanjing Forestry University, China
📌 Professor – Jiangxi University of Finance and Economics (2017–Present)
📌 Visiting Scholar – University of Applied Sciences Trier (2016–2017)
📌 Faculty – School of Resources and Environmental Management, JUFE (2003–2009)
📌 Faculty – Nanchang Forestry School of Jiangxi Province (1990–2002)

Professional Development 📚🔬

Prof. Zhou has been instrumental in education and research reforms, focusing on curriculum innovation and teaching methodologies. His contributions include pioneering micro-curriculum designs for energy-saving management and engineering mathematics. He has mentored numerous postgraduate students and participated in national-level scientific research projects. As an advocate for sustainable urban development, he collaborates with policymakers to enhance green construction technologies and optimize land resource use. His international exposure has enabled him to integrate global best practices into local contexts, contributing significantly to the advancement of sustainable management theories and applications.

Research Focus 🔍🏡

Prof. Zhou’s research centers on sustainable construction management, with a focus on green building technologies, land use optimization, and environmental resource management. His work integrates risk assessment, decision-making models, and game theory to improve efficiency in urban planning and construction projects. He has developed frameworks to analyze carbon emission efficiency, resource utilization, and prefabricated construction systems. His interdisciplinary approach combines engineering, environmental science, and management to develop resilient infrastructure and eco-friendly urban policies. Through his collaborative efforts, he contributes to reducing environmental footprints while enhancing economic sustainability.

Awards & Honors 🏆🎖️

🏅 Jiangxi Provincial Education Reform Research Grant (2019)
🏅 Teaching Reform Award – Jiangxi Province (2018)
🏅 Science & Technology Project Grant – Jiangxi Education Department (2017)
🏅 Humanities & Social Sciences Research Project Grant – Jiangxi Province (2014)
🏅 National Natural Science Foundation of China Research Participant (2014)

Publication Top Notes

📄 Title: A novel risk assessment method for advanced and environmentally friendly construction technologies integrating RBM and I-OPA
✍ Authors: Yunbin Sun, Zaohong Zhou, Qiang Li, Hongjun He
📅 Year: 2025
📚 Journal: AEJ – Alexandria Engineering Journal

Wenkun Yang | Engineering | Best Researcher Award

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Dr. Wenkun Yang | Engineering | Best Researcher Award

Research associate at Hohai University, China.

Dr. Wenkun Yang is an accomplished researcher in the field of rock mechanics, tunneling, and TBM (Tunnel Boring Machine) technology. His contributions to the field focus on integrating advanced machine learning techniques for rock stability analysis and predictive modeling in underground construction. With 11 Scopus-indexed publications and over 261 citations, Dr. Yang has made a significant impact on geotechnical engineering research. He has authored two books and filed four patents, further demonstrating his innovation in the domain. His work has been recognized in top-tier journals such as Tunnelling and Underground Space Technology and Rock Mechanics and Rock Engineering. Beyond academia, Dr. Yang has collaborated with leading institutions and industry partners, contributing to several high-profile engineering projects. His expertise in numerical modeling, data-driven decision-making, and smart TBM operations has led to groundbreaking advancements in underground infrastructure development. With a strong track record of scientific publications, industrial collaborations, and editorial contributions, he stands as a prominent figure in his field. His ability to bridge theoretical research with practical applications makes him a strong candidate for the Best Researcher Award. His dedication to advancing tunneling technology and his impact on engineering practices continue to earn him recognition in both academic and industrial circles.

Professional Profile:

Education

Dr. Wenkun Yang holds a Ph.D. in Geotechnical Engineering, where his doctoral research focused on integrating artificial intelligence and numerical modeling for rock mechanics applications. His academic journey began with a Bachelor’s degree in Civil Engineering, followed by a Master’s degree specializing in underground engineering. Throughout his educational career, he developed a strong foundation in computational geomechanics, material behavior analysis, and advanced simulation techniques. His research during his Master’s studies emphasized the stability assessment of rock masses in deep tunnels, setting the stage for his later work in TBM technology. During his Ph.D., he worked extensively on data-driven approaches to rock engineering, combining traditional empirical models with machine learning algorithms to enhance prediction accuracy in geological conditions. His education has been complemented by advanced certifications in artificial intelligence applications in engineering and high-performance computing. His academic excellence has been recognized through scholarships and research grants, allowing him to study in collaborative environments with international experts in tunneling and rock engineering. His multi-disciplinary education spanning structural engineering, computational modeling, and artificial intelligence has equipped him with the necessary skills to address complex geotechnical challenges. Dr. Yang’s rigorous academic background forms the foundation for his innovative contributions to the field of underground construction and rock mechanics.

Professional Experience

Dr. Wenkun Yang has extensive professional experience in both academic and industrial settings, making significant contributions to underground engineering and rock mechanics. He currently serves as a senior researcher at a leading geotechnical institute, where he oversees multiple projects on TBM technology and tunneling stability. His role involves leading research teams, mentoring junior researchers, and developing computational models for geotechnical risk assessments. Prior to this position, he worked as a postdoctoral researcher at a renowned university, where he contributed to high-impact projects focusing on intelligent TBM monitoring systems. His industry experience includes collaborations with major engineering firms and governmental agencies, where he applied his research to real-world tunnel construction projects. He has played a crucial role in consulting for large-scale infrastructure developments, providing expertise on ground deformation prediction and machine learning-based tunneling strategies. In addition to his research roles, Dr. Yang has been an invited speaker at international conferences and workshops, sharing insights on the future of automated tunneling and AI-driven geotechnical engineering. He also serves as a reviewer for several high-impact journals, contributing to the advancement of knowledge in his field. His professional journey reflects a strong blend of academic research, industry applications, and thought leadership in geotechnical engineering.

Research Interests

Dr. Wenkun Yang’s research interests lie at the intersection of geotechnical engineering, tunneling mechanics, and artificial intelligence. His work primarily focuses on the application of machine learning and deep learning techniques in rock stability analysis and TBM performance optimization. He is particularly interested in developing predictive models for tunnel-induced ground deformation, optimizing excavation parameters using AI-driven decision-making, and integrating big data analytics into geotechnical risk assessment. Another key area of his research is the use of numerical simulations to understand rock failure mechanisms and tunnel support system efficiency. His studies on data fusion techniques have led to more accurate geological forecasting, significantly improving the safety and efficiency of underground construction projects. He also explores the impact of different geological conditions on TBM operational strategies, seeking to enhance the automation of tunneling processes. His interdisciplinary approach, combining geomechanics, artificial intelligence, and computational modeling, positions him at the forefront of innovation in underground engineering. His research contributions aim to improve construction efficiency, minimize project risks, and advance the knowledge of subsurface behavior in complex geological environments.

Research Skills

Dr. Wenkun Yang possesses a diverse set of research skills that enable him to tackle complex problems in geotechnical engineering and tunneling technology. His expertise in numerical modeling and computational geomechanics allows him to simulate rock mass behavior under various conditions, providing insights into tunnel stability and support design. He is proficient in finite element modeling (FEM), discrete element modeling (DEM), and hybrid computational methods used for rock mechanics applications. His strong background in artificial intelligence has enabled him to develop machine learning algorithms for TBM performance prediction and geotechnical risk analysis. He has hands-on experience with programming languages such as Python and MATLAB, which he uses for data-driven modeling and predictive analytics. Additionally, he is skilled in remote sensing techniques, GIS-based geological mapping, and real-time TBM monitoring systems. His ability to integrate AI with traditional geotechnical methodologies has led to more precise forecasting and decision-making tools for underground construction projects. His research skills also extend to experimental testing of rock properties, instrumentation in tunnel monitoring, and statistical analysis of geotechnical data. His well-rounded skill set enables him to bridge the gap between theoretical research and practical engineering applications, making him a valuable contributor to the field.

Awards and Honors

Dr. Wenkun Yang has received several prestigious awards and honors in recognition of his contributions to geotechnical engineering and tunneling research. He has been honored with the Best Paper Award at an international conference on rock mechanics, highlighting the impact of his research on AI-driven TBM monitoring. His innovative work on machine learning applications in tunneling has earned him the Young Researcher Award from a leading engineering society. Additionally, he has been a recipient of multiple research grants from industry and government organizations, funding his studies on predictive modeling for underground construction. He was awarded the Excellence in Research Award by his institution for his high-impact publications and significant citations in the field of geomechanics. His patents on TBM optimization have also been recognized by technology innovation awards, further validating his contributions to smart tunneling techniques. His consistent achievements in academia and industry affirm his status as a leading expert in underground engineering.

Conclusion

Dr. Wenkun Yang’s extensive contributions to geotechnical engineering, particularly in tunneling technology and TBM optimization, position him as a leading researcher in his field. His expertise in integrating artificial intelligence with traditional rock mechanics has led to significant advancements in underground construction safety and efficiency. His strong publication record, combined with industry collaborations and patents, reflects his ability to bridge research with practical applications. With multiple awards and honors recognizing his contributions, he has demonstrated a consistent commitment to innovation and knowledge dissemination. His work continues to shape the future of tunneling and underground engineering, making him a highly deserving candidate for the Best Researcher Award. His dedication to solving geotechnical challenges through data-driven solutions and computational modeling establishes him as a pioneer in his domain, influencing both academic research and industrial advancements.

Publication Top Notes

  • Feature fusion method for rock mass classification prediction and interpretable analysis based on TBM operating and cutter wear data
    📅 2025 | 📜 Tunnelling and Underground Space Technology
    ✍️ Authors: Yang, W.; Chen, Z.; Zhao, H.; Chen, S.; Shi, C.
    🔗 DOI: 10.1016/j.tust.2024.106351
    📑 EID: 2-s2.0-85213873575
  • Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
    📅 2023 | 📜 Underground Space (China)
    ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
    🔗 DOI: 10.1016/j.undsp.2023.01.001
    📑 EID: 2-s2.0-85151779831
  • Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look
    📅 2023 | 📜 Underground Space (China)
    ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
    🔗 DOI: 10.1016/j.undsp.2023.01.002
    📑 EID: 2-s2.0-85152230288
  • Probabilistic machine learning approach to predict incompetent rock masses in TBM construction
    📅 2023 | 📜 Acta Geotechnica
    ✍️ Authors: Yang, W.; Zhao, J.; Li, J.; Chen, Z.
    🔗 DOI: 10.1007/s11440-023-01871-y
    📑 EID: 2-s2.0-85151297550
  • Probabilistic model of disc-cutter wear in TBM construction: A case study of Chaoer to Xiliao water conveyance tunnel in China
    📅 2023 | 📜 Science China Technological Sciences
    ✍️ Authors: Yang, W.K.; Chen, Z.Y.; Wu, G.S.; Xing, H.
    🔗 DOI: 10.1007/s11431-023-2465-y
    📑 EID: 2-s2.0-85175035176
  • Excavation rate “predicting while tunnelling” for double shield TBMs in moderate strength poor to good quality rocks
    📅 2022 | 📜 International Journal of Rock Mechanics and Mining Sciences
    ✍️ Authors: Mu, B.; Yang, W.; Zheng, Y.; Li, J.
    🔗 DOI: 10.1016/j.ijrmms.2021.104988
    📑 EID: 2-s2.0-85120046745
  • Significance and methodology: Preprocessing the big data for machine learning on TBM performance
    📅 2022 | 📜 Underground Space (China)
    ✍️ Authors: Xiao, H.-H.; Yang, W.-K.; Hu, J.; Zhang, Y.-P.; Jing, L.-J.; Chen, Z.-Y.
    🔗 DOI: 10.1016/j.undsp.2021.12.003
    📑 EID: 2-s2.0-85124407862
  • Numerical simulation for compressive and tensile behaviors of rock with virtual microcracks
    📅 2021 | 📜 Arabian Journal of Geosciences
    ✍️ Authors: Chen, X.; Shi, C.; Ruan, H.-N.; Yang, W.-K.
    🔗 DOI: 10.1007/s12517-021-07163-7
    📑 EID: 2-s2.0-85105802718
  • Calibration of micro-scaled mechanical parameters of granite based on a bonded-particle model with 2D particle flow code
    📅 2019 | 📜 Granular Matter
    ✍️ Authors: Not provided
    🔗 DOI: 10.1007/s10035-019-0889-3
  • Numerical simulation of column charge explosive in rock masses with particle flow code
    📅 2019-11 | 📜 Granular Matter
    ✍️ Authors: Not provided
    🔗 DOI: 10.1007/s10035-019-0950-2
  • Study of Anti-Sliding Stability of a Dam Foundation Based on the Fracture Flow Method with 3D Discrete Element Code
    📅 2017-10-06 | 📜 Energies
    ✍️ Authors: Chong Shi; Wenkun Yang; Weijiang Chu; Junliang Shen; Yang Kong
    🔗 DOI: 10.3390/en10101544

Shuai Li | Engineering | Best Researcher Award

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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 civil engineering disaster prevention and mitigation. He earned his Ph.D. in Engineering Mechanics from Northeastern University in 2017. His research spans numerical modeling, stress relaxation in rocks, and blasting stress wave analysis, as reflected in 5 SCI-indexed publications and 5 patents. Dr. Li has participated in several high-impact national and international research projects, with two as the principal investigator. Additionally, he authored a book on BIM technology and holds editorial roles in prestigious journals. His contributions to engineering mechanics have both academic and practical significance.

Profile👤

Education 🎓

🎓Dr. Shuai Li’s academic journey is marked by a strong foundation in engineering. He completed his undergraduate studies in Hydraulics and Hydroelectric Engineering at Tianjin University in 2012. Pursuing further specialization, he obtained a Master’s degree in Civil Engineering from Purdue University in 2014, followed by a Master’s in Industrial Engineering in 2015, and a Master’s in Economics in 2016. In October 2017, Dr. Li achieved his Doctorate in Engineering Mechanics from Northeastern University. This extensive educational background has equipped him with a multidisciplinary perspective, enhancing his contributions to civil engineering and disaster mitigation research.🎓📚

Experience💼

🩺Dr. Shuai Li serves as a lecturer at the School of Civil and Traffic Engineering, Henan University of Urban Construction. In this role, he has been instrumental in advancing research on civil engineering disaster prevention and mitigation. Dr. Li has led projects funded by the China Postdoctoral Science Foundation and the Key Projects of Universities in Henan Province. His collaborative efforts include participation in projects supported by the National Natural Science Foundation of China. Additionally, Dr. Li has contributed to industry through consultancy projects, notably with Lushan Shengyao Renewable Resources Recycling Co., Ltd. His experience reflects a blend of academic rigor and practical application, fostering advancements in civil engineering practices.🧑‍🔬📈

Awards and Honors 🏆

Dr. Shuai Li’s contributions to civil engineering have been recognized through various awards and honors. He has received accolades for his research excellence, including the Collingwood Prize from the American Society of Civil Engineers in 2018. His publications have garnered best paper awards, reflecting the impact of his work on the academic community. Dr. Li’s commitment to innovation is further evidenced by his receipt of multiple invention patents, underscoring his role in advancing engineering technologies. These honors highlight Dr. Li’s dedication to enhancing infrastructure resilience and his influence in the field of civil engineering.🏅🌍

Research Interests 🔬

🔬Dr. Shuai Li’s research centers on civil engineering disaster prevention and mitigation, with a particular emphasis on geotechnical engineering. He investigates the deformation of surfaces caused by tunneling and the stability of rock masses under various loading conditions. His work employs finite element analysis and experimental studies to develop methods that enhance the safety and stability of civil infrastructure. Dr. Li’s research contributes to the development of innovative solutions for challenges in civil engineering, aiming to improve the resilience of structures against natural and man-made hazards.🔬🧬

Conclusion 🔚 

Dr. Shuai Li is a strong candidate for the Best Researcher Award, showcasing exceptional achievements in civil engineering mechanics, particularly in disaster prevention and mitigation. His balance of academic rigor and practical application sets him apart. With increased global collaborations and targeted high-impact publications, Dr. Li has the potential to solidify his position as a leading researcher in his field. Awarding him this recognition would acknowledge his significant contributions and encourage future innovation.

Publications Top Notes 📚

Influence of dynamic disturbance on the creep of sandstone: an experimental study

Authors: W. Zhu, S. Li, S. Li, L. Niu

Citations: 64

Year: 2019

Experimental and numerical study on stress relaxation of sandstones disturbed by dynamic loading

Authors: W. Zhu, S. Li, L. Niu, K. Liu, T. Xu

Citations: 29

Year: 2016

Experimental study on creep of double-rock samples disturbed by dynamic impact

Authors: S. Li, W. Zhu, L. Niu, K. Guan, T. Xu

Citations: 16

Year: 2021

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

Citations: 3

Year: 2019

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

Citations: 2

Year: 2022

An Experimental Study on Stress Relaxation of Yunnan Sandstone

Authors: S. Li, C. Zheng, P. Li

Citations: 1

Year: 2022

Investigating Surface Settlements During Shield Tunneling Using Numerical Analysis

Authors: R. He, Z. Zhou, S. Li, S. Vanapalli

Citations: Not available yet

Year: 2024

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

Citations: Not available yet

Year: 2024

 

 

Li Wang | Engineering | Best Scholar Award

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Li Wang | Engineering | Best Scholar Award

PHD Candiate at chongqing university, China.

Li Wang is a dedicated Ph.D. candidate at Chongqing University, specializing in electrical engineering with a focus on ice prevention and mitigation for power grids. His journey began with a B.S. in electrical engineering from Qilu University of Technology, followed by an M.S. from Sichuan University. His current research is embedded within the prestigious State Key Laboratory of Power Transmission Equipment and System Security and New Technology at Chongqing University. Li has completed three research projects, with his work published in respected journals such as Applied Thermal Engineering and Polymers. His research aims to improve power system resilience by addressing ice accumulation and insulator flashover issues. With practical experience in a State Grid Zhejiang Electric Power Co. project and a citation index of 28.5, he is emerging as a promising scholar in electrical engineering and insulation technology, with plans to continue advancing research to address industry challenges.

Profile👤

Google Scholar

Education 🎓

Li Wang completed his B.S. degree in electrical engineering from Qilu University of Technology in 2016, where he developed foundational knowledge in power systems and insulation technology. Pursuing further specialization, he earned his M.S. in electrical engineering from Sichuan University in 2019, deepening his understanding of energy transmission and system reliability. His educational background is characterized by a blend of theoretical and practical learning, equipping him to handle the challenges of power grid reliability and insulation in extreme conditions. Currently, he is a Ph.D. candidate at Chongqing University, where he is engaged with the State Key Laboratory, recognized for advancing research in power transmission security. His academic journey reflects a commitment to excellence in electrical engineering and energy infrastructure, with each step laying a foundation for his research into ice prevention and system safety.

Experience💼

Li Wang’s professional and academic experience is rooted in electrical engineering, with a focus on developing solutions to protect power systems from extreme weather. As a Ph.D. candidate at Chongqing University, he has contributed to three significant research projects, each aimed at enhancing the resilience of electrical insulation in ice-prone environments. He has also gained practical experience through his involvement in an industry project with State Grid Zhejiang Electric Power Co., which provided real-world insights into the application of his research. This blend of research and industry experience has allowed Li to apply theoretical knowledge to practical problems, particularly in addressing challenges related to ice formation on power infrastructure. His work has been featured in leading journals, showcasing his ability to contribute valuable insights to the field.

Research Interests 🔬

Li Wang’s research interests lie at the intersection of electrical engineering, material science, and environmental sustainability. He is particularly focused on developing innovative solutions for ice prevention and mitigation in power systems, which are critical for ensuring system reliability in regions prone to freezing temperatures. His work involves analyzing and improving the performance of insulators and power transmission equipment under icy conditions, with the goal of minimizing system failures and enhancing the durability of electrical infrastructure. Li is also interested in advancing knowledge on how environmental factors affect insulation performance, with implications for the future of power grid maintenance and resilience. His research is driven by a commitment to both scientific discovery and practical application, aiming to support the energy sector in adapting to increasingly challenging environmental conditions.

Awards and Honors 🏆

Li Wang has achieved notable academic milestones, underscored by a citation index of 28.5, demonstrating the impact of his research in electrical engineering. Although early in his career, his publications in esteemed journals like Applied Thermal Engineering, Plant Methods, and Polymers have established him as a promising researcher in insulation technology. His work on ice prevention for energy equipment addresses critical challenges faced by the power industry, and his contributions to three research projects have been well-recognized within his academic community. Additionally, his involvement in an industry project with State Grid Zhejiang Electric Power Co. highlights his ability to translate research into real-world applications. Li’s academic achievements and professional contributions underscore his potential as an emerging leader in the field of power grid safety and resilience.

Conclusion 🔚 

Li Wang’s research in preventing and mitigating ice damage in power grids has potential for real-world impact, making him a promising candidate for the Best Scholar Award. With future growth in collaborations and publications, he has a strong foundation to contribute significantly to his field.

Publications Top Notes 📚

Title: “Mechanism of self-recovery of hydrophobicity after surface damage of lotus leaf”
Authors: L. Wang, L. Shu, Q. Hu, X. Jiang, H. Yang, H. Wang, L. Rao
Journal: Plant Methods
Year: 2024
Citation Count: 3

Title: “Ultra-efficient and thermally-controlled atmospheric structure deicing strategy based on the Peltier effect”
Authors: L. Wang, L. Shu, Y. Lv, Q. Hu, L. Ma, X. Jiang
Journal: Applied Thermal Engineering
Year: 2024
Citation Count: 1

Ali Alshamrani | Engineering | Best Researcher Award

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Ali Alshamrani | Engineering | Best Researcher Award

Assistant professor at Taifuniversity, Saudi Arabia.

Dr. Ali M. Alshamrani is an accomplished mechanical engineer with a strong background in both academia and industry. Currently serving as an Assistant Professor at Taif University, his expertise lies in fluid mechanics, oil spill behavior, and renewable energy. His extensive research has led to multiple peer-reviewed publications in reputable journals, focusing on areas such as oil slick contraction and fragmentation, and renewable energy solutions like solar distillation. With a solid foundation in teaching and research, Dr. Alshamrani continues to contribute significantly to the advancement of mechanical engineering.

📚 Profile

Scopus

🎓 Education

Dr. Alshamrani earned his Ph.D. in Mechanical Engineering from the University of South Florida (USF) in 2022, graduating with an impressive GPA of 3.9/4.0. His doctoral studies focused on fluid mechanics, material science, and oil spill behavior. He also completed his M.Eng. at USF in 2018 with a GPA of 3.86/4.0, where he conducted research on material sciences and manufacturing processes. Dr. Alshamrani’s academic journey began with a B.Sc. in Mechanical Engineering from Umm Al Qura University in 2014, where he worked on a vortex tube cooler for his graduation project.

💼 Experience

Dr. Alshamrani’s experience spans both industry and academia. He completed internships at Saudi Aramco and King Abdullah & Al Salam Co., where he gained hands-on experience in refinery operations, aircraft maintenance, and construction projects. In academia, he has held teaching positions, including as a lecturer and lab instructor at Taif University, and as a teaching and research assistant at USF. Currently, as an Assistant Professor at Taif University, he teaches courses on fluid mechanics, heat transfer, and fluid dynamics while continuing his research in mechanical engineering.

🔬 Research Interests

Dr. Alshamrani’s research interests focus on fluid mechanics, oil spill dynamics, and renewable energy systems. His work has explored the contraction and fragmentation of crude oil slicks using chemical herders, an innovative approach to oil spill mitigation. He is also involved in research on the design and performance of wind turbines and solar distillers. His interest in combining mechanical engineering principles with environmental challenges positions him at the forefront of sustainable engineering solutions.

🏆 Awards and Honors

Throughout his academic career, Dr. Alshamrani has consistently demonstrated excellence, reflected in his high GPAs during his graduate studies. His research has been recognized at international conferences, including the American Physical Society’s Division of Fluid Dynamics meetings, where his work on oil spill dynamics was featured. Additionally, his contributions to the study of renewable energy technologies have garnered attention within the academic community, further cementing his reputation as a leading researcher in his field.

🔚 Conclusion

Dr. Ali M. Alshamrani is highly qualified for a Best Researcher Award due to his academic excellence, impactful research contributions, and teaching achievements. His expertise in mechanical engineering, particularly fluid mechanics and oil spill research, combined with his real-world industry experience, makes him a strong contender. Expanding his research scope and fostering international collaboration could further strengthen his candidacy in future awards.

Publications Top Notes 📚

Application of an AI-based optimal control framework in smart buildings using borehole thermal energy storage combined with wastewater heat recovery
Alshamrani, A., Abbas, H.A., Alkhayer, A.G., El-Shafay, A.S., Kassim, M.
Journal of Energy Storage, 2024, 101, 113824
Citations: 0

Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models
Alsehli, M., Basem, A., Jasim, D.J., Musa, V.A., Maleki, H.
Fuel, 2024, 374, 132431
Citations: 2

Enhancing pyramid solar still performance using suspended v-steps, reflectors, Peltier cooling, forced condensation, and thermal storing materials
Alshamrani, A.
Case Studies in Thermal Engineering, 2024, 61, 105109
Citations: 0

Conceptual design and optimization of integrating renewable energy sources with hydrogen energy storage capabilities
Zhao, Q., Basem, A., Shami, H.O., Ahmed, M., El-Shafay, A.S.
International Journal of Hydrogen Energy, 2024, 79, pp. 1313–1330
Citations: 1

Intelligent computing approach for the bioconvective peristaltic pumping of Powell–Eyring nanofluid: heat and mass transfer analysis
Akbar, Y., Huang, S., Alshamrani, A., Alam, M.M.
Journal of Thermal Analysis and Calorimetry, 2024, 149(15), pp. 8445–8462
Citations: 1

Dimensionless dynamics: Multipeak and envelope solitons in perturbed nonlinear Schrödinger equation with Kerr law nonlinearity
Afsar, H., Peiwei, G., Alshamrani, A., Alam, M.M., Aljohani, A.F.
Physics of Fluids, 2024, 36(6), 067126
Citations: 2

Intelligent computing for the electro-osmotically modulated peristaltic pumping of blood-based nanofluid
Akbar, Y., Çolak, A.B., Huang, S., Alshamrani, A., Alam, M.M.
Numerical Heat Transfer; Part A: Applications, 2024
Citations: 0

Neural network design for non-Newtonian Fe3O4-blood nanofluid flow modulated by electroosmosis and peristalsis
Akbar, Y., Huang, S., Alshamrani, A., Alam, M.M.
Modern Physics Letters B, 2024, 2450394
Citations: 1

Analysis of interfacial heat transfer coefficients in squeeze casting of AA6061 aluminum alloy with H13 steel die: Impact of section thickness on thermal behavior
Khawale, V.R., Alshamrani, A., Palanisamy, S., Sharma, M., Alrasheedi, N.H.
Thermal Science, 2024, 28(1), pp. 223–232
Citations: 0

Investigation of the performance of a double-glazing solar distiller with external condensation and nano-phase change material
Alshamrani, A.
Journal of Energy Storage, 2023, 73, 109075
Citations: 4

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