Xin Bai | Mechanical Engineering | Best Researcher Award

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Assist. Prof. Dr. Xin Bai | Mechanical Engineering
|Best Researcher Award

Assist. Prof. Institute of Metal Research, Chinese Academy of Sciences, China.

 

Assoc. Prof. Dr. Xin Bai is a distinguished researcher at the Institute of Metal Research, Chinese Academy of Sciences, and a member of the Youth Innovation Promotion Association. Renowned for his pioneering work in fatigue fracture and structural reliability, Dr. Bai has significantly advanced methods for predicting fatigue performance from minimal experimental data. His research is both innovative and impactful, addressing critical needs in materials engineering and structural integrity. His commitment to developing cost-effective and efficient reliability assessment tools and software has garnered recognition across academia and industry. Dr. Bai’s sustained research excellence, leadership, and contributions to cutting-edge reliability science make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

 

Assoc. Prof. Dr. Xin Bai is a distinguished researcher at the Institute of Metal Research, Chinese Academy of Sciences, and a member of the Youth Innovation Promotion Association. Renowned for his pioneering work in fatigue fracture and structural reliability, Dr. Bai has significantly advanced methods for predicting fatigue performance from minimal experimental data. His research is both innovative and impactful, addressing critical needs in materials engineering and structural integrity. His commitment to developing cost-effective and efficient reliability assessment tools and software has garnered recognition across academia and industry. Dr. Bai’s sustained research excellence, leadership, and contributions to cutting-edge reliability science make him a compelling candidate for the Best Researcher Award.

🎓 Education 

Dr. Xin Bai received comprehensive training in materials science and engineering, culminating in his doctoral studies at the prestigious Institute of Metal Research, Chinese Academy of Sciences (CAS). His academic path reflects a strong foundation in mechanical behavior, fracture mechanics, and fatigue analysis. He has also engaged in postdoctoral research and advanced studies in failure physics, enhancing his expertise in structural reliability. His educational journey combined rigorous scientific coursework with hands-on research in laboratory environments, allowing him to acquire the necessary skills for leading complex experimental and theoretical investigations. His continued affiliation with CAS exemplifies the high caliber of his education and research orientation.

🏢 Work Experience 

Dr. Xin Bai serves as an Associate Professor at the Institute of Metal Research, Chinese Academy of Sciences, and is actively involved in advanced fatigue and reliability studies. His professional journey includes extensive experience in developing fatigue reliability methods based on physical failure mechanisms, small-scale testing, and predictive modeling. He has led multiple research projects focusing on translating laboratory-scale data into accurate, full-scale structural performance assessments. His work integrates mechanical engineering, software development, and statistical modeling to address real-world engineering problems. As a member of the Youth Innovation Promotion Association of CAS, he collaborates with leading scientists nationwide, contributing to China’s strategic goals in materials reliability and engineering safety.

🏅 Awards and Honors 

Dr. Xin Bai has been honored as a member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences—an elite recognition awarded to promising young scientists. This distinction underscores his contributions to material reliability and fatigue research. He has received accolades for his innovative research methods and impactful findings, with invitations to present at top conferences and collaborations with national-level research teams. His software development efforts for fatigue prediction have been adopted in academic and industrial settings, further establishing his influence in the field. His work continues to earn national and institutional praise, positioning him among China’s rising stars in materials science and engineering.

🔬 Research Focus 

Dr. Xin Bai’s research centers on developing low-cost, high-efficiency methods for assessing fatigue reliability based on failure physics. His focus areas include: (1) structural fatigue reliability assessment using minimal testing data, enabling accurate predictions without extensive experimentation; (2) techniques for extrapolating full-scale component fatigue performance from small specimen data, significantly reducing testing time and cost; and (3) software development to support fatigue fracture analysis and reliability modeling. His interdisciplinary approach combines materials science, mechanical engineering, and data-driven modeling to advance the understanding and prediction of structural behavior under cyclic loads. His innovations have broad applications in aerospace, automotive, and infrastructure industries, helping ensure long-term structural safety and performance.

📊 Publication Top Notes:

  • Song Zhou; Zhaoxing Qian; Xin Bai (2024). Static properties evaluation for laser deposition repaired TA15 components based on a constitutive model considering annealing heat treatment. Engineering Failure Analysis.

  • Xin Bai; Peng Zhang; Shuo Liu; Rui Liu; Bingfeng Zhao; Zhefeng Zhang (2023). Fatigue strength prediction of large-size component through size effect measurement and determination. International Journal of Fatigue.

  • X. Bai; P. Zhang; Q. Wang; R. Liu; Z. J. Zhang; Q. Q. Duan; E. N. Yang; H. Bo; Z. F. Zhang (2022). A New Dominance Distribution Method to Select Materials with Higher Fatigue Resistance under Property Scatter and Load Uncertainty. Journal of Materials Engineering and Performance.

  • Zhiming Xie; Peng Wang; Bin Wang; P. Zhang; Xin Bai; Zhefeng Zhang (2022). Effects of Heat Treatment on Fatigue Properties of Double Vacuum Smelting High‐Carbon Chromium‐Bearing Steel. Advanced Engineering Materials.

  • Shuo LIU; Bin Wang; P. Zhang; Xin Bai; Qiqiang Duan; Xuegang Wang; Zhefeng Zhang (2022). The Effect of Microstructure Inhomogeneity on Fatigue Property of EA4T Axle Steel. steel research international.

  • Bingfeng Zhao; Liyang Xie; Yu Zhang; Jungang Ren; Xin Bai; Bo Qin (2021). An improved dynamic load-strength interference model for the reliability analysis of aero-engine rotor blade system. Journal of Aerospace Engineering.

  • Lei Wang; Bingfeng Zhao; Lei Wang; Zhiyong Hu; Song Zhou; Xin Bai (2021). A new multiaxial fatigue life prediction model for aircraft aluminum alloy. International Journal of Fatigue.

  • Xin Bai; Peng Zhang; Enna Yang; Qiqiang Duan; Hao Bo; Zhefeng Zhang (2020). Dominance distributions for fatigue performance of materials and its application in material selection. Preprint on Authorea.

  • Xin Bai; Peng Zhang; Zhen‐jun Zhang; Rui Liu; Zhe‐feng Zhang (2019). New method for determining P‐S‐N curves in terms of equivalent fatigue lives. Fatigue & Fracture of Engineering Materials & Structures.

  • Xin Bai; Liyang Xie; Ruijin Zhang; Ruoyi Guan; Anshi Tong; Enjun Bai (2017). Measurement and estimation of probabilistic fatigue limits using Monte-Carlo simulations. International Journal of Fatigue.

Mehmet Senturk |Structural Engineering | Best Researcher Award

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Dr. Mehmet Senturk | Structural Engineering
| Best Researcher Award

 

Tutor in Engineering at Coventry University, United Kingdom .

Dr. Mehmet Senturk is a distinguished engineering tutor at Coventry University, with a PhD in Structural Engineering. His work integrates seismic design, finite element analysis, and sustainable construction, bridging academic innovation with industrial application. With over ten years of global academic and consultancy experience, Dr. Senturk has led and collaborated on funded research projects, produced high-impact publications, and holds several national patents. His expertise spans structural health monitoring, sensor technologies, image processing, and additive manufacturing. His interdisciplinary approach enhances structural resilience and digital engineering. With 140 citations and an h-index of 6, Dr. Senturk’s commitment to cutting-edge innovation and international collaboration makes him an ideal candidate for the Best Researcher Award.

🌍 Professional Profile:

Orcid 

Scopus

Google scholar

🏆 Suitability for the Best Researcher Award

Dr. Mehmet Senturk exemplifies research excellence through his impactful contributions to structural and earthquake engineering. With a PhD in Structural Engineering and over a decade of academic and industry experience, he has led pioneering work in seismic design, sustainable structures, and smart monitoring technologies. His three national patents, 140+ Google Scholar citations, and extensive publication record in top-tier journals showcase his innovative approach and commitment to advancing engineering science. Dr. Senturk’s interdisciplinary skills—spanning robotics, image processing, and additive manufacturing—have fueled international collaborations and transformative research projects. His ability to bridge theory with real-world applications makes him a leader in engineering innovation and a highly deserving candidate for the Best Researcher Award.

🎓 Education 

Dr. Mehmet Senturk’s academic journey reflects a strong foundation in civil and structural engineering. He holds a PhD in Structural Engineering, where his research focused on advanced modeling and resilience of structural systems under seismic and thermal loads. His MSc in Earthquake Engineering provided expertise in seismic risk mitigation, retrofitting, and dynamic analysis. He began his academic pursuit with a BSc in Civil Engineering, establishing core competencies in materials science, construction practices, and geotechnical fundamentals. This progression has allowed Dr. Senturk to integrate theory with real-world applications, culminating in a comprehensive educational background ideal for interdisciplinary research and innovation in structural and sustainable engineering.

🏢 Work Experience 

Dr. Mehmet Senturk has over a decade of combined academic and industrial experience. He currently serves as a Tutor in Engineering at Coventry University, where he mentors future engineers and contributes to pioneering research. His career includes contributions to over 20 industry projects, with a focus on structural diagnostics, seismic assessment, and smart infrastructure systems. Dr. Senturk has collaborated with institutions such as the University of Sheffield, Istanbul Technical University, and Istanbul Rumeli University. His cross-functional work includes the design and testing of cold-formed steel, bolted precast systems, and high-temperature-resistant components. His experience spans robotics, sensor integration, and image processing, positioning him at the intersection of civil, digital, and structural engineering.

🏅 Awards and Honors 

Dr. Mehmet Senturk’s research achievements have earned national recognition through multiple Turkish patents, reflecting his contributions to innovative structural systems and testing technologies. His patented inventions include a two-piece high-temperature test furnace and advanced connection systems for reinforced concrete. He has been a prolific reviewer for leading journals such as Engineering Structures and Structures (Elsevier), completing over 30 peer-reviews. His role in collaborative projects with renowned academics from institutions like the University of Sheffield and Istanbul Technical University highlights his influence in global research. With 140 Google Scholar citations and an h-index of 6, Dr. Senturk’s consistent excellence in research, collaboration, and innovation underlines his strong suitability for awards recognizing outstanding research contributions.

🔬 Research Focus 

Dr. Mehmet Senturk’s research focuses on enhancing structural resilience through the integration of traditional civil engineering with advanced digital tools. His core areas include seismic performance of structures, finite element modeling, and sustainable construction. He investigates structural systems under complex load conditions—thermal, seismic, and axial—using both experimental and numerical methods. His research incorporates sensor technologies, structural health monitoring, and robotics platforms like Arduino and Raspberry Pi for real-time diagnostics. He is also active in additive manufacturing and digital prototyping of test systems. Dr. Senturk’s work supports the development of smarter, safer infrastructure through interdisciplinary innovation, evidenced by his patents, publications in top-tier journals, and ongoing collaborations across Europe and Turkey.

📊 Publication Top Notes:

  1. Senturk, M., Ilki, A., & Hajirasouliha, I. (2025).
    Replaceable monolithic-like beam-to-beam precast connection for RC frames: Concept development and design procedure.
    Structures.
    https://doi.org/10.1016/j.istruc.2025.108875

  2. Öztürk, F., Mojtabaei, S. M., Senturk, M., Pul, S., & Hajirasouliha, I. (2022).
    Buckling behaviour of cold-formed steel sigma and lipped channel beam–column members.
    Thin-Walled Structures, 173, 108963.
    https://doi.org/10.1016/j.tws.2022.108963

  3. Pul, S., Senturk, M., Ilki, A., & Hajirasouliha, I. (2021).
    Experimental and numerical investigation of a proposed monolithic-like precast concrete column-foundation connection.
    Engineering Structures, 239, 113090.
    https://doi.org/10.1016/j.engstruct.2021.113090

  4. Pul, S., Atasoy, A., Senturk, M., & Hajirasouliha, I. (2021).
    Structural performance of reinforced concrete columns subjected to high-temperature and axial loading under different heating-cooling scenarios.
    Journal of Building Engineering, 43, 102477.
    https://doi.org/10.1016/j.jobe.2021.102477

  5. Senturk, M., Pul, S., Ilki, A., & Hajirasouliha, I. (2020).
    Development of a monolithic-like precast beam-column moment connection: Experimental and analytical investigation.
    Engineering Structures, 206, 110057.
    https://doi.org/10.1016/j.engstruct.2019.110057

  6. Pul, S., & Senturk, M. (2017).
    A bolted moment connection model for precast column-beam joint.
    World Congress on Civil, Structural, and Environmental Engineering.
    https://doi.org/10.11159/icsenm17.129

Hazrat Bilal | Robotics Engineering | Young Scientist Award

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Dr. Hazrat Bilal |Robotics Engineering
| Young Scientist Award

Postdoctoral Research Fellow at Shenzhen University, China .

Dr. Hazrat Bilal is a highly accomplished researcher and Postdoctoral Fellow at Shenzhen University, China. With a Ph.D. in Control Science & Engineering from the University of Science & Technology of China, he brings exceptional expertise in intelligent robotics, deep learning, and fault diagnosis. Dr. Bilal has authored over 30 high-impact publications and has earned 1200+ citations (h-index: 12). His outstanding academic record is complemented by rich industry experience and several prestigious fellowships and awards. A dedicated IEEE member and registered engineer with PEC, he is passionate about pushing the boundaries of intelligent automation. His remarkable early-career accomplishments and leadership in research make him a strong candidate for the Young Scientist Award.

🌍 Professional Profile:

Scopus 

Orcid

Google scholar

🏆 Suitability for the Best Researcher Award


Dr. Hazrat Bilal is a highly deserving candidate for the Young Scientist Award, with an exceptional academic record (Ph.D., USTC, GPA 3.88/4.00; M.S., NUST, GPA 3.85/4.00). As a Postdoctoral Fellow at Shenzhen University, he has made significant contributions to intelligent robotics, fault diagnosis, and AI-driven control systems. With over 15 high-impact Q1/Q2 journal publications—including in the IEEE IoT Journal, Soft Computing, Bioengineering, and Human-centric Computing—his research demonstrates innovation and practical value. His interdisciplinary work in blockchain-enabled IoRT, fuzzy-ADRC control, and AI for medical and UAV systems reflects his leadership in emerging technologies. With over 1200 citations and global collaborations, Dr. Bilal stands out as a promising young researcher committed to advancing intelligent and sustainable technologies.

🎓 Education 

Dr. Hazrat Bilal’s academic journey reflects a commitment to excellence in engineering and research. He earned his Ph.D. in Control Science & Engineering from the University of Science & Technology of China (2018–2024), maintaining an exceptional GPA of 3.88/4.00. Prior to that, he completed his Master’s degree in the same field at Nanjing University of Science & Technology (2015–2018) with a GPA of 3.85/4.00 and received an Outstanding Graduate Certificate. His undergraduate studies in Electrical (Electronics) Engineering were completed at FUUAST Islamabad, Pakistan (2010–2014), earning a GPA of 3.18/4.00. This strong academic foundation underpins his work in robotics, automation, and advanced control systems.

🏢 Work Experience 

Dr. Bilal brings interdisciplinary and global experience from academia and industry. He worked as a Performance Software Engineer at ZF Friedrichshafen, Shanghai, where he developed vision algorithms and optimized embedded system performance. Prior to that, he served as a Telecom Engineer at Real Solution Pvt. Ltd., Pakistan, contributing to 3G system installations and RF optimization. He also worked at Dargai Hydropower Plant as a Control Engineer, where he led power generation and preventive maintenance operations. Additionally, he completed internships and technical training at the National Telecommunication Corporation, University of Tennessee (USA), and CAE Pakistan. These roles have refined his expertise in automation, control systems, robotics, and smart grid technologies.

🏅 Awards and Honors 

Dr. Hazrat Bilal has received numerous awards recognizing his academic excellence and innovation. He is a recipient of the CAS-TWAS Fellowship Award at USTC and the NMG-NUST Joint Scholarship for his master’s studies. His exceptional performance earned him the Outstanding Graduate Certificate from Nanjing University of Science & Technology. As an undergraduate, he was awarded scholarships from Akhuwat Foundation and Pakistan Bait-ul-Mal, along with a Prime Minister’s Laptop under the national scheme. He secured funding for his final-year project through the National Grassroots ICT R&D Program. Dr. Bilal also received a Letter of Appreciation from IEEE FUUAST and continues to excel as an IEEE member and professional contributor to engineering societies.

🔬 Research Focus 

Dr. Hazrat Bilal’s research lies at the intersection of robotic control systems, artificial intelligence, and fault diagnosis. His recent works emphasize hybrid deep learning algorithms for the Internet of Robotic Things (IoRT), adaptive control of flexible manipulators, and trajectory tracking. He has proposed advanced techniques integrating fuzzy logic, ADRC, CNNs, LSTMs, and blockchain for secure, intelligent, and resilient systems. His current research includes generative AI for traffic simulation, nanorobot control, and intelligent medical diagnosis using AI. Dr. Bilal’s work contributes significantly to the future of smart manufacturing, healthcare, and autonomous vehicles. His focus on interdisciplinary, experimental, and data-driven approaches positions him as a leader in intelligent control systems.

📊 Publication Top Notes:

  • Title: A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things
    Authors: H. Xu, Z. Sun, Y. Cao, H. Bilal
    Journal: Soft Computing, 27(19), 14469–14481
    Citations: 172
    Year: 2023

  • Title: Pruning filters with L1-norm and capped L1-norm for CNN compression
    Authors: A. Kumar, A. M. Shaikh, Y. Li, H. Bilal, B. Yin
    Journal: Applied Intelligence, 51(2), 1152–1160
    Citations: 147
    Year: 2021

  • Title: A practical study of active disturbance rejection control for rotary flexible joint robot manipulator
    Authors: H. Bilal, B. Yin, M. S. Aslam, Z. Anjum, A. Rohra, Y. Wang
    Journal: Soft Computing, 27(8), 4987–5001
    Citations: 128
    Year: 2023

  • Title: Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach
    Authors: H. Bilal, B. Yin, A. Kumar, M. Ali, J. Zhang, J. Yao
    Journal: Soft Computing, 27(7), 4029–4039
    Citations: 118
    Year: 2023

  • Title: Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties
    Authors: H. Bilal, W. Yao, Y. Guo, Y. Wu, J. Guo
    Conference: 2017 36th Chinese Control Conference (CCC), 4192–4197
    Citations: 115
    Year: 2017

  • Title: Real-time lane detection and tracking for advanced driver assistance systems
    Authors: H. Bilal, B. Yin, J. Khan, L. Wang, J. Zhang, A. Kumar
    Conference: 2019 Chinese Control Conference (CCC), 6772–6777
    Citations: 101
    Year: 2019

  • Title: Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles
    Authors: Q. Wu, X. Li, K. Wang, H. Bilal
    Journal: Soft Computing, 27(23), 18195–18213
    Citations: 90
    Year: 2023

  • Title: Reduction of multiplications in convolutional neural networks
    Authors: M. Ali, B. Yin, A. Kunar, A. M. Sheikh, H. Bilal
    Conference: 2020 39th Chinese Control Conference (CCC), 7406–7411
    Citations: 85
    Year: 2020

  • Title: Second-order convolutional network for crowd counting
    Authors: L. Wang, Q. Zhai, B. Yin, H. Bilal
    Conference: Fourth International Workshop on Pattern Recognition, 11198, 158–163
    Citations: 83
    Year: 2019

  • Title: A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways
    Authors: H. Dou, Y. Liu, S. Chen, H. Zhao, H. Bilal
    Journal: Soft Computing, 27(21), 16373–16388
    Citations: 82
    Year: 2023

  • Title: Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection
    Authors: M. Ali, B. Yin, H. Bilal, A. Kumar, A. M. Shaikh, A. Rohra
    Journal: Multimedia Tools and Applications, 83(12), 36307–36327
    Citations: 50
    Year: 2024