Genfeng Liu | Engineering | Best Researcher Award

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Dr. Genfeng Liu | Engineering | Best Researcher Award

Research Scholar at Henan University of Technology, China

Genfeng Liu is a highly qualified candidate for the Best Researcher Award, with a strong background in control science and engineering, specializing in data-driven control, adaptive control, and fault-tolerant systems. His research spans intelligent transportation, multiagent systems, and nonlinear systems, contributing to high-impact IEEE journals such as IEEE Transactions on Cybernetics (IF: 19.118) and IEEE Transactions on Neural Networks and Learning Systems (IF: 14.255). As a reviewer for leading journals, he holds strong academic credibility. His work on model-free adaptive control and cybersecurity applications demonstrates real-world relevance. To enhance his profile, he could expand international collaborations, increase industry applications, and lead large-scale research projects. While his contributions are highly significant, further engagement in technology transfer and interdisciplinary research would strengthen his impact. Overall, his extensive publication record and research influence make him a strong contender for the award, with potential for even greater contributions in the future.

Professional Profile

Education

Genfeng Liu received his Ph.D. in Control Science and Engineering from Beijing Jiaotong University, China, in 2021. His doctoral research focused on advanced control methodologies, including data-driven control, iterative learning control, and fault-tolerant control, which have significant applications in intelligent transportation and nonlinear systems. Throughout his academic journey, he developed expertise in adaptive control and multiagent systems, contributing to cutting-edge research in automation and cybernetics. His education provided a strong foundation in both theoretical and applied control engineering, enabling him to publish in prestigious IEEE journals. Additionally, his academic background equipped him with the analytical and problem-solving skills necessary to address complex challenges in system automation and intelligent control. His commitment to continuous learning and research excellence is evident in his contributions to high-impact scientific literature and his role as a reviewer for renowned international journals, solidifying his reputation as an expert in his field.

Professional Experience

Genfeng Liu is currently a Lecturer at the College of Electrical Engineering, Henan University of Technology, Zhengzhou, China. His professional experience revolves around advanced control engineering, with a focus on data-driven control, adaptive control, and fault-tolerant systems. As a researcher, he has made significant contributions to intelligent transportation systems, multiagent systems, and nonlinear control, publishing extensively in high-impact IEEE journals. Beyond his research, he actively participates in academic peer review for prestigious journals such as IEEE Transactions on Cybernetics and IEEE Transactions on Intelligent Vehicles, reinforcing his role as a respected scholar in the field. His expertise extends to supervising students and collaborating on interdisciplinary projects, bridging the gap between theoretical advancements and practical applications. His ongoing work in model-free adaptive control and cybersecurity-related control mechanisms further strengthens his impact in academia and industry, positioning him as a leader in modern control systems and intelligent automation research.

Research Interest

Genfeng Liu’s research interests lie in advanced control engineering, with a strong focus on data-driven control, adaptive control, and fault-tolerant control. His work explores iterative learning control and model-free adaptive control techniques, particularly in applications related to intelligent transportation systems, nonlinear systems, and multiagent systems. He is also interested in cybersecurity aspects of control systems, such as defense mechanisms against false data injection attacks. His research aims to enhance the efficiency, safety, and reliability of automation in modern transportation and industrial systems. By integrating artificial intelligence with control theory, he seeks to develop innovative solutions for complex, real-world engineering challenges. His studies have been published in top-tier journals, reflecting his commitment to advancing theoretical and applied knowledge in control science. Additionally, his expertise in intelligent transportation and system optimization continues to drive impactful contributions to the fields of automation, cybernetics, and industrial informatics.

Award and Honor

Genfeng Liu has received several accolades and recognition for his outstanding contributions to the field of control science and engineering. His research publications in prestigious IEEE journals, such as IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems, have earned him significant recognition within the academic community. As an active reviewer for renowned international journals, he has been acknowledged for his critical evaluations and contributions to the peer-review process. His innovative work in data-driven control, adaptive control, and fault-tolerant systems has positioned him as a leading researcher in intelligent transportation and nonlinear systems. Additionally, his participation in high-profile conferences and collaborations with esteemed researchers further highlight his impact in the field. While his research achievements are commendable, pursuing national and international research grants and awards would further enhance his recognition and establish him as a distinguished leader in control engineering and automation.

Research Skill

Genfeng Liu possesses strong research skills in advanced control engineering, specializing in data-driven control, adaptive control, and fault-tolerant control. He is proficient in developing and implementing iterative learning control and model-free adaptive control strategies for complex nonlinear and multiagent systems. His expertise extends to intelligent transportation systems, where he applies innovative control techniques to enhance automation and safety. He is highly skilled in mathematical modeling, algorithm development, and system optimization, enabling him to solve real-world engineering challenges effectively. His ability to conduct in-depth theoretical analysis and translate findings into practical applications is evident in his numerous high-impact publications in top-tier IEEE journals. Additionally, his experience as a reviewer for prestigious academic journals demonstrates his critical thinking and analytical skills. His research capabilities, combined with his ability to collaborate on interdisciplinary projects, make him a valuable contributor to the fields of cybernetics, automation, and industrial informatics.

Conclusion

Genfeng Liu is a highly suitable candidate for the Best Researcher Award due to his exceptional research output, high-impact publications, and contributions to control engineering and intelligent transportation systems. To further strengthen his candidacy, increasing international collaborations, practical industry applications, and leadership roles in large-scale projects would make his research even more impactful.

Publications Top Noted

  • Title: Improved Model-Free Adaptive Predictive Control for Nonlinear Systems with Quantization Under Denial of Service Attacks
    Authors: Genfeng Liu, Jinbao Zhu, Yule Wang, Yangyang Wang
    Year: 2025
    Citation: DOI: 10.3390/sym17030471

  • Title: Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2024
    Citation: DOI: 10.1109/TNNLS.2022.3185080

  • Title: Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2022
    Citation: DOI: 10.1109/TCYB.2020.2986006

  • Title: RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TSMC.2019.2957299

  • Title: Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TITS.2020.2970000

  • Title: A Model-Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude
    Authors: Gaoyang Jiang, Genfeng Liu, Hansong Yu
    Year: 2021
    Citation: DOI: 10.3390/sym13020347

  • Title: A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems
    Authors: Xian Yu, Shangtai Jin, Genfeng Liu, Ting Lei, Ye Ren
    Year: 2020
    Citation: DOI: 10.1109/ACCESS.2020.3038629

  • Title: Model-Free Adaptive Direct Torque Control for the Speed Regulation of Asynchronous Motors
    Authors: Ziwei Zhang, Shangtai Jin, Genfeng Liu, Zhongsheng Hou, Jianmin Zheng
    Year: 2020
    Citation: DOI: 10.3390/pr8030333

Azhar Ali | Engineering | Best Researcher Award

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Dr. Azhar Ali | Engineering | Best Researcher Award

Researcher at Dalian university of technology,China

Dr. Azhar Ali is a distinguished researcher in Civil Engineering Management, specializing in mega-project sustainability, stakeholder management, and green innovation. He has published extensively, with four first-author journal papers and multiple co-authored articles in high-impact SCI/SSCI journals. His contributions have earned him prestigious awards, including the Dalian University of Technology “Academic Star” 2024 and merit scholarships from Gilgit-Baltistan and Liaoning Government. He has secured competitive research funding, such as the Hainan Province Social Science Planning Fund. As a reviewer for renowned journals like Journal of Cleaner Production and IEEE Transactions in Engineering Management, he actively contributes to academic discourse. Beyond research, Dr. Ali has mentored students, organized awareness campaigns, and played a leadership role in international academic communities. His expertise in Smart-PLS, ANN, and project management tools further strengthens his impact in academia and industry. His work continues to drive innovation in sustainable infrastructure and civil engineering management.

Professional Profile 

Education

Dr. Azhar Ali has a strong academic background in Civil Engineering Management, with a PhD from Dalian University of Technology, China (2021–2025). Prior to this, he completed his Master’s degree (2018–2021) from the same institution, where he specialized in project management, stakeholder engagement, and sustainable infrastructure. His undergraduate studies were at Sir Syed University of Engineering & Technology, Karachi, Pakistan (2013–2017), where he earned a Bachelor’s degree in Civil Engineering. Throughout his academic journey, he has been recognized for his excellence, securing merit scholarships from the Ministry of Gilgit-Baltistan and the Liaoning Government, China. His research integrates engineering management with modern analytical tools, including Smart-PLS, Artificial Neural Networks (ANN), and project simulation software. With a commitment to academic excellence and innovation, Dr. Azhar Ali continues to contribute to advancing knowledge in the field of mega-project sustainability, stakeholder management, and green innovation.

Professional Experience

Dr. Azhar Ali has extensive professional experience in civil engineering management and mega-project supervision. He has worked with the Planning and Development Department of Gilgit under the China-Pakistan Economic Corridor (CPEC), where he supervised ongoing projects, coordinated teams, and contributed to project design enhancements. Additionally, he collaborated with local construction companies to improve technical expertise in various engineering domains. His experience also includes working with Firdous Ahmed Govt. Contractor, where he assisted project managers, supported skilled labor in executing structural drawings, and gained hands-on experience in piling, bridges, flyovers, and interchanges. Dr. Ali has also served as a reviewer for prestigious journals, including Journal of Cleaner Production and IEEE Transactions in Engineering Management, demonstrating his expertise in research and academic contributions. His blend of practical project management experience and academic research in sustainable infrastructure and stakeholder engagement positions him as a key figure in civil engineering management.

Research Interest

Dr. Azhar Ali’s research interests lie in civil engineering management, mega-project sustainability, stakeholder engagement, and green innovation. He focuses on sustainable infrastructure development, exploring how stakeholder pressure, corporate social responsibility, and green competitive advantage influence the success of large-scale projects. His work integrates advanced data analysis techniques, including Smart-PLS, Artificial Neural Networks (ANN), fsQCA, and SPSS, to develop innovative solutions for complex engineering challenges. Dr. Ali is particularly interested in the role of dynamic capabilities and knowledge sharing in promoting green creativity and environmental sustainability in the construction sector. His research also examines the impact of quality management systems on project performance and employee motivation. Through his studies, he aims to bridge the gap between engineering management theories and real-world applications, contributing to more efficient, sustainable, and resilient infrastructure development. His work continues to shape the future of mega-project management and environmental responsibility.

Award and Honor

Dr. Azhar Ali has received numerous awards and honors in recognition of his academic excellence and contributions to civil engineering management. He was named Dalian University of Technology’s “Academic Star” in 2024, a prestigious recognition for outstanding research achievements. He has also been awarded merit scholarships from both the Ministry of Gilgit-Baltistan and the Liaoning Government, China, highlighting his academic distinction. Beyond his research accolades, Dr. Ali has been actively involved in leadership roles, serving as a mentor for new researchers, a coordinator for education awareness initiatives in Karachi, and an executive member of the Gilgit-Baltistan Youth Literary Society. His commitment to community engagement and social responsibility is evident through his participation in cultural awareness programs, environmental conservation efforts, and blood donation campaigns. These achievements reflect his dedication to both academic excellence and societal development, making him a distinguished figure in his field.

Research Skill

Dr. Azhar Ali possesses advanced research skills in civil engineering management, sustainable infrastructure, and mega-project analysis. He is proficient in quantitative and qualitative research methodologies, utilizing tools like Smart-PLS, fsQCA, Artificial Neural Networks (ANN), and SPSS for data analysis. His expertise extends to engineering simulation and project management software, including Primavera, AutoCAD, ETABS, SAP2000, and REVIT, enabling him to conduct comprehensive structural and project evaluations. Dr. Ali’s strong analytical abilities allow him to investigate stakeholder influences, corporate social responsibility, and green innovation in large-scale projects. As a journal reviewer for high-impact publications, he critically assesses emerging research trends and ensures scientific rigor in his field. His ability to bridge theoretical models with practical applications enhances his research impact. Through his multidisciplinary approach, Dr. Ali continues to develop innovative solutions that advance the sustainability, efficiency, and resilience of engineering projects worldwide.

Conclusion

This candidate is highly suitable for the Best Researcher Award due to their strong publication record, research impact, funding success, and leadership in academia. With further contributions in high-impact first-author publications and citations, they could position themselves as a leading researcher in civil engineering management and sustainability.

Publications Top Noted

  • Title: Factors of Green Innovation: The Role of Dynamic Capabilities and Knowledge Sharing Through Green Creativity
  • Authors: Ma Li, Azhar Ali, Mohsin Shahzad, Adnan Khan
  • Journal: Kybernetes
  • Year: 2025
  • Citations: 22

Babatunde Ogunbayo | Engineering | Best Researcher Award

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Mr. Babatunde Ogunbayo | Engineering | Best Researcher Award

Research Student at aof Engineering and Built Environment, University of Johannesburg, South Africa

Mr. Babatunde Ogunbayo is an accomplished professional specializing in Quantity Surveying and Construction Management with a robust academic and industry background. His expertise spans project cost management, budgeting, contract administration, and construction project planning, making him an essential contributor to his field. Known for his analytical approach, Ogunbayo has played key roles in various construction projects, ensuring efficient resource allocation and cost control. He is also actively engaged in academia, providing guidance to students and participating in research to advance construction management practices. Through his work, he bridges the gap between theory and practice, enabling future industry professionals to gain insights grounded in real-world applications. Mr. Ogunbayo’s contributions, marked by a strong commitment to quality and precision, have positioned him as a respected figure in the field, impacting both industry standards and educational practices in construction management.

Professional Profile

Education

Mr. Babatunde Ogunbayo has a strong academic foundation in Quantity Surveying and Construction Management, which underpins his expertise and professional contributions. His educational journey includes advanced studies in these fields, equipping him with critical skills in cost estimation, budgeting, and contract management. Through rigorous training, he has developed a keen analytical perspective essential for project planning and resource allocation. His academic qualifications not only reflect his commitment to excellence but also support his active involvement in research and teaching, where he imparts valuable knowledge to emerging professionals in construction management. Mr. Ogunbayo’s educational background aligns seamlessly with his hands-on experience, allowing him to effectively bridge theoretical concepts with practical applications. This blend of education and practical experience makes him a knowledgeable resource in Quantity Surveying, enabling him to uphold high standards of precision and efficiency in his field.

Professional Experience

Mr. Babatunde Ogunbayo brings extensive professional experience in Quantity Surveying and Construction Management, marked by his roles in cost management, budgeting, and contract administration on diverse projects. Known for his expertise in efficient resource allocation and cost control, Ogunbayo has overseen various stages of construction projects, from planning and estimation to project execution and post-completion review. His hands-on approach and attention to detail ensure that projects adhere to budget and quality standards, while his strategic insights contribute to optimizing workflows and minimizing waste. Alongside his industry roles, Ogunbayo is active in academia, where he mentors students and contributes to research in construction management, furthering best practices in the field. His combination of technical skills and project oversight experience has earned him a reputation as a reliable and effective leader in the industry, making a lasting impact on both practical and academic circles in construction management.

Research Interests

Mr. Babatunde Ogunbayo’s research interests center on advancing construction management practices, with a particular focus on cost control, project efficiency, and sustainable building solutions. He is deeply invested in exploring methods to optimize resource allocation and reduce waste, aiming to improve the financial and environmental impact of construction projects. His work emphasizes the integration of innovative cost-estimation models and advanced budgeting techniques, providing frameworks for more accurate financial forecasting in large-scale projects. Additionally, Ogunbayo is interested in sustainable construction, investigating materials and methods that minimize ecological footprints without compromising quality. His research also includes developing strategies for effective contract management and exploring digital tools to streamline project management processes. Through these efforts, Ogunbayo contributes to building industry knowledge, fostering practices that support both economic efficiency and environmental responsibility, and positioning him as a forward-thinking leader in construction research.

Awards and Honors

Mr. Babatunde Ogunbayo has received notable awards and honors that underscore his contributions and commitment to excellence in Quantity Surveying and Construction Management. His accolades highlight both his technical skills and leadership qualities in the field. Recognized for his expertise in cost management, budgeting, and resource optimization, Ogunbayo has been celebrated for his role in enhancing project efficiency and precision in construction practices. His dedication to advancing sustainable and innovative construction solutions has also earned him industry acknowledgment. Additionally, his academic achievements and involvement in mentoring emerging professionals have been commended by his peers, reflecting his influence in both educational and professional circles. These awards underscore Ogunbayo’s impact on construction management, recognizing his contributions to developing high standards in cost control and contract administration, as well as his commitment to fostering growth and knowledge within the industry.

Conclusion

Bamgbose is a strong candidate for the Research for Excellence in Best Researcher Award based on his extensive experience, academic achievements, and contributions to construction management and building technology. His impact on the field is evident through his hands-on project management roles and commitment to industry standards. Addressing areas like research publications and international certifications would further enhance his qualifications and elevate his standing in the competitive landscape for research awards.

Publication Top Noted

  • Title: Inhibiting Factors to the Implementation of Preferential Procurement Policy in the South African Construction Industry
    Authors: Tau, L.J., Ogunbayo, B.F., Aigbavboa, C.O.
    Year: 2024
    Citations: 0
  • Title: A Systematic Review of the Applications of AI in a Sustainable Building’s Lifecycle
    Authors: Adewale, B.A., Ene, V.O., Ogunbayo, B.F., Aigbavboa, C.O.
    Year: 2024
    Citations: 1
  • Title: Barriers to Building Information Modelling Adoption in Small and Medium Enterprises: Nigerian Construction Industry Perspectives
    Authors: Bamgbose, O.A., Ogunbayo, B.F., Aigbavboa, C.O.
    Year: 2024
    Citations: 2
  • Title: A Principal Component Analysis of Corporate Dispositions for Sustainable Building Construction in South Africa
    Authors: Emere, C.E., Aigbavboa, C.O., Oguntona, O.A., Ogunbayo, B.F.
    Year: 2024
    Citations: 0
  • Title: Assessing Monitoring and Evaluation Effectiveness for Projects in the Construction Industry
    Authors: Ogunbayo, B.F., Aigbavboa, C.O., Ahmed, S., Stevens, M.
    Year: 2024
    Citations: 0
  • Title: A Review of Applicable Approaches to Safety Incentive Schemes Design in the Construction Industry
    Authors: Ogundipe, K.E., Aigbavboa, C.O., Ogunbayo, B.F.
    Year: 2024
    Citations: 0
  • Title: Strategies for Successful Monitoring and Evaluation Practices in Construction Projects
    Authors: Ogunbayo, B.F., Ramabodu, M.S., Adewale, B.A., Ogundipe, K.E.
    Year: 2024
    Citations: 0
  • Title: Encumbrances to Social Media Applications in the South African Construction Industry
    Authors: Oguntona, O.A., Ndoda, U., Akinradewo, O., Ogunbayo, B.F., Aigbavboa, C.O.
    Year: 2024
    Citations: 0
  • Title: Assessing Current Health and Safety Practices in the Construction Industry in the Fourth Industry Revolution
    Authors: Abina, O.G., Ogunbayo, B.F., Aigbavboa, C.
    Year: 2024
    Citations: 0
  • Title: A Review of Barriers to Safety Incentives Design and Implementation in the Construction Industry
    Authors: Ogundipe, K.E., Ogunbayo, B.F., Aigbavboa, C.O.
    Year: 2024
    Citations: 0

Mohammadreza Esmaeilidehkordi | Engineering | Best Researcher Award

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Mr. Mohammadreza Esmaeilidehkordi | Engineering | Best Researcher Award

Author at Isfahan University of Technology, Iran

Mohamadreza Esmaeilidehkordi is an accomplished electrical engineer and researcher with expertise in control systems, machine learning, and nonlinear observation. He has a strong technical background and extensive hands-on experience in control systems and artificial intelligence, which he applies in interdisciplinary research projects. Known for his innovative approach to problem-solving, he has made notable contributions to fields like control system design, tumor detection, and fault detection in industrial systems. With a drive for academic excellence, Mohamadreza has authored impactful research publications and actively seeks to push the boundaries of his field through advanced techniques and new applications.

Professional Profile

Education

Mohamadreza completed his Master’s in Electrical Engineering (Control Systems) at Isfahan University of Technology (IUT), one of Iran’s leading institutions. He achieved a high GPA (3.90/4), with a thesis on “Online Sequential Type-2 Fuzzy Wavelet Extreme Learning Machine” that applied advanced machine learning techniques to nonlinear observer problems. His academic journey began with a Bachelor’s degree in Electrical Engineering from the Islamic Azad University of Najafabad, where he worked on fuzzy systems to control twin rotor systems. His rigorous coursework in neuro-fuzzy networks, adaptive control, and system identification provided a foundation that has deeply informed his research trajectory and professional work.

Professional Experience

Mohamadreza has over five years of professional experience in electrical engineering and research roles. His career began with an internship and later a position as an electrical engineer at Pars Taban Zagros Engineering Technical Company, where he developed and maintained electrical control panels. Concurrently, he served as a teaching and research assistant at IUT, focusing on linear control and fault detection of three-phase motors using fuzzy wavelet algorithms. His project management experience within IUT’s Scientific Association, where he led a fault detection project, speaks to his organizational skills and ability to apply academic research in practical, industrial contexts.

Research Interests

Mohamadreza’s research interests are rooted in control systems, nonlinear control, and artificial intelligence. He is particularly drawn to the integration of machine learning algorithms in control systems, aiming to enhance fault detection accuracy and develop adaptive models for complex systems. His interdisciplinary pursuits have led him to apply AI-driven techniques, such as fuzzy wavelet algorithms, to medical fields like tumor detection, demonstrating the versatility of his expertise. With a focus on real-world applications, he actively explores innovative methods to improve system efficiency and reliability, contributing meaningful advancements to both engineering and health sciences.

Awards and Honors

Throughout his academic and professional journey, Mohamadreza has been recognized for his exceptional aptitude and dedication. Notably, he ranked in the top 0.1% on Iran’s National Graduate Entrance Exam for Electrical Engineering, securing a full-tuition scholarship at IUT. Additionally, he achieved top 0.5% placement in the national exam for his undergraduate program. His academic excellence has been consistently recognized, underscoring his standing as a leading figure among his peers. Complementing his awards, he has also completed high-impact certifications in machine learning and programming, showcasing his commitment to continuous improvement and leadership in his field.

Conclusion

Overall, Mohamadreza Esmaeilidehkordi possesses a robust profile suitable for consideration for the Best Researcher Award. His strong technical foundation, focused research contributions, and dedication to control systems and machine learning applications make him a promising candidate. Addressing areas such as international exposure and language skills could further enhance his standing in future award considerations.

Publication Top Noted

  • Online Sequential Type-2 Fuzzy Wavelet Extreme Learning Machine: A Nonlinear Observer Application
    Authors: M. Esmaeilidehkordi, M. Zekri, I. Izadi, F. Sheikholeslam
    Year: 2024
    Citation: Esmaeilidehkordi, M., Zekri, M., Izadi, I., & Sheikholeslam, F. (2024). Online Sequential Type-2 Fuzzy Wavelet Extreme Learning Machine: A Nonlinear Observer Application. Fuzzy Sets and Systems, 108897.
  • Attention U-net approach in predicting Intensity Modulated Radiation Therapy dose distribution in brain glioma tumor
    Authors: M. Naeemi, M. R. Esmaeili, I. Abedi
    Year: 2023
    Citation: Naeemi, M., Esmaeili, M. R., & Abedi, I. (2023). Attention U-net approach in predicting Intensity Modulated Radiation Therapy dose distribution in brain glioma tumor. arXiv preprint arXiv:2305.07033.
  • Utilizing Armchair and Zigzag Nanoribbons for Improved Detection of So2 Toxicity with Graphene Biosensor
    Authors: M. Ramezani Farani, M. Esmaeilidehkordi, I. Alipourfard, M. Azarian, …
    Year: 2023
    Citation: Ramezani Farani, M., Esmaeilidehkordi, M., Alipourfard, I., Azarian, M., & others. (2023). Utilizing Armchair and Zigzag Nanoribbons for Improved Detection of So2 Toxicity with Graphene Biosensor. Available at SSRN 4852941.
  • Fuzzy Wavelet Online Sequential Extreme Learning Machine Applied as an Observer for Nonlinear Systems
    Authors: [Author names not provided, but should be included]
    Year: [Year not provided, please specify if known]
    Citation: [Citation information not provided, please specify if known].