Saibo She | Electrical Engineering | Best Researcher Award

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Dr Saibo She | Electrical Engineering | Best Researcher Award 

Ph.D Student, University of Manchester, United Kingdom

Saibo She is a dedicated researcher specializing in electromagnetic non-destructive testing and sensor design. Currently pursuing a PhD at the University of Manchester, UK, he previously earned his bachelor’s degree from Hunan University, China. With a strong foundation in electrical engineering, Saibo has actively contributed to various innovative research projects, focusing on defect detection and material evaluation. He is passionate about applying artificial intelligence to enhance diagnostic methodologies. Beyond academia, Saibo has demonstrated leadership in multiple competitions, reflecting his commitment to innovation and collaboration. His work has led to numerous publications and patents, marking him as a rising star in his field.

Profile

Scopus

Strengths for the Award

  1. Extensive Research Experience: Saibo has been involved in numerous significant research programs, focusing on advanced topics in non-destructive testing and electromagnetic evaluation. His work is supported by prestigious funding, such as the National Natural Science Foundation of China.
  2. Innovative Contributions: He has made substantial contributions to the field, as evidenced by multiple patents and published papers in reputable journals like the IEEE Sensors Journal and IEEE Transactions on Instrumentation and Measurement. His research on eddy current sensors demonstrates a blend of innovation and practical application.
  3. Strong Publication Record: Saibo has co-authored several papers with impactful findings, showcasing his ability to engage in high-quality research and contribute to scientific knowledge. His work on defect detection and materials evaluation reflects a commitment to advancing the field.
  4. Awards and Scholarships: His accolades, including the IEEE Instrumentation and Measurement Graduate Fellowship Award and various scholarships, highlight his academic excellence and recognition by peers and institutions.
  5. Leadership Experience: His role as a group leader in several competitions suggests strong leadership and teamwork skills, which are crucial for collaborative research environments.

Areas for Improvement

  1. Broader Impact: While his research is innovative, exploring avenues to increase the practical impact of his work in industrial applications could enhance his profile. Engaging with industry partners for real-world testing and implementation could broaden his research’s reach.
  2. Interdisciplinary Collaboration: Saibo could benefit from engaging with researchers from different fields to foster interdisciplinary collaboration, which can lead to new perspectives and innovative solutions to complex problems.
  3. Communication Skills: While his publication record is strong, focusing on enhancing presentation and outreach skills could help him communicate his research findings more effectively to diverse audiences, including policymakers and industry stakeholders.

Education

Saibo She is currently pursuing a PhD at the University of Manchester, UK, from September 2022 to June 2026. He previously obtained his bachelor’s degree from Hunan University, China, where he studied from September 2019 to June 2022. His education has provided him with a solid foundation in electrical engineering and materials science, equipping him with the knowledge and skills needed for advanced research. At both institutions, Saibo excelled academically, receiving several scholarships and awards that recognized his outstanding performance. His studies have been complemented by hands-on research experiences, enabling him to apply theoretical concepts to practical challenges in non-destructive testing and sensor technology. Saibo’s educational journey reflects a commitment to excellence and a strong desire to contribute to advancements in his field.

Experience 

Saibo She has extensive research experience, starting in July 2019, where he has been involved in multiple significant projects. His work includes analyzing mechanical stress wave mechanisms in silicon carbide power electronic devices and exploring damage mechanisms using nonlinear electromagnetic acoustic emission methods. He has contributed to research funded by the National Natural Science Foundation of China and participated in projects related to non-destructive testing techniques. Saibo’s main responsibilities include the simulation and analysis of electromagnetic fields, the design and evaluation of electromagnetic sensors, and hardware circuit design. He has also constructed experimental platforms for testing and validation purposes. His involvement in these projects showcases his technical expertise and ability to tackle complex engineering problems, making him a valuable asset in the field of non-destructive evaluation.

Awards and Honors 

Saibo She has received numerous awards and honors throughout his academic career. In March 2023, he was awarded the IEEE Instrumentation and Measurement Graduate Fellowship Award. He is a recipient of the China Scholarship Council (CSC) and University of Manchester Joint Scholarship, covering the period from 2022 to 2026. During his time at Hunan University, he received several accolades, including the Graduate Student National Scholarship for two consecutive years (2020-2021 and 2019-2020) and the Academic First-Class Scholarship. Additionally, he was recognized as an Outstanding Graduate Student for the 2019-2020 academic year. His achievements in competitions include the Central China Second Prize in the China Sensor Innovation and Entrepreneurship Competition and multiple awards in electronic design competitions. These recognitions underscore his dedication to research excellence and innovation in engineering.

Research Focus

Saibo She’s research focuses on the design of eddy current array sensors and the evaluation of ferromagnetic materials, particularly through the study of hysteresis loops and Barkhausen magnetic noise. He is keenly interested in defect diagnosis and identification utilizing artificial intelligence algorithms, aiming to enhance the capabilities of non-destructive testing techniques. His work addresses challenges in materials science and engineering, particularly in improving the reliability and efficiency of sensor technologies. By integrating machine learning approaches into traditional testing methods, Saibo seeks to push the boundaries of current evaluation techniques. His research not only contributes to academic knowledge but also has practical implications for industries requiring advanced non-destructive testing solutions. Saibo’s commitment to innovation and his technical expertise position him as a leading researcher in the field, with the potential to significantly advance the understanding and application of electromagnetic testing methods.

Publication Top Notes

  • Flexible Differential Butterfly-Shape Eddy Current Array Sensor for Defect Detection of Screw Thread 📄
  • Flexible Floral Eddy Current Probe for Detecting Flaws in Metal Plate 📄
  • Optimal Design of Remote Field Eddy Current Testing Probe for Ferromagnetic Pipeline Inspection 📄
  • An Innovative Eddy Current Sensor with E-Core Ferrite Resistant to Lift-Off and Tilt Effects 📄
  • Inspection of Defects Depth for Stainless-Steel Sheets Using Four-Coil Excitation Sensor and Deep Learning 📄
  • Evaluation of Defects Depth for Metal Sheets Using Four-Coil Excitation Array Eddy Current Sensor and Improved ResNet18 Network 📄
  • Thickness Measurement and Surface-Defect Detection for Metal Plate Using Pulsed Eddy Current Testing and Optimized Res2Net Network 📄
  • Simultaneous Measurements of Metal Plate Thickness and Defect Depth Using Low Frequency Sweeping Eddy Current Testing 📄
  • Size-Distinguishing Miniature Electromagnetic Tomography Sensor for Small Object Detection 📄
  • Diffusion Velocity of Eddy Current in Metallic Plates Using Point-Tracing Method 📄
  • Temperature Monitoring of Vehicle Brake Drum Based on Dual Light Fusion and Deep Learning 📄

Conclusion

Saibo She is an excellent candidate for the Research for Best Researcher Award due to his impressive research accomplishments, innovative contributions, and strong leadership capabilities. By addressing the areas for improvement, such as expanding the practical impact of his research and enhancing interdisciplinary collaborations, he can further strengthen his profile. His trajectory indicates a promising future in research and innovation, making him a worthy recipient of this award.

 

 

Sonia Akram | Applied Mathematics | Best Researcher Award

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Ms. Sonia Akram | Applied Mathematics | Best Researcher Award

Researcher at University of Gujrat, Pakistan

Sonia Akram, born on February 4, 1998, in Gujrat, Pakistan, is a dedicated researcher in the field of Applied Mathematics, specializing in Soliton Theory. She completed her M.Phil from the University of Gujrat in August 2023, achieving a perfect CGPA of 4.00. With a remarkable portfolio of 20 published research articles, Sonia’s work is recognized in reputable peer-reviewed journals. Passionate about advancing mathematical physics, she aims to incorporate artificial intelligence into her future research. Sonia actively participates in conferences to share her insights and collaborate with fellow researchers. Her academic journey reflects her commitment to contributing to the advancement of knowledge in her field.

Profile:

Scopus Profile

Strengths for the Award:

  1. Strong Academic Background:
    • Sonia holds an M.Phil in Applied Mathematics with a perfect CGPA, showcasing her academic excellence.
    • Her thesis focuses on a relevant and advanced topic in mathematical physics, indicating a deep understanding of her field.
  2. Significant Research Output:
    • She has published 20 research articles in reputable peer-reviewed journals, highlighting her productivity and commitment to research.
    • Her recent work involves complex analyses such as Lie Symmetry, Bifurcation, Chaos, and Sensitivity Analysis, demonstrating her versatility and depth of knowledge.
  3. Innovative Research Interests:
    • Sonia’s plan to integrate Neural Network modeling into her research indicates a forward-thinking approach, bridging traditional mathematics with modern artificial intelligence.
  4. Recognition and Awards:
    • She has received a gold medal for her performance in her Master’s program and has been recognized with a merit-based laptop from the Prime Minister of Pakistan, showcasing her achievements.
  5. International Engagement:
    • Participation in international conferences indicates her willingness to engage with the global research community and stay updated with current trends.

Areas for Improvement:

  1. Networking and Collaboration:
    • While she has published multiple articles, fostering collaborations with researchers from different fields could further enhance her research profile and introduce her to new methodologies.
  2. Broader Impact:
    • Emphasizing the practical applications of her research in real-world scenarios could strengthen her research narrative and appeal to a wider audience.
  3. Public Outreach:
    • Engaging in community outreach or educational initiatives to promote mathematics and its applications could enhance her visibility and demonstrate her commitment to the field beyond academia.
  4. Diversity of Research Topics:
    • While her current focus is impressive, diversifying her research topics could open new avenues and strengthen her overall impact in the field.

Education:

Sonia Akram holds an M.Phil in Applied Mathematics from the University of Gujrat, where she graduated with a perfect CGPA of 4.00 in August 2023. Her thesis focused on the “Wave Structure of Some Nonlinear Dynamical Models Arise in Mathematical Physics,” demonstrating her expertise in advanced mathematical concepts. Prior to her M.Phil, she earned a Master’s degree in Mathematics from the same institution (2018-2021) and a Bachelor’s degree in Science (2016-2018), laying a solid foundation in mathematics and statistics. Sonia’s academic training encompasses various areas of natural sciences, enhancing her analytical and problem-solving skills. Her educational achievements underscore her commitment to excellence and her potential to contribute significantly to the field of applied mathematics.

Experience:

Sonia Akram has gained valuable experience in research and academia through her extensive work in Applied Mathematics. During her M.Phil studies, she focused on soliton and lump solutions of nonlinear dynamical models, contributing to her field with 20 research publications. Her collaboration with prominent researchers has enabled her to engage in significant studies, including modulation instability analysis and bifurcation analysis. In addition to her research, Sonia has attended several academic conferences, such as the “1st International Alumni’s Mathematics UET Conference” in February 2022, where she presented her findings and networked with fellow researchers. This experience has allowed her to refine her communication skills and enhance her professional network. Sonia’s commitment to advancing mathematical research positions her as an emerging expert in her field.

Awards and Honors:

Sonia Akram has received several prestigious awards recognizing her academic excellence and research contributions. She was honored as a gold medalist during her Master’s program in Mathematics at the University of Gujrat, highlighting her exceptional academic performance. Additionally, she was awarded a merit-based laptop from the Prime Minister of Pakistan, which underscores her dedication and hard work in the field of mathematics. These accolades not only reflect Sonia’s academic achievements but also serve as motivation for her continued pursuit of knowledge and research excellence. Her accomplishments position her as a strong candidate for further awards and recognition in her academic and research endeavors, inspiring future generations of mathematicians.

Research Focus:

Sonia Akram’s research focuses on soliton theory and nonlinear dynamical models in both classical and fractional forms. She employs linear stability theory to analyze modulation instability and has published 20 articles in reputable peer-reviewed journals. Her work explores advanced topics such as Lie Symmetry Analysis, Bifurcation Analysis, and Chaos Theory, showcasing her versatility in applied mathematics. Recently, she has extended her research to include sensitivity analysis of various nonlinear models, further enhancing the depth of her studies. Sonia is also planning to integrate neural network modeling in artificial intelligence into her future research, bridging the gap between traditional mathematics and modern computational methods. Her commitment to advancing knowledge in applied mathematics reflects her passion for solving complex problems and contributing to the scientific community.

Publications Top Notes:

  1. Soliton solutions for some higher order nonlinear problems of mathematical engineering, Nonlinear Engineering. Modeling and Application (2023).
  2. Soliton solutions and sensitive analysis to nonlinear wave model arising in optics, Physica Scripta, 2024.
  3. Propagation of solitary wave solutions to (4+1)-dimensional Davey–Stewartson–Kadomtsev–Petviashvili equation arise in mathematical physics and stability analysis, 2024.
  4. Stability analysis and solitonic behaviour of Schrödinger’s nonlinear (2+1) complex conformable time fractional model, Optical and Quantum Electronics, 2024.
  5. Dispersive optical soliton solutions to the truncated time M-fractional paraxial wave equation with its stability analysis, 2024.
  6. Dynamical behaviors of analytical and localized solutions to the generalized Bogoyavlvensky–Konopelchenko equation arising in mathematical physics, 2024.
  7. Stochastic wave solutions of fractional Radhakrishnan–Kundu–Lakshmanan equation arising in optical fibers with their sensitivity analysis, 2024.
  8. Analysis of bifurcation, chaotic structures, lump and M − W-shape soliton solutions to (2 + 1) complex modified Korteweg-de-Vries system, 2024.
  9. Retrieval of diverse soliton, lump solutions to a dynamical system of the nonlinear Biswas–Milovic equation and stability analysis, 2024.
  10. Stability analysis and soliton solutions of truncated M-fractional Heisenberg ferromagnetic spin chain model via two analytical methods, 2024.
  11. Analysis of new soliton type solutions to generalized extended (2 + 1)-dimensional Kadomtsev-Petviashvili equation via two techniques, 2024.

Conclusion:

Sonia Akram is a promising researcher with a solid foundation in applied mathematics and a notable publication record. Her innovative approach to integrating AI into her research, coupled with her academic achievements, positions her as a strong candidate for the Best Researcher Award. By addressing some areas for improvement, particularly in networking and broader impact, she can further elevate her research profile and contribution to the field. Recognizing her efforts with this award would not only honor her achievements but also encourage her continued growth and innovation in mathematical research.

Qi Liang | Pattern Recognition | Excellence in Research

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Mr Qi Liang | Pattern Recognition | Excellence in Research

Master in Tongji University at China

Qi Liang is a dedicated researcher and master’s student at Tongji University, PR China, specializing in mechanical engineering. With a strong foundation in industrial engineering from Jiangsu University of Science and Technology, Qi has a keen interest in advancing technology through innovative research. Recognized for introducing self-supervised learning methods in semiconductor applications, Qi’s work aims to solve complex challenges in pattern recognition. Their publication in Engineering Applications of Artificial Intelligence reflects a commitment to high-impact research. With multiple ongoing projects and a focus on practical applications, Qi is paving the way for efficient solutions in the semiconductor industry.

Profile

Google Scholar

Strengths for the Award

  1. Innovative Research: Qi Liang has introduced a self-supervised learning method for few-shot learning in semiconductor applications, demonstrating originality and a significant contribution to the field.
  2. Publication Record: The recent publication in Engineering Applications of Artificial Intelligence showcases a commitment to high-quality research, adding to the credibility of the work.
  3. Diverse Research Interests: With a focus on computer vision, multi-modal learning, and fault diagnosis, Qi’s work spans multiple cutting-edge areas, which increases the potential impact of the research.
  4. Practical Applications: The research addresses real-world challenges in the semiconductor industry, offering low-cost, efficient methods that have immediate applicability.
  5. Academic Engagement: Qi’s active involvement in ongoing projects and industry collaborations indicates a robust engagement with both academic and practical aspects of research.

Areas for Improvement

  1. Broader Collaboration: Expanding collaborations with international researchers could enhance the research’s visibility and applicability on a global scale.
  2. Increased Publication Volume: While the current publication is commendable, a more extensive publication record could further establish Qi’s expertise and leadership in the field.
  3. Outreach and Communication: Engaging in more outreach activities, such as conferences and seminars, could help disseminate findings and foster connections within the research community.

Education 

Qi Liang graduated with a Bachelor’s degree in Industrial Engineering from Jiangsu University of Science and Technology, where foundational principles of engineering and technology were mastered. Currently, Qi is pursuing a Master’s degree in Mechanical Engineering at Tongji University, one of China’s prestigious institutions, now in their third year of the program. This advanced education has allowed Qi to engage deeply with cutting-edge topics, particularly in computer vision and machine learning. Through rigorous coursework and research, Qi has developed expertise in areas such as pattern recognition, self-supervised learning, and fault diagnosis, equipping them with the skills necessary to tackle complex engineering problems and contribute significantly to both academic and industrial advancements.

Experience

Qi Liang has gained substantial experience through multiple research projects, totaling five completed or ongoing initiatives that emphasize practical applications of machine learning in semiconductor manufacturing. In addition to academic research, Qi has participated in three consultancy and industry-sponsored projects, bridging the gap between theoretical knowledge and real-world applications. Their collaborative efforts in research have led to valuable partnerships and a broader understanding of the industry’s challenges and needs. As the first to implement self-supervised learning techniques in few-shot learning tasks related to wafer map pattern recognition, Qi has showcased exceptional innovation. This unique approach has opened new avenues for cost-effective and efficient solutions within the semiconductor sector, positioning Qi as an emerging leader in their field.

Research Focus 

Qi Liang’s research focuses on the intersection of computer vision and machine learning, with a strong emphasis on pattern recognition, keypoint detection, and image retrieval. Specializing in self-supervised and multi-modal learning, Qi aims to develop innovative methodologies that minimize the reliance on labeled data while maximizing efficiency and applicability in industrial contexts. Current research projects explore dynamic adaptation mechanisms for few-shot learning, specifically tailored for wafer map pattern recognition in the semiconductor industry. Qi is also interested in signal processing and fault diagnosis, seeking to improve reliability and performance in manufacturing processes. This research direction not only contributes to the academic community but also addresses pressing industry challenges, promoting advancements in automation and smart manufacturing.

Publication Top Notes

  • Masked Autoencoder with Dynamic Multi-Loss Adaptation Mechanism for Few Shot Wafer Map Pattern Recognition 📄

Conclusion

Qi Liang’s innovative contributions to the field of mechanical engineering and computer vision make a strong case for the Excellence in Research award. The unique approach to self-supervised learning in few-shot learning for wafer map pattern recognition signifies both a breakthrough in methodology and practical application in the semiconductor industry. With a few strategic improvements, Qi has the potential to further amplify the impact of their research and cement their status as a leading researcher in their field.

Ojong Ojong | Optimization and Control | Best Researcher Award

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Dr Ojong Ojong | Optimization and Control | Best Researcher Award

Lecturer II and Ag. HoD Chemical Engineering at University of Calabar in Nigeria.

Engr. Dr. Ojong Elias Ojong is a distinguished Lecturer II in the Department of Chemical Engineering at the University of Calabar, Nigeria. Born on September 30, 1990, in Bendeghe/Ekiem, Cross River State, he is dedicated to advancing the field of chemical engineering through research and education. Ojong holds a B.Tech., M.Tech., and is currently pursuing a Ph.D. at Rivers State University. With a robust academic background and practical experience in industry, he is committed to fostering the next generation of engineers. Ojong is also actively involved in community services and professional associations, promoting collaboration and innovation in engineering.

Profile

Scopus

Strengths

  1. Academic Qualifications: Ojong has an impressive academic background, with a B.Tech (First Class) in Chemical Engineering, an M.Tech, and a Ph.D. in progress. His strong academic foundation sets him apart as a dedicated and capable researcher.
  2. Teaching Experience: With extensive teaching responsibilities over the past five years at the University of Calabar, he has proven his commitment to education and mentoring. His involvement in key chemical engineering courses demonstrates both depth and breadth in his teaching abilities.
  3. Research Output: Ojong has published several peer-reviewed articles in reputable journals, covering topics such as chemical kinetics, reactor functions, and heat exchanger modeling. His research has practical applications in engineering, as demonstrated by his work on the design and simulation of chemical plants and energy systems.
  4. Professional Engagement: Ojong has attended and presented at multiple national and international conferences, showcasing his research and staying updated on the latest trends in his field. This engagement in the broader academic community is a critical factor in research leadership.
  5. Awards and Scholarships: His scholarships, such as the Ph.D. Scholarship by Tetfund and the Agbami Undergraduate Scholarship, underscore his academic excellence and promise.
  6. Membership in Professional Bodies: Ojong is a Corporate Member of the Nigerian Society of Chemical Engineers and a COREN-registered engineer. This formal recognition within professional bodies highlights his commitment to adhering to high standards in engineering practice.

Areas for Improvement

  1. Research Citations: While Ojong has published significant research, his citation metrics on Google Scholar, Scopus, and ORCID are relatively modest, with h-index values of 1-2. Increasing his citation count through collaborative and impactful research could enhance his academic influence.
  2. International Collaboration: Strengthening collaborations with international researchers and participating in joint research projects could further improve the global impact of his work.

Education

Dr. Ojong Elias Ojong’s academic journey showcases a strong foundation in chemical engineering. He earned his B.Tech. in Chemical Engineering with first-class honors from Rivers State University in 2014. His passion for the field led him to pursue an M.Tech. at the same institution, which he completed in 2019. Currently, he is a Ph.D. candidate at Rivers State University, focusing on optimizing chemical processes. His educational endeavors are supported by notable scholarships, including a PhD Scholarship from Tetfund and an Agbami Scholarship during his undergraduate studies. Ojong’s dedication to academic excellence and continuous learning reflects his commitment to making significant contributions to chemical engineering.

Experience 

Engr. Dr. Ojong Elias Ojong has accumulated extensive teaching and industrial experience in chemical engineering. He began his academic career as an Assistant Lecturer at the University of Calabar in October 2019 and was promoted to Lecturer II in October 2021. His teaching responsibilities include various undergraduate courses such as Process Dynamics and Control, Chemical Reaction Engineering, and Thermodynamics. Ojong also supervises undergraduate projects, enhancing students’ practical skills. Prior to academia, he worked as a Graduate Trainee at Indorama Eleme Fertilizer and Chemicals Limited, gaining valuable industry insights. His experience is complemented by a National Youth Service Corps (NYSC) position at Erisco Food Nigeria Limited. Through his roles, he aims to bridge the gap between theoretical knowledge and practical application in the chemical engineering field.

Awards and Honors 

Dr. Ojong Elias Ojong’s academic achievements have been recognized through several prestigious awards and scholarships. In 2021, he received a PhD Scholarship from Tetfund, reflecting his research potential and commitment to advancing knowledge in chemical engineering. During his undergraduate studies, he was awarded the Agbami Scholarship, which further motivated his pursuit of academic excellence. These recognitions underscore his dedication to his field and his efforts to contribute meaningfully to engineering education and research. Ojong’s accolades not only highlight his personal achievements but also serve as an inspiration for his students and peers. His recognition in academia reinforces the importance of supporting and nurturing emerging talents in the engineering sector.

Research Focus 

Dr. Ojong Elias Ojong’s research interests center on optimizing chemical processes and control systems. His current Ph.D. research investigates advanced techniques in the performance evaluation of PID and fuzzy logic controllers for urea reactor functions. Ojong’s work aims to enhance efficiency and effectiveness in chemical engineering applications, particularly in process dynamics and control. He has published several articles in reputable journals, contributing to knowledge in areas such as naphtha reforming and corrosion effects in chemical systems. His research not only addresses theoretical challenges but also emphasizes practical solutions that can be implemented in industrial settings. Dr. Ojong is committed to fostering innovation in chemical engineering through his research and collaborative projects.

Publication Top Notes

  • Resolving systems of ordinary differential equations in naphtha reforming process: Comparison of laplace transform and numerical methods. 📊
  • Estimation of Kinetic Parameters of Naphtha Lump Feeds. 📈
  • Design of Engineering Project Planning Software: A case Study. 💻
  • Two-way ANOVA for comparison of remedial nutrient solution and enhanced natural attenuation using SPSS for treating petroleum contaminated soils. 🧪
  • Mathematical modelling of operating temperature variations of shell-tube-heat exchanger (10-E-01). 🔥
  • Validation of MATLAB algorithm to implement a two-step parallel pyrolysis model for the prediction of maximum % char yield. ⚙️
  • Effect of Surface Finish on Corrosion and Microstructure of Carbon Steel-(C-1020) and Stainless Steel-(SS304). 🛠️
  • The Use of Models to Evaluate Corrosion Effects on Mild Steel Heat Exchanger in Water and Mono Ethanol Amine (MEA). 🌊

Conclusion

Ojong Elias Ojong’s academic achievements, research output, and professional activities make him a strong contender for the Best Researcher Award. His diverse work in chemical engineering, teaching experience, and research productivity highlight his dedication to advancing both the academic and practical aspects of his field.