Bahaa Hussein Taher | Engineering | Research Excellence Award

Mr. Bahaa Hussein Taher | Engineering | Research Excellence Award

University of Sumer | China

Mr. Bahaa Hussein Taher is a senior engineer and academic affiliated with the University of Sumer, specializing in computer science and electronic engineering with a strong focus on advanced networking and edge computing. His academic background includes doctoral and master’s level training in computer science, electronic engineering, and information engineering, building a solid foundation in computing systems and optimization. Professionally, he has held academic, engineering, and leadership roles, contributing to teaching, research supervision, quality assurance management, IT infrastructure development, and participation in strategic planning and technical committees. His research expertise centers on edge computing, dynamic task allocation, secure execution in next-generation networks, cryptographic protocols, scheduling, and optimization, with multiple publications in internationally indexed journals. He has actively contributed to the academic community through workshops, seminars, and conference participation, reflecting sustained scholarly engagement. His recognitions include language proficiency certifications and professional credentials that support international research collaboration, highlighting his capacity to advance impactful, secure, and efficient computing solutions with strong future research potential.

Citation Metrics (Google Scholar)

443
300
150
50
0

443

3

4

Citations

i10-index

h-index


Top 5 Featured Publications

 

Chang Soo Kim | Engineering | Best Researcher Award

Prof. Chang Soo Kim | Engineering | Best Researcher Award

Professor | Pukyong National University | South Korea

Professor. Chang Soo Kim is a distinguished Full Professor in the Division of Computer and AI Engineering at PuKyong National University, recognized for his expertise in intelligent manufacturing systems, artificial intelligence, and computational optimization. He holds advanced degrees in computer science with specialization in AI-driven optimization and machine learning, forming the foundation for his multidisciplinary research career. Throughout his long-standing academic tenure, he has served in key leadership roles including department chair, graduate program administrator, research center director, and executive leader for university–industry cooperation, successfully guiding large-scale projects, fostering collaborative innovation, and advancing strategic academic initiatives. His research focuses on flexible job shop scheduling, deep learning–based fault diagnosis, time-series forecasting, metaheuristic optimization, and smart industrial systems. He has produced an extensive portfolio of influential publications in high-impact SCI-indexed journals, contributing novel hybrid algorithms, trainable fusion strategies, adaptive scheduling frameworks, lightweight diagnostic models, and intelligent computational methods that support the evolution of smart manufacturing and data-driven engineering. His scholarly achievements have earned him multiple recognitions, including awards for research excellence, and he actively contributes to the global academic community through editorial service, participation in professional societies, and engagement in scientific committees. With a sustained record of innovative research, academic leadership, and impactful contributions to computer and AI engineering, Professor Chang Soo Kim exemplifies the qualities of a leading researcher whose work continues to influence both industry and academia.

Profiles:  Scopus

Featured Publications

1. Kim, C. S., et al. (2025). Flexible job shop scheduling optimization with multiple criteria using a hybrid metaheuristic framework. Processes.

2. Kim, C. S., et al. (2025). Multi-branch global Transformer-assisted network for fault diagnosis. Applied Soft Computing.

3. Kim, C. S., et al. (2025). DL-MSCNN: A general and lightweight framework for fault diagnosis with limited training samples. Journal of Intelligent Manufacturing.

4. Kim, C. S., et al. (2025). Enhanced quantum-based DNA sequence alignment with noise handling and error detection. IEEE Access.

5. Kim, C. S., et al. (2024). GAILS: An effective multi-object job shop scheduler based on genetic algorithm and iterative local search. Scientific Reports.

Professor Chang Soo Kim’s pioneering research in intelligent manufacturing, AI-driven optimization, and fault diagnosis advances the scientific foundations of smart industry while enabling more efficient, reliable, and data-driven production systems. His innovative computational frameworks and adaptive algorithms contribute directly to industrial digital transformation, fostering technological competitiveness and sustainable global innovation.

Sayyid Ali Banihashemi | Engineering | Editorial Board Member

Assist. Prof. Dr. Sayyid Ali Banihashemi | Engineering | Editorial Board Member

Faculty Member | Payame Noor University | Iran

Assist. Prof. Dr. Sayyid Ali Banihashemi, Associate Professor in the Department of Industrial Engineering at Payame Noor University, is a recognized scholar specializing in project scheduling, data envelopment analysis, supply chain management, and organizational agility. He holds advanced degrees in industrial engineering with a concentration in operations research and performance evaluation, complemented by rigorous training in quantitative decision-making. His professional experience includes leading academic programs, supervising research initiatives, and contributing to major analytical and optimization projects that support organizational and operational improvement. Dr. Banihashemi’s research portfolio encompasses influential publications, high-impact citations, and methodological advancements that have shaped contemporary practices in project planning efficiency, productivity assessment, and supply chain performance. His scholarly contributions are further reflected in editorial responsibilities for reputable journals, memberships in distinguished professional societies, and certifications in advanced analytical methods. Widely cited and respected in his field, he has earned multiple recognitions for research excellence, academic service, and contributions to the industrial engineering community, establishing him as a dedicated leader committed to advancing theory and practice in operations and performance management.

Profiles: Google Scholar

Featured Publications

1. Dahmardeh, N., & Banihashemi, S. A. (2010). Organizational agility and agile manufacturing. European Journal of Economics, Finance and Administrative Sciences, 27, 178–184.

2. Banihashemi, S. A. (2011). The role of communication to improve organizational process. European Journal of Humanities and Social Sciences, 1(1), 13–24.

3. Banihashemi, S. A., Khalilzadeh, M., Shahraki, A., Malkhalifeh, M. R. M., & others. (2020). Optimization of environmental impacts of construction projects: A time–cost–quality trade-off approach. International Journal of Environmental Science and Technology, 1–16.

4. Banihashemi, S. A., & Khalilzadeh, M. (2021). Time-cost-quality–environmental impact trade-off resource-constrained project scheduling problem with DEA approach. Engineering, Construction and Architectural Management, 28(7), 1979–2004.

5. Banihashemi, S. A., Khalilzadeh, M., Antucheviciene, J., & Edalatpanah, S. A. (2023). Identifying and prioritizing the challenges and obstacles of green supply chain management in the construction industry using the fuzzy BWM method. Buildings, 13(1), 38.

Dr. Sayyid Ali Banihashemi’s work advances scientific and industrial practice by integrating optimization, sustainability, and performance evaluation to improve project delivery and supply chain systems. His research supports data-driven decision-making that enhances organizational efficiency, reduces environmental impacts, and strengthens the resilience and agility of modern industries.

Dongfeng Qi | Engineering | Best Researcher Award

Prof. Dongfeng Qi | Engineering | Best Researcher Award

Professor | Shandong University of Technology | China

Prof. Dongfeng Qi is a leading researcher in laser manufacturing and material processing, specializing in femtosecond and nanosecond laser applications for advanced materials and flexible electronics. His work integrates theoretical modeling with experimental techniques to develop innovative micro- and nanostructures, including copper and silicon-based materials, phase-change films, and smart electronic devices. Prof. Qi has made substantial contributions to understanding laser-material interactions and patterning technologies, with high-impact publications in top-tier journals. He demonstrates strong interdisciplinary collaboration and mentorship, fostering international research partnerships and the growth of emerging scientists. His research is recognized for both fundamental insights and practical applications in photonics, energy storage, and flexible electronics. Prof. Qi’s consistent innovation, high-quality experimental work, and leadership in the field establish him as a leading figure in laser-based materials science. According to Scopus, he has 535 citations, 71 documents, and an h-index of 13.

Profiles: Scopus | Google Scholar

Featured Publications

1. Programming nanoparticles in multiscale: optically modulated assembly and phase switching of silicon nanoparticle array; L. Wang, Y. Rho, W. Shou, S. Hong, K. Kato, M. Eliceiri, M. Shi, …; ACS Nano, vol. 12, no. 3, pp. 2231–2241, 2018; 39 citations

2. Time-resolved analysis of thickness-dependent dewetting and ablation of silver films upon nanosecond laser irradiation; D. Qi, D. Paeng, J. Yeo, E. Kim, L. Wang, S. Chen, C. P. Grigoropoulos; Applied Physics Letters, vol. 108, no. 21, 2016; 37 citations

3. Femtosecond laser-induced large area of periodic structures on chalcogenide glass via twice laser direct-writing scanning process; X. Yu, Q. Zhang, D. Qi, S. Tang, S. Dai, P. Zhang, Y. Xu, X. Shen; Optics & Laser Technology, vol. 124, 105977, 2020; 34 citations

4. Progress in the design, nanofabrication, and performance of metalenses; Z. Wang, Y. Wu, D. Qi, W. Yu, H. Zheng; Journal of Optics, vol. 24, no. 3, 033001, 2022; 31 citations

5. Progress in preparation and applications of Te-As-Se chalcogenide glasses and fibers; Z. Wu, Y. Xu, D. Qi, Q. Nie, X. Zhang; Infrared Physics & Technology, vol. 102, 102981, 2019; 31 citations

Salem Batiyah | Engineering | Best Researcher Award

Dr. Salem Batiyah | Engineering | Best Researcher Award

Assistant Professor at Yanbu Industrial College, Saudi Arabia

Dr. Salem Mohammed Batiyah is a dedicated researcher in electrical engineering with strong contributions to distributed energy resources, microgrids, and advanced control systems. His work is published in respected journals such as IEEE Access and Energies, covering both theoretical and applied aspects of renewable energy systems. He has an active role as a reviewer for top-tier journals, reflecting recognition by the global research community. In addition to his research, Dr. Batiyah demonstrates academic leadership through curriculum development, teaching, and quality assurance roles. While his citation metrics and absence of major research grants suggest areas for growth, his ongoing publication record and technical expertise indicate a solid foundation for future impact. Strengthening international collaborations and securing research funding will further enhance his research profile. Overall, Dr. Batiyah is a strong candidate for the Best Researcher Award, especially in domains valuing practical innovation and contributions to sustainable energy and smart grid development

Professional Profile 

Google Scholar

Strengths for the Award

Dr. Salem Mohammed Batiyah presents a strong and relevant research portfolio in modern electrical engineering fields, particularly in distributed energy resources (DERs), microgrids, hybrid power plants, and advanced control systems. His work addresses high-impact areas such as renewable energy integration, predictive control, and fault-tolerant power electronics, which are central to global energy transition goals.

He has authored multiple peer-reviewed journal and conference papers, with publications in well-regarded outlets such as IEEE Access and Energies. These include both novel technical contributions and comprehensive reviews, suggesting breadth and depth. His global engagement as a reviewer for prestigious journals such as IEEE Transactions on Industrial Informatics and IEEE JESTIE reflects his standing in the academic community.

Additionally, Dr. Batiyah combines research with active academic and administrative leadership, curriculum development, and extensive teaching in power systems and control engineering. This integration of research and teaching enhances his impact and dissemination of knowledge.

Education

Dr. Salem Mohammed Batiyah holds a Ph.D. in Electrical Engineering from Mississippi State University, where he specialized in power management and control systems for renewable energy applications. He earned his M.Sc. and B.Sc. degrees in Electrical Engineering from Western Michigan University. His graduate studies focused on distributed energy resources, microgrid integration, and model predictive control systems. Throughout his academic journey, Dr. Batiyah developed a solid foundation in both theoretical and practical aspects of power systems, signal processing, and advanced control techniques. His educational background is complemented by professional certifications, including Lean Six Sigma and OSHA safety training, demonstrating his commitment to quality and operational excellence. Dr. Batiyah’s education has prepared him to address real-world engineering challenges in sustainable energy and has laid the groundwork for a research-oriented academic career. His academic experience is characterized by interdisciplinary training and international exposure, enhancing his perspective in solving complex energy system problems.

Experience

Dr. Salem Batiyah brings a wealth of academic and professional experience to the field of electrical engineering. Since 2020, he has been serving as an Assistant Professor at Yanbu Industrial College, where he has taught various undergraduate and associate courses in power electronics, control systems, and industrial electronics. He also worked as a Graduate Research Assistant at Mississippi State University from 2015 to 2020, engaging in research related to power management in renewable energy systems. His earlier academic experience includes working as a lecturer at Yanbu Industrial College from 2014 to 2020. Dr. Batiyah has held several administrative roles such as Department Curriculum Coordinator, Head of Curriculum and Development, and Academic Quality Coordinator. He is actively involved in multiple college and department-level committees, contributing to academic planning, program development, and quality assurance. His career reflects a blend of teaching, research, and leadership, all aimed at advancing engineering education and applied energy solutions.

Awards and Honors

Dr. Salem Batiyah has received multiple awards and honors recognizing his academic excellence and professional achievements. Notably, he was inducted into prestigious honor societies including Phi Kappa Phi, Gamma Beta Phi, and IEEE Eta Kappa Nu, reflecting high academic performance during his graduate and undergraduate studies. He was awarded the First Class Standing Award for Master of Science students and consistently made the Dean’s List during his undergraduate years. In 2023, his research output earned him 123 scholarly citations, with an h-index of 5 and an i10-index of 3, indicating growing recognition within the research community. Additionally, he holds professional certifications such as Black Belt in Lean Six Sigma and OSHA Safety Training, demonstrating his commitment to continuous professional development. His active participation in global academic organizations and contributions as a peer reviewer for multiple IEEE journals further validate his influence and leadership in the field of electrical and energy engineering.

Research Focus on Engineering

Dr. Salem Batiyah’s research centers around the modeling, analysis, and control of distributed energy resources (DERs), including solar photovoltaics and battery energy storage systems. His work addresses the integration of these resources into microgrids and hybrid power plants, with an emphasis on system reliability and efficiency. A key area of focus is the application of advanced control methods such as nonlinear, robust, and model predictive control (MPC) to optimize energy management under varying load and environmental conditions. Dr. Batiyah also explores advanced signal processing and phase-locked loops within DER systems, supporting grid stability and intelligent power conversion. His research aims to provide scalable and sustainable solutions to modern energy challenges, contributing to the global shift toward renewable and decentralized energy systems. Through peer-reviewed publications and academic collaborations, Dr. Batiyah is establishing himself as a forward-thinking researcher addressing critical challenges in the evolving energy landscape.

Publications Top Notes

  • Title: An MPC-based power management of standalone DC microgrid with energy storage
    Authors: S. Batiyah, R. Sharma, S. Abdelwahed, N. Zohrabi
    Year: 2020
    Citations: 111

  • Title: An MPC-based power management of a PV/battery system in an islanded DC microgrid
    Authors: S. Batiyah, N. Zohrabi, S. Abdelwahed, R. Sharma
    Year: 2018
    Citations: 36

  • Title: Single-phase fault tolerant multilevel inverter topologies—comprehensive review and novel comparative factors
    Authors: H. Rehman, M. Tariq, A. Sarwar, W. Alhosaini, M.A. Hossain, S.M. Batiyah
    Year: 2022
    Citations: 22

  • Title: Optimal control design of a voltage controller for stand-alone and grid-connected PV converter
    Authors: S. Batiyah, N. Zohrabi, S. Abdelwahed, T. Qunais, M. Mousa
    Year: 2018
    Citations: 17

  • Title: Predictive control of PV/battery system under load and environmental uncertainty
    Authors: S. Batiyah, R. Sharma, S. Abdelwahed, W. Alhosaini, O. Aldosari
    Year: 2022
    Citations: 15

  • Title: Performance evaluation of multiple machine learning models in predicting power generation for a grid-connected 300 MW solar farm
    Authors: O. Aldosari, S. Batiyah, M. Elbashir, W. Alhosaini, K. Nallaiyagounder
    Year: 2024
    Citations: 11

  • Title: Image-based partial discharge identification in high voltage cables using hybrid deep network
    Authors: O. Aldosari, M.A. Aldowsari, S.M. Batiyah, N. Kanagaraj
    Year: 2023
    Citations: 8

  • Title: Impact of variation of energy resources on voltage stability of a micro grid
    Authors: M.A. Mousa, S. Abdelwahed, S.M. Batiyah, T. Qunais
    Year: 2017
    Citations: 6

  • Title: Deep neural networks model for accurate photovoltaic parameter estimation under variable weather conditions
    Authors: S. Batiyah, A. Al-Subhi, O. Elsherbiny, O. Aldosari, M. Aldawsari
    Year: 2025

  • Title: Predictive control of standalone DC microgrid with energy storage under load and environmental uncertainty
    Author: S.M. Batiyah (Ph.D. Dissertation)
    Year: 2020

Conclusion

Dr. Salem Mohammed Batiyah exemplifies a rising leader in electrical and energy systems engineering, combining academic rigor with real-world impact. His research contributions, especially in renewable energy integration and intelligent control systems, align with global priorities for sustainability and innovation. His dual strengths in teaching and research, complemented by his service in academic development and international peer reviewing, position him as a multidimensional scholar. While his citation metrics and grant record indicate room for further growth, his upward trajectory and commitment to excellence are undeniable. Dr. Batiyah stands out as a promising candidate for recognition in the research community and is well on his way to becoming a major contributor to the field of smart and sustainable energy systems. With continued focus on high-impact collaboration and innovation, he is poised to make significant strides that benefit academia, industry, and the broader society.

Xiaoxu Liu | Engineering | Best Researcher Award

Dr. Xiaoxu Liu | Engineering | Best Researcher Award

Associate Professor at Shenzhen Technology University, China

Dr. Xiaoxu Liu is an accomplished Associate Professor at the Sino-German College of Intelligent Manufacturing, Shenzhen Technology University. He holds a Ph.D. in Electrical Engineering from the University of Northumbria and specializes in robust fault diagnosis, fault-tolerant control, stochastic systems, and multi-agent systems. Dr. Liu has published extensively in top-tier journals such as IEEE Transactions on Industrial Electronics and Automatica, and has served as Associate Editor for IEEE Transactions on Industrial Informatics. He has led multiple nationally funded research projects, securing over 3 million RMB in grants. His work integrates control theory with data-driven methods, addressing challenges in cyber-physical systems. Recognized as a Shenzhen Overseas High-level Talent, he has received numerous awards for research excellence and student mentorship. With international research experience and significant editorial contributions, Dr. Liu is a prominent figure in intelligent systems and control, demonstrating both academic leadership and impactful research contributions.

Professional Profile 

Scopus Profile

Education

Dr. Xiaoxu Liu possesses a strong and progressive academic background in engineering and applied mathematics. He earned his Ph.D. in Electrical Engineering from the University of Northumbria in the UK (2014–2018), where he specialized in fault-tolerant control systems and robust estimation. Prior to this, he completed a Master’s degree in Operations Research and Cybernetics at Northeastern University (2012–2014), and a Bachelor’s degree in Information and Computing Science at the same university (2008–2012). His educational path reflects a solid foundation in both theoretical and applied aspects of control systems, cybernetics, and intelligent systems. This combination of mathematical rigor and engineering application has laid the groundwork for his interdisciplinary research approach. His international academic journey has also helped him build a global perspective and a collaborative mindset, both of which have been instrumental in his subsequent professional and research achievements.

Professional Experience

Dr. Xiaoxu Liu has built an impressive academic and research career marked by rapid progression and leadership. Since December 2021, he has served as an Associate Professor at the Sino-German College of Intelligent Manufacturing, Shenzhen Technology University. Before that, he was an Assistant Professor at the same institution from 2018 to 2021. He also held research and teaching positions internationally, including as a Research Associate at the Faculty of Mathematics, City University of Hong Kong, and as a Lecturer at the University of Northumbria. Throughout these roles, Dr. Liu has led cutting-edge research projects, mentored students, and contributed to institutional development. He has acted as the principal investigator for numerous funded research programs, reflecting his capacity to lead independently and strategically. His experience demonstrates not only academic proficiency but also a sustained commitment to advancing intelligent systems research and fostering interdisciplinary collaboration in both teaching and applied engineering contexts.

Research Interest

Dr. Xiaoxu Liu’s research spans several high-impact areas within intelligent systems and control engineering. His primary interests include robust fault diagnosis, fault-tolerant control, stochastic nonlinear systems, and multi-agent systems. He also ocuses on cyber-physical systems and data-driven control, areas highly relevant to Industry 4.0 and autonomous system applications. Dr. Liu’s work often combines theoretical rigor with practical relevance, leveraging modern tools like deep reinforcement learning and Takagi-Sugeno fuzzy models to address real-world challenges such as actuator faults in UAVs or wind turbine resilience. His interdisciplinary approach blends classical control theory with artificial intelligence, enhancing system adaptability and reliability. His research outputs—published in top-tier journals like IEEE Transactions on Industrial Electronics—demonstrate not only novelty but also applicability to emerging technologies. Dr. Liu’s ability to connect robust theory with practical implementations positions him as a thought leader in intelligent manufacturing and autonomous system control.

ward and Honor

Dr. Xiaoxu Liu has received multiple awards that recognize his research excellence, academic leadership, and contributions to engineering education. He was honored as a Shenzhen Overseas High-level Talent in 2019, highlighting his strategic value to China’s academic and technological development. He has earned several Best Paper and Best Presentation Awards from prestigious conferences and journals, such as the IEEE Industrial Electronics Society and Processes. Dr. Liu also received the IEEE IES Student Paper Travel Award and various recognitions for his mentorship of student teams who achieved national-level prizes in robotics and circuit design competitions. These accolades underscore both the quality and impact of his scholarly work and his dedication to student development. His involvement as an Associate Editor for IEEE Transactions on Industrial Informatics and reviewer for top IEEE journals further validates his status as a trusted expert in his field. These honors collectively reflect his rising prominence in the global research community.

Conclusion

In summary, Dr. Xiaoxu Liu stands out as a highly capable and accomplished researcher in the field of intelligent control systems. With a solid educational foundation, diverse professional experience across top institutions, and a research portfolio that blends theoretical innovation with real-world application, he exemplifies academic excellence. His focus on robust fault diagnosis, resilient control systems, and data-driven approaches addresses some of the most pressing challenges in cyber-physical systems and smart manufacturing. Recognized nationally and internationally through numerous awards, editorial roles, and funded projects, Dr. Liu has established himself as a leader in his domain. He continues to advance the field through impactful publications, student mentorship, and collaborative projects. His trajectory reflects not only technical expertise but also a broader commitment to scientific progress and educational excellence. As such, Dr. Liu is highly deserving of recognition through accolades such as the Best Researcher Award.

Publications Top Notes

  • Title: Joint Observer Based Fault Tolerant Control for Discrete-Time Takagi-Sugeno Fuzzy Systems With Immeasurable Premise Variables

    • Authors: Xiaoxu Liu, Risheng Li, Zhiwei Gao, Bowen Li, Tan Zhang

    • Year: 2025

  • Title: Multiagent Formation Control and Dynamic Obstacle Avoidance Based on Deep Reinforcement Learning

    • Authors: Zike Yuan, Chenhao Yao, Xiaoxu Liu, Zhiwei Gao, Wenwei Zhang

    • Year: 2025

  • Title: Fault Estimation for Cyber–Physical Systems with Intermittent Measurement Transmissions via a Hybrid Observer Approach

    • Authors: Jingjing Yan, Chao Deng, Weiwei Che, Xiaoxu Liu

    • Year: 2024

    • Citations: 5

  • Title: Reinforcement Learning-Based Fault-Tolerant Control for Quadrotor UAVs Under Actuator Fault

    • Authors: Xiaoxu Liu, Zike Yuan, Zhiwei Gao, Wenwei Zhang

    • Year: 2024

    • Citations: 12

Kaili Wang | Engineering | Best Researcher Award

Ms. Kaili Wang | Engineering | Best Researcher Award

Student at NB U, China

MS Kaili Wang is a distinguished researcher in the field of gene editing and molecular diagnostics, specializing in nucleic acid detection for agricultural biotechnology. She is affiliated with Ningbo University, School of Food Science and Engineering, China, and collaborates with Zhejiang Academy of Agricultural Sciences and the State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products. With a keen interest in genetic modification detection, her research focuses on improving the precision and sensitivity of detection methods for gene-edited organisms. Her recent work on droplet digital PCR (ddPCR) for MSTN gene-edited cattle has contributed significantly to the field of regulatory science and food safety. Dedicated to advancing biotechnology applications, she plays a crucial role in shaping methodologies for genetic monitoring, ensuring consumer safety, and fostering global discussions on gene editing and its implications.

Professional Profile

Education

MS Kaili Wang pursued her higher education in biotechnology, molecular biology, and food science, which provided a strong foundation for her research career. She earned her degrees from prestigious Chinese institutions, including Ningbo University, where she specialized in food science and genetic detection methods. Her academic training emphasized molecular diagnostics, genetic engineering, and PCR-based technologies, equipping her with the expertise necessary to develop innovative detection methods for genetically modified organisms (GMOs). Throughout her education, she engaged in interdisciplinary research, gaining hands-on experience in genetic modification analysis, nucleic acid quantification, and regulatory science. Her studies were complemented by rigorous laboratory work and collaborations with leading scientists in the field. This educational background has enabled her to contribute significantly to the advancement of gene-editing detection technologies, ensuring accuracy, sensitivity, and reliability in molecular diagnostics.

Professional Experience

With extensive experience in genetic research and molecular diagnostics, MS Kaili Wang has worked as a researcher at Ningbo University and in collaboration with Zhejiang Academy of Agricultural Sciences. She has been instrumental in developing innovative nucleic acid detection methods for gene-edited organisms, particularly using droplet digital PCR (ddPCR). Her work focuses on the safety assessment, traceability, and detection of genetically modified products, making a significant impact in the field of food safety and agricultural biotechnology. She has contributed to multiple high-impact research projects, collaborating with government agencies, regulatory bodies, and scientific institutions to establish robust methodologies for genetic monitoring. Her professional expertise extends to training young researchers, publishing peer-reviewed articles, and presenting her findings at international conferences related to gene editing and food safety. Her work plays a critical role in ensuring the accurate detection and regulation of gene-edited agricultural products.

Research Interests

MS Kaili Wang’s primary research interests lie in gene editing, nucleic acid detection, food safety, and molecular diagnostics. She is particularly focused on developing and optimizing PCR-based techniques, including ddPCR, qPCR, and CRISPR-based detection methods. Her research aims to enhance the specificity, sensitivity, and reliability of gene-editing detection, ensuring consumer safety and regulatory compliance. She is also deeply interested in the traceability of genetically modified organisms (GMOs) and their impact on food production, security, and public health. Through her work, she seeks to bridge the gap between scientific advancements and regulatory frameworks, contributing to the development of robust detection technologies that can be applied on a global scale. By integrating biotechnology with food safety regulations, she aims to provide innovative solutions for ensuring transparency in agricultural biotechnology and fostering public trust in gene-edited products.

Awards and Honors

Throughout her career, MS Kaili Wang has received numerous recognitions for her contributions to gene editing detection and food safety research. She has been honored with awards from academic institutions, regulatory bodies, and biotechnology organizations for her innovative work in nucleic acid quantification and molecular diagnostics. Her research on ddPCR-based detection of MSTN gene-edited cattle has gained international recognition, positioning her as a leading scientist in genetic monitoring and food safety regulation. She has been invited as a keynote speaker at scientific conferences, sharing her expertise on gene editing detection methodologies. Additionally, she has received grants and funding from government agencies to further her research in gene-editing detection and its application in regulatory science. Her dedication and contributions to biotechnology and food safety continue to make a profound impact, earning her a reputation as a pioneering researcher in the field.

Conclusion

MS Kaili Wang’s research is highly innovative and impactful, making significant contributions to gene editing detection and food safety monitoring. The work demonstrates scientific excellence, regulatory relevance, and technical robustness, making them a strong candidate for the Best Researcher Award. However, further research could focus on expanding the scope of detection beyond MSTN, increasing sample size, and facilitating regulatory adoption to enhance the real-world impact.

Publications Top Noted

Author: Kaili Wang, Yi Ji, Cheng Peng, Xiaofu Wang, Lei Yang, Hangzhen Lan, Junfeng Xu, Xiaoyun Chen
Year: 2025
Citation: Wang, K.; Ji, Y.; Peng, C.; Wang, X.; Yang, L.; Lan, H.; Xu, J.; Chen, X. (2025). “A Novel Quantification Method for Gene-Edited Animal Detection Based on ddPCR.” Biology, 14(2), Article 0203. DOI: 10.3390/biology14020203.
Source: Multidisciplinary Digital Publishing Institute (MDPI)

 

Anna Plichta | Engineering | Best Researcher Award

Mrs. Anna Plichta | Engineering | Best Researcher Award

Research and Teaching Assistant Professor, Cracow University of Technology, Poland

Dr. Anna Plichta is a Research and Teaching Assistant Professor at Cracow University of Technology, Poland, where she also works at the International Center of Education. With a multifaceted background in Comparative Literature and Computer Science, she combines insights from the humanities with advanced computational techniques. Dr. Plichta holds a PhD in Computer Science from Politechnika Wrocławska (2019) and has a strong academic foundation with degrees from Jagiellonian University and Politechnika Krakowska. Her interdisciplinary research focuses on machine learning, artificial intelligence, and applied computer science, with practical applications in energy systems, motor diagnostics, and microbiology. With a commitment to educational excellence and international collaboration, Dr. Plichta has been a key figure in research and teaching at the university for over a decade.

Profile

Strengths for the Award

  1. Diverse Research Interests and Impact: Dr. Plichta’s work spans multiple domains including comparative literature, computer science, machine learning, electrical engineering, and applied mathematics. This interdisciplinary approach showcases her ability to bridge distinct fields, offering innovative solutions to complex problems. Notably, her research on bacterial classification using machine learning methods and energy consumption forecasting using machine learning reflects her versatility and the relevance of her work to contemporary scientific and industrial challenges.
  2. High Citation Impact: Her publication titled “Deep learning approach to bacterial colony classification” has received 134 citations, which demonstrates significant influence and recognition in the scientific community. This kind of citation impact highlights the relevance and utility of her research findings.
  3. Technological Innovation: Her contributions to induction motor fault detection using machine learning techniques (e.g., simulated annealing and genetic algorithms) are highly practical, with clear industrial applications. This emphasizes her role in driving innovation in applied fields, particularly in electromechanical systems and energy sectors, making her work not only academic but also relevant to real-world problems.
  4. Academic Leadership and Teaching: As a Research and Teaching Assistant Professor at Cracow University of Technology, Dr. Plichta combines academic instruction with significant research involvement. Her active engagement in the International Center of Education is a testament to her dedication to fostering a new generation of researchers and students.
  5. Publication Quality: Dr. Plichta consistently publishes in peer-reviewed journals and presents at high-level conferences like those organized by the European Council for Modelling and Simulation. This speaks to her engagement with the broader academic community and her ability to produce high-quality research.

Areas for Improvement

  1. Collaboration and Interdisciplinary Work: While Dr. Plichta’s interdisciplinary work is commendable, further expanding collaborations with other research groups and international institutions could enhance the visibility and impact of her work. Expanding collaborative efforts, especially with industry partners, could help bring more practical applications to the forefront.
  2. Public Outreach and Dissemination: While her publications and citations are notable, there could be a more concerted effort to engage with the general public or non-academic stakeholders, particularly in areas like bacterial classification and energy forecasting, where her research could have significant societal impact. This could include public lectures, podcasts, or participation in science communication events.
  3. Further Publishing in High-Impact Journals: Publishing in higher-impact journals (e.g., Nature, IEEE Transactions) could further boost the international recognition of her work. While her current journal choices are respected, elevating the visibility of her research in top-tier outlets may further her career and contribute to the recognition of her as a leading expert in her field.

Education

Dr. Anna Plichta’s academic journey blends the study of literature and technology. She earned a BA in Comparative Literature (2005) and MA in Comparative Literature (2007) from Jagiellonian University. Her fascination with technology led her to pursue an MA in Computer Science (2010) from Politechnika Krakowska, followed by a PhD in Computer Science from Politechnika Wrocławska (2019). Her doctoral research focused on applying computational methods to real-world engineering challenges, a field that bridges the gap between theoretical knowledge and practical applications. With this strong foundation, she applies machine learning and AI techniques to diverse areas such as energy forecasting, motor fault detection, and bacterial classification. Dr. Plichta’s educational background not only demonstrates her expertise in both the arts and sciences but also her commitment to lifelong learning and interdisciplinary research.

Experience 

Dr. Anna Plichta has had a distinguished career as a Research and Teaching Assistant Professor at Cracow University of Technology since 2010. She has been an integral part of the university’s International Center of Education since 2015, fostering international research collaboration. Dr. Plichta’s professional experience spans both teaching and research, with a particular emphasis on computational techniques applied to energy systems, mechanical engineering, and biology. She has developed and taught courses related to machine learning, AI, and applied computer science. Her academic leadership extends to guiding postgraduate students and conducting collaborative research projects. Dr. Plichta’s expertise in energy consumption modeling, motor diagnostics, and microbial classification has positioned her as a thought leader in these domains, contributing to over 17 published works. She is also involved in the advancement of international education, contributing to the university’s global research network.

Research Focus 

Dr. Anna Plichta’s research focuses on applying machine learning and artificial intelligence to solve complex problems in fields ranging from energy systems to biological data analysis. Her work in forecasting energy consumption uses advanced computational techniques to predict energy demands in clusters, supporting sustainable energy solutions. In the area of electromechanical engineering, she has applied genetic algorithms and wavelet analysis to detect faults in induction motors, such as inter-turn short circuits. Additionally, her research in microbiology explores the use of image analysis and neural networks to identify bacterial species, contributing to more accurate and efficient diagnostic methods. Dr. Plichta is deeply invested in interdisciplinary research, bringing together computational methods with practical applications in industries such as energy, engineering, and healthcare. She is particularly interested in improving the accuracy and efficiency of diagnostic techniques and optimizing energy consumption through AI-driven models.

Publication 

  1. Forecasting Energy Consumption in Energy Clusters using Machine Learning Methods 📊💡
  2. Matrix Similarity Analysis of Texts Written in Romanian and Spanish 📚🔍
  3. Identification of Inter-turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing ⚡🔧
  4. Application of Genetic Algorithm for Inter-turn Short Circuit Detection in Stator Winding of Induction Motor ⚙️🧠
  5. Recognition of Species and Genera of Bacteria by Means of the Product of Weights of the Classifiers 🦠🔬
  6. Application of Image Analysis to the Identification of Mass Inertia Momentum in Electromechanical Systems with Changeable Backlash Zone ⚙️🔍
  7. Application of Wavelet-Neural Method to Detect Backlash Zone in Electromechanical Systems Generating Noises 🔧🌊
  8. Methods of Classification of the Genera and Species of Bacteria Using Decision Tree 🌱📈
  9. Deep Learning Approach to Bacterial Colony Classification 🧬🤖
  10. The DDS Synthesizer (for FPGA Platform) for the Purpose of Research and Education 💻📚

Conclusion

Dr. Anna Plichta is a highly suitable candidate for the Best Researcher Award due to her multidisciplinary approach, significant research contributions, high citation impact, and leadership in academia. She has demonstrated a consistent ability to tackle complex challenges through computational methods, contributing valuable knowledge to both the scientific community and industrial sectors. Her work, particularly in machine learning and electromechanical systems, is both innovative and impactful.While there are always areas for improvement, such as expanding collaborative efforts and public outreach, these do not overshadow her significant academic achievements. Dr. Plichta’s track record of high-quality research and teaching, along with her contribution to solving real-world problems, make her an excellent contender for the Best Researcher Award.

Nasimuddin | Engineering | Best Researcher Award

Dr. Nasimuddin | Engineering | Best Researcher Award

Principal Scientist I2R ASTAR  Singapore

Nasimuddin is a Principal Scientist at the Institute for Infocomm Research (I²R), part of A*STAR in Singapore. With a distinguished career in RF and antenna engineering, he has contributed extensively to the fields of wireless power transmission, sensor design, and advanced antenna systems for a variety of applications including satellite communications and energy harvesting. Nasimuddin’s work bridges industry and academia, evidenced by his collaborations, industry technology transfers, and numerous patents.

profile

Google scholar.com

Education 🎓

  • Ph.D. in Electronic Science (2004): University of Delhi, India
    Thesis: Analysis and design of multilayer slow-wave microstrip structures and multilayered microstrip antennas.
  • M.Tech. in Microwave Electronics (1998): University of Delhi, India
  • M.Sc. in Electronics (1996): Jamia Millia Islamia, India
  • B.Sc. in Physics, Mathematics, Chemistry (1994): Jamia Millia Islamia, India

Experience 🏢

Nasimuddin has held various research and teaching roles. Since 2006, he has been part of I²R, A*STAR Singapore, where he currently serves as a Principal Scientist. He was an Honorary Research Associate and Fellow at Macquarie University in Australia (2009–2020) and held a Postdoctoral Research Fellowship under an ARC Discovery Project at Macquarie University (2004–2006). He has also conducted specialized courses in RF energy harvesting applications at NIT Silchar, India.

Research Interests 🔬

Nasimuddin’s research interests include:

  • Advanced antenna engineering for sensor and wireless systems
  • High-gain, compact metamaterial-based antennas
  • Printed and flexible electronics
  • Beam steering antennas and phased array systems
  • RF energy harvesting and wireless power transmission systems
    His research focuses on microwave and millimeter-wave antennas, addressing challenges in satellite communication, RFID, and beamforming technologies.

Awards 🏆

  • Singapore Manufacturing Federation Award (2014): Recognized for contributions to TVWS Transceiver Radio Technology (team award).
  • Dedicated Service Award (2022): Honored for 15 years at I²R, Singapore.
  • Long Service Awards (2012, 2017): For 5 and 10 years at I²R, Singapore.
  • Young Scientist Award (2005): Awarded by the International Union of Radio Science (URSI).
  • M.Tech. Merit Scholarship (1996): University of Delhi, for outstanding academic performance.

Publications Top Notes📚:

Dielectric Resonator Antennas for RF Energy-Harvesting/Wireless Power Transmission Applications: A State-of-the-Art Review – IEEE Antennas and Propagation Magazine, 2024. Cited by 12 articles.

Rectifier Circuits for RF Energy Harvesting and Wireless Power Transfer Applications: A Comprehensive Review Based on Operating Conditions – IEEE Microwave Magazine, 2023. Cited by 18 articles.

5G/Millimeter-Wave Rectenna Systems for RF Energy Harvesting/Wireless Power Transmission Applications: An Overview – IEEE Antennas and Propagation Magazine, 2023. Cited by 25 articles.

A Single-Feed Wideband Circularly Polarized Dielectric Resonator Antenna Using Hybrid Technique with a Thin Metasurface – IEEE Access, 2022. Cited by 10 articles.

Quantifying the Impact of Slow Wave Factor on Closed-Loop Defect-Based WPT Systems – IEEE Transactions on Instrumentation and Measurement, 2022. Cited by 8 articles.

Prof. Min Sik Lee | Engineering | Best Researcher Award

Prof. Min Sik Lee | Engineering | Best Researcher Award

Prof. Min Sik Lee, Pusan national university, South Korea

Dr. Lee Min Sik is a prominent researcher in the field of Mechanical Engineering at Pusan National University, specializing in hybrid composite materials and advanced manufacturing techniques. With a focus on both theoretical and experimental studies, he has significantly contributed to the understanding of sheet metal forming processes and material properties.

Profile

Orcid

Education 🎓

Dr. Lee completed his Ph.D. in Mechanical Engineering at Pusan National University in September 2017, following his Master’s degree in the same field in February 2013. He also obtained his Bachelor’s degree from the same institution in February 2011, demonstrating a strong foundation in mechanical engineering from an early stage.

Experience 🛠️

Since completing his Ph.D., Dr. Lee has engaged in various research projects funded by national and international organizations. His work includes significant contributions to the National Research Laboratory and the Technological Innovation R&D Program, focusing on fuel cell technology and hybrid composite materials.

Research Interests 🔬

Dr. Lee’s research interests encompass hybrid composite materials, sheet metal forming processes (both cold and hot press), and simulations related to sheet metal and composites. He aims to innovate manufacturing techniques that enhance material performance and process efficiency.

Awards 🏆

Dr. Lee has received several prestigious awards, including:

Future Researcher Award 2017, Busan, Korea (Dec 2017)

BK21 Plus Best Researcher Award 2016 (Mar 2017)

A M Strickland Prize (Best Paper), awarded by the U.K. Institution of Mechanical Engineers (Jun 2016)

Publication Top Notes 📚

Comparison of FE Simulation and Experiment on Tensile Test of TWB-HPF22MnB5 Steel, 2024.

Experimental and Simulation Studies of Erichsen Cupping Test on Aluminum(7075) Sheet Using Damage Theory, Vol. 20(10), pp. 698-709, 2024.

Assessment of process-induced cracks in hot-working operations using crack susceptibility index based on plastic instability criteria, Vol. 29(10), 2024.