Shaohui Lei | Service AI | Best Researcher Award

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Assist. Prof. Dr. Shaohui Lei | Service AI | Best Researcher Award

Dr, Southwest Jiaotong University, China.

Dr. Shaohui Lei is an Assistant Professor at the School of Economics and Management, Southwest Jiaotong University, China. His research specializes in service marketing, consumer behavior, and technology-enabled service interactions, particularly focusing on the behavioral impact of artificial intelligence in service environments. He has authored impactful articles in prestigious journals including Journal of Business Ethics, Journal of Service Research, Applied Psychology, Journal of Business Research, and International Journal of Hospitality Management. Dr. Lei’s interdisciplinary research uniquely blends behavioral science, marketing theory, and AI technology, addressing timely issues such as customer misbehavior in robotized service settings. His work continues to advance the frontier of human-AI interaction and its implications for ethical, psychological, and practical aspects of modern service economies.

🌐Author Profile

🏆 Strengths for the Award

  1. Focused Expertise in Service Marketing & Consumer Behavior
    Dr. Lei’s research centers on timely and high-impact areas such as service robot personas, customer misbehavior, and ethical consumer interactions—critical to both academic inquiry and real-world service industries.

  2. Publications in High-Impact Journals
    His work has appeared in prestigious peer-reviewed journals such as:

    • Journal of Business Ethics

    • Journal of Service Research

    • Applied Psychology

    • Journal of Business Research

    • International Journal of Hospitality Management

    • Journal of Product & Brand Management
      This reflects scholarly credibility and sustained contributions to his field.

  3. Innovative and Interdisciplinary Research
    The study “Servant” versus “Partner”: Investigating the effect of service robot personas on customer misbehavior demonstrates a forward-thinking integration of AI, robotics, and behavioral marketing, offering novel insights into human-machine interaction.

  4. Global Relevance and Practical Applications
    His research addresses consumer behavior patterns that are increasingly relevant in a tech-driven global service economy, particularly amid the rise of AI and automation in retail and hospitality sectors.

🎓 Education 

Shaohui Lei completed his academic training with a focus on management science and behavioral research. While the exact institutions and degree progression are not detailed in the CV, his academic path reflects rigorous preparation in marketing, consumer psychology, and applied economics. He has developed a strong academic foundation enabling cross-disciplinary contributions at the intersection of management and emerging service technologies. His education likely includes a PhD in a relevant domain—such as Business Administration, Marketing, or Psychology—and postgraduate specialization in service operations and behavior. This academic trajectory supports his current research output, which integrates empirical methods with theoretical frameworks to study customer behavior in technologically mediated environments.

🔬 Research Focus on Service AI

Dr. Lei’s research explores the intersection of service marketing, consumer behavior, and artificial intelligence, focusing on how consumers interact with AI-driven service technologies like robots and virtual agents. He investigates customer misbehavior, perception, and trust in service contexts shaped by human-machine dynamics. His studies offer insights into how robot personas (e.g., servant vs. partner roles) affect customer behavior, with implications for designing AI interactions that balance efficiency, ethics, and user satisfaction. His research also addresses psychological drivers behind unethical or deviant behaviors in service encounters. Dr. Lei leverages experimental designs and behavioral modeling to produce actionable knowledge that supports AI deployment in retail, hospitality, and public services. This line of inquiry contributes both to theory development and managerial decision-making in an increasingly digitized service landscape.

📚Publication Top Notes

“Servant” versus “Partner”: Investigating the Effect of Service Robot Personas on Customer Misbehavior

  • Authors: Shaohui Lei

  • Published In: Journal of Business Research

  • Summary:
    This article examines how the assigned role of a service robot—whether perceived as a “servant” or a “partner”—influences customer misbehavior. Using behavioral experiments, Dr. Lei and colleagues uncover that customers are more likely to act disrespectfully or abusively toward robots that adopt a submissive “servant” persona. In contrast, partner-like robots foster mutual respect and reduce misconduct. The findings illuminate the psychological and ethical dimensions of AI-driven service experiences and provide actionable design recommendations for businesses integrating robots into frontline operations. This work contributes to service marketing, ethical AI design, and human-robot interaction literature.

✅ Conclusion

Dr. Shaohui Lei is a highly suitable candidate for the Best Researcher Award, particularly in fields intersecting consumer behavior, service marketing, and technology-enabled service environments. His robust publication record in top-tier journals, combined with his innovative focus on ethical and behavioral implications of service automation, mark him as a thought leader in his domain. With strategic

Sirmayanti | AI and Digital Communication | Women Researcher Award

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Assoc. Prof. Dr. Sirmayanti | AI and Digital Communication | Women Researcher Award

Associate Professor at Politeknik Negeri Ujung Pandang, Indonesia.

Assoc. Prof. Dr. Sirmayanti, Ph.D., is a prominent Indonesian scholar in telecommunication engineering, currently serving at the State Polytechnic of Ujung Pandang (PNUP). With a career spanning over two decades, she combines academic excellence, cutting-edge research, and impactful community engagement. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia, with Cum Laude distinction. Her research explores a wide spectrum—from 5G and RF engineering to artificial intelligence, IoT, and green communication technologies. As Head of the Telecommunication Network Engineering Technology program and the Centre for Applied Telecommunications Technology Research at PNUP, she has mentored numerous scholars and led various national and international innovation projects. Her prolific output includes over 60 publications, 15 technical books, and numerous patented inventions. A passionate advocate for education and digital equity, she’s been widely recognized for her contributions, including multiple presidential awards and the Australia Awards Alumni honor.

🌐Author Profile

🎓 Education 

Sirmayanti’s educational journey reflects a trajectory of excellence across multiple continents. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia (2015), where she graduated cum laude for her innovative research in digital RF architectures. Her Master of Engineering from the same university (2008) was awarded with First-Class Honors, reinforcing her expertise in signal processing and software radio systems. Her Bachelor of Engineering in Telecommunications, from Hasanuddin University, Indonesia (2001), was completed with High Distinction. She began her technical training at SMK Telkom Sandhy Putra 2 (1997), where she laid the foundation for her career in communications technology. In 2024, she was formally recognized as an Insinyur Profesional Utama (IPU), ASEAN Engineer, and APEC Engineer, further validating her technical leadership at the global level. Her continuous professional development includes numerous international certifications in Open RAN, IoT, AI, and RF engineering, demonstrating her commitment to lifelong learning.

Strengths for the Award

  1. Academic Excellence & Global Exposure:

    • Holds a Ph.D. with Cum Laude honors in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia.

    • Her academic track includes top honors at every level, showcasing consistent high performance.

  2. Extensive Multidisciplinary Research:

    • Research covers Wireless Communications, IoT, AI, RF Engineering, Satellite Communications, and 5G.

    • Led and contributed to over 35+ funded research projects from national and international agencies (e.g., BRIN, KEMENRISTEKDIKBUD, DIKTI, AFS, Australia Direct Aid).

  3. High Research Output:

    • Over 60 peer-reviewed publications, including Scopus-indexed journal articles (Q1–Q3) and IEEE conference proceedings.

    • Significant work on metaheuristic algorithms for diabetes prediction, 5G networks, and digital RF systems.

  4. Innovation and Patents:

    • Holds multiple patents and copyrights (2020–2025) on innovations such as:

      • Wireless Bridge Mi-Fi 4G

      • Techniques for RF Spectrum Image Cancellation

      • Tools for lithium battery drop testing

    • Demonstrates intellectual property development at a national level.

  5. Leadership and Capacity Building:

    • Head of the Telecommunication Network Engineering Program and Center for Applied Telecommunications Technology Research (PNUP).

    • National assessor, ISO lead auditor, and active in curriculum reform and national education initiatives.

💼 Professional Experience 

Since 2001, Sirmayanti has served as a Lecturer and Researcher at the State Polytechnic of Ujung Pandang (PNUP), where she currently leads both the Telecommunication Network Engineering Technology Program and the Centre for Applied Telecommunications Technology Research (CATTAR). Her experience spans academia, national policy, quality assurance, and technology innovation. She is a certified lead auditor for ISO 9001 and an assessor for national educational programs like BKD, BIP, and Praktisi Mengajar under Indonesia’s Ministry of Research and Education. Her leadership in internal quality systems and curriculum design has transformed vocational education frameworks. She has led over 40 funded research and community service projects across 5G, AI, IoT, and rural connectivity systems. Internationally, she collaborates with UiTM Malaysia and is a certified practitioner in Open RAN, AI, and RF planning. Her blend of administrative leadership, hands-on technical innovation, and commitment to community service uniquely positions her in advancing AI and digital communication technologies.

🏅 Awards and Honors

Assoc. Prof. Sirmayanti has been decorated with some of Indonesia’s highest honors for civil service, including the Satyalancana Karya Satya 20 Tahun and 10 Tahun awards from the President of the Republic of Indonesia in 2024 and 2018. She is also among the “70 Outstanding Alumni” of Australian institutions recognized by Australia Awards Indonesia. She received the PIN LEMHANNAS award in 2024 and the prestigious INSINAS Research Grant Award from BRIN for her research on distortion mitigation in 5G systems. Her academic journey was supported by multiple competitive scholarships, such as the Australian Development Scholarship (ADS) and DIKTI Fellowships for postgraduate research. Beyond institutional honors, her contributions to community service and STEM education have garnered attention from various national and international programs. Her consistent record of excellence in education, research, innovation, and social impact establishes her as a leading figure in digital communication and AI technology in Southeast Asia.

🔬 Research Focus on AI and Digital Communication

Sirmayanti’s research sits at the intersection of artificial intelligence, wireless communication, and digital signal processing. Her key focus areas include 5G/6G networks, RF engineering, IoT-based infrastructure, AI-powered healthcare diagnostics, and green communication technologies. She applies metaheuristic algorithms and machine learning models to enhance wireless performance, such as through feature optimization for disease prediction or spectral efficiency in OFDM systems. Notably, she has developed low-power RF transmitters, digital baseband tuning systems, and hardware innovations like dual-band filters for 5G. She also works on rural connectivity solutions using mini BTS and community Wi-Fi networks. Her projects are not only technically advanced but socially driven—such as her work on digital inclusion in remote Indonesian islands and health monitoring systems. Her patents and intellectual property showcase strong emphasis on applied research, while her academic publications reflect rigorous peer-reviewed contributions to the fields of AI, digital modulation, and next-generation communication protocols.

📚 Publication Top Notes

1. E-Government dan Digitalisasi Layanan Publik

Authors: M. Mirfan, M. Mariana, S. Abdullah, A. Nurfadly, S. Suhada, S. Sirmayanti
Publisher: Yayasan Kita Menulis (2025)
Type: Book/Handbook
Summary:
This publication explores the digital transformation of public services through e-government frameworks. It addresses how Indonesian government institutions adopt ICT-based platforms for public administration, service efficiency, and transparency. Sirmayanti’s contribution lies in her analysis of telecommunication infrastructure enabling smart governance and public service digitization. The book combines theoretical frameworks with empirical insights and practical models for policymakers and technocrats.

2. A Systematic Literature Review of Diabetes Prediction Using Metaheuristic Algorithm-Based Feature Selection: Algorithms and Challenges Method

Authors: FR Sirmayanti, Pulung Hendro Prastyo, Mahyati
Journal: Applied Computer Science, 21(1), pp. 126–142, 2025 (Scopus Q3)
DOI: Applied Computer Science
Summary:
This systematic review focuses on AI-based diabetes prediction models utilizing metaheuristic feature selection algorithms. The authors classify and analyze recent algorithms (GWO, PSO, ACO, etc.), evaluating their effectiveness in reducing feature dimensionality while maintaining high diagnostic accuracy. Challenges such as computational cost, algorithm convergence, and overfitting are critically discussed. The work contributes a taxonomy of algorithmic strategies and highlights future research gaps for clinical decision support systems in e-health.

3. An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection

Authors: M. Sirmayanti, Pulung Hendro Prastyo
Conference: 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 96–101
Summary:
This conference paper proposes an improved version of the Grey Wolf Optimizer (GWO) for feature selection by incorporating opposition-based learning, mutation, and local search mechanisms. The hybridized GWO algorithm demonstrated enhanced exploration-exploitation balance and was tested on medical and telecom datasets. Results showed better performance compared to standard GWO and other algorithms, with significant implications for AI-driven feature selection in classification systems.

4. Design of Dual-Band Bandpass Filter (DBPF) for 5G Applications

Authors: M. Mimsyad, A. Bazergan, S. Sirmayanti
Journal: AIP Conference Proceedings, Vol. 3140, 040018 (2024)
DOI: 10.1063/5.0222871
Summary:
This study presents the design and simulation of a dual-band bandpass filter (DBPF) aimed at 5G wireless applications. Utilizing advanced substrate materials and electromagnetic modeling, the design supports frequency bands relevant for mid-band 5G use cases. Sirmayanti contributed to the filter miniaturization and optimization phase. The paper outlines how the DBPF improves signal selectivity and reduces interference, with potential application in small-cell architectures and 5G-enabled devices.

5. Rekayasa Mitigasi Kebocoran Gas LPG dengan Sistem Monitoring Telegram Bot Berbasis Internet of Things (IoT)

Authors: S. Sirmayanti, E. Dwi Melda
Conference: Seminar Nasional Teknik Elektro dan Informatika (SNTEI), Vol. 9(1), pp. 223–228, 2023
Summary:
This paper discusses the development of an IoT-based real-time monitoring system for LPG gas leak detection, integrated with a Telegram bot alert system. The authors designed and implemented an Arduino-based sensor framework to monitor gas concentration in households and notify users immediately through messaging. Sirmayanti led the system integration and sensor calibration. The project highlights practical IoT use cases in smart safety systems for domestic environments.

✅ Conclusion

Assoc. Prof. Sirmayanti is highly suitable for the Women Researcher Award. Her extensive academic, research, innovation, and community engagement portfolio, combined with her leadership in engineering education and commitment to women’s empowerment in STEM, exemplify the spirit of the award. Her work not only advances telecommunications and AI research but also bridges technology with sustainable development and inclusive education—hallmarks of a transformative woman researcher.

Sajjad Hashemi Abasabadi | Quantum thermodynamics | Best Researcher Award

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Mr. Sajjad Hashemi Abasabadi | Quantum thermodynamics | Best Researcher Award

PhD Candidate, Vali-e-Asr University of Rafsanjan, Iran

Sajjad Hashemi Abasabadi is an emerging physicist and a dedicated PhD candidate in Optics and Laser Physics at Vali-e-Asr University of Rafsanjan, Iran. With a Master’s degree in Atomic and Molecular Physics and a solid foundation in laser spectroscopy, Sajjad is spearheading theoretical innovations in quantum thermodynamics and energy-efficient heat engines. His work intricately combines quantum optics, information theory, and thermodynamic modeling to advance nanoscale energy systems. His growing publication record in high-impact journals and strong conceptual grasp of quantum systems position him as a promising young researcher in the frontier of quantum technologies. 🌟

👨‍🔬 Author Profile

✅ Strengths for the Award

Sajjad Hashemi Abasabadi has demonstrated notable potential and commitment to advancing the field of quantum thermodynamics and quantum heat engines, particularly within the context of quantum optics and information. As a Ph.D. candidate, his contributions reflect a deep theoretical understanding and novel analytical approaches. His published works in reputable journals like Scientific Reports and International Communications in Heat and Mass Transfer indicate the scholarly merit and international visibility of his research.

  • Development of a Quantum Otto engine model with a Pöschl–Teller potential, contributing to energy efficiency at the nanoscale.

  • Exploration of non-thermal reservoirs and their impact on work and efficiency, which broadens the understanding of thermodynamic behavior in quantum systems.

  • Innovative analysis of endoreversible quantum heat engines under strong coupling, offering insight into irreversibility and system performance trade-offs.

His work addresses fundamental challenges in energy-efficient technologies and emerging quantum devices, aligning with cutting-edge priorities in modern physics and quantum engineering.

🎓 Education

Sajjad began his academic journey at Vali-e-Asr University of Rafsanjan, where he earned his M.Sc. in Physics, specializing in Atomic and Molecular Physics. His thesis focused on the spectroscopic characterization of molecular transitions under various pressure conditions, revealing key insights into atomic behavior in dynamic environments. Driven by a passion for precision measurement and quantum mechanics, he continued his academic path at the same university, currently pursuing a Ph.D. in Physics (Optics and Laser). His doctoral research is centered on laser-based high-resolution imaging and quantum metrology, where he explores applications ranging from ultrafast laser dynamics to the mechanics of quantum heat engines. 🎓🔬

👨‍🔬 Experience

During his academic career, Sajjad has contributed to several research endeavors that reflect both depth and innovation. His collaborative work extends across multiple domains of quantum physics, from thermodynamic cycle modeling to non-classical reservoir dynamics. He has presented his findings at national conferences, gaining recognition for tackling complex theoretical models with practical significance in quantum engines. He has also participated in interdisciplinary projects involving ultrafast laser dynamics, contributing to the design of precision instruments in optical physics. His evolving expertise is evidenced by peer-reviewed publications in Scientific Reports and International Communications in Heat and Mass Transfer. 📊🧪

🔍 Research Focus on Quantum thermodynamics

Sajjad’s research bridges quantum thermodynamics, optics, and non-equilibrium heat engine modeling, with a primary focus on Quantum Otto heat engines. He explores how non-standard reservoir dynamics and system-bath interactions influence performance, including studies on Pöschl–Teller potential models for enhanced efficiency, the role of coherent and non-thermal reservoirs, and the impact of strong coupling in endoreversible engines. Through analytical and numerical modeling, his work supports the development of nanoscale thermal machines relevant to quantum information processing and energy conversion technologies. 🔭⚛️

📚 Publications Top Notes

Quantum Otto Heat Engine with Pöschl–Teller Potential in Contact with Coherent Thermal Bath

Authors: Sajjad Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: Scientific Reports, Volume 13, Article 10522, 2023
Publisher: Nature Portfolio
DOI: 10.1038/s41598-023-37681-1
Summary:
This paper explores the behavior of a quantum Otto heat engine using a Pöschl–Teller potential as the working medium, coupled to a coherent thermal reservoir. By incorporating quantum coherence into the thermal bath, the study demonstrates measurable improvements in efficiency and work output. The authors establish that coherence can be leveraged to enhance the performance of nanoscale thermal machines beyond classical thermodynamic limits, offering a pathway toward the realization of quantum-enhanced energy devices.

Endoreversible Quantum Heat Engine Affected by Strong Coupling with Thermal Reservoir

Authors: Sajjad Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: International Communications in Heat and Mass Transfer, Volume 167, Article 109309, 2025
Publisher: Elsevier
DOI: 10.1016/j.icheatmasstransfer.2025.109309

🔍 Summary:
In this work, the authors examine a quantum endoreversible Otto engine operating under strong coupling between the system and its thermal environment. Unlike weak coupling models that simplify energy exchange, this study reveals how strong interactions affect irreversibility, power output, and overall thermodynamic efficiency. The analysis uncovers trade-offs between performance and system-bath coupling strength, providing critical insights into the design of realistic quantum thermal engines operating in non-ideal conditions.

Efficiency and Work Quantum Otto Machine in Contact with Non-Thermal Reservoir

Authors: S. Hashemi Abasabadi, S.Y. Mirafzali, H.R. Baghshahi
Journal: Quarterly Journal of Optoelectronic, Volume 6, Issue 1, Pages 51–58, 2023
DOI: https://doi.org/10.30473/jphys.2023.69525.1170

🔍 Summary:
This article investigates the performance of a quantum Otto engine interacting with a non-thermal reservoir, extending conventional thermodynamic models. By introducing non-thermal bath characteristics such as squeezed states or engineered distributions, the paper analyzes their impact on the engine’s efficiency and work extraction capacity. Results show that non-thermal reservoirs can be engineered to outperform thermal baths, marking a significant step forward in optimizing quantum energy systems.

🧠 Conclusion

Sajjad Hashemi Abasabadi is a visionary early-career researcher whose work bridges theoretical physics and applied quantum technologies. His groundbreaking studies on quantum heat engines have unveiled fundamental relationships between coherence, coupling strength, and engine performance, shaping a new understanding of how quantum machines can operate efficiently in realistic environments. Despite being at the outset of his career, Sajjad has already carved a niche in quantum thermodynamics and optics, showing the potential to lead transformative research in the field.

Chao Yang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Chao Yang | Mechanical Engineering | Best Researcher Award

Associate Professor, Jiaxing University, China.

Dr. Chao Yang, born in 1982, is currently a Lecturer in the School of Mechanical Engineering at Jiaxing University, China. He holds a Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University (2019), preceded by an M.E. in Engineering Mechanics from Dalian University of Technology (2009) and a B.E. in Process Equipment & Control Engineering from Zhengzhou University of Light Industry (2005). Since joining academia, Dr. Yang has consistently contributed to advancing the mechanics of parallel manipulators, focusing on kinematics, dynamics, stiffness analysis, and intelligent optimization algorithms. With over 20 peer-reviewed publications in reputable international journals and conferences, he has become a recognized figure in robot modeling and design. Dr. Yang also serves as a reviewer for journals like Mechanism and Machine Theory and Applied Mathematical Modelling, further reflecting his deep academic engagement. His work bridges theoretical innovation and practical engineering applications in robotics and precision mechanisms.

🧾Author Profile

🏆 Strengths for the Award

1. Deep Specialization in Parallel Manipulators

Dr. Yang’s research is highly focused and impactful in the domains of kinematics, stiffness modeling, dynamics, and optimization of parallel manipulators. His work addresses both theoretical foundations and practical design aspects, which are critical to modern robotics and automation systems.

2. Robust and Consistent Publication Record

Since 2018, Dr. Yang has published 20+ peer-reviewed articles, many in high-impact international journals such as:

  • Mechanism and Machine Theory

  • Mechanical Sciences

  • Chinese Journal of Mechanical Engineering

  • Robotica

  • International Journal of Control

3. Novel Contributions in Modeling Techniques

Dr. Yang has proposed and validated novel methodologies including:

  • Elastodynamic and elastostatic modeling of over-constrained systems

  • Use of neural networks and principal component analysis for multi-objective optimization

  • Finite-time tracking control techniques for underwater vehicles
    These show innovation, interdisciplinary thinking, and a mastery of mechanical system modeling.

4. International Journal Reviewership

Dr. Yang serves as a reviewer for journals like Applied Mathematical Modelling and Mechanism and Machine Theory, which reflects recognition of his expertise by the academic community.

🎓 Education 

Dr. Chao Yang began his academic journey in mechanical disciplines with a Bachelor’s degree in Process Equipment & Control Engineering from Zhengzhou University of Light Industry, China, in 2005. He continued to sharpen his analytical and mathematical expertise by completing his Master’s in Engineering Mechanics at Dalian University of Technology in 2009, where he laid the groundwork for his future in dynamic systems and mechanical modeling. In 2019, he earned his Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University, focusing on advanced dynamic analysis and optimization techniques for parallel robotic manipulators. His academic training integrates control theory, mechanical design, and computational modeling—making him uniquely positioned to tackle cutting-edge problems in modern robotics. This rich educational background directly contributes to his current research, which blends multi-body dynamics, elastostatics, and AI-based optimization in robotic mechanisms.

🔬 Research Focus On Mechanical Engineering

Dr. Chao Yang’s research focuses on the mechanics, modeling, and optimization of parallel manipulators—key elements in robotics and precision automation. His work revolves around four core themes: kinematics, stiffness modeling, dynamic analysis, and multi-objective optimization. He explores how over-constrained or hybrid manipulator systems can be optimized using neural networks, principal component analysis, and evolutionary algorithms. His innovative modeling methods extend to both elastostatic and elastodynamic domains, enabling more precise and adaptive control systems. He also delves into applications such as underwater robotics and hybrid robot platforms. By avoiding Lagrangian multipliers in modeling and adopting screw theory in kinetostatic design, he simplifies computational complexity while maintaining physical accuracy. His contributions fill a crucial gap in designing robust, high-performance robotic systems that are used in manufacturing, aerospace, and intelligent automation. Dr. Yang’s research is practical, interdisciplinary, and driven by the demands of next-generation robotics.

📚 Publications Top Notes

1. A hybrid algorithm for the dimensional synthesis of parallel manipulators

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Yang, C.; Zhang, H.; Huang, F.; Ye, W.
Summary: This study presents a novel hybrid algorithm integrating evolutionary computing and deterministic search for optimizing the geometry of parallel manipulators. It addresses trade-offs in workspace, stiffness, and dexterity with improved computational performance.

2. Elastodynamic modeling and analysis of a 4SRRR overconstrained parallel robot

Journal: Mechanical Sciences, 2025
Authors: Wang, B.; Zhao, Y.; Yang, C.; Hu, X.; Zhao, Y.
Summary: Investigates vibration and dynamic response of a 4SRRR parallel robot. The study contributes to better understanding structural deformation under motion, crucial for high-speed precision applications.

3. Kinematic Analysis and Optimization Design of 2-PRU-PRRPa Parallel Mechanism

Journal: Transactions of the Chinese Society for Agricultural Machinery, 2025
Authors: Zhang, W.; Feng, S.; Yuan, X.; Sun, P.; Yang, C.; Lu, Y.
Summary: Offers a systematic study of a novel parallel mechanism applied in agricultural automation, optimizing motion paths and actuator placement.

4. Multibody elastodynamic modeling of parallel manipulators based on the Lagrangian equations without Lagrangian multipliers

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Gong, Y.; Lou, J.; Yang, C.; Ye, W.
Summary: This paper introduces a novel elastodynamic modeling approach for parallel manipulators that bypasses the use of Lagrangian multipliers. The methodology improves numerical efficiency and simplifies model derivation, making it suitable for real-time control and simulation of complex parallel robotic systems.

5. Dynamic modeling and performance analysis of the 2PRU-PUU parallel mechanism

Journal: Mechanical Sciences, 2024
Authors: Sun, T.; Ye, W.; Yang, C.; Huang, F.
Summary: Focuses on the dynamic modeling of a 2PRU-PUU architecture parallel robot. Through simulation and performance metrics evaluation, the study demonstrates how structural configurations affect system response and highlights its suitability for precision tasks in constrained workspaces.

Conclusion

Dr. Chao Yang is highly suitable for the Research for Best Researcher Award—particularly in domains of mechanical systems design, parallel robots, and multi-objective optimization. His contributions are academically rich, technically deep, and steadily expanding. While early in his career stage as a lecturer, the maturity and depth of his publication portfolio, coupled with innovative methodologies, clearly reflect a rising star in mechanical engineering research.

Jiabin lv | Molecular Breeding | Best Researcher Award

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Dr. Jiabin lv | Molecular Breeding | Best Researcher Award

Dr. Jiabin lv, Anhui Agricultural University, China

Lv Jiabin is a dedicated researcher at Anhui Agricultural University, specializing in forest tree genetics and breeding. With extensive experience in molecular-assisted and stress-resistance breeding of both economic (e.g., Camellia oleifera, Carya illinoinensis) and timber (e.g., Eucalyptus) forest species, Lv Jiabin has significantly advanced the understanding of forest tree genomics. His work has contributed to enhancing breeding strategies and sustainable forestry practices in China and beyond. He has led and collaborated on several national and provincial research initiatives and continues to make impactful scientific contributions in the field of plant molecular biology and forestry biotechnology. 🌿🧪

Professional Profile

Scopus Profile

🏆 Strengths for the Award:

  • Focused Expertise in Forest Tree Genetics & Molecular Breeding 🌳
    Lv Jiabin has specialized in the molecular breeding and stress-resistance research of economically valuable and timber forest species such as Camellia oleifera, Carya illinoinensis, and Eucalyptus. This area is highly relevant to environmental sustainability, agroforestry productivity, and climate-resilient ecosystems.

  • Notable Publications in High-Impact Journals 📚

    • 2024 (BMC Plant Biology): Genome-wide identification and expression analysis of GRAS gene family in Eucalyptus grandis
      This article showcases cutting-edge genomic approaches and contributes to understanding gene expression under stress conditions.

    • 2020 (Industrial Crops and Products): Genetic diversity analysis of Eucalyptus cloeziana
      This paper highlights the use of microsatellite markers to inform breeding strategies, demonstrating applied research strength.

  • Active Role in Research Grants 💼
    He has led the Anhui Provincial Natural Science Foundation Youth Project and co-participated in prestigious national initiatives like the National Key R&D Program and the Forestry Public Welfare Industry Major Project, indicating leadership and teamwork in collaborative research.

  • Consistent Research Output 🔬
    With 7 research projects completed/ongoing, he maintains a strong output despite a relatively early-to-mid stage career, suggesting consistent productivity.

🎓 Education

Lv Jiabin received formal training in forest tree genetics and breeding, which laid a strong foundation for his research in molecular biology and forest biotechnology. He has continuously expanded his academic knowledge through active participation in academic research and advanced training programs. His academic journey is reflected in his thorough understanding of the genomics of economically valuable tree species and the integration of this knowledge into practical breeding programs. 🎓📚

🧑‍🔬 Experience

Over the years, Lv Jiabin has been actively engaged in both teaching and scientific research at Anhui Agricultural University. He has led numerous research projects, including those funded by the Anhui Provincial Natural Science Foundation and the Anhui Provincial University Natural Science Foundation. In addition, he has played significant roles in national-level programs such as the National Key R&D Program, National Forestry Public Welfare Industry Research Project, and the Guangxi Zhuang Autonomous Region Innovation-Driven Development Project. His professional involvement extends to managing laboratory work, mentoring students, and publishing high-quality research. Through these activities, he has developed a reputation as a meticulous, innovative, and collaborative scientist in the forestry research community. 🌱👨‍🏫

🔬 Research Focus On Molecular Breeding

Lv Jiabin’s primary research interests lie in the molecular breeding of forest trees, with an emphasis on stress resistance breeding and molecular marker-assisted selection. His research spans across the identification of key genetic traits, development of core germplasm resources, and improvement of economically and ecologically important tree species. Focusing on Eucalyptus, Camellia oleifera, and Carya illinoinensis, he has conducted in-depth studies using genomic tools such as microsatellite markers and genome-wide expression analysis, which have yielded practical tools for forest tree breeding. His research helps improve productivity, stress tolerance, and sustainability in forestry systems. 🌳🔍🧬

📘 Publication Top Note

Title: Genome-wide identification and expression analysis of GRAS gene family in Eucalyptus grandis
Authors: Lu H., Xu J., Li G., Zhong T., Chen D., Lv Jiabin* (corresponding author)
Journal: BMC Plant Biology
DOI: 10.1186/s12870-024-05288-x
Citations: 5 (as of latest available record)
Summary: This study presents a comprehensive genome-wide analysis of the GRAS gene family in Eucalyptus grandis, an important commercial timber species. GRAS genes are known for their crucial roles in plant development, signaling, and responses to abiotic and biotic stresses.

✅ Conclusion

In recognition of his exceptional contributions to molecular forest tree breeding, scholarly publications, and research leadership, Lv Jiabin is a highly deserving candidate for the Best Researcher Award. His work bridges the gap between fundamental genomics and practical forestry applications, promoting sustainable development and genetic resilience in economically significant trees. With a deep commitment to scientific excellence and innovation, Lv Jiabin stands out as a leader in forest molecular breeding and a true asset to the academic and research community. 🏅🌲🔬

Waseem Khan | Oil and Gas | Best Researcher Award

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Mr. Waseem Khan | Oil and Gas | Best Researcher Award

PhD Scholar, University of Science and Technology of China, China.

Waseem Khan is an emerging geoscientist from Pakistan with a strong background in petrography, geochemistry, sedimentology, and geochronology. Born on May 27, 1992, he has built an impressive research and professional profile across academia and industry. He holds a Master’s degree from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, where he was awarded the prestigious ANSO scholarship. Waseem has contributed to multiple high-impact publications on salt range provenance, Jurassic reservoir characterization, and paleogeographic reconstructions in journals like Gondwana Research and Carbonates and Evaporites. His cross-disciplinary expertise includes U-Pb-Hf isotopic analysis, LA-ICP-MS, reservoir modeling, and GIS-based mapping. With professional experience ranging from QA/QC engineering in Qatar to exploration geology in Pakistan, he bridges the gap between theoretical research and field practice. Waseem is recognized for his ability to combine analytical geoscience tools with hands-on industry applications, making him a valuable contributor to both academic and energy sectors.

🔹Author Profile

🔹 Education 

Waseem Khan earned his Master’s in Earth Sciences from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), China, with a CGPA of 3.74 in 2024. His thesis focused on the provenance and paleogeography of the Salt Range Formation in Pakistan. His undergraduate studies were completed at the University of Haripur, where he earned a BS in Geology with a CGPA of 3.5. His BS thesis investigated microfacies and diagenesis in the Middle Jurassic Samana Suk Formation in the Nizampur Basin. Both degrees emphasized fieldwork, lab-based petrography, sedimentology, and tectonics. Waseem’s academic journey has been supported by competitive scholarships and enriched by international exposure and certified training from global institutions such as the University of Toronto, Macquarie University, and Duke University. This foundation has equipped him with expertise in detrital zircon geochronology, geospatial analysis, petroleum systems, and sedimentary provenance, bridging classical geology with advanced analytical techniques.

🔹Strengths for the Award

  1. Diverse and Deep Research Portfolio:

    • Waseem Khan has published seven peer-reviewed journal articles (2024–2025), including in prestigious venues like Gondwana Research, Palaeogeography, Palaeoclimatology, Palaeoecology, and Carbonates and Evaporites.

    • His research spans a wide array of geological sub-disciplines: petrography, sedimentology, reservoir characterization, detrital zircon geochronology, and paleogeography, with a regional focus on the Western Himalayas, Tethys, and Tibetan Plateau.

    • He has contributed to both applied (e.g., oil and gas reservoir studies) and fundamental research (e.g., Gondwana paleogeography reconstruction).

  2. Technical and Analytical Expertise:

    • Demonstrated strong technical proficiency with tools like LA-ICP-MS, XRF, ArcGIS, and IOLITE.

    • Conducted advanced U-Pb-Hf isotopic work, showing deep specialization in detrital zircon analysis and geochronology.

  3. Global Academic Exposure and Collaboration:

    • Completed his Master’s at the Chinese Academy of Sciences (CAS) — one of Asia’s premier research institutions — under the ANSO scholarship, indicating high academic merit.

    • Worked with globally recognized geoscientists like Eduardo Garzanti, enhancing the academic quality and international visibility of his research.

  4. Professional Experience and Applied Knowledge:

    • Extensive multidisciplinary experience across QA/QC in materials engineering, nuclear gauge operation, and mineral exploration, which enriches his research with applied industrial insights.

    • Worked on high-impact projects like Mohmand Dam Hydro Project and M-9 Motorway Construction with organizations such as FWO, NESPAK, and NHA.

  5. Training and Certifications:

    • Completed over ten international certified courses, including in GIS, petroleum engineering, environmental safety, and ISO accreditation standards, reflecting a commitment to continuous learning.

🔹 Experience 

Waseem Khan’s experience spans six diverse roles across academia, industry, and international research institutions. He most recently worked as a Research Assistant at the Chinese Academy of Sciences (2020–2025), where he conducted geochronological and geochemical analysis (U-Pb-Hf, LA-ICP-MS). He also served as a QA/QC Officer in Qatar (2021–2022), ensuring compliance with international testing standards and ISO certifications. His prior roles include Assistant Geologist at China Gezhouba Group (Mohmand Dam project), Exploration Geologist for base metals in Khyber Pakhtunkhwa, Research Associate at University of Haripur, and Material Engineer for the M-9 Motorway project with FWO. His work has included core logging, XRF sampling, seismic interpretations, reservoir assessments, and site-level geological mapping. His well-rounded field and lab experience, combined with his ability to manage geotechnical and QA/QC processes, make him uniquely suited to bridge scientific exploration with applied oil and gas geology.

🔹 Awards and Honors 

Waseem Khan has received several academic and professional accolades. Most notably, he was awarded the Alliance of International Science Organizations (ANSO) Scholarship for his Master’s studies at the prestigious Chinese Academy of Sciences, which recognizes outstanding students from developing countries in scientific research. He was also awarded a government-issued laptop for securing over 80% marks in his undergraduate program—an initiative by Pakistan’s Higher Education Commission to support merit-based excellence. In addition to formal awards, his certifications reflect a proactive approach to continuous learning. These include ISO 17025 and ISO 17020 accreditations, radiation protection training, and multiple Coursera credentials from leading universities in petroleum engineering, environmental safety, and GIS analysis. These honors underscore his commitment to excellence, scientific integrity, and professional development, positioning him as a dedicated researcher capable of contributing to global energy and environmental challenges.

🔹 Research Focus on Oil and Gas

Waseem Khan’s research centers on the petrological and geochemical evolution of sedimentary basins, with particular emphasis on reservoir potential, tectonic reconstruction, and paleogeography. He specializes in U-Pb-Hf zircon geochronology, detrital zircon provenance analysis, and basin tectonics, applying advanced tools like LA-ICP-MS, XRF, and GIS modeling. His work investigates processes within the Western Himalayas, Salt Range, and the Tibetan Plateau, unraveling Earth’s tectono-sedimentary history through integrative datasets. He bridges academic research with industrial applications, especially in the oil and gas sector, focusing on carbonate and sandstone reservoirs, diagenetic processes, and subsurface characterization. His collaborative projects span stratigraphy, seismic interpretations, and paleoclimatic reconstructions. By integrating isotopic dating with sedimentological observations, Waseem contributes to both the understanding of ancient paleoenvironments and the exploration of hydrocarbon systems, positioning him as a versatile researcher in petroleum geology and tectonics.

🔹 Publications Top Notes

1. Petrophysical characterization and reservoir potential of the lower Goru sandstone

Journal: Journal of Natural Gas Geoscience, June 2025
Contributors: Waseem Khan et al.
Summary: This study evaluates reservoir properties of Lower Goru sandstone through petrophysical logs, thin-section analysis, and core measurements. Results highlight moderate to good reservoir quality with effective porosity and permeability ranges ideal for gas production. The study provides key insights for exploration in Pakistan’s Sindh Basin.

2. Reservoir potential of middle Jurassic carbonates in the Nizampur Basin:

Journal: Physics and Chemistry of the Earth, June 2025
Contributors: Waseem Khan et al.
Summary: The paper explores Jurassic carbonates using microfacies analysis and diagenetic markers to assess reservoir viability. It finds that early marine cementation followed by dissolution-enhanced porosity created suitable reservoir zones, contributing to future petroleum exploration in Khyber Pakhtunkhwa.

3. Petrography and geochemistry of Early Cambrian phosphorites from Abbottabad:

Journal: Carbonates and Evaporites, May 2025
Contributors: Waseem Khan et al.
Summary: The authors investigate phosphorite deposits to interpret depositional environments and trace element enrichment. Their geochemical signatures suggest upwelling-driven sedimentation under anoxic to dysoxic conditions, offering a paleoceanographic perspective on Cambrian phosphorus cycles.

4. Decoding the Ediacaran Enigma: Gondwana paleogeography revisited through a provenance study of the Salt Range Formation

Journal: Gondwana Research, April 2025
Contributors: Waseem Khan et al.
Summary: This landmark paper applies detrital zircon dating to reconstruct Gondwana’s paleogeography, revealing sediment routing from northeastern Africa to the Salt Range. It reshapes tectonic models of the western Himalayas during the late Neoproterozoic.

Conclusion

Waseem Khan is a highly capable and emerging researcher in the field of geosciences with a strong academic foundation, hands-on field and lab expertise, and a growing international publication record. His combination of advanced analytical skills, cross-disciplinary work experience, and recent high-impact journal articles make him a strong contender for the Best Researcher Award, particularly in the Earth and Environmental Sciences category.

Jack Mathebula | Planning and Operations | Best Researcher Award

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Mr. Jack Mathebula | Planning and Operations | Best Researcher Award

Research Manager, Eskom, South Africa

Jack Mathebula is a veteran energy systems strategist with over two decades of experience in power system planning, operations, and renewable energy integration. Currently serving as Acting Research Manager at Eskom RT&D, Jack leads strategic grid innovation efforts aligned with global sustainability goals. His journey began with technical roles in HVDC plant operations and evolved into thought leadership in transmission planning, capital budgeting, and smart grid transformation. Jack has authored multiple conference papers and peer-reviewed articles focusing on HVDC planning and MCDA methodologies. A recipient of several international conference honors, he has also chaired global forums and mentored young engineers across Africa. He is a registered Professional Technologist (Pr Tech Eng) and a Senior Member of SAIEE, with a growing academic profile. Jack’s work directly supports energy transition efforts in South Africa and beyond, combining academic insight with real-world applications to meet the energy challenges of tomorrow.

📘Author Profile

🎓 Education

Jack Mathebula is currently pursuing a PhD in Electrical Engineering at the University of South Africa (UNISA), building on a Cum Laude MSc from the University of Pretoria (2015). His master’s thesis focused on optimizing HVDC scheme planning. He also holds a BSc Honours in Applied Sciences–Electrical from the University of Pretoria (2004), a B-Tech in Power Engineering from Technikon Pretoria (2001), and a National Diploma in Electrical Engineering from Technikon Witwatersrand (1998). His academic training blends strong theoretical knowledge with practical energy systems expertise. Jack further expanded his leadership acumen through specialized programs, including a Project Management Programme from UNISA’s School of Business Leadership and the Middle Managers Programme (MMP) via Henley Business School in collaboration with Eskom. His education reflects a lifelong dedication to combining engineering excellence with strategic project management in the energy sector.

🛠️ Experience

Jack’s career spans over 25 years at Eskom, where he has held progressively senior roles in grid planning, transmission strategy, and renewable integration. Since April 2024, he serves as Acting Research Manager (Distribution) in Eskom’s RT&D division, leading strategic energy projects and guiding national/international technical initiatives. Between 2008 and 2024, Jack was Middle Manager for Grid Planning and Operation, overseeing research portfolios and contract/resource management. Previously, he contributed to capital planning (2006–2008), network investment (2005–2006), and master planning (2000–2005). His career began in 1999 at the Apollo HVDC Converter Station, where he optimized plant performance. Jack’s career reflects deep technical competency coupled with leadership in digital transformation, grid simulation (RTDS), and policy-relevant research on EV infrastructure and hosting capacity assessments. He continues to mentor emerging engineers and drive forward-thinking energy planning initiatives.

🔬 Research Focus 

Jack Mathebula’s research concentrates on power system planning, HVDC optimization, renewable integration, and electric mobility infrastructure. He is particularly known for applying multi-criteria decision analysis (MCDA) and TOPSIS models in selecting optimal grid expansion strategies. His ongoing PhD explores advanced planning tools for dynamic energy systems under uncertainty, contributing to resilient grid development. His technical projects include hosting capacity assessments, RTDS-based simulation, and distribution-level renewable integration via DSTATCOMs. Jack is also involved in shaping EV-ready grid infrastructure and tariff structures, through cross-border collaborations with institutions like the Danish Technical University. His commitment to applied systems thinking is evident in his work linking technical feasibility, policy formulation, and national energy planning. His research is impactful not only in scholarly terms but also in operationalizing energy transition strategies for utilities and regulators.

📚 Publication Top Notes

Application of TOPSIS in Power Systems: A Review

Authors: J. Mathebula, N. Mbuli
Conference: 2024 International Conference on Electrical, Computer and Energy Engineering
Citations: 2
Summary:
This comprehensive review explores the use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in addressing multi-criteria challenges in power systems. The paper synthesizes over a decade of applications, detailing how TOPSIS has been utilized for substation site selection, transmission route optimization, and renewable energy prioritization. It emphasizes the method’s effectiveness in quantifying trade-offs between conflicting objectives like cost, reliability, and environmental impact. The authors also discuss emerging trends such as hybrid TOPSIS models and their role in decision support systems for utilities.

Potential Factors for Multi-Criteria Evaluation of HVDC Compared to HVAC in Power Transmission

Authors: J. Mathebula, N. Mbuli
Conference: 2024 International Conference on Green Energy, Computing and Sustainable Technologies
Citations: 2
Summary:
This paper provides a structured framework for evaluating High Voltage Direct Current (HVDC) and High Voltage Alternating Current (HVAC) systems using multi-criteria analysis. Technical aspects like voltage stability, power losses, and system compatibility are considered alongside economic (CAPEX/OPEX) and environmental parameters. The study offers guidance for policymakers and transmission planners by identifying the most influential factors when choosing transmission technology for large-scale power corridors, particularly in developing countries with expanding renewable capacity.

Approach for Screening and Ranking Potential Receiving End Points in Planning New HVDC Schemes

Authors: J. Mathebula, M.N. Gitau, N. Mbuli, J.H.C. Pretorious
Conference: 2018 IEEE PES/IAS PowerAfrica
Citations: 1
Summary:
This research introduces a structured screening and ranking method to determine optimal receiving terminals for HVDC links. Using a case study in the South African grid, the authors apply decision matrix techniques based on projected load growth, geographic accessibility, system redundancy, and cost. The proposed framework supports utilities in identifying HVDC endpoints that align with long-term energy planning and enhances strategic transmission deployment in emerging economies.

Simplified Negative Load-Based Approach Versus Full HVDC Modeling in Assessing Options for the Cape Network

Authors: J. Mathebula, M.N. Gitau, N. Mbuli
Conference: 2013 13th International Conference on Environment and Electrical Engineering
Citations: 1
Summary:
This study contrasts two methodologies for evaluating HVDC implementation in the Cape region of South Africa: a simplified negative load approach and a full HVDC model. By comparing simulation results and cost-efficiency, the paper discusses the limitations and applicability of each method. The simplified model offers quicker decision support, while the full model yields greater accuracy. The work provides guidance on the trade-offs between modeling complexity and planning effectiveness in early-stage transmission projects.

Application of TOPSIS for MCDA in Power Systems: A Systematic Literature Review

Authors: J. Mathebula, N. Mbuli
Journal: Energies (2025)
Summary:
This peer-reviewed article presents a rigorous literature review of the integration of TOPSIS with multi-criteria decision analysis (MCDA) in power system engineering. Covering applications from renewable site selection to grid reinforcement prioritization, it categorizes studies by criteria sets, modeling tools, and decision contexts. The authors propose a future research agenda emphasizing the integration of real-time data, stakeholder weighting schemes, and AI-enhanced decision-making in power systems. The review positions TOPSIS as a valuable, yet underutilized, tool for navigating the complexity of modern grids.

Potential Factors for HVDC Evaluation in Selection of the Suitable Location Within HVAC System

Authors: J. Mathebula, N. Mbuli
Conference: 2024 ICECCME
Summary:
The paper investigates the suitability of integrating HVDC terminals within existing HVAC networks. Key criteria include system stability impact, proximity to generation/load centers, infrastructure compatibility, and future scalability. The study proposes a location scoring model tailored for hybrid AC-DC systems in grid modernization scenarios. Case illustrations from the South African transmission system reinforce the practical relevance of the proposed methodology, particularly for utilities preparing for high renewable penetration.

Design Options for Thermal Uprate of a Transmission Line: A Case Study in the South African Power System

Authors: J. Mathebula, N. Mbuli, S. Mushabe
Conference: 2024 International Conference on Electrical, Communication and Computer Engineering (ECCCE)
Summary:
This case study explores cost-effective design modifications to increase the thermal capacity of aging transmission lines. Options include conductor replacement, dynamic line rating, and advanced monitoring systems. Using a real-world line segment in South Africa, the paper evaluates each method based on cost, downtime, and long-term benefits. The findings aid transmission operators in choosing appropriate uprate techniques to meet increasing demand without incurring full infrastructure replacement costs.

Conclusion

Jack Mathebula is a highly suitable and deserving candidate for the Best Researcher Award, particularly in the domain of power systems, HVDC planning, and renewable energy integration. His blend of technical depth, leadership, applied research, and mentorship exemplifies the qualities of an impactful researcher driving innovation in the energy sector.

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

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Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same conference, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.

Anna Giuliano | Prevent Oropharyngeal Cancer | Best Researcher Award

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Prof. Anna Giuliano | Prevent Oropharyngeal Cancer | Best Researcher Award

Professor, Moffitt Cancer Center, United States

Dr. Anna R. Giuliano, Ph.D., is a distinguished professor, cancer epidemiologist, and visionary public health leader dedicated to the prevention of virus-associated cancers. She currently serves as Founding Director of the Center for Immunization and Infection Research in Cancer at the H. Lee Moffitt Cancer Center, where she pioneers research at the intersection of infectious diseases and oncology.

👩‍🔬Professional Profile

ORCID

🎓 Education

Dr. Giuliano earned her Ph.D. in Nutritional Biochemistry from Tufts University (1990), where she explored calcium transport mechanisms in intestinal cells. 🧫 Prior to that, she completed an M.S. in Nutrition and a B.A. in Psychology and Anthropology from SUNY Stony Brook, graduating summa cum laude and Phi Beta Kappa 🎓. Her postdoctoral training included an NCI Fellowship in Cancer Prevention and Etiology at the University of Arizona, further anchoring her in epidemiological research. 🧪

🧑‍🏫 Experience

With over three decades of academic and research leadership, Dr. Giuliano has held multiple tenured faculty positions at the University of Arizona and the University of South Florida. 🏛️ She served as Chair and Program Leader of Cancer Epidemiology at Moffitt Cancer Center (2004–2011) and has been a tenured professor in multiple departments since 2004.

Her international consulting roles span from Haiti 🇭🇹 to Nepal 🇳🇵 to Kenya 🇰🇪, where she collaborated with CARE, USAID, and ADRA on critical nutrition and health security assessments. She is widely respected for her culturally sensitive, data-driven, and community-based health interventions across global settings. 🌐

🔬 Research Focus On Prevent Oropharyngeal Cancer

Dr. Giuliano’s research centers on HPV-related cancer prevention, vaccine efficacy, and the natural history of HPV infection in both men and women. 💉 She has been instrumental in expanding the evidence base supporting HPV vaccination for males, leading to global policy shifts and improved cancer prevention outcomes. She also explores the role of biomarkers and epigenetics in early cancer detection, with a strong translational research approach. 🧬

🏅 Awards & Honors

Dr. Giuliano’s contributions have been celebrated with numerous accolades, including:

  • 🏆 Joseph F. Fraumeni Jr. Distinguished Achievement Award (2019)

  • 🌟 Moffitt Cancer Center Researcher of the Year (2011, 2022)

  • 📊 Top Publication Awards, JAMA Oncology (2022)

  • 👩‍⚕️ American Cancer Society Clinical Research Professor Award (2018–2023)

  • 🌸 President Elect, International Papillomavirus Society (2021–present)

  • 🎖️ Most Cited Faculty Member, Moffitt Cancer Center (2007)

📚 Publications Top Notes

1. Immunogenicity of the 9-valent human papillomavirus vaccine: Post hoc analysis from five phase 3 studies

Journal: Human Vaccines & Immunotherapeutics
Publication Date: December 31, 2025
DOI: 10.1080/21645515.2024.2425146
Contributors: Anna R. Giuliano, Joel M. Palefsky, Stephen E. Goldstone, Jacob Bornstein, Ilse De Coster, Ana María Guevara, Ole Mogensen, Andrea Schilling, Pierre Van Damme, Corinne Vandermeulen, et al.

Summary:
This comprehensive post hoc analysis combined data from five Phase 3 clinical trials to evaluate the immunogenicity of the 9-valent HPV vaccine across different demographics. Results demonstrated robust antibody responses in males and females aged 9–26 years, with especially high titers in adolescents. Differences in responses by gender and sexual orientation were also noted. The study confirms the vaccine’s strong and consistent immunogenic profile, supporting its global use for both cancer and genital wart prevention across populations.

2. Natural history of HPV-16 E6 serology among cancer-free men in a multicenter longitudinal cohort study

Journal: JNCI: Journal of the National Cancer Institute
Publication Date: May 1, 2025
DOI: 10.1093/jnci/djae326
Contributors: Jaimie Z. Shing, Anna R. Giuliano, Nicole Brenner, Birgitta Michels, Allan Hildesheim, Sudhir Srivastava, Bradley A. Sirak, John Schussler, Danping Liu, Wendy Wang, et al.

Summary:
This study tracked HPV-16 E6 antibody prevalence and persistence in nearly 4,000 cancer-free men from Brazil, Mexico, and the U.S. It found that while E6 seropositivity was rare (0.35%), it was highly persistent and more common in older men. A strong association was observed between oral HPV-16 DNA and E6 seropositivity. The findings suggest that HPV-16 E6 antibodies could serve as early biomarkers for oropharyngeal cancer risk, aiding in future screening strategies.

3. Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer

Journal: Cancer Prevention Research
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.CAPR-23-0317
Contributors: Brittney L. Dickey, Ryan M. Putney, Michael J. Schell, Anders E. Berglund, Antonio L. Amelio, Jimmy J. Caudell, Christine H. Chung, Anna R. Giuliano

Summary:
In this cutting-edge translational study, researchers used genome-wide methylation data to identify a panel of biomarkers for the early detection of HPV-associated oropharyngeal cancer. The biomarker panel demonstrated high sensitivity and specificity in distinguishing early cancer cases from healthy individuals. Dr. Giuliano’s contribution to this work supports the development of non-invasive screening tools that could revolutionize early cancer detection and improve patient outcomes.

4. Data from Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer

Platform: Preprint
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.c.7160199
Contributors: Same as above

Summary:
This preprint presents the raw and supplementary data associated with the biomarker panel study (see item 3). It provides detailed methodologies, validation datasets, and analytical workflows used in detecting methylation changes associated with early HPV-driven cancers. This open-access resource enables replication and further exploration by other cancer prevention researchers.

5. Data from Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer (Version 1)

Platform: Preprint (Version 1)
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.c.7160199.v1
Contributors: Same as above

Summary:
This version 1 dataset provides the initial analysis and raw findings from the methylation study. The release of multiple versions reflects a commitment to transparency and continuous scientific improvement. These data have supported downstream publications and are foundational to future clinical applications in HPV cancer diagnostics.

🎯 Conclusion

Dr. Anna R. Giuliano is a pioneering figure whose lifelong commitment to cancer prevention has reshaped global health paradigms. From pioneering HPV vaccine research in men to improving cancer screening among marginalized populations, her impact spans continents, disciplines, and generations. 🌍 Her leadership, scholarship, and advocacy for equitable healthcare make her a truly deserving candidate for this award. 🏅

Xiangyu Zhang | Public Health | Best Researcher Award

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Mr. Xiangyu Zhang | Public Health | Best Researcher Award

Doctoral Researcher, CAS Institute of Automation, China

Dr. Xiangyu Zhang is a doctoral researcher at the Institute of Automation, Chinese Academy of Sciences (CASIA). With a strong foundation in mechanical engineering and robotics, Dr. Zhang transitioned into the realm of social computing and artificial intelligence. His research addresses real-world crises by developing cognitive AI agents and multi-agent systems for public health emergency response. His collaborative work with the Chinese CDC and the Beijing CDC has resulted in intelligent models that predict disease outbreaks and optimize response strategies using advanced machine learning and large language models. His forward-thinking research contributes significantly to the growing intersection between AI and epidemiology, aiming to build resilient, data-driven health systems. A rising voice in the AI and public health community, Dr. Zhang continues to lead impactful research published in leading journals and presented at top international conferences.

Author’s Profile

🎓 Education

Dr. Zhang’s educational journey exemplifies his interdisciplinary approach. He is currently pursuing his Ph.D. in Social Computing at CASIA under the University of Chinese Academy of Sciences. His research integrates AI, epidemiology, and intelligent systems at the State Key Laboratory of Multimodal Artificial Intelligence Systems. Prior to this, he earned his M.Sc. in Mechanical Engineering from the University of Electronic Science and Technology of China (UESTC), where he specialized in biomimetic actuators in robotics. His undergraduate studies in Mechanical Design (B.Eng., UESTC) laid the foundation for his engineering acumen, particularly in robotic arm systems and control mechanisms. This unique blend of mechanical design and cognitive AI enables him to craft deeply technical yet socially responsive research, blending physical systems with computational intelligence to solve contemporary global health challenges.

🏢 Experience

Dr. Zhang’s research experience spans across both engineering innovation and public health intelligence. As a Ph.D. researcher at CASIA, he is engaged in groundbreaking work on cognitive agent-based systems for pandemic response and crisis management. He has worked extensively with high-stakes research projects funded by the National Natural Science Foundation of China and the Next-Generation AI Development Plan (2015–2030). His key contributions include developing simulation models for outbreak prediction and adaptive intervention frameworks using LLMs and multi-agent systems. He has collaborated directly with the Chinese CDC and Beijing CDC, grounding his work in critical, real-world public health needs. His previous research in robotics labs focused on elastic actuators and autonomous learning systems, bringing an engineering lens to his AI-driven innovations. Despite his early career stage, his experience is already making waves in intelligent public health system design.

🔬 Research Focus

Dr. Zhang’s research centers on the development of intelligent decision-support systems for public health emergencies. His primary focus lies in the integration of multi-agent systems, epidemiological modeling, and large language models (LLMs) to create dynamic, real-time frameworks for disease surveillance, prediction, and intervention. He has proposed agent-based simulation architectures that replicate human-like decision-making in health crisis contexts, enabling systems to not only forecast disease spread but also adaptively respond with optimal strategies. His novel work in spatiotemporal forecasting for urban epidemics (IEEE ISI 2025) and cognitive frameworks for crisis modeling (Frontiers of Engineering Management) positions him as a forward-thinking researcher in AI for social good. His contributions are pioneering the use of AI to enhance epidemiological intelligence and resilience against future pandemics—an area of urgent global need.

📚 Publication Top Notes

  1. Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
    Technologies, 2025, 13, 272.
    This paper reviews the current methodologies and advancements in agent-based modeling (ABM) for epidemics. Dr. Zhang explores how ABMs simulate human behaviors and interactions to better understand disease spread and the effectiveness of policy interventions.

  2. Large Language Models: Technology, Intelligence, and Thought 
    Frontiers of Engineering Management
    Co-authored with Z. Cao and D. Zeng, this paper examines the philosophical and functional implications of large language models (LLMs) in understanding intelligence and cognition, particularly in the context of public service applications.

  3. LLM-Driven Spatiotemporal Forecasting of Urban Infectious Diseases
    IEEE ISI 2025 Conference
    A real-world case study on Haidian District, this work presents a cutting-edge LLM-integrated system for forecasting infection patterns, enabling early interventions based on data-driven spatial and temporal analysis.

  4. ShadowPainter: Robotic Painting via Active Learning
    Journal of Intelligent & Robotic Systems, 2022, 105(3), 61
    This research introduces an AI-powered robotic system capable of learning artistic techniques through visual replication. While outside public health, it showcases Dr. Zhang’s skills in human-like machine learning.

  5. Novel Multi-Configuration Elastic Actuator
    Advanced Intelligent Systems, 2024, 6(10): 2400079
    This paper introduces an elastic actuator capable of energy modulation, relevant for dynamic robotic systems. It highlights Dr. Zhang’s prior contributions in robotics and mechanical innovation.

Conclusion 

Dr. Xiangyu Zhang presents a strong and promising research portfolio marked by high-impact publications, innovative interdisciplinary work, and societal relevance in AI-driven public health crisis management. While still early in his career, his contributions clearly demonstrate leadership potential and research excellence, especially in emerging fields.