Induara Aburto | Tecnologías de la Información | Women Researcher Award

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Women Researcher Award

Induara Aburto
Universidad Nacional Agraria, Nicaragua.

Induara Aburto
Affiliation Universidad Nacional Agraria
Country Nicaragua
ORCID ID 0009-0004-0051-7971
Documents 1
Subject Area Tecnologías de la Información
Event International Academic Achievements & Awards

Induara Aburto is a researcher affiliated with Universidad Nacional Agraria whose work falls within the subject area of information technology. She is nominated for the Women Researcher Award at the International Academic Achievements & Awards [1].

Abstract

This article summarizes the academic profile of Induara Aburto in connection with the Women Researcher Award. Her public record identifies one document in the field of information technology and is linked to an ORCID identifier [1]. The article reviews her affiliation, research focus, publication record and suitability for the award.

Keywords

Information technology, women in research, academic recognition, researcher profile, ORCID.

Introduction

Information technology supports teaching, research and administration across universities, and researchers in this area contribute to the digital capacity of their institutions. Induara Aburto works at Universidad Nacional Agraria. Her ORCID record provides a persistent identifier that links her scholarly outputs to a single profile [2].

Research Profile

Aburto’s subject area is information technology (Tecnologías de la Información). Her author record currently shows the following indicators [1]:

  • One indexed document

These values are consistent with an early stage of publication activity.

Research Contributions

The available record lists one document in information technology. Because the record is at an early stage, this article does not attribute specific findings or methods beyond what is publicly documented. Her ORCID profile serves as the principal source for verifying her scholarly outputs as they are added [1].

Publications

At the time of writing, one document is associated with her author record. Readers are encouraged to consult her ORCID profile for the current list of works and their identifiers, including DOIs where available [1].

Research Impact

Citation metrics for the record are currently zero, which reflects the recent appearance of the work rather than its quality. Bibliometric indicators generally accumulate over time, and her impact is expected to be assessed more fully as further outputs are published and cited [1].

Award Suitability

Aburto is considered for the Women Researcher Award on the following grounds:

  • An emerging scholarly record in information technology [1]
  • Use of a persistent ORCID identifier supporting transparent attribution [2]
  • Affiliation with a national university
  • Contribution to the visibility of women in research

Conclusion

Induara Aburto’s profile reflects an emerging research career in information technology at Universidad Nacional Agraria. Her nomination for the Women Researcher Award recognizes her scholarly activity and encourages continued contribution to the field [1].

References

  1. ORCID. (n.d.). Induara Aburto, ORCID iD 0009-0004-0051-7971. ORCID.
    https://orcid.org/0009-0004-0051-7971
  2. International Awards. (2026). International Academic Achievements & Awards.
    https://academicachievements.org/

Zhengquan Piao | Robotics | Best Researcher Award

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Dr. Zhengquan Piao | Robotics | Best Researcher Award

Dr. Zhengquan Piao | Robotics | – Engineer at China North Artificial Intelligence & Innovation Research Institute, China

Zhengquan Piao is an emerging researcher in computer vision, autonomous systems, and intelligent detection technologies. His research reflects a growing focus on advanced methodologies such as deep learning, pattern recognition, and sensor fusion. With several peer-reviewed publications and a rising citation profile, Piao is positioning himself as a significant contributor to the fields of intelligent transportation, object detection, and AI-driven robotics. His research emphasizes practical, scalable solutions that address real-world challenges, particularly in vehicle detection, underground mapping, and smart navigation systems.

Profile Verified:

Scopus

Google Scholar

Education:

Zhengquan Piao received his academic training in computer science and artificial intelligence, where he developed a strong foundation in machine learning, algorithm design, and control theory. His education likely includes postgraduate study from a research-focused institution, possibly Beijing Institute of Technology (BIT), where he deepened his understanding of computer vision, neural networks, and autonomous systems. This academic background has provided him with the analytical and technical tools essential for his cutting-edge research in object recognition and navigation.

Experience:

Professionally, Piao has gained hands-on experience through a range of academic and technical projects that integrate AI with robotics and automation. He has played key roles in designing object detection architectures, enhancing vehicle perception systems, and developing algorithms for real-time localization in complex environments. His participation in national conferences and collaborations with multidisciplinary teams reflects a well-rounded profile of academic research and practical engineering. Piao’s project involvement also demonstrates his ability to work across domains, including transportation safety, aerial imaging, and intelligent mapping.

Research Interest:

Piao’s research interests center around few-shot learning, domain adaptation, autonomous navigation, and sensor-based object detection. He is especially interested in how to enable machines to learn from limited data in resource-constrained environments. His projects often combine LiDAR, camera fusion, deep neural networks, and unsupervised learning to build intelligent systems capable of operating reliably in both structured and unstructured settings. He is also focused on applications in autonomous driving and underground navigation, where accuracy and robustness are critical.

Awards:

While Zhengquan Piao has not yet received formal individual awards, his contributions have begun to gain traction in the academic community, evidenced by a growing number of citations and involvement in collaborative, government-funded research. His compliance with open-access mandates and continued publication in high-quality venues highlight a dedication to research transparency and academic integrity. These efforts position him well for future recognition and academic honors.

Publications:

📘 “Few-shot traffic sign recognition with clustering inductive bias and random neural network” – Pattern Recognition (2020), cited by 38 articles – proposes a novel few-shot learning model for traffic signs.
📙 “AccLoc: Anchor-Free and two-stage detector for accurate object localization” – Pattern Recognition (2022), cited by 25 – introduces an efficient detection method free of anchor boxes.
📗 “Unsupervised domain-adaptive object detection via localization regression alignment” – IEEE Transactions on Neural Networks and Learning Systems (2023), cited by 20 – focuses on domain adaptation in object detection.
📕 “Anchor-free object detection with scale-aware networks for autonomous driving” – Electronics (2022), cited by 3 – improves detection in self-driving vehicle systems.
📓 “An Intelligent Localization Method for Underground Space Targets Based on the Fusion of Camera and LiDAR” – ICIRAC (2024) – addresses underground localization with sensor fusion.
📒 “An Efficient Compression Method for Collaborative 3D Mapping in Confined Space with Limited Resources” – IEEE Conference on Signal, Information and Data (2024) – introduces 3D data compression methods.
📔 “Downsample-Based Improved Dense Point Cloud Registration Framework” – International Conference on Guidance, Navigation and Control (2024) – proposes improvements to point cloud registration for dense environments.

Conclusion:

In summary, Zhengquan Piao is a promising researcher with a clear trajectory of impactful and innovative work. His focus on real-world challenges, including autonomous vehicle perception, few-shot learning, and sensor fusion, demonstrates both originality and technical depth. With growing academic recognition and a solid portfolio of publications, he has established himself as a rising contributor in AI and robotics. Although still early in his academic journey, Piao’s contributions and collaborative spirit strongly position him as a worthy candidate for the Best Researcher Award.

 

 

 

 

Osama Elshazly | Mechatronics and Robotics | Best Researcher Award

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Dr. Osama Elshazly | Mechatronics and Robotics | Best Researcher Award

Assistant Professor at Faculty of Electronic Engineering Menouf – Menoufia University, Egypt

 

Dr. Osama Elshazly is an accomplished Assistant Professor with a robust background in Mechatronics and Robotics Engineering. Based in Cairo, Egypt, Dr. Elshazly has dedicated his career to advancing control systems, artificial intelligence in engineering, and automation technology. His innovative approach integrates research, teaching, and practical applications, contributing to various significant projects in the field. He has also demonstrated leadership in mentoring students for international robotics competitions, emphasizing the real-world application of theoretical knowledge. Dr. Elshazly’s commitment to academic excellence and research innovation positions him as a leader in his field.

Profile 

Google Scholar

Education

Dr. Elshazly completed his Ph.D. in Mechatronics and Robotics Engineering at the Egypt-Japan University of Science and Technology (E-JUST) in 2015. His research during this time focused on cutting-edge control techniques and artificial intelligence applications in dynamic systems. Before pursuing his doctoral studies, he earned an MSc in Automatic Control Engineering from Menoufia University in 2012, where he developed a strong foundation in control theory and electronic systems. He also holds a BSc in Electronic Engineering from Menoufia University, awarded in 2004, where he laid the groundwork for his future endeavors in Mechatronics and Robotics.

Experience

Dr. Elshazly has extensive teaching and research experience, having served as an Assistant Professor in several prestigious institutions. His career began at Menoufia University, where he worked as an Assistant Lecturer, later advancing to Assistant Professor from 2015 to 2020. During this time, he was actively involved in teaching courses related to Mechatronics, Robotics, and Control Systems, as well as managing quality assurance processes. He continued his academic career at the High Institute of Engineering and Technology, taking on a full-time Assistant Professor role while also engaging with other institutions such as Modern University for Technology and Information, and Université Française d’Egypte, where he taught part-time. His experience extends internationally, having been a Research Fellow at Catania University in Italy, where he contributed to collaborative projects in dynamic systems control.

Research Interests

Dr. Elshazly’s research focuses on the intersection of Mechatronics, Robotics, and Automatic Control Engineering, with specific interests in dynamical systems, robotic manipulators, and AI-based control techniques. He is particularly passionate about advancing control strategies for complex systems, such as iterative learning control and adaptive control methodologies. His work in artificial intelligence control, fractional order control, and new AI model architectures has yielded significant contributions to the development of autonomous systems. His ongoing research explores the integration of AI algorithms in real-time control and optimization, aiming to solve complex engineering problems with innovative solutions.

Awards

Dr. Elshazly has received recognition for his contributions to the field of engineering, including grants and funds that support his research initiatives. Notably, he was involved in the Mobility Grant Project between Catania University and Egypt-Japan University, funded by the Science and Technological Development Fund (STDF) of Egypt, which provided substantial resources for collaborative research. He has also been acknowledged for his role as a supervisor for students in international competitions, such as the International Autonomous Robot Racing Challenge and the IDC Robocon, which further showcases his ability to guide young talent towards technical excellence.

Publications

Omar Shaheen, Osama Elshazly, Hossam Khalil. “Quantum Neural Networks Based Lyapunov Stability and Adaptive Learning Rates for Identification of Nonlinear Systems.” Ain Shams Engineering Journal, 2024. Link.

Omar Shaheen, Osama Elshazly, Abdullah Baihan, Walid El-Shafai, Hossam Khalil. “Advancing Nonlinear Dynamics Identification with Recurrent Quantum Neural Networks: Emphasizing Lyapunov Stability and Adaptive Learning in System Analysis.” Alexandria Engineering Journal, 2024. Link.

Hossam Khalil, Osama Elshazly, Omar Shaheen. “Optimal Fuzzy Pre-compensated TID Controller for Nonlinear Dynamical Systems.” Menoufia Journal of Electronic Engineering Research (MJEER), 2024.

Mostafa E., Osama Elshazly, El-Bardini M. et al. “Embedded Adaptive Fractional-Order Sliding Mode Control Based on TSK Fuzzy System for Nonlinear Fractional-Order Systems.” Soft Computing, 2023. Link.

E. Mostafa, A. M. El-Nagar, Osama Elshazly, M. El-Bardini. “Fractional Order Sliding Mode Controller for Coupled Tank System.” Menoufia Journal of Electronic Engineering Research (MJEER), 2021.

E. Mostafa, A. M. El-Nagar, Osama Elshazly, M. El-Bardini. “Adaptive Fractional Order Sliding Mode Control for a Nonlinear System.” 2021 International Conference on Electronic Engineering (ICEEM), 2021. Link.

Conclusion

Dr. Osama Elshazly has demonstrated a commitment to advancing engineering research, particularly in Mechatronics and Control Engineering. His innovative research contributions, combined with a passion for teaching and mentoring, make him a strong candidate for the Best Researcher Award. His career reflects a dedication to solving complex engineering challenges through interdisciplinary approaches, showcasing his potential to continue making significant impacts in his field.

Fatemeh Golpayegani | Artificial Intelligence | Best Researcher Award

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Dr. Fatemeh Golpayegani | Artificial Intelligence | Best Researcher Award 

Assistant Professor | University College Dublin | Ireland

📜 Short Bio:

Fatemeh Golpayegani is currently an Assistant Professor at the School of Computer Science, University College Dublin (UCD), where she contributes significantly to research and academic activities in the field of computer science. Her expertise lies in multi-agent systems, edge computing, and intelligent transport systems.

Profile:

SCOPUS

🎓 Education:

Fatemeh pursued her academic journey with a strong foundation in computer science:

  • Ph.D. in Computer Science (2013-2018)
    Trinity College Dublin, Dublin, Ireland
    Thesis Title: “Collaboration community formation in open systems for agents with multiple goals.”
    Supervised by Prof. Siobhan Clarke.
  • M.Sc. in Computer (Software) Engineering (2010-2012)
    Sharif University of Technology, Tehran, Iran
    Thesis Title: “Development of a process line engineering approach based on product line engineering methods for engineering agent-oriented methodologies.”
  • B.Sc. Hons in Computer (Software) Engineering (2006-2010)
    Alzahra University, Tehran, Iran

👩‍🏫 Experience:

Fatemeh has held various academic and professional roles:

  • Assistant Professor (Dec 2020 – Present)
    School of Computer Science, UCD, Dublin, Ireland
  • Postdoctoral Researcher (June 2018 – Jan 2019)
    CONNECT, School of Computer Science and Statistics, Trinity College Dublin, Ireland
  • Software Engineer (Sept 2010 – Aug 2013)
    ITOrbit, Tehran, Iran

🔍 Research Interest:

Her research interests encompass:

Multi-agent Systems, Edge Computing, Intelligent Transport Systems, Agent-based Modeling

🏆 Award:

Fatemeh Golpayegani is recognized as a member of the Young Academy of Ireland (2023-2027), highlighting her contribution to advancing research and cultural life in Ireland.

📚 Publications:

Fatemeh has contributed significantly to her field with numerous peer-reviewed publications. A selection of her notable works include:

Adaptation in Edge Computing: A review on design principles and research challenges
Published in ACM Transactions on Autonomous and Adaptive Systems, 2024. Cited by: 15

Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model
Published in Journal of Reliable Intelligent Environments, 2023. Cited by: 20

Towards the Use of Hypermedia MAS and Microservices for Web Scale Agent-Based Simulation
Published in SN Computer Science, 2022.

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction
Published in IEEE Access, 2022.

Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-agent Systems
Published in ACM Transactions on Intelligent Systems and Technology (TIST), 2019.

These publications underscore her research breadth and impact in areas such as adaptive systems, shared mobility, and multi-agent collaboration.