Mohammad Awad | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Mohammad Awad
Affiliation Hashemite University
Country Jordan
Google Scholar ID BlOlGg0AAAAJ
Documents 3
Citations 2
h-index 1
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0009-0004-1009-7923

Mohammad Awad
Hashemite University, Jordan

Mohammad Awad of Hashemite University, Jordan, is associated with engineering research concerning the structural behavior and numerical assessment of fiber-reinforced polymer (FRP) reinforced concrete columns. His recent publications examine axial compression, load–strain response, database-based analysis, and finite element validation, establishing a focused research direction within contemporary structural engineering.

Abstract

Mohammad Awad’s research profile reflects an engineering focus on the structural performance of concrete columns strengthened or reinforced with fiber-reinforced polymer systems. The work combines empirical database analysis with finite element modelling to investigate axial compression and load–strain behavior. Such approaches can support improved understanding of structural response and provide numerical evidence for evaluating FRP-reinforced concrete systems. [1]

Keywords

Fiber-reinforced polymer; FRP reinforced concrete; concrete columns; axial compression; finite element analysis; load–strain behavior; structural engineering.

Introduction

FRP reinforcement has become an important research topic in structural engineering because its mechanical and durability characteristics offer alternatives to conventional reinforcement systems. Numerical modelling and experimental databases are frequently used together to characterize structural response and evaluate predictive approaches. Awad’s recent research addresses this area through finite element assessment and database-oriented investigation of concrete columns subjected to axial loading. [2]

Research Profile

The research profile is centered on computational structural analysis, particularly the relationship between reinforcement configuration and the mechanical response of concrete columns. The use of finite element methods provides a framework for examining load transfer and strain development, while database analysis allows broader comparison across structural cases. This combination represents a methodologically focused contribution to engineering research.

Research Contributions

  • Investigation of axial compression behavior in FRP-reinforced concrete columns.
  • Application of finite element analysis to structural load–strain response.
  • Use of database analysis to support broader evaluation and validation of structural behavior.

Publications

“Axial compression behavior of FRP reinforced concrete columns based on database analysis and finite element validation” was published in Discover Civil Engineering on 27 August 2026. The article examines database evidence alongside finite element validation of axial compression behavior. [3]

“Finite element analysis of concrete columns reinforced with fiber-reinforced polymer (FRP): Load-strain behavior” was published in Scientific Research and Essays on 31 August 2025. The study focuses on finite element evaluation of load–strain behavior in FRP-reinforced concrete columns. [4]

Research Impact

The available scholarly profile records 3 documents, 2 citations, and an h-index of 1. These quantitative indicators represent a developing publication record and should be interpreted in relation to the researcher’s recent publication activity. The subject concentration in Engineering and the focus on FRP-based structural systems provide a clearly defined technical research direction.

Award Suitability

The Innovative Research Award recognizes research characterized by methodological development, relevant technical investigation, and potential contribution to its field. Awad’s work demonstrates innovation through the combined use of finite element modelling and database analysis to study FRP-reinforced concrete columns. The alignment between the research topic, engineering specialization, and recent peer-reviewed publications provides a substantive basis for consideration.

Conclusion

Mohammad Awad presents an emerging engineering research profile focused on computational and analytical assessment of FRP-reinforced concrete columns. His recent publications establish a coherent direction in structural behaviour, finite element analysis, and database-supported validation, making the profile relevant to research recognition in innovative engineering studies.

References

  1. Elsevier. (n.d.). Google Scholar author details: Mohammad Awad, Author ID BlOlGg0AAAAJ.
    https://scholar.google.com/citations?hl=en&user=BlOlGg0AAAAJ
  2. Awad, M. (2025). Finite element analysis of concrete columns reinforced with fiber-reinforced polymer (FRP): Load-strain behavior. Scientific Research and Essays.
    https://doi.org/10.5897/sre2025.6801
  3. Awad, M. (2026). Axial compression behavior of FRP reinforced concrete columns based on database analysis and finite element validation. Discover Civil Engineering.
    https://doi.org/10.1007/s44290-026-00602-y
  4. Awad, M. (2025). Finite element analysis of concrete columns reinforced with fiber-reinforced polymer (FRP): Load-strain behavior. Scientific Research and Essays, ISSN 1992-2248.
    https://doi.org/10.5897/sre2025.6801
  5. International Organization for Standardization. Structural engineering terminology and principles for concrete and reinforcement assessment.

Behzad Motallebi Azar | Engineering | Best Researcher Award

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Best Researcher Award

Behzad Motallebi Azar
Sahand University of Technology, Iran

Behzad Motallebi Azar
Affiliation Sahand University of Technology
Country Iran
Scopus ID 57221133046
Documents 7
Citations 45
h-index 4
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-7964-0507

Behzad Motallebi Azar is an engineering researcher whose documented scholarly work addresses contemporary energy-system challenges, including prosumer participation, peer-to-peer energy trading, renewable-energy integration, energy storage, hydrogen power systems, and intelligent forecasting. His publication record includes journal, conference, and book-chapter contributions spanning optimization, reinforcement learning, transactive energy, and hybrid energy systems. The available bibliometric information records 7 documents, 45 citations, and an h-index of 4 in Scopus. [1]

Abstract

The research profile of Behzad Motallebi Azar is characterized by work in energy-system engineering and computational approaches to decentralized electricity systems. His publications examine peer-to-peer energy trading, prosumer behavior, net-load forecasting, hydrogen-based power systems, transactive energy, and interconnected hybrid energy systems. Recent work combines deep reinforcement learning and blockchain-based settlement concepts for prosumer markets, reflecting the growing role of intelligent computational methods in energy-system coordination. [2] [3]

Keywords

  • Energy Systems
  • Peer-to-Peer Energy Trading
  • Deep Reinforcement Learning
  • Prosumer Markets
  • Renewable Energy

Introduction

Modern energy systems increasingly require methods capable of coordinating distributed generation, flexible demand, storage, and prosumer participation. Research in this area has consequently expanded toward decentralized markets, intelligent forecasting, and computational optimization. Motallebi Azar’s documented publications fit within this broader engineering context, addressing both market mechanisms and operational strategies for interconnected energy resources. [4]

Research Profile

The research portfolio demonstrates an interdisciplinary connection between energy engineering, artificial intelligence, optimization, and distributed energy management. The 2026 Journal of Cleaner Production article investigates optimal prosumer participation in dual peer-to-peer markets through multi-agent deep reinforcement learning, fuzzy satisfaction levels, and blockchain settlement. [2] A 2025 conference contribution examines household-prosumer net-load forecasting using deep reinforcement learning. [3] Earlier book chapters extend the profile into techno-economic hydrogen systems, transactive energy, and coalition operation of hybrid energy systems. [5] [6]

Research Contributions

  • Application of multi-agent deep reinforcement learning to prosumer participation and peer-to-peer energy markets.
  • Investigation of deep reinforcement learning for household net-load forecasting.
  • Analysis of techno-economic considerations in centralized green-hydrogen power systems.
  • Review and analysis of transactive energy and peer-to-peer trading applications.
  • Study of coalition-based operation in interconnected hybrid energy systems incorporating renewable resources, storage, and local conversion technologies.

Publications

  1. Optimal prosumer participation in dual peer-to-peer markets using multi-agent deep reinforcement learning, fuzzy satisfaction level, and blockchain settlement. Journal of Cleaner Production, 2026.
  2. Net Load Forecasting of Household Prosumers Considering Deep Reinforcement Learning. 2025 33rd International Conference on Electrical Engineering (ICEE), 2025.
  3. Techno-Economic Analysis for Centralized GH2 Power Systems. Book chapter, 2024.
  4. Transactive Energy and Peer-to-Peer Trading Applications in Energy Systems: An Overview. Book chapter, 2023.
  5. Optimal Coalition Operation of Interconnected Hybrid Energy Systems Containing Local Energy Conversion Technologies, Renewable Energy Resources, and Energy Storage Systems. Book chapter, 2022.

Research Impact

The available Scopus indicators record 7 documents, 45 citations, and an h-index of 4 for the researcher identified by Scopus Author ID 57221133046. [1] These indicators provide a bibliometric snapshot of the documented research output and citation visibility. The publication portfolio also shows continuity across several related themes, progressing from hybrid-energy-system operation and transactive-energy applications toward data-driven forecasting and intelligent peer-to-peer market coordination.

Award Suitability

Based on the supplied publication record and bibliometric information, Behzad Motallebi Azar presents a research profile relevant to consideration for the Best Researcher Award at the International Forensic Scientist Awards. The suitability assessment can be grounded in documented scholarly output, multidisciplinary energy-system research, peer-reviewed and scholarly publications, and measurable citation indicators rather than unsupported claims of distinction. Final award decisions remain subject to the applicable evaluation criteria and review process.

Conclusion

Behzad Motallebi Azar’s documented research focuses on emerging methods for intelligent and decentralized energy systems. His work connects peer-to-peer electricity markets, prosumer participation, reinforcement learning, forecasting, hydrogen power systems, transactive energy, renewable resources, and energy storage. The combination of publication activity and recorded citation indicators provides a concise basis for recognizing his continuing contribution to engineering research. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Behzad Motallebi Azar, Author ID 57221133046. Scopus.
    https://www.scopus.com/pages/authors/57221133046
  2. Motallebi Azar, B., et al. (2026). Optimal prosumer participation in dual peer-to-peer markets using multi-agent deep reinforcement learning, fuzzy satisfaction level, and blockchain settlement. Journal of Cleaner Production.
    https://doi.org/10.1016/j.jclepro.2026.149041
  3. Motallebi Azar, B., et al. (2025). Net Load Forecasting of Household Prosumers Considering Deep Reinforcement Learning. 2025 33rd International Conference on Electrical Engineering (ICEE).
    https://doi.org/10.1109/icee67339.2025.11213685
  4. Motallebi Azar, B., et al. (2023). Transactive Energy and Peer-to-Peer Trading Applications in Energy Systems: An Overview. Book chapter.
    https://doi.org/10.1007/978-3-031-35233-1_3
  5. Motallebi Azar, B., et al. (2024). Techno-Economic Analysis for Centralized GH2 Power Systems. Book chapter.
    https://doi.org/10.1007/978-3-031-52429-5_3
  6. Motallebi Azar, B., et al. (2022). Optimal Coalition Operation of Interconnected Hybrid Energy Systems Containing Local Energy Conversion Technologies, Renewable Energy Resources, and Energy Storage Systems. Book chapter.
    https://doi.org/10.1007/978-3-030-87653-1_7

Suganya | Engineering | Best Researcher Award

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Dr. Suganya | Engineering | Best Researcher Award

Assistant Professor | SRM Institute of Science and Technology | India

Y. Suganya, M.E., (Ph.D.), is a dedicated academic professional with over 14 years of teaching experience in Computer Science and Engineering. Her research focuses on machine learning and deep learning techniques for ovarian cyst classification and prediction. She has contributed significantly to academia through publications, conference presentations, and departmental leadership.

Professional profile👤

Google Scholar

Strengths for the Awards✨

  • Research Excellence: Y. Suganya has a strong research background, with a focus on machine learning and deep learning applications in medical imaging, particularly ovarian cyst classification. Her Ph.D. work aligns well with contemporary research trends in AI-driven healthcare.
  • Publication Record: She has published extensively in reputable journals and conferences, including Springer and IEEE Xplore, with multiple papers indexed in Scopus. These publications demonstrate the depth and quality of her research.
  • Teaching and Mentorship: Nearly 15 years of teaching experience, with a proven track record of producing 100% results in several semesters, indicates her commitment to education and mentorship.
  • Leadership and Service: She has taken on significant responsibilities such as coordinating accreditation processes (NBA), acting as Chief Superintendent for examinations, and serving as a journal reviewer. These roles highlight her leadership skills and service to the academic community.
  • Technical Proficiency: Proficiency in programming languages like Python, C, C++, and Java supports her research in machine learning and data analysis, making her technically well-equipped.

🎓 Education

  • Ph.D. (Computer Science and Engineering)
    • Annamalai University, Tamil Nadu, India
    • Thesis: “Classification and Prediction of Ovarian Cysts Using Machine Learning and Deep Learning Techniques” (Thesis Submitted: October 16, 2023)
  • Master of Engineering (Computer Science and Engineering)
    • MIET College of Engineering (Affiliated to Anna University), Tamil Nadu, India (June 2011) – CGPA: 8.22 (First Class)
  • Bachelor of Engineering (Computer Science and Engineering)
    • Royal College of Engineering (Affiliated to Anna University), Tamil Nadu, India (April 2005) – Percentage: 66.6% (First Class)

💼 Experience

  • Assistant Professor
    • Mookambigai College of Engineering, Tamil Nadu, India (June 24, 2011 – Present)
  • Lecturer
    • Idhaya College of Engineering for Women, Tamil Nadu, India (January 2, 2006 – April 15, 2007)

🔬 Research Interests On Engineering

  • Machine Learning and Deep Learning
  • Medical Image Processing
  • Ovarian Cyst Classification and Prediction
  • Artificial Intelligence in Healthcare

🏆 Awards

  • Annual Membership in ACM Professional Membership (ID: 5666897)
  • Reviewer for Journal Manuscript (Signal, Image, and Video Processing – Springer Nature) – November 8, 2024
  • Chief Superintendent for Theory Examination (Nov/Dec 2023), Anna University
  • Coordinated NBA Accreditation Work for the Computer Science Department

📝 Publications

  • Title: A diagnosis of ovarian cyst using deep learning neural network with XGBoost algorithm
    Authors: Y Suganya, S Ganesan, P Valarmathi, T Suresh
    Year: 2023
    Citations: 17

  • Title: Ultrasound ovary cyst image classification with deep learning neural network with Support vector machine
    Authors: Y Suganya, S Ganesan, P Valarmathi
    Year: 2022
    Citations: 11

  • Title: Classification of medical x-ray images for automated annotation
    Authors: S Ganesan, TS Subashini
    Year: 2014
    Citations: 10

  • Title: Novel approach of internet of things (IoT) based smart ambulance system for patient’s health monitoring
    Authors: A Bekkanti, R Aishwarya, Y Suganya, P Valarmathi, S Ganesan, …
    Year: 2021
    Citations: 7

  • Title: Classification of X-rays using statistical moments and SVM
    Authors: S Ganesan, TS Subashini, K Jayalakshmi
    Year: 2014
    Citations: 7

  • Title: An approach toward the efficient indexing and retrieval on medical X-ray images
    Authors: S Ganesan, TS Subashini
    Year: 2013
    Citations: 7

  • Title: A content based approach to medical X-Ray image retrieval using texture features
    Authors: S Ganesan, TS Subashini
    Year: 2014
    Citations: 6

  • Title: A Comparative Study on Consumer Courts in Tamil Nadu & Kerala States-A Statistical Survey Report
    Authors: BY Krishna, Y Suganya
    Year: 2011
    Citations: 5

  • Title: Fuzzy based detection and swarm based authenticated routing in MANET
    Authors: K Shanthi, T Jebarajan, P Sampath, W AMITABH, D RAMYA, …
    Year: 2014
    Citations: 3

  • Title: Comparative analysis of ovarian images classification for identification of cyst using ensemble method machine learning approach
    Authors: Y Suganya, S Ganesan, P Valarmathi
    Year: 2022
    Citations: 2

📊 Conclusion

Y. Suganya is a passionate educator and researcher who has made remarkable contributions to machine learning and medical image processing, specifically focusing on ovarian cyst classification and prediction. With a wealth of teaching experience, active participation in departmental responsibilities, and numerous research publications, she continues to inspire and shape the future of computer science students.

Zejie Yu | Engineering | Best Researcher Award

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Prof. Dr. Zejie Yu | Engineering | Best Researcher Award

Zhejiang University | China

Zejie Yu is a tenure-track professor at Zhejiang University in Hangzhou, China. With a strong foundation in optical and electronics engineering, he has contributed significantly to the fields of integrated photonics, nonlinear optics, and microwave photonics. His career began with his undergraduate studies at Zhejiang University, followed by a Ph.D. and postdoctoral research at The Chinese University of Hong Kong. He currently holds a prominent role in advancing photonic technologies and has made notable contributions to research in photonic chips and optical modulators.

Profile

Google Scholar

Orcid

Scopus

Strengths for the Awards

  • Academic and Professional Background:
    • Zejie Yu holds a B.S. in Optical Engineering and a Ph.D. in Electronics Engineering, with a strong track record of postdoctoral research and current tenure-track professorship at Zhejiang University. His expertise spans integrated photonics, nonlinear optics, and microwave photonics, which are highly relevant to contemporary technological advancements.
  • Research Output:
    • His extensive publication list, featuring high-quality papers in top-tier journals like Advanced Science, Nanophotonics, Laser & Photonics Reviews, and ACS Photonics, showcases his active and innovative contributions to the field of photonics.
    • His work often addresses cutting-edge technologies, such as electro-optic modulators, supercontinuum generation, and high-bandwidth integrated photonics.
  • Professional Engagement:
    • Zejie Yu’s roles as a TPC member for conferences like ACP 2020 and 2021, as well as his position as a guest editor for JOSA B and Chinese Optics Letters, illustrate his leadership in advancing the field.
    • He has been invited to give talks at major international conferences, signifying his recognition and influence among peers.
  • Research Funding:
    • His involvement in prestigious research projects funded by the National Natural Science Foundation of China and other significant national programs highlights his ability to secure funding for innovative and impactful research.

Education🎓

Zejie Yu’s academic journey began with a Bachelor of Science in Optical Engineering from Zhejiang University, China, in 2015. His focus on advanced engineering education led him to the prestigious Chinese University of Hong Kong, where he earned his Ph.D. in Electronics Engineering in 2019. He continued his research there as a postdoctoral fellow before joining Zhejiang University as a faculty member in 2020.

Experience📈

After completing his Ph.D., Zejie Yu worked as a postdoctoral researcher at The Chinese University of Hong Kong, where he honed his expertise in photonics and optics. In 2020, he transitioned to Zhejiang University as a tenure-track professor, where he continues to lead significant research projects. He also actively contributes to the scientific community as a young editor for Chinese Optics Letters and as a guest editor for JOSA B.

Research Interest On Engineering🔬

Zejie Yu’s primary research interests include integrated photonics, nonlinear optics, and microwave photonics. His work focuses on developing advanced photonic devices, including high-performance optical modulators, photonic chips, and the application of lithium niobate in integrated photonics. His ongoing projects aim to push the boundaries of photonic technologies for future communication and computing applications.

Award🏆

Zejie Yu’s innovative contributions to photonics have earned him recognition in the scientific community. His research has been featured in top-tier journals and presented at various international conferences. While specific awards are not mentioned, his contributions to the field have undoubtedly positioned him as a leading figure in photonics research.

Publication📚

  • Photonic integrated circuits with bound states in the continuum
    • Authors: Z Yu, X Xi, J Ma, HK Tsang, CL Zou, X Sun
    • Year: 2019
    • Citations: 179
  • Genetic-algorithm-optimized wideband on-chip polarization rotator with an ultrasmall footprint
    • Authors: Z Yu, H Cui, X Sun
    • Year: 2017
    • Citations: 139
  • Acousto-optic modulation of photonic bound state in the continuum
    • Authors: Z Yu, X Sun
    • Year: 2020
    • Citations: 133
  • High-dimensional communication on etchless lithium niobate platform with photonic bound states in the continuum
    • Authors: Z Yu, Y Tong, HK Tsang, X Sun
    • Year: 2020
    • Citations: 128
  • Ultralow‐loss silicon photonics beyond the singlemode regime
    • Authors: L Zhang, S Hong, Y Wang, H Yan, Y Xie, T Chen, M Zhang, Z Yu, Y Shi, …
    • Year: 2022
    • Citations: 89
  • Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities
    • Authors: Z Yu, H Cui, X Sun
    • Year: 2017
    • Citations: 89
  • Inverse-designed low-loss and wideband polarization-insensitive silicon waveguide crossing
    • Authors: Z Yu, A Feng, X Xi, X Sun
    • Year: 2018
    • Citations: 60
  • Gigahertz acousto-optic modulation and frequency shifting on etchless lithium niobate integrated platform
    • Authors: Z Yu, X Sun
    • Year: 2021
    • Citations: 57
  • Ultranarrow-band metagrating absorbers for sensing and modulation
    • Authors: A Feng, Z Yu, X Sun
    • Year: 2018
    • Citations: 56
  • Compact electro-optic modulator on lithium niobate
    • Authors: B Pan, H Cao, Y Huang, Z Wang, K Chen, H Li, Z Yu, D Dai
    • Year: 2022
    • Citations: 55

Conclusion🔮

Zejie Yu’s academic and professional trajectory exemplifies a commitment to advancing the field of integrated photonics. His impressive educational background, extensive research, and leadership in numerous professional activities reflect his significant contributions to photonics. His continued success as a tenure-track professor at Zhejiang University promises further innovation and breakthroughs in the field of photonic engineering.