Mohammadmahdi Amini | Structural health monitoring | Best Researcher Award

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Mr. Mohammadmahdi Amini | Structural health monitoring | Best Researcher Award

Innovation & Technology Manager at Laskaridis Shipping Co. LTD, Greece

🎓 Mohammadmahdi Amini, a skilled BIM Modeler born in 1995, has over 3 years of professional expertise in Revit-based Building Information Modeling (BIM). 🌍 Based in Damghan, Semnan, Iran, he has authored three Q1 Elsevier journal papers exploring the effects of magnetic fields on concrete properties. 🏗️ Proficient in Autodesk Revit, AutoCAD, and advanced design software, Mohammadmahdi excels in architectural design, construction documentation, and quantity surveying. ✍️ Fluent in English with an IELTS score of 6, he thrives in collaborative environments, showcasing a passion for innovative civil engineering solutions.

Publication Profile

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Education🎓

Mohammadmahdi holds a Bachelor’s degree in Civil Engineering from Semnan University, Iran (2014–2019). 🏫 Specializing in structural analysis and concrete technologies, he developed a foundational understanding of construction methodologies and project management. 📚 With a GPA of 13.73, his academic journey laid the groundwork for his advanced research in magnetic fields’ effects on concrete, culminating in contributions to high-impact journals. ✨ Semnan University was instrumental in shaping his technical and analytical abilities, inspiring his pursuit of excellence in BIM modeling and civil engineering research.

Experience 💼

As a BIM Modeler at Agourconstruction (Dec 2020–Feb 2024), Mohammadmahdi specialized in Revit-based architectural drafting, quantity surveying, and cost estimation. 📊 His role extended to supervision assistance and resident engineering, ensuring project execution met quality standards. 🏗️ With a keen eye for detail, he collaborated with multidisciplinary teams to deliver efficient construction documentation. ✨ Leveraging his Revit and AutoCAD expertise, he optimized workflows and developed innovative solutions for construction challenges. 🌟 His commitment to excellence has consistently driven successful project outcomes.

Awards and Honors 🏅

Elsevier Recognition: Published three Q1 journal papers in 2024, advancing research in magnetic fields’ effects on concrete. Academic Achievement: Recognized for contributing innovative methodologies to concrete technologies at Semnan University Innovation Awards: Praised for applying novel magnetic approaches in structural engineering solutions. Professional Excellence: Earned commendations for delivering high-quality BIM projects and advancing Revit-based construction workflows.

Research Focus 🔬

Mohammadmahdi’s research centers on leveraging magnetic fields to enhance concrete’s mechanical properties. 🧲 His studies delve into the compressive strength of concrete enriched with silica sand, ferrosilicon, and nano-silica. 📖 His publications include experimental and numerical investigations of magnetic field effects, aiming to improve concrete’s durability and magnetization. 💡 A pioneering approach integrates nanotechnology and magnetic innovations for advanced construction materials. ✨ His work bridges theory and application, inspiring sustainable and efficient civil engineering solutions.

Publications 📖

1. Numerical Investigation on the Impact of Alternating Magnetic Fields on the Mechanical Properties of Concrete with Various Silica Sand and Ferrosilicon Compositions

Authors: Ghanepour, M.; Amini, M.M.; Rezaifar, O.
Journal: Results in Engineering
Volume: 24
Article ID: 103631
Year: 2024
Citations: 0
This study investigates the mechanical behavior of concrete exposed to alternating magnetic fields, focusing on compositions incorporating silica sand and ferrosilicon. Advanced numerical simulations provide insights into how magnetic fields influence concrete’s structural performance and durability. This work serves as a significant step in optimizing construction materials for modern infrastructure.

2. Experimental Analysis of the Impact of Alternating Magnetic Fields on the Compressive Strength of Concrete with Various Silica Sand and Microsilica Compositions

Authors: Amini, M.M.; Ghanepour, M.; Rezaifar, O.
Journal: Case Studies in Construction Materials
Volume: 21
Article ID: e03487
Year: 2024
Citations: 3
This experimental study explores the compressive strength enhancement of concrete treated with alternating magnetic fields. It emphasizes how the integration of silica sand and microsilica alters the concrete’s properties under magnetic exposure. The findings highlight innovative strategies to improve concrete performance in high-demand applications.

3. A Novel Magnetic Approach to Improve Compressive Strength and Magnetization of Concrete Containing Nano Silica and Steel Fibers

Authors: Rezaifar, O.; Ghanepour, M.; Amini, M.M.
Journal: Journal of Building Engineering
Volume: 91
Article ID: 109342
Year: 2024
Citations: 7
This paper presents a groundbreaking approach to enhancing concrete’s compressive strength and magnetization through the inclusion of nano silica and steel fibers. The application of magnetic fields during the curing process demonstrates significant improvements in both mechanical and magnetic properties. This research has profound implications for the construction of magnetically sensitive and structurally robust materials.

Conclusion

Mohammadmahdi Amini demonstrates significant potential for the Research for Best Researcher Award due to his impactful publications, technical expertise, and innovative research on concrete properties. However, improving language proficiency, further diversifying research topics, and showcasing exceptional academic achievements could make his profile even more compelling for international recognition. Overall, he is a strong candidate for the award.

Jinxia Zhang | Defect detection | Best Researcher Award

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Assoc Prof Dr. Jinxia Zhang | Defect detection | Best Researcher Award

 Associate Professor at Southeast University, China

Assoc Prof Dr. Jinxia Zhang is an Associate Professor at Southeast University, Nanjing, China, specializing in saliency detection, visual attention, computer vision, and deep learning. With a Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology, he has extensive experience in artificial intelligence research. His journey includes time as a visiting scholar at Harvard Medical School and numerous prestigious research projects funded by national foundations. Assoc Prof Dr. Jinxia Zhang leads key AI initiatives, driving innovations in multimodal understanding, defect analysis, and object detection. His academic and professional contributions have positioned him as a prominent researcher in visual computing and AI.

Publication Profile

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Education 🎓

Assoc Prof Dr. Jinxia Zhang  earned his M.Sc. and Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology in 2015. His doctoral research laid a foundation for his interest in artificial intelligence, particularly in areas like visual attention and computer vision. Prior to his postgraduate work, he completed his B.Sc. in Computer Science and Technology at the same institution in 2009, where he developed a solid understanding of computational theories and applications. His education has provided him with both theoretical knowledge and practical skills that are central to his current research on AI and deep learning.Assoc Prof Dr. Jinxia Zhang  is currently an Associate Professor at Southeast University, Nanjing, a role he has held since 2019. From 2016 to 2019, he served as a Lecturer at the same university, where he significantly contributed to AI teaching and research. His early career included a prestigious stint as a Visiting Scholar at Harvard Medical School, USA, between 2012 and 2014, where he collaborated on cutting-edge AI-driven healthcare projects. His international exposure and academic roles have enriched his teaching and research, particularly in computer vision and AI, making him a key figure in the field.

Awards and Honors  🏆

Assoc Prof Dr. Jinxia Zhang  has received numerous accolades for his research excellence and contributions to the field of AI. He was awarded the National Natural Science Foundation of China grant in 2018 for his project on salient object detection. In 2017, he secured the Jiangsu Natural Science Foundation Grant for his innovative research on visual cognitive characteristics. Additionally, his work in defect diagnosis for photovoltaic modules was recognized as part of the National Key Research and Development Plan. These prestigious awards underscore his pioneering contributions in artificial intelligence and computer vision research.

Research Focus  🔬

Assoc Prof Dr. Jinxia Zhang ‘s research focuses on the intersection of visual attention, saliency detection, and deep learning within artificial intelligence. He leads projects on multimodal understanding and e-commerce applications, and is a Principal Investigator for research into AI-based fruit and vegetable recognition. His earlier work in defect diagnosis for photovoltaic modules and salient object detection in complex scenes has been supported by prominent grants. His innovative approach combines perceptual grouping and visual attention to develop cutting-edge solutions in computer vision, making significant advancements in how machines perceive and interact with visual data.

Conclusion

The candidate demonstrates an impressive body of work across several domains of artificial intelligence, particularly in salient object detection, visual cognition, and multimodal learning. Their academic achievements, project leadership, and dedication to advancing AI make them a strong contender for the Best Researcher Award. By continuing to broaden their industry collaborations and expanding the scope of their research impact, they can become a globally recognized leader in AI and computer vision.

Publication  Top Notes

  • Towards the Quantitative Evaluation of Visual Attention Models (2015)
    • Citation: 75
    • Journal: Vision Research
    • Key Contributors: Z. Bylinskii, E.M. DeGennaro, R. Rajalingham, H. Ruda, J. Zhang, J.K. Tsotsos
    • Highlights: Focuses on quantitative approaches to evaluate visual attention models, essential for improving saliency detection.
  • A Novel Graph-Based Optimization Framework for Salient Object Detection (2017)
    • Citation: 63
    • Journal: Pattern Recognition
    • Key Contributors: J. Zhang, K.A. Ehinger, H. Wei, K. Zhang, J. Yang
    • Highlights: Presents a new graph-based optimization method for enhancing the accuracy of salient object detection.
  • Salient Object Detection by Fusing Local and Global Contexts (2020)
    • Citation: 60
    • Journal: IEEE Transactions on Multimedia
    • Key Contributors: Q. Ren, S. Lu, J. Zhang, R. Hu
    • Highlights: This paper integrates both local and global visual contexts to refine salient object detection in multimedia applications.
  • Inter-Hour Direct Normal Irradiance Forecast with Multiple Data Types and Time-Series (2019)
    • Citation: 36
    • Journal: Journal of Modern Power Systems and Clean Energy
    • Key Contributors: T. Zhu, H. Zhou, H. Wei, X. Zhao, K. Zhang, J. Zhang
    • Highlights: Introduces a time-series forecasting model for direct normal irradiance, benefiting renewable energy systems.
  • Winter is Coming: How Humans Forage in a Temporally Structured Environment (2015)
    • Citation: 35
    • Journal: Journal of Vision
    • Key Contributors: D. Fougnie, S.M. Cormiea, J. Zhang, G.A. Alvarez, J.M. Wolfe
    • Highlights: Examines human visual foraging behavior in dynamically changing environments.
  • Domain Adaptation for Epileptic EEG Classification Using Adversarial Learning and Riemannian Manifold (2022)
    • Citation: 25
    • Journal: Biomedical Signal Processing and Control
    • Key Contributors: P. Peng, L. Xie, K. Zhang, J. Zhang, L. Yang, H. Wei
    • Highlights: This paper explores domain adaptation techniques to improve epileptic EEG classification through adversarial learning.
  • A Lightweight Network for Photovoltaic Cell Defect Detection in Electroluminescence Images (2024)
    • Citation: 23
    • Journal: Applied Energy
    • Key Contributors: J. Zhang, X. Chen, H. Wei, K. Zhang
    • Highlights: Develops a lightweight neural network for detecting defects in photovoltaic cells using knowledge distillation.
  • Salient Object Detection via Deformed Smoothness Constraint (2018)
    • Citation: 21
    • Journal: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: X. Wu, X. Ma, J. Zhang, A. Wang, Z. Jin
    • Highlights: Proposes a deformed smoothness constraint approach for improving salient object detection.
  • Character Recognition via a Compact Convolutional Neural Network (2017)
    • Citation: 20
    • Conference: International Conference on Digital Image Computing
    • Key Contributors: H. Zhao, Y. Hu, J. Zhang
    • Highlights: Develops a compact CNN for robust character recognition in natural scene images.
  • A Prior-Based Graph for Salient Object Detection (2014)
    • Citation: 23
    • Conference: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: J. Zhang, K.A. Ehinger, J. Ding, J. Yang
    • Highlights: Uses a prior-based graph model to enhance the performance of salient object detection algorithms.

ebraheem menda | Signal Processing | Best Researcher Award

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Mr. ebraheem menda |Signal Processing | Best Researcher Award

Assistant professor at GITAM University, India

Dr. Menda Ebraheem is an innovative and results-driven Assistant Professor with over 15 years of experience in electrical and electronics engineering education. Passionate about integrating cutting-edge research and technology, he focuses on advancing engineering methodologies through experimental design and quantitative analysis. 💡 Dr. Ebraheem is dedicated to mentoring emerging talent, fostering innovation, and contributing to the growth of the scientific community through high-quality publications. His ability to simplify complex ideas into actionable solutions has earned him respect among his peers and students

Publication Profile

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Education 🎓

Dr. Menda Ebraheem holds a B.E. in Electrical and Electronics Engineering from Andhra University College of Engineering (2000-2004) and an M.Tech from GVP College of Engineering (2005-2007). His academic background provided him with a solid foundation in engineering principles, which he has since built upon through a career dedicated to both education and research. 📖 During his studies, he honed his skills in quantitative analysis, research design, and applied electrical engineering, which would later play a pivotal role in his professional career. 📘

Experience💼

Dr. Menda Ebraheem has been serving as an Assistant Professor at GITAM University since 2009. Over the past decade, he has become known for his ability to translate complex electrical engineering concepts into understandable material for students. ✍️ Prior to his role at GITAM, he worked as an Assistant Professor at Pydah College of Engineering and Technology (2006-2009), where he first began honing his teaching and research skills. His career spans over 15 years in academia, where he has actively contributed to the development of future engineers while also collaborating on various cross-functional research projects. 📊

Awards and Honors🏆 

Dr. Menda Ebraheem has received recognition for his dedication to research and teaching. His outstanding contributions to electrical and electronics engineering have been acknowledged with accolades that highlight his excellence in both academic and experimental research. 🎖️ In addition to delivering impactful research publications, Dr. Ebraheem has been commended for his mentorship efforts, guiding students to reach their full potential. His numerous awards reflect his commitment to innovation and the broader scientific community, fostering a culture of learning and excellence within the university. 🌟

Research Focus 🔬 

Dr. Menda Ebraheem’s research focuses on electrical and electronics engineering, with a particular emphasis on leveraging quantitative analysis, experimental design, and technology-driven solutions. 📈 His work explores advancements in power systems, control systems, and circuit design, contributing to cutting-edge developments in the field. 📊 He has a strong publication record in reputable journals and is actively involved in cross-functional research collaborations aimed at driving innovation. His commitment to translating theoretical concepts into practical applications ensures his research makes a meaningful impact on both industry and academia. 💡

Publication  Top Notes

  1. Comparative performance evaluation of teaching learning-based optimization against genetic algorithm on benchmark functions
    📖 M. Ebraheem, T.R. Jyothsna (2015)
    Published in the IEEE Power, Communication and Information Technology Conference, this study compares the performance of Teaching Learning Based Optimization (TLBO) with Genetic Algorithms (GA) on benchmark functions. It focuses on assessing optimization algorithms’ efficiency for solving complex engineering problems. 📊
  2. Performance analysis of transient behavior of PMSG model with sudden load variations: Part-2
    📚 T.R. Jyothsna, M. Ebraheem (2018)
    This paper, presented at the Technologies for Smart-City Energy Security and Power Conference, investigates the transient behavior of Permanent Magnet Synchronous Generator (PMSG) models under sudden load variations, focusing on the implications for smart energy systems. 💡
  3. Modeling and Analysis of Wind Energy System
    📘 S. Medikonda, G. Vanitha, M. Ebraheem (2022)
    In this conference paper, Dr. Ebraheem and co-authors analyze wind energy systems, providing modeling insights to optimize the performance of wind energy conversion systems, especially for intelligent controllers. 🌬️
  4. Hybrid sand cat-galactic swarm optimization-based adaptive maximum power point tracking and blade pitch controller for wind energy conversion system
    🌀 M. Ebraheem (2024)
    Published in the International Journal of Adaptive Control and Signal Processing, this innovative paper introduces a hybrid optimization algorithm for adaptive maximum power point tracking and control in wind energy systems, showcasing advancements in renewable energy technologies. 🌍
  5. ATC Calculation using Power Transfer Distribution Factor for Large System
    ⚡ P.M. Ebraheem Menda, Aravind Kumar Kondaji (2022)
    Published in NeuroQuantology, this research addresses Available Transfer Capability (ATC) calculation using power transfer distribution factors, a critical issue in managing large power systems. 🚀
  6. Performance analysis of transient behaviour of PMSG model with sudden load variations part-1
    ⚙️ T.R. Jyothsna, M. Ebraheem (2018)
    This paper provides an in-depth analysis of the PMSG model’s performance under transient conditions, emphasizing the system’s response to load fluctuations and its implications for renewable energy integration. 🌿

Conclusion

With an extensive body of work, innovative research contributions, and a proven track record of mentoring emerging talent, Menda Ebraheem is well-suited for the Best Researcher Award. His dedication to advancing electrical and electronics engineering, particularly in renewable energy systems and digital signal processing, marks him as a leader in his field. By addressing areas for further growth, he will continue to contribute significantly to both academia and industry.