Mohamed Zakaria | Engineering | Best Researcher Award

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Dr. Mohamed Zakaria | Engineering | Best Researcher Award

Kafrelsheikh University Faculty of Engineering, Egypt

Dr. Mohamed H. Zakaria, an Assistant Professor in Civil Engineering at Kafrelsheikh University, Egypt, is a dedicated researcher specializing in Soil Mechanics, Foundation Engineering, Highway Engineering, and Reinforced Concrete. With a Ph.D. from Menoufia University and a consistent academic trajectory, he has published extensively in reputable international journals, contributing innovative research on structural behavior, excavation systems, and the integration of advanced techniques such as machine learning and finite element modeling. His recent work addresses critical infrastructure challenges, reflecting both technical depth and practical relevance. Dr. Zakaria maintains active profiles on ORCID, Scopus, and ResearchGate, demonstrating his engagement with the global research community. His research reflects strong potential for collaboration and societal impact. While he could further enhance his profile through increased citations, international projects, and mentorship roles, his achievements and commitment make him a highly suitable candidate for the Best Researcher Award, with significant promise for future contributions.

Professional Profile 

Education🎓

Dr. Mohamed H. Zakaria has pursued a robust and progressive academic path in the field of Civil Engineering. He earned his Ph.D. in Civil Engineering from Menoufia University, Egypt, where he focused on advanced geotechnical and structural engineering concepts. Prior to this, he obtained a Master of Science degree in Civil Engineering from Kafrelsheikh University, further deepening his expertise in soil mechanics and foundation engineering. His academic journey began at Kafrelsheikh University, where he laid a strong foundation in engineering principles. Throughout his educational career, Dr. Zakaria demonstrated academic excellence, dedication to research, and a commitment to innovation. His studies have equipped him with both theoretical knowledge and practical problem-solving skills, which are evident in his applied research and numerous publications. His educational background not only reflects a high level of specialization in his chosen field but also positions him well for continued contributions to civil engineering education and research.

Professional Experience📝

Dr. Mohamed H. Zakaria has amassed extensive professional experience in the field of Civil Engineering, primarily through his longstanding association with Kafrelsheikh University in Egypt. He began his academic career as a Demonstrator in 2014, steadily progressing to the position of Assistant Lecturer in 2019, and currently serves as an Assistant Professor in the Civil Engineering Department. His roles have encompassed teaching, mentoring, and conducting impactful research in soil mechanics, foundation engineering, and highway engineering. Dr. Zakaria has contributed significantly to the academic community through his involvement in experimental investigations, numerical modeling, and structural analysis. His research has been published in numerous high-impact journals, reflecting both academic rigor and practical relevance. Through his professional journey, he has demonstrated a strong commitment to advancing civil engineering knowledge and fostering innovation. His experience positions him as a capable educator, active researcher, and a valuable contributor to both academic and applied engineering projects.

Research Interest🔎

Dr. Mohamed H. Zakaria’s research interests are rooted in the core areas of Civil Engineering, with a particular focus on Soil Mechanics, Foundation Engineering, Highway Engineering, and Reinforced Concrete. He is especially passionate about understanding and improving the behavior of structural systems under various loading and environmental conditions. His work explores critical challenges such as settlement mitigation, bearing capacity enhancement, and the structural performance of pile walls and reinforced concrete elements. Dr. Zakaria is also interested in the application of advanced techniques like finite element modeling, machine learning, and experimental methods to optimize design and construction practices. His interdisciplinary approach combines theoretical modeling with practical experimentation, aiming to develop innovative and sustainable engineering solutions. Through his research, he seeks to enhance the safety, durability, and efficiency of infrastructure systems, making a tangible impact on both academic knowledge and engineering practice. His work invites collaboration and has strong potential for global relevance.

Award and Honor🏆

Dr. Mohamed H. Zakaria has earned recognition for his dedication to research and academic excellence in Civil Engineering. While specific named awards and honors are not extensively listed in public records, his consistent publication of high-quality research in reputable, peer-reviewed international journals reflects his scholarly impact and recognition within the academic community. His achievements in developing innovative solutions for geotechnical and structural engineering challenges, such as enhancing the performance of secant pile walls and utilizing machine learning in structural prediction, demonstrate both technical expertise and thought leadership. His rising citation metrics and growing international research collaborations also highlight his influence and professional standing. Dr. Zakaria’s academic progression—from Demonstrator to Assistant Professor at Kafrelsheikh University—illustrates his merit and recognition by peers and institutions. As he continues to contribute significantly to his field, he is well-positioned to receive further honors and awards in acknowledgment of his impactful research and academic leadership.

Research Skill🔬

Dr. Mohamed H. Zakaria possesses a diverse and well-developed set of research skills that span both theoretical and practical aspects of Civil Engineering. He is highly proficient in experimental design and laboratory testing, particularly in the areas of soil mechanics, foundation behavior, and reinforced concrete structures. His ability to conduct complex analyses is complemented by his expertise in numerical modeling, including the use of finite element methods for simulating structural and geotechnical behavior. Additionally, Dr. Zakaria has demonstrated skill in applying advanced technologies such as machine learning to predict structural performance, showcasing his adaptability and innovation in solving engineering problems. He is also adept at conducting comprehensive literature reviews, synthesizing technical data, and publishing findings in high-impact journals. His collaborative approach and strong communication skills enhance his ability to work across multidisciplinary teams. Overall, his research skillset makes him a valuable contributor to academic advancements and practical engineering solutions.

Conclusion💡

Dr. Mohamed H. Zakaria is a highly promising and dedicated researcher with a strong and focused track record in civil engineering. His steady academic career, continuous publication record, and exploration of advanced methods like machine learning and FE modeling in civil applications showcase technical excellence and innovative thinking.

Publications Top Noted✍️

  1. Title: Mitigating Settlement and Enhancing Bearing Capacity of Adjacent Strip Footings Using Sheet Pile Walls: An Experimental Approach
    Authors: Ali Basha, Ahmed Yousry Akal, Mohamed H. Zakaria
    Year: 2025
    Citation: Infrastructures, 2025, DOI: 10.3390/infrastructures10040083

  2. Title: A Comparative Study of Terrestrial Laser Scanning and Photogrammetry: Accuracy and Applications
    Authors: Mohamed H. Zakaria, Hossam Fawzy, Mohammed El-Beshbeshy, Magda Farhan
    Year: 2025
    Citation: Civil Engineering Journal, March 2025, DOI: 10.28991/cej-2025-011-03-021

  3. Title: Cantilever Piled-Wall Design Criteria in Cohesionless Soil: A Review
    Authors: Mohamed Hamed Zakaria, Ali Basha
    Year: 2024
    Citation: World Journal of Engineering, 2024, DOI: 10.1108/WJE-01-2024-0038

  4. Title: Prediction of RC T-Beams Shear Strength Based on Machine Learning
    Authors: Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. Shahin
    Year: 2024
    Citation: International Journal of Concrete Structures and Materials, 2024, DOI: 10.1186/S40069-024-00690-Z

  5. Title: Effect of Insufficient Tension Lap Splices on the Deformability and Crack Resistance of Reinforced Concrete Beams: A Comparative Study Techniques and Experimental Study
    Authors: Roba Osman, Boshra El-taly, Ahmed Fahmy, Mohamed Zakaria
    Year: 2024
    Citation: Engineering Research Journal, Nov 2024, DOI: 10.21608/erjm.2024.296635.1337

  6. Title: Predicting the Maximum Axial Capacity of Secant Pile Walls Embedded in Sandy Soil
    Authors: Ali M. Basha, Mohamed H. Zakaria, Maher T. El-Nimr, Mohamed M. Abo-Raya
    Year: 2024
    Citation: Geotechnical and Geological Engineering, July 2024, DOI: 10.1007/s10706-023-02734-9

  7. Title: Two-Dimensional Numerical Approaches of Excavation Support Systems: A Comprehensive Review of Key Considerations and Modelling Techniques
    Authors: Mohamed Hamed Zakaria, Ali Basha
    Year: 2024
    Citation: Journal of Contemporary Technology and Applied Engineering, July 2024, DOI: 10.21608/jctae.2024.299692.1030

  8. Title: Interfacial Shear Behavior of Composite Concrete Substrate to High-Performance Concrete Overly After Exposure to Elevated Temperature
    Authors: Nagat M. Zalhaf, Sabry Fayed, Mohamed H. Zakaria
    Year: 2024
    Citation: International Journal of Concrete Structures and Materials, March 2024, DOI: 10.1186/s40069-023-00654-9

Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559

Najeeb ur rehman Malik | Engineering | Best Researcher Award

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Dr. Najeeb ur rehman Malik | Engineering | Best Researcher Award

Assistant Professor at DHA Suffa University, Pakistan

Dr. Najeeb Ur Rehman Malik is a dedicated researcher and electronics engineer specializing in computer vision, deep learning, and image processing. He holds a Ph.D. from Universiti Teknologi Malaysia (UTM), where his research focused on multi-view human action recognition using convolutional neural networks (CNNs) and pose features. His expertise spans artificial intelligence, embedded systems, and digital signal processing. With multiple peer-reviewed publications, including work on COVID-19 detection using X-ray images and AI-driven healthcare solutions, he has significantly contributed to applied AI research. He has industry experience as an Assistant Manager at PTCL and has led technical events at the university and national levels. His proficiency in MATLAB, Python, and embedded systems complements his research acumen. While he has made impactful contributions, further global collaborations, research funding, and high-impact citations would enhance his academic influence. Dr. Malik continues to innovate in AI and computer vision, driving advancements in intelligent systems.

Professional Profile 

Education

Dr. Najeeb Ur Rehman Malik has a strong academic background in electronics engineering and communication systems. He is currently pursuing a Ph.D. at Universiti Teknologi Malaysia (UTM), where his research focuses on multi-view human action recognition using deep learning and convolutional neural networks (CNNs). He earned his Master of Engineering (M.E.) in Communication Systems and Networks from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan, graduating with a CGPA of 3.40. His master’s research explored speeded-up robust features (SURF) for image retrieval systems. Prior to that, he completed his Bachelor of Engineering (B.E.) in Electronics Engineering from MUET with a CGPA of 3.45, gaining expertise in power electronics, automation, digital signal processing, and embedded systems. His academic journey reflects a strong foundation in artificial intelligence, image processing, and computer vision, positioning him as a key contributor to advancements in intelligent systems and AI-driven technologies.

Professional Experience

Dr. Najeeb Ur Rehman Malik has diverse professional experience in both academia and industry, specializing in electronics engineering, communication systems, and artificial intelligence. He served as an Assistant Manager at PTCL in Hyderabad, Sindh, Pakistan, from February 2017 to June 2018, where he gained hands-on experience in telecommunications, networking, and system management. Prior to that, he completed an internship at the National Telecommunication Corporation (NTC) in Karachi during June-July 2010, where he worked on networking infrastructure and telecommunication protocols. In addition to his industry experience, he has been actively engaged in research at Universiti Teknologi Malaysia (UTM), focusing on deep learning applications for multi-view human action recognition. His technical expertise spans MATLAB, Python, embedded systems, and digital signal processing, making him a well-rounded professional. With a strong blend of research and industry exposure, Dr. Malik continues to contribute to advancements in AI, image processing, and communication technologies.

Research Interest

Dr. Najeeb Ur Rehman Malik’s research interests lie at the intersection of computer vision, deep learning, image processing, and artificial intelligence. His primary focus is on multi-view human action recognition, where he integrates convolutional neural networks (CNNs) and pose estimation techniques to enhance accuracy in real-world scenarios. He has also explored content-based image retrieval, developing robust techniques using Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). His work extends to healthcare applications, including AI-driven COVID-19 detection from chest X-ray images and the role of wearable technology in pandemic management. Additionally, he is interested in embedded systems, automation, and signal processing, particularly in developing intelligent and efficient computing solutions. His expertise in MATLAB, Python, and FPGA-based system design enables him to innovate in these areas. Dr. Malik aims to contribute to the advancement of AI-driven technologies for healthcare, surveillance, and human-computer interaction.

Award and Honor

Dr. Najeeb Ur Rehman Malik has been recognized for his contributions to computer vision, deep learning, and artificial intelligence through various academic and professional honors. His research in multi-view human action recognition and AI-driven healthcare solutions has been published in reputed journals, highlighting his impact in the field. During his academic career, he actively participated in technical events, conferences, and research forums, further solidifying his reputation as a dedicated scholar. He has also played a key role in organizing and volunteering at national and university-level exhibitions and competitions, showcasing his leadership and commitment to knowledge dissemination. His work on COVID-19 detection using AI and image processing techniques has received significant attention, demonstrating real-world applications of his research. While he has made commendable contributions, further recognition in the form of best paper awards, patents, and international research grants would enhance his standing in the global research community.

Research Skill

Dr. Najeeb Ur Rehman Malik possesses advanced research skills in computer vision, deep learning, and image processing, making significant contributions to AI-driven solutions. He is proficient in MATLAB and Python, leveraging machine learning frameworks like TensorFlow and PyTorch to develop multi-view human action recognition systems using convolutional neural networks (CNNs) and pose estimation techniques. His expertise extends to content-based image retrieval, feature extraction (SURF & SIFT), and embedded system design, enabling efficient AI model deployment. He is skilled in handling large datasets, performing statistical analysis, and optimizing deep learning architectures for real-world applications, including COVID-19 detection from chest X-ray images. Additionally, he has experience in academic writing, research methodology, and experimental design, ensuring high-quality publications. His ability to analyze complex problems, design innovative solutions, and collaborate on interdisciplinary research projects positions him as a strong contributor to advancements in AI, healthcare, and intelligent automation.

Conclusion

Najeeb Ur Rehman Malik is a strong candidate for the Best Researcher Award due to his technical expertise, interdisciplinary research contributions, and published works in computer vision and AI. However, improving citation metrics, securing research funding, and enhancing global collaboration would further strengthen his profile. If he has additional awards, patents, or high-impact projects, those should be highlighted in the application to maximize competitiveness.

Publications Top Noted

  • Cascading pose features with CNN-LSTM for multiview human action recognition

    • Authors: NR Malik, SAR Abu-Bakar, UU Sheikh, A Channa, N Popescu
    • Year: 2023
    • Citations: 23

  • Robust Technique to Detect COVID-19 using Chest X-ray Images

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 23

  • Multi-view human action recognition using skeleton based-FineKNN with extraneous frame scrapping technique

    • Authors: NUR Malik, UU Sheikh, SAR Abu-Bakar, A Channa
    • Year: 2023
    • Citations: 18

  • Managing COVID-19 Global Pandemic With High-Tech Consumer Wearables: A Comprehensive Review

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 17

  • Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

    • Authors: A Memon, MWB Mustafa, TA Jumani, M Olatunji Obalowu, NR Malik
    • Year: 2021
    • Citations: 10

  • Performance comparison between SURF and SIFT for content-based image retrieval

    • Authors: NUR Malik, AG Airij, SA Memon, YN Panhwar, SAR Abu-Bakar
    • Year: 2019
    • Citations: 8

  • Multiview human action recognition system based on OpenPose and KNN classifier

    • Authors: NUR Malik, SAR Abu Bakar, UU Sheikh
    • Year: 2022
    • Citations: 5

  • Association of stride rate variability and altered fractal dynamics with ageing and neurological functioning

    • Authors: A Channa, N Popescu
    • Year: 2021
    • Citations: 3

  • Localized Background Subtraction Feature-Based Approach for Vehicle Counting

    • Authors: MA El-Khoreby, SAR Abu-Bakar, MM Mokji, SN Omar, NUR Malik
    • Year: 2019
    • Citations: 3

Arash Yazdanpanah Goharrizi | Engineering | Best Innovation Award

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Prof. Arash Yazdanpanah Goharrizi | Engineering | Best Innovation Award

Shahid Beheshti University, Iran

Dr. Arash Yazdanpanah Goharrizi is a distinguished professor in electrical engineering at Shahid Beheshti University, Tehran, Iran. His research focuses on nanotechnology, semiconductor devices, and electronic transport properties, with contributions to optimizing transistor performance, nanoribbon-based sensors, and first-principles calculations of novel materials. He has published extensively in high-impact journals, collaborating with international researchers to advance the field of microelectronics and nanostructures. In addition to research, Dr. Goharrizi actively reviews scientific manuscripts and contributes to academic peer-review processes.

Professional Profile

Education

Dr. Arash Yazdanpanah Goharrizi earned his academic qualifications from Shahid Beheshti University, Tehran, Iran. He initially served as an assistant professor in electrical engineering at the same institution, where he developed expertise in semiconductor physics, nanomaterials, and device modeling. His academic training provided him with a strong foundation in theoretical and applied aspects of electronic devices, paving the way for his contributions to advanced semiconductor research.

Professional Experience

Dr. Goharrizi currently serves as a professor at Shahid Beheshti University, where he leads research in electrical engineering, with a focus on micro- and nanostructures. Over the years, he has conducted groundbreaking studies on electronic and transport properties of advanced materials like phosphorene, antimonene, and germanene. His work has led to numerous publications in esteemed journals such as ACS Applied Electronic Materials, IEEE Transactions on Electron Devices, and Physica E. Beyond research, he contributes to academia through peer reviewing and mentoring graduate students in semiconductor device physics and nanoelectronics.

Research Interests

Dr. Arash Yazdanpanah Goharrizi’s research interests lie in the fields of nanoelectronics, semiconductor devices, and computational materials science. He focuses on the electronic, optical, and transport properties of low-dimensional materials such as phosphorene, antimonene, graphene, and germanene nanoribbons, utilizing first-principles calculations and device modeling to optimize their performance. His studies contribute to advancements in transistor design, Bragg grating-based sensors, and tunneling field-effect transistors (TFETs). Additionally, he explores strain engineering and doping control to enhance device efficiency and scalability. His interdisciplinary research integrates physics, electrical engineering, and material science, aiming to develop next-generation electronic and optoelectronic devices for high-performance computing and sensing applications.

Awards and Honors

Dr. Goharrizi has been recognized for his contributions to semiconductor research and nanoelectronics through various academic and professional honors. His high-impact publications in prestigious journals and collaborations with international researchers reflect his standing in the scientific community. As a peer reviewer for leading journals, he has contributed to the advancement of materials science and electrical engineering. He has also received recognition for his mentorship and guidance of graduate students in advanced semiconductor device research. His work on nanostructured materials and electronic transport properties continues to earn him accolades within the academic and research communities, further establishing his reputation as a leading expert in the field.

Publications Top Noted

  1. Modeling of lightly doped drain and source graphene nanoribbon field effect transistors
    • Authors: M Saremi, M Saremi, H Niazi, AY Goharrizi
    • Journal: Superlattices and Microstructures
    • Year: 2013
    • Citations: 94
  2. Armchair graphene nanoribbon resonant tunneling diodes using antidote and BN doping
    • Authors: AY Goharrizi, M Zoghi, M Saremi
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2016
    • Citations: 93
  3. Band gap tuning of armchair graphene nanoribbons by using antidotes
    • Authors: M Zoghi, AY Goharrizi, M Saremi
    • Journal: Journal of Electronic Materials
    • Year: 2017
    • Citations: 77
  4. A numerical study of line-edge roughness scattering in graphene nanoribbons
    • Authors: A Yazdanpanah, M Pourfath, M Fathipour, H Kosina, S Selberherr
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2011
    • Citations: 71
  5. Device performance of graphene nanoribbon field-effect transistors in the presence of line-edge roughness
    • Authors: AY Goharrizi, M Pourfath, M Fathipour, H Kosina
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2012
    • Citations: 67
  6. Tuning electronic, magnetic, and transport properties of blue phosphorene by substitutional doping: a first-principles study
    • Authors: F Safari, M Fathipour, A Yazdanpanah Goharrizi
    • Journal: Journal of Computational Electronics
    • Year: 2018
    • Citations: 44
  7. An analytical model for line-edge roughness limited mobility of graphene nanoribbons
    • Authors: AY Goharrizi, M Pourfath, M Fathipour, H Kosina, S Selberherr
    • Journal: IEEE Transactions on Electron Devices
    • Year: 2011
    • Citations: 41
  8. SOI LDMOSFET with up and down extended stepped drift region
    • Authors: M Saremi, M Saremi, H Niazi, M Saremi, AY Goharrizi
    • Journal: Journal of Electronic Materials
    • Year: 2017
    • Citations: 40
  9. A new method for classification and identification of complex fiber Bragg grating using the genetic algorithm
    • Authors: A Rostami, A Yazdanpanah-Goharriz
    • Journal: Progress In Electromagnetics Research
    • Year: 2007
    • Citations: 31
  10. Strain-induced armchair graphene nanoribbon resonant-tunneling diodes
  • Authors: M Zoghi, AY Goharrizi
  • Journal: IEEE Transactions on Electron Devices
  • Year: 2017
  • Citations: 30

Amir Reza Rahimi | Computer | Best Researcher Award

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Dr. Amir Reza Rahimi | Computer | Best Researcher Award

PHD at University of Valencia, Spain

Dr. Amir Reza Rahimi is a Ph.D. candidate at the University of Valencia, specializing in language, literature, culture, and their applications. With extensive experience teaching English at universities, high schools, and language institutes in Iran, he is actively involved in research projects like FORTHEM and SOCIEMOVE, focusing on fostering socioemotional skills through virtual exchange. Dr. Rahimi has conducted workshops for language teachers on integrating technology into English teaching and has published extensively in prestigious journals such as Computer-Assisted Language Learning and Computers in Human Behavior Reports. His research has been presented at international conferences, and he is recognized for introducing innovative educational methodologies, earning the Best Research Award in Innovation in Data Analysis. His expertise spans psycholinguistics, CALL, MOOCs, virtual exchange, and teacher education. With a passion for advancing language learning, Dr. Rahimi continues to make significant contributions to the intersection of technology and education.

Professional Profile 

Education

Dr. Amir Reza Rahimi has an extensive academic background, beginning with a Bachelor’s degree in English Language Teaching from the University of Mohaghegh Ardabili in Iran, completed between 2014 and 2017. He then pursued a Master’s degree in English Language Teaching at Shahid Rajaee Teacher Training University in Tehran, Iran, where he conducted research on the impact of massive open online courses (MOOCs) on Iranian EFL learners’ self-regulation and motivation. Dr. Rahimi is currently a Ph.D. candidate at the University of Valencia, Spain, where he is studying language, literature, culture, and their applications. His doctoral research is focused on exploring innovative methods in language learning, particularly through virtual exchange and computer-assisted language learning (CALL). Throughout his educational journey, Dr. Rahimi has continuously demonstrated a commitment to advancing the field of language education through research, publications, and participation in international academic projects.

Professional Experience

Dr. Amir Reza Rahimi has a rich and diverse professional experience in the field of language education. He has taught English at various institutions, including universities, high schools, and language institutes in Iran, where he developed expertise in teaching English as a foreign language (EFL). His teaching career spans over several years, during which he contributed to curriculum development and language instruction. Dr. Rahimi is currently involved in the FORTHEM Research Project and the SOCIEMOVE project, where he serves as a mentor researcher and focuses on developing socioemotional skills through virtual exchange. Additionally, he has conducted workshops for language teachers, helping them incorporate technology into their teaching practices. His research, which bridges the gap between language learning and technology, has led to numerous publications in high-impact journals. Dr. Rahimi’s professional experience reflects his dedication to enhancing language education through innovative methodologies and research-driven approaches.

Research Interest

Dr. Amir Reza Rahimi’s research interests primarily focus on the intersection of language education, technology, and learner motivation. His work explores various aspects of computer-assisted language learning (CALL), particularly how digital tools and virtual exchanges can enhance language learning experiences. Dr. Rahimi is deeply interested in the role of massive open online courses (MOOCs) and the development of self-regulation and motivation in online language learners. He also delves into psycholinguistics, exploring how emotional and psychological factors influence language acquisition. His research further investigates the impact of socioemotional skills on language learners, especially through virtual exchange programs like SOCIEMOVE. Additionally, he examines theory development in education, with a particular emphasis on innovative research designs, such as bisymmetric approaches. Dr. Rahimi’s work aims to bridge the gap between technology and language teaching, contributing to the advancement of both educational theory and practice in the digital age.

Award and Honor

Dr. Amir Reza Rahimi has received several prestigious awards and honors for his outstanding contributions to language education and research. Notably, he won the Best Research Award in Innovation in Data Analysis from ScienceFather for introducing a new research design to the field of education, specifically a bisymmetric research design. This recognition highlights his innovative approach to research methodology, particularly in the context of computer-assisted language learning (CALL). Dr. Rahimi’s research has also earned him multiple publications in top-tier journals such as Computer-Assisted Language Learning, Computers in Human Behavior Reports, and Education and Information Technologies, where his work on language learning, virtual exchange, and online motivation has gained significant academic attention. His accomplishments have been further acknowledged through his active participation in international conferences, including the TESOL International Convention and the World CALL Conference. Dr. Rahimi’s honors reflect his commitment to advancing language education through technology and innovation.

Conclusion

Amir Reza Rahimi is a highly accomplished researcher whose contributions to CALL, psycholinguistics, and educational technology make him a strong contender for the Best Researcher Award. His innovative approaches, impactful publications, and leadership in international projects are commendable. To further solidify his candidacy, increased interdisciplinary collaboration, a focus on societal impact, and broader dissemination of his work are recommended. Overall, his profile aligns well with the criteria for excellence in research, making him a suitable nominee for this award.

Publications Top Noted

  • The role of university teachers’ 21st-century digital competence in their attitudes toward ICT integration in higher education: Extending the theory of planned behavior
    Authors: AR Rahimi, D Tafazoli
    Year: 2022
    Citation: The JALT CALL Journal, 18(2), 1832-4215
  • Unifying EFL learners’ online self‑regulation and online motivational self‑system in MOOCs: A structural equation modeling approach
    Authors: AR Rahimi, Z Cheraghi
    Year: 2022
    Citation: Journal of Computers in Education, 9(4)
  • EFL learners’ attitudes toward the usability of LMOOCs: A qualitative content analysis
    Authors: AR Rahimi, D Tafazoli
    Year: 2022
    Citation: The Qualitative Report, 27(1), 158-173
  • The role of EFL learners’ L2 self-identities, and authenticity gap on their intention to continue LMOOCs: Insights from an exploratory partial least approach
    Author: AR Rahimi
    Year: 2023
    Citation: Computer Assisted Language Learning, 1-32
  • Online motivational self-system in MOOC: A qualitative study
    Author: AR Rahimi
    Year: 2021
    Citation: From emotion to knowledge: emerging ecosystems in language learning, 79-86
  • Beyond digital competence and language teaching skills: The bi-level factors associated with EFL teachers’ 21st-century digital competence to cultivate 21st-century digital skills
    Author: AR Rahimi
    Year: 2024
    Citation: Education and Information Technologies, 29(8), 9061-9089
  • A bi-phenomenon analysis to escalate higher educators’ competence in developing university students’ information literacy (HECDUSIL): The role of language lecturers’ conceptual …
    Author: AR Rahimi
    Year: 2024
    Citation: Education and Information Technologies, 29(6), 7195-7222
  • The role of twenty-first century digital competence in shaping pre-service teacher language teachers’ twenty-first century digital skills: the Partial Least Square Modeling …
    Authors: AR Rahimi, Z Mosalli
    Year: 2024
    Citation: Journal of Computers in Education
  • A tri-phenomenon perspective to mitigate MOOCs’ high dropout rates: the role of technical, pedagogical, and contextual factors on language learners’ L2 motivational selves, and …
    Author: AR Rahimi
    Year: 2024
    Citation: Smart Learning Environments, 11(1), 11
  • Determinants of Online Platform Diffusion during COVID-19: Insights from EFL Teachers’ Perspectives
    Authors: AR Rahimi, S Atefi Boroujeni
    Year: 2022
    Citation: Journal of Foreign Language Teaching and Translation Studies, 7(4), 111-136
  • The role of ChatGPT readiness in shaping language teachers’ language teaching innovation and meeting accountability: A bisymmetric approach
    Authors: AR Rahimi, A Sevilla-Pavón
    Year: 2024
    Citation: Computers and Education: Artificial Intelligence, 7, 100258
  • Exploring the direct and indirect effects of EFL learners’ online motivational self-system on their online language learning acceptance: the new roles of current L2 self and …
    Authors: AR Rahimi, Z Mosalli
    Year: 2024
    Citation: Asian-Pacific Journal of Second and Foreign Language Education, 9(1), 49