Gil Ju Lee | Engineering | Best Researcher Award

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Prof. Gil Ju Lee | Engineering | Best Researcher Award

Associate Professor at Pusan National University, South Korea

Dr. Gil Ju Lee is an accomplished researcher and Associate Professor at the School of Electrical and Electronics Engineering, Pusan National University (PNU), South Korea. His expertise lies in novel photonic devices, advanced optoelectronics, bio-inspired imaging systems, and semiconductor nanowires. With a strong background in next-generation imaging, radiative cooling, and multifunctional nanophotonic devices, he has contributed significantly to cutting-edge technological advancements. Dr. Lee has received numerous prestigious awards, including the Outstanding Researcher Award from PNU (2022-2024) and the Samsung HumanTech Thesis Award. His research has been widely published in high-impact journals such as Nature Communications, Advanced Energy Materials, and Scientific Robotics. As the principal investigator of multiple national research projects, he continues to drive innovation in optoelectronics and nanophotonics.

Professional Profile 

Education

Dr. Gil Ju Lee earned his Integrated M.S./Ph.D. degree from the Gwangju Institute of Science and Technology (GIST), Korea, in February 2021, under the prestigious GIST Presidential Fellowship. His research at GIST focused on cutting-edge photonic and optoelectronic technologies under the mentorship of Prof. Young Min Song. Prior to this, he completed his Bachelor of Science (Summa Cum Laude) in Electronics Engineering from Pusan National University, Korea, in February 2016. His early academic career was marked by exceptional performance, earning him several scholarships and research awards. His education has provided him with a solid foundation in electrical engineering, photonic systems, and nanotechnology, enabling him to excel in both theoretical and applied research.

Professional Experience

Dr. Lee has been an Associate Professor at Pusan National University since March 2025, following his tenure as an Assistant Professor from September 2021 to February 2025. Prior to joining PNU, he worked as a Postdoctoral Research Associate at the School of Electrical Engineering and Computer Science, GIST, Korea, from March to August 2021. Throughout his career, Dr. Lee has led groundbreaking research in optoelectronics, nanophotonics, and imaging devices. His research contributions have been supported by national and international funding agencies, and he has collaborated with leading academic and industrial institutions. His extensive research experience, combined with his leadership in high-impact projects, makes him a key figure in advancing innovative technologies in photonics and electronics.

Research Interests

Dr. Gil Ju Lee’s research focuses on cutting-edge advancements in optoelectronics, photonic devices, and nanophotonics. His expertise spans bio-inspired imaging systems, semiconductor nanowires, radiative cooling, and multifunctional nanophotonic devices. He is particularly interested in developing next-generation imaging and sensing technologies, leveraging nanostructured materials for energy-efficient optical systems. His research integrates machine learning with photonic device engineering to enhance imaging performance and energy efficiency. Dr. Lee also explores novel applications in metasurfaces, perovskite optoelectronics, and smart photonic materials to revolutionize future electronic and photonic systems.

Awards and Honors

Dr. Lee has received numerous accolades for his contributions to science and technology. Notably, he was honored with the Outstanding Researcher Award from Pusan National University (2022-2024) and the prestigious Samsung HumanTech Thesis Award. He has also been recognized with multiple Best Paper Awards from international conferences in photonics and optoelectronics. His research excellence has secured funding from leading national and international agencies, further solidifying his reputation as a pioneer in advanced photonic technologies.

Research Skills

Dr. Lee possesses strong expertise in nanofabrication, optoelectronic device characterization, computational photonics, and semiconductor processing. He has extensive experience in designing and developing photonic metasurfaces, perovskite-based optoelectronic systems, and bio-inspired imaging technologies. His technical skills include finite-difference time-domain (FDTD) simulations, COMSOL Multiphysics, and deep learning-based image analysis. Additionally, he is proficient in fabrication techniques such as electron-beam lithography, atomic layer deposition, and nanoimprinting. His ability to integrate theoretical modeling with experimental validation has been instrumental in advancing high-performance nanophotonic devices for diverse applications.

Conclusion

Dr. Gil Ju Lee is a highly qualified candidate for the Best Researcher Award. His extensive contributions to optoelectronics, bio-inspired imaging, and photonic device research, coupled with high-impact publications and substantial funding, make him a strong contender. While he already has significant national recognition, expanding international collaborations, industry partnerships, and the commercialization of his work would further enhance his profile.

Publications Top Noted

  • Human eye-inspired soft optoelectronic device using high-density MoS₂-graphene curved image sensor array
    Authors: C Choi, MK Choi, S Liu, M Kim, OK Park, C Im, J Kim, X Qin, GJ Lee, …
    Year: 2017
    Citations: 520

  • Curved neuromorphic image sensor array using a MoS₂-organic heterostructure inspired by the human visual recognition system
    Authors: C Choi, J Leem, M Kim, A Taqieddin, C Cho, KW Cho, GJ Lee, H Seung, …
    Year: 2020
    Citations: 263

  • Bioinspired artificial eyes: Optic components, digital cameras, and visual prostheses
    Authors: GJ Lee†, C Choi†, DH Kim, YM Song
    Year: 2018
    Citations: 251

  • Colored, daytime radiative coolers with thin‐film resonators for aesthetic purposes
    Authors: GJ Lee, YJ Kim, HM Kim, YJ Yoo, YM Song
    Year: 2018
    Citations: 215

  • Wearable force touch sensor array using a flexible and transparent electrode
    Authors: JK Song, D Son, J Kim, YJ Yoo, GJ Lee, L Wang, MK Choi, J Yang, M Lee, …
    Year: 2017
    Citations: 194

  • A Janus emitter for passive heat release from enclosures
    Authors: SY Heo†, GJ Lee†, DH Kim, YJ Kim, S Ishii, MS Kim, TJ Seok, BJ Lee, …
    Year: 2020
    Citations: 177

  • An aquatic-vision-inspired camera based on a monocentric lens and a silicon nanorod photodiode array
    Authors: MS Kim†, GJ Lee†, C Choi†, MS Kim†, M Lee, S Liu, KW Cho, HM Kim, …
    Year: 2020
    Citations: 131

  • Bio‐inspired artificial vision and neuromorphic image processing devices
    Authors: MS Kim, MS Kim, GJ Lee, SH Sunwoo, S Chang, YM Song, DH Kim
    Year: 2022
    Citations: 104

  • Revisiting silk: a lens-free optical physical unclonable function
    Authors: MS Kim†, GJ Lee†, JW Leem, S Choi, YL Kim, YM Song
    Year: 2022
    Citations: 93

  • Outdoor‐Useable, Wireless/Battery‐Free Patch‐Type Tissue Oximeter with Radiative Cooling
    Authors: MH Kang†, GJ Lee†, JH Lee, MS Kim, Z Yan, JW Jeong, KI Jang, …
    Year: 2021
    Citations: 81

  • An amphibious artificial vision system with a panoramic visual field
    Authors: M Lee†, GJ Lee†, HJ Jang†, E Joh, H Cho, MS Kim, HM Kim, KM Kang, …
    Year: 2022
    Citations: 66

  • Efficient light absorption by GaN truncated nanocones for high-performance water splitting applications
    Authors: YJ Kim, GJ Lee, S Kim, JW Min, SY Jeong, YJ Yoo, S Lee, YM Song
    Year: 2018
    Citations: 64

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

Suihong Liu | Engineering | Young Scientist Award

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Mr. Suihong Liu | Engineering | Young Scientist Award

Postdoc at Penn State University, United States

Dr. Suihong Liu is a dedicated researcher specializing in 3D bioprinting, biofabrication, and tissue engineering. He obtained a double Ph.D. degree in Mechanical Manufacture and Automation from Shanghai University and Biomedical Engineering from Technische Universität Dresden. Currently, he is a Postdoctoral Fellow at Penn State University in Prof. Ibrahim T. Ozbolat’s lab. With an impressive research portfolio, Dr. Liu has published 31 papers, including 12 as first or co-first author, accumulating over 500 citations and an H-index of 14. His work focuses on multi-material 3D bioprinting, bioinks, and osteochondral regeneration, earning him multiple national scholarships and awards. He has contributed to book chapters, holds five Chinese patents, and actively participates in international conferences. Dr. Liu is also a reviewer for prestigious journals. His expertise in bioprinting and biomaterials, coupled with strong leadership and collaborative skills, positions him as a promising young scientist in the field of biomedical engineering.

Professional Profile 

Education

Dr. Suihong Liu has a strong academic background in engineering and biomedical sciences. He completed a Bachelor of Engineering in Mechanical Design, Manufacture, and Automation from the University of Shanghai for Science and Technology, ranking in the top 4% of his class. He then pursued a Master-Ph.D. joint program at Shanghai University, specializing in Mechanical Manufacture and Automation, where he focused on advanced 3D bioprinting technologies. His academic excellence placed him in the top 5% of his cohort. Additionally, he undertook a joint Ph.D. program at Technische Universität Dresden in Germany, earning a double Ph.D. degree in Biomedical Engineering. His doctoral research emphasized multi-material 3D bioprinting for osteochondral regeneration and clinical translation. Dr. Liu’s interdisciplinary education, combining mechanical engineering with biomedical applications, has equipped him with cutting-edge expertise in biofabrication and tissue engineering, laying a strong foundation for his contributions to scientific innovation and translational research.

Professional Experience

Dr. Suihong Liu has extensive professional experience in 3D bioprinting, biofabrication, and tissue engineering. He is currently a Postdoctoral Fellow at Penn State University in Prof. Ibrahim T. Ozbolat’s lab, where he focuses on advanced bioprinting techniques for tissue regeneration. Prior to this, he served as a Postdoctoral Scholar at the Shanghai Institute of Ceramics, Chinese Academy of Sciences, under Prof. Chengtie Wu, where he contributed to pioneering research in biomaterials and tissue engineering. Throughout his academic and professional career, Dr. Liu has been involved in interdisciplinary research, bridging mechanical engineering with biomedical applications. His expertise includes multi-material bioprinting, bioink development, and osteochondral regeneration. He has actively participated in international conferences, collaborated with leading researchers, and contributed to high-impact publications and patents. Dr. Liu’s strong research background, technical expertise, and collaborative approach make him a valuable asset in the field of biomedical engineering and regenerative medicine.

Research Interest

Dr. Suihong Liu’s research interests lie at the intersection of 3D bioprinting, biofabrication, and tissue engineering, with a strong focus on developing innovative biomaterials for regenerative medicine. His work explores multi-material 3D bioprinting techniques to create complex tissue structures that mimic natural biological systems. He is particularly interested in bioink formulation, electrospinning, EHD-jet printing, and melt electrowriting for fabricating functional tissue scaffolds. His research aims to enhance osteochondral regeneration and advance clinical translation of bioprinted constructs for medical applications. Dr. Liu is also engaged in investigating novel crosslinking methods for hydrogel composites to improve their mechanical properties and biocompatibility. Through interdisciplinary collaboration, he seeks to push the boundaries of biofabrication by integrating engineering, biomaterials science, and cell biology. His ultimate goal is to contribute to the development of personalized tissue grafts and organ-on-chip models for disease modeling, drug testing, and regenerative therapies.

Award and Honor

Dr. Suihong Liu has received numerous awards and honors in recognition of his academic excellence, research contributions, and leadership skills. He was a recipient of the prestigious Chinese National Scholarship twice during his Ph.D., as well as the Chinese National Aspiration Scholarship. His outstanding academic performance earned him the Scholarship for Academic Excellence and a Corporate Scholarship. Dr. Liu demonstrated exceptional innovation and technical expertise by securing first prizes in both the National Mechanical Design Competition and the Shanghai Machinery Innovation Competition. In addition to his research and technical achievements, he was recognized for his leadership and service, receiving awards for Outstanding Student Leadership, Excellent Volunteers, and Excellent Graduate. Furthermore, he was awarded the China Scholarship Council (CSC) Scholarship, supporting his international research endeavors. These accolades reflect his dedication to advancing the field of biofabrication and 3D bioprinting while maintaining a strong commitment to academic and professional excellence.

Research Skill

Dr. Suihong Liu possesses extensive research skills in biofabrication, 3D bioprinting, and tissue engineering, making significant contributions to the field. His expertise includes CAD/CAM software for precise modeling and fabrication, electrospinning techniques for creating nanofiber structures, and advanced 3D (bio)printing technologies such as EHD-jet printing and melt electrowriting. He has hands-on experience in cell culture, biochemistry testing, and developing multi-material bioinks for biomedical applications. Dr. Liu’s research focuses on enhancing biomaterial properties for osteochondral regeneration and clinical translation, as evidenced by his high-impact publications in top-tier journals. Additionally, his ability to conduct interdisciplinary research is demonstrated by his collaborations across mechanical engineering, biomedical sciences, and material sciences. His strong analytical skills, innovative approach to problem-solving, and ability to manage complex research projects have led to multiple patents and invited peer reviews for renowned scientific journals, further solidifying his expertise in the field.

Conclusion

Suihong Liu is a highly suitable candidate for the Young Scientist Award, given his strong research contributions, high-impact publications, international collaborations, and innovation through patents. His work in 3D bioprinting and biofabrication aligns well with cutting-edge advancements in biomedical engineering. To further enhance his profile, he could focus on independent research leadership, securing research funding, and increasing his scientific outreach. With continued progress, he has the potential to become a leading researcher in his field.

Publications Top Noted

  • Title: Interparticle Crosslinked Ion-responsive Microgels for 3D and 4D (Bio) printing Applications
    Authors: V Pal, D Gupta, S Liu, I Namli, SHA Rizvi, YO Yilmaz, L Haugh, …
    Year: 2025
    Citations: Not available (new publication)

  • Title: Synergy of engineered gelatin methacrylate-based porous microspheres and multicellular assembly to promote osteogenesis and angiogenesis in bone tissue reconstruction
    Authors: X Hu, Q Hu, S Liu, H Zhang
    Year: 2024
    Citations: Not available (new publication)

  • Title: Electrospinning drug-loaded polycaprolactone/polycaprolactone-gelatin multi-functional bilayer nanofibers composite scaffold for postoperative wound healing of cutaneous injuries
    Authors: Y Song, Q Hu, S Liu, G Yao, H Zhang
    Year: 2024
    Citations: Not available (new publication)

  • Title: 3D printed biomimetic composite scaffolds with sequential releasing of copper ions and dexamethasone for cascade regulation of angiogenesis and osteogenesis
    Authors: Y Song, Q Hu, S Liu, Y Wang, L Jia, X Hu, C Huang, H Zhang
    Year: 2024
    Citations: 9

  • Title: Electrospinning/3D printing drug-loaded antibacterial polycaprolactone nanofiber/sodium alginate-gelatin hydrogel bilayer scaffold for skin wound repair
    Authors: Y Song, Q Hu, S Liu, Y Wang, H Zhang, J Chen, G Yao
    Year: 2024
    Citations: 24

  • Title: A 5+1-axis 3D printing platform for producing customized intestinal fistula stents
    Authors: Q Hu, J Cui, H Zhang, S Liu, M Ramalingam
    Year: 2023
    Citations: 3

  • Title: Bioinks for space missions: the influence of long‐term storage of alginate‐methylcellulose‐based bioinks on printability as well as cell viability and function
    Authors: J Windisch, O Reinhardt, S Duin, K Schütz, NJN Rodriguez, S Liu, A Lode, …
    Year: 2023
    Citations: 16

  • Title: Synergy of inorganic and organic inks in bioprinted tissue substitutes: construct stability and cell response during long-term cultivation in vitro
    Authors: S Liu, A Bernhardt, K Wirsig, A Lode, Q Hu, M Gelinsky, D Kilian
    Year: 2023
    Citations: 11

  • Title: Building a 3D printed osteocytic network by differentiation of primary human osteoblasts towards construction of a 3D printed in vitro bone model
    Authors: A Bernhardt, K Wirsig, AR Akkineni, L Suihong, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: Influence of long-term storage of cell-laden alginate-methylcellulose based bioinks on printability as well as cell viability and function
    Authors: J Windisch, K Schuetz, O Reinhardt, S Duin, S Liu, A Lode, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: A novel eggwhite powder-enhanced bioink stimulates cell proliferation and response in 3D bioprinted tissue substitutes
    Authors: S Liu, D Kilian, A Bernhardt, A Lode, Q Hu, M Gelinsky
    Year: 2023
    Citations: Not available

  • Title: 3D Bioprinting tissue analogs: Current development and translational implications
    Authors: S Liu, L Cheng, Y Liu, H Zhang, Y Song, JH Park, K Dashnyam, JH Lee, …
    Year: 2023
    Citations: 12

 

Daniel Akerele | Engineering | Best Researcher Award

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Mr. Daniel Akerele | Engineering | Best Researcher Award

Research Assistant at University of Washington, United States

Daniel D. Akerele is a Ph.D. candidate in Construction Management at the University of Washington, specializing in rapid-set materials for concrete pavement repair, sustainability, and AI-driven material science. With an extensive academic background, including an MSc in Civil Engineering and a Graduate Certificate in Construction Project Management from Columbia University, he has demonstrated expertise in material optimization, performance evaluation, and infrastructure sustainability. His research contributions include several peer-reviewed publications and journal reviews. Beyond academia, he has significant industry experience as a Project Engineer at Turner Construction and a Research Assistant at the Center for Education and Research in Construction Lab. Daniel has also been recognized with multiple awards, including the College of Built Environment’s Top Scholar Award and PNWCMAA Student Scholarship. A dedicated educator, he mentors students and serves as a reviewer for esteemed journals. His leadership, technical acumen, and research impact make him a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Daniel D. Akerele has a strong academic background in civil engineering and construction management. He is currently pursuing a Ph.D. in Construction Management at the University of Washington, focusing on rapid-set materials for concrete pavement repair, sustainability, and AI-driven material science. He earned his Master of Science in Civil Engineering from Columbia University, where he also obtained a Graduate Certificate in Construction Project Management, demonstrating his expertise in both technical and managerial aspects of the field. His academic journey is marked by excellence, with a strong emphasis on material optimization, performance evaluation, and infrastructure sustainability. Throughout his studies, Daniel has been actively involved in research, contributing to peer-reviewed publications and journal reviews. His dedication to education is further reflected in his mentorship of students and leadership roles in academic and professional organizations. His diverse and multidisciplinary educational background positions him as a leading researcher in construction materials and engineering.

Professional Experience

Daniel D. Akerele has extensive professional experience in civil engineering, construction management, and material science. He has worked on various high-profile infrastructure projects, specializing in concrete pavement repair, sustainable materials, and AI-driven construction techniques. As a researcher at the University of Washington, he has contributed significantly to developing rapid-set materials for concrete repairs, enhancing durability and efficiency in infrastructure maintenance. His previous roles include project management and engineering positions where he oversaw construction planning, quality control, and material performance assessments. Daniel has also collaborated with industry leaders and government agencies, applying his expertise to real-world construction challenges. In addition to his technical work, he is an active mentor and peer reviewer, supporting academic and professional development in his field. His combination of research excellence and hands-on industry experience makes him a respected expert in construction materials and infrastructure sustainability.

Research Interest

Daniel D. Akerele’s research interests lie at the intersection of civil engineering, material science, and advanced construction technologies. His work focuses on developing sustainable and high-performance construction materials, with a particular emphasis on rapid-setting concrete for infrastructure repairs. He is passionate about exploring innovative solutions to enhance the durability, resilience, and sustainability of construction materials, integrating nanotechnology, AI-driven material optimization, and green construction practices. His research also delves into pavement engineering, investigating ways to improve road durability through advanced material formulations and predictive modeling. Daniel is committed to bridging the gap between academic research and industry applications, working closely with government agencies and private sector stakeholders to implement his findings in real-world construction projects. Through his research, he aims to contribute to the development of smart, eco-friendly infrastructure solutions that align with global sustainability goals while improving efficiency and cost-effectiveness in the construction industry.

Award and Honor

Daniel D. Akerele has received numerous awards and honors in recognition of his outstanding contributions to civil engineering and materials science. His excellence in research and innovation has earned him prestigious academic and professional accolades, including best paper awards at international engineering conferences. He has been honored by professional organizations for his pioneering work in sustainable construction materials and pavement engineering. Daniel has also received research grants and fellowships from esteemed institutions, supporting his investigations into advanced construction technologies. His dedication to bridging academic research with industry applications has been acknowledged through awards for impactful contributions to infrastructure development. Additionally, he has been recognized as an emerging leader in engineering by various professional bodies, highlighting his commitment to advancing the field. Through these accolades, Daniel continues to inspire young researchers and professionals, reinforcing his reputation as a distinguished scholar and innovator in civil and structural engineering.

Research Skill

Daniel D. Akerele possesses exceptional research skills that have significantly contributed to advancements in civil engineering and materials science. His expertise spans experimental analysis, data interpretation, and computational modeling, enabling him to develop innovative solutions for sustainable infrastructure. He excels in laboratory testing of construction materials, utilizing advanced characterization techniques to assess performance and durability. Daniel is proficient in statistical analysis and simulation tools, allowing him to model complex engineering phenomena accurately. His ability to synthesize interdisciplinary knowledge enhances his research impact, bridging gaps between materials science, structural engineering, and environmental sustainability. He is skilled in grant writing and proposal development, securing funding for pioneering research projects. Additionally, his strong analytical thinking and problem-solving abilities make him adept at tackling engineering challenges with practical, evidence-based solutions. Through his rigorous research methodology, Daniel continues to push the boundaries of knowledge, contributing to the evolution of modern construction and engineering practices.

Conclusion

Daniel D. Akerele is a highly suitable candidate for the Best Researcher Award due to his strong research contributions, innovative applications in construction materials, leadership in academia and industry, and commitment to sustainability. Strengthening his publication record, interdisciplinary collaborations, and patent contributions would further solidify his reputation as a top-tier researcher in construction engineering and material science.

Publications Top Noted

  • Title: A study on pharmacovigilance of herbal medicines in Lagos West Senatorial District, Nigeria
    Authors: O. Awodele, A. Daniel, T.D. Popoola, E.F. Salami
    Year: 2013
    Citations: 31

  • Title: Analysis of maize value addition among entrepreneurs in Taraba State, Nigeria
    Authors: P.I. Ater, G.C. Aye, A. Daniel
    Year: 2018
    Citations: 17

  • Title: Evaluating the Impact of CO2 on Calcium SulphoAluminate (CSA) Concrete
    Authors: D.D. Akerele, F. Aguayo
    Year: 2024
    Citations: 4

  • Title: An Assessment of Saltwater Intrusion in Coastal Regions of Lagos, Nigeria
    Authors: O. Callistus, A.D. Daniel, A.O. Pelumi, O. Somtobe, O. Kunle, O.S. Echezona, et al.
    Year: 2024
    Citations: 4

  • Title: Assessment of Physicochemical and Bacteriological Parameters of Borehole Water: A Case Study from Lekki, Lagos, Nigeria
    Authors: D.D. Akerele, C. Obunadike, P.O. Abiodun
    Year: 2023
    Citations: 3

  • Title: Portland Limestone Cement in Concrete Pavement and Bridge Decks: Performance Evaluation and Future Directions
    Authors: D.D. Akerele, F. Aguayo, L. Wu
    Year: 2025
    Citations: Not available

  • Title: Effect of Geotextile on Lime Stabilized Lateritic Soils under Unsoaked Condition
    Authors: D.D. Akerele, P. Aduwenye
    Year: 2023
    Citations: Not available

  • Title: Solving Lime Stabilization Issues Using Woven Geotextile in Soaked Conditions
    Authors: D.D. Akerele
    Year: 2023
    Citations: Not available

Amr Shafik | Engineering | Best Researcher Award

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Mr. Amr Shafik | Engineering | Best Researcher Award

Civil Engineering Department at Virginia Tech, United States

Amr Shafik is a dedicated researcher specializing in transportation systems engineering, with over seven years of academic and industry experience in transportation planning, traffic engineering, and intelligent mobility solutions. Currently a Ph.D. candidate in Civil and Environmental Engineering at Virginia Tech, his research focuses on optimizing eco-driving systems for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He has published extensively in IEEE Transactions on Intelligent Transportation Systems and presented at prestigious conferences such as the IEEE Smart Mobility Conference and the Transportation Research Board Annual Meetings. Amr has collaborated with global organizations like the World Bank and EBRD on large-scale mobility projects. With expertise in simulation modeling, data science, and machine learning, he contributes to sustainable transportation innovations. Additionally, he has extensive teaching experience, mentoring students in traffic engineering and transportation planning. His technical skills include Python, R, AutoCAD, GIS, and advanced traffic simulation tools.

Professional Profile

Education

Amr Shafik holds a strong academic background in transportation engineering and data-driven mobility solutions. He is currently pursuing a Ph.D. in Civil and Environmental Engineering at Virginia Tech, where his research focuses on eco-driving optimization for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He earned his Master’s degree in Transportation Engineering from Cairo University, where he specialized in traffic flow theory, simulation modeling, and intelligent transportation systems. His thesis explored data-driven approaches to optimizing urban traffic networks. Prior to that, he completed his Bachelor’s degree in Civil Engineering from Cairo University with distinction, laying the foundation for his expertise in infrastructure design, traffic analysis, and sustainable mobility. Throughout his academic journey, he has engaged in interdisciplinary research, collaborated with global institutions, and honed advanced technical skills in Python, GIS, and transportation simulation tools. His education equips him to tackle real-world transportation challenges with innovative solutions.

Professional Experience

Amr Shafik has extensive professional experience in transportation engineering, data-driven mobility solutions, and intelligent transportation systems. He has worked as a Research Assistant at Virginia Tech, contributing to projects on eco-driving optimization, stochastic traffic signal control, and predictive modeling for connected and automated vehicles. Prior to this, he served as a Transportation Engineer at a leading consultancy, where he specialized in traffic flow analysis, microsimulation modeling, and urban mobility planning. His expertise extends to working with big data analytics, GIS applications, and machine learning for transportation systems. He has collaborated with government agencies and research institutions to develop sustainable and efficient mobility solutions. Additionally, he has experience in teaching and mentoring students in transportation engineering concepts. His strong analytical skills, combined with his hands-on experience in software tools like Python, MATLAB, and traffic simulation platforms, position him as a key contributor to the advancement of smart and sustainable transportation networks.

Research Interest

Amr Shafik’s research interests lie at the intersection of transportation engineering, intelligent mobility, and data-driven traffic management. He focuses on optimizing traffic flow and enhancing transportation efficiency through connected and automated vehicle technologies, eco-driving strategies, and stochastic traffic signal control. His work integrates machine learning, big data analytics, and artificial intelligence to develop predictive models for traffic behavior and mobility patterns. He is particularly interested in sustainable urban transportation, leveraging smart mobility solutions to reduce congestion, emissions, and energy consumption. His research also explores the application of Geographic Information Systems (GIS) and simulation modeling in transportation planning. By collaborating with industry partners and academic institutions, he aims to contribute to the development of next-generation intelligent transportation systems that improve safety, efficiency, and environmental sustainability. His passion for innovation and interdisciplinary research drives him to address real-world transportation challenges through advanced computational and analytical techniques.

Awards and honor

Amr Shafik has received numerous awards and honors in recognition of his contributions to transportation engineering and intelligent mobility research. He has been honored with prestigious research grants and fellowships for his work on data-driven traffic management and sustainable transportation solutions. His innovative research has earned him accolades at international conferences, where he has received Best Paper and Outstanding Research awards. He has also been recognized by professional engineering societies for his significant advancements in traffic optimization and eco-driving strategies. Additionally, he has been awarded competitive scholarships for academic excellence and leadership in the field of intelligent transportation systems. His contributions to collaborative projects with industry and government agencies have further solidified his reputation as a leading researcher in the field. Through his dedication to advancing transportation science, Amr Shafik continues to receive recognition for his impactful work in shaping the future of smart and sustainable mobility solutions.

Research skill

Amr Shafik possesses a diverse set of research skills that contribute to his expertise in transportation engineering and intelligent mobility solutions. He excels in data analysis, statistical modeling, and machine learning applications for traffic flow optimization and predictive analytics. His proficiency in programming languages such as Python, MATLAB, and R enables him to develop advanced algorithms for real-time traffic monitoring and control. He is skilled in using Geographic Information Systems (GIS) and simulation software like VISSIM and SUMO to model transportation networks and assess the impact of smart mobility solutions. Additionally, he has a strong background in sensor data processing and Internet of Things (IoT) applications for connected and autonomous vehicles. His ability to conduct interdisciplinary research, collaborate with industry stakeholders, and publish high-impact studies demonstrates his analytical thinking, problem-solving abilities, and dedication to innovation in the field of intelligent transportation systems and sustainable urban mobility.

Conclusion

Amr Shafik is a strong candidate for the Best Researcher Award due to his extensive contributions to transportation engineering, expertise in traffic optimization, and impactful research in connected and automated vehicles. His impressive academic and industry experience, along with publications in top-tier conferences and journals, showcases his research excellence. To further strengthen his profile, expanding interdisciplinary collaborations, securing independent research funding, and pursuing patents or industry partnerships would be beneficial.

Publications Top Noted

  • Optimization of vehicle trajectories considering uncertainty in actuated traffic signal timings

    • Authors: AK Shafik, S Eteifa, HA Rakha
    • Year: 2023
    • Citations: 19

  • Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: 5

  • Environmental Impacts of MSW Collection Route Optimization Using GIS: A Case Study of 10th of Ramadan City, Egypt

    • Authors: A Shafik, M Elkhedr, D Said, A Hassan
    • Year: 2022
    • Citations: 4

  • Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 3

  • Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles Towards Uncertainty of Actuated Traffic Signals

    • Authors: A Shafik, S Eteifa, HA Rakha, E Center
    • Year: 2023
    • Citations: 3

  • Development of Online VISSIM Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

    • Authors: AK Shafik, A Hassan, AM Saied, AE & Abdelmegeed
    • Year: 2022
    • Citations: 2

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Traffic Signal Switch Times

    • Authors: S Eteifa, AK Shafik, H Eldardiry, HA Rakha
    • Year: 2024
    • Citations: 1

  • Kalman Filter-based Real-Time Traffic State Estimation and Prediction using Vehicle Probe Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals

    • Authors: S Eteifa, A Shafik, H Eldardiry, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Real-Time Turning Movement, Queue Length and Traffic Density Estimation and Prediction from Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

  • Real-Time Traffic State Estimation and Short-Term Prediction Using Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: N/A

  • Queue Estimation and Consideration in Vehicle Trajectory Optimization at Actuated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

Zainab Mahdi Saleh | Engineering | Women Researcher Award

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Mrs. Zainab Mahdi Saleh | Engineering | Women Researcher Award

An engineer at the Iraqi Ministry of Health at University of Babylon, Iraq

Mrs. Zainab Mahdi Saleh is an accomplished mechanical engineer specializing in thermodynamics, currently pursuing a Ph.D. at the University of Babylon. She holds a Master’s degree from the University of Wasit and has conducted significant research on energy-efficient cooling systems, publishing multiple papers on desiccant wheel performance and heat transfer enhancement. With extensive experience in mechanical systems, she has held various leadership roles in hospital infrastructure management, overseeing central cooling, generators, and medical oxygen systems. Proficient in ANSYS and other engineering software, she combines theoretical expertise with practical applications. A dedicated educator, she serves as an Assistant Lecturer and is an active member of the Iraqi Engineers Union. Her strong English proficiency and technical skills make her a valuable contributor to the field. To further enhance her impact, she aims to expand her research internationally, secure funding, and mentor young engineers, particularly women in STEM.

Professional Profile

Education

Mrs. Zainab Mahdi Saleh has a strong academic background in mechanical engineering, specializing in thermodynamics. She earned her Bachelor’s degree in Mechanical Engineering from the University of Thi Qar in 2008 and later pursued a Master’s degree in Mechanical Engineering at the University of Wasit, which she completed in 2020. Currently, she is a Ph.D. candidate at the University of Babylon, focusing on advanced research in thermodynamics. Her academic journey reflects a commitment to scientific excellence and continuous learning. Throughout her studies, she has developed expertise in energy-efficient cooling systems and heat transfer enhancement, contributing to innovative research in her field. She has also undertaken specialized courses in mechanical engineering, ANSYS software, and teaching methodologies, further strengthening her technical and instructional capabilities. Her dedication to education and research positions her as a leading figure in engineering, striving to make meaningful contributions to both academia and industry.

Professional Experience

Mrs. Zainab Mahdi Saleh has extensive professional experience in mechanical engineering, specializing in thermodynamics and energy systems. She has held various leadership positions in healthcare infrastructure management, overseeing critical mechanical systems such as central cooling, generators, and medical oxygen units. Her career began as a Maintenance Unit Supervisor at Al-Hay Health Sector in 2009, followed by roles at Al-Karama Teaching Hospital and Badra Model Health Center, where she managed mechanical and generator maintenance. She later advanced to Assistant Head of the Mechanical Division at Al-Zahraa Teaching Hospital, eventually becoming the Supervisor of both the Central Cooling and Medical Oxygen Units. In addition to her technical expertise, she serves as an Assistant Lecturer, contributing to academic research and mentoring students in mechanical engineering. Her combined experience in practical engineering applications and academia positions her as a leader in the field, bridging the gap between research and real-world industrial challenges.

Research Interest

Mrs. Zainab Mahdi Saleh’s research interests lie in the fields of thermodynamics, heat transfer enhancement, and energy-efficient cooling systems. She focuses on optimizing the performance of desiccant wheel technology to reduce latent heat loads in air conditioning systems, contributing to improved energy efficiency and sustainability. Her work also explores innovative heat transfer techniques in double-pipe heat exchangers, utilizing advanced methods such as wavy edge twisted tapes with varying twist ratios and perforated diameters to enhance thermal performance. With a strong background in both theoretical and experimental studies, she aims to develop practical solutions for industrial and environmental applications. Additionally, her expertise in mechanical systems, including medical oxygen and central cooling units, allows her to bridge the gap between research and real-world engineering challenges. By expanding her studies to include renewable energy integration, she seeks to further advance sustainable thermal management technologies for future applications.

Award and Honor

Mrs. Zainab Mahdi Saleh has earned recognition for her contributions to mechanical engineering, particularly in the field of thermodynamics and energy-efficient cooling systems. As an accomplished researcher, she has published multiple scientific papers in reputable university journals, showcasing her expertise in heat transfer enhancement and desiccant wheel technology. Her dedication to academia and research has positioned her as a respected scholar in her field. In addition to her academic achievements, she has held leadership roles in various healthcare institutions, demonstrating her ability to apply engineering principles to critical infrastructure management. Her commitment to education is evident in her role as an Assistant Lecturer, where she mentors and guides students in mechanical engineering. As a member of the Iraqi Engineers Union, she actively contributes to the engineering community. While she continues to advance her research, further recognition through national and international awards would strengthen her impact and professional standing.

Research Skill

Mrs. Zainab Mahdi Saleh possesses strong research skills in thermodynamics, heat transfer, and energy-efficient cooling systems. She excels in both theoretical and experimental research, demonstrated by her studies on desiccant wheel performance and heat exchangers. Her expertise includes conducting experimental setups, data analysis, and computational simulations using ANSYS software, enhancing the accuracy and efficiency of her findings. She is skilled in designing and optimizing mechanical systems to improve energy performance, particularly in HVAC and industrial cooling applications. Her ability to integrate engineering principles with real-world applications is evident in her research on moisture adsorption materials and innovative heat transfer techniques. Additionally, she is proficient in academic writing and has successfully published her work in university journals. Her analytical approach, problem-solving abilities, and technical expertise make her a valuable contributor to the field. As she advances in her Ph.D. research, her skills continue to evolve, driving innovation in mechanical engineering.

Conclusion

Zainab Mahdi Saleh is a strong candidate for the Women Researcher Award, given her academic achievements, research contributions, technical expertise, and leadership in the field of mechanical engineering. Her work on energy-efficient cooling and heat transfer enhancement is highly relevant to sustainability and industrial advancements.

To further enhance her candidacy, she could focus on expanding her research to international platforms, securing research funding, and mentoring the next generation of engineers, particularly women in STEM. Overall, her profile reflects dedication, technical excellence, and leadership, making her a deserving contender for this prestigious award.

Publications Top Noted

  • Title: “Theoretical Performance of Silica Gel Desiccant Wheel”

    • Authors: ZM Salih, ADM Hassan, AM Al-Dabagh
    • Journal: Wasit Journal of Engineering Sciences, Volume 7, Issue 3, Pages 66-74
    • Year: 2019
    • Citations: 1

  • Title: “The Experimentally Studying of Solid Desiccant Wheel Performance Combined with the System of Air Conditioning”

    • Authors: ZM Salih, ADM Hassen, AM Al-Dabagh
    • Journal: Journal of University of Babylon for Engineering Sciences, Pages 50-59
    • Year: 2019
    • Citations: 1

 

 

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

Mohammad Ali Balafar | Computer Science | Best Researcher Award

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Prof. Dr. Mohammad Ali Balafar | Computer Science | Best Researcher Award

Prof at University of Tabriz, Iran

Prof. Dr. Mohammad Ali Balafar is a distinguished researcher in Artificial Intelligence and Multimedia Systems. With an h-index of 24 (Google Scholar) and inclusion in Stanford’s top 2% most-cited authors, his work is widely recognized for its impact. He leads the Intelligent Information Technology and Multimedia Research Laboratory at Tabriz University, focusing on deep learning, image processing, machine learning, and graph neural networks. His research projects address real-world problems, including image encryption, stock price prediction, and medical diagnosis through brain image segmentation. Dr. Balafar has authored numerous high-impact publications in reputable journals like IEEE Transactions and Chaos, Solitons & Fractals. Fluent in four languages, he fosters collaboration across diverse academic and cultural landscapes. His work blends innovation with application, making him a pioneer in intelligent systems. A strong advocate of interdisciplinary research, Dr. Balafar’s contributions exemplify excellence in both theoretical advancements and practical implementations.

Professional Profile

Education

Prof. Dr. Mohammad Ali Balafar has a strong academic foundation, specializing in Artificial Intelligence and Multimedia Systems. He earned his Bachelor’s degree in Computer Engineering, laying the groundwork for his expertise in computational systems and programming. Pursuing advanced studies, he obtained a Master’s degree in Software Engineering, where he focused on algorithm development and software methodologies. Dr. Balafar then completed his Ph.D. in Computer Engineering, concentrating on cutting-edge technologies such as image processing, data mining, and deep learning. Throughout his educational journey, he honed his skills in machine learning, graph neural networks, and intelligent information systems, which later became central to his research. His academic excellence was complemented by multilingual proficiency (Azerbaijani, English, Farsi, and Turkish), facilitating collaboration in diverse research environments. These educational milestones have equipped Dr. Balafar with the theoretical knowledge and technical expertise essential for pioneering innovations in artificial intelligence and intelligent multimedia technologies.

Professional  Experience

Prof. Dr. Mohammad Ali Balafar is a seasoned academic and researcher with extensive experience in Artificial Intelligence and Multimedia Systems. Currently, he serves as a faculty member in the Department of Electrical and Computer Engineering at Tabriz University. He is the founder and head of the Intelligent Information Technology and Multimedia Research Laboratory, established in 1391 (2012), where he leads innovative projects in areas such as image processing, machine vision, and robotics. Dr. Balafar has been instrumental in advancing intelligent multimedia systems through diverse research initiatives, including expert recommendation systems, stock price prediction, and medical imaging for diagnosing diseases like MS. He has authored numerous high-impact publications and collaborated with leading scholars, contributing to advancements in fields such as deep learning and data mining. With fluency in multiple languages and a global academic network, his professional career reflects a blend of academic rigor, research innovation, and leadership in cutting-edge technology development.

Research Interests

Prof. Dr. Mohammad Ali Balafar’s research interests are deeply rooted in the fields of Artificial Intelligence, Machine Learning, and Multimedia Systems, with a focus on addressing complex computational challenges. His expertise spans a wide range of cutting-edge topics, including Deep Learning, Image Processing, Computer Vision, and Graph Neural Networks. He is particularly interested in developing intelligent systems that can process and analyze visual data, such as creating efficient algorithms for image encryption, clustering, and anomaly detection. Dr. Balafar’s work also delves into Data Mining, where he applies advanced techniques to uncover patterns and insights in domains such as medical diagnostics, stock price prediction, and emergency service optimization. His contributions aim to bridge the gap between theory and application, advancing technologies that enhance real-world decision-making. This interdisciplinary approach not only pushes the boundaries of innovation but also showcases his dedication to solving impactful societal and scientific problems.

Awards and Honors

Prof. Dr. Mohammad Ali Balafar is a highly acclaimed researcher whose contributions have been recognized through various awards and honors. Notably, he has been included in Stanford University’s list of the top 2% most-cited scientists worldwide, based on a one-year performance metric—a testament to his impactful research and global influence in Artificial Intelligence and Multimedia Systems. Dr. Balafar’s scholarly achievements, reflected in his impressive h-index of 24 (Google Scholar) and over 2,380 citations, underscore his standing as a leading researcher in fields like Deep Learning, Image Processing, and Graph Neural Networks. His role as the head of the Intelligent Information Technology and Multimedia Research Laboratory further highlights his leadership in advancing innovative solutions for complex technological challenges. These accolades, combined with his extensive publication record in top-tier journals, position Dr. Balafar as a pioneer in his domain, earning him well-deserved recognition in the academic and research communities.

Conclusion

Dr. Mohammad Ali Balafar is a highly accomplished researcher with a solid track record of impactful publications, innovative research, and academic leadership. His diverse skill set, coupled with his contributions to AI and multimedia systems, makes him a strong candidate for the Best Researcher Award. Enhancing his global collaborations and industry engagement could further solidify his standing as a leading figure in his field.

Publications Top Noted

  • Review of brain MRI image segmentation methods
    • Authors: MA Balafar, AR Ramli, MI Saripan, S Mashohor
    • Year: 2010
    • Citations: 643
  • Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts
    • Authors: M Dashtban, M Balafar
    • Year: 2017
    • Citations: 167
  • A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem
    • Authors: MM Alipour, SN Razavi, MR Feizi Derakhshi, MA Balafar
    • Year: 2018
    • Citations: 134
  • A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation
    • Authors: M Asgari-Chenaghlu, MA Balafar, MR Feizi-Derakhshi
    • Year: 2019
    • Citations: 131
  • Gene selection for tumor classification using a novel bio-inspired multi-objective approach
    • Authors: M Dashtban, M Balafar, P Suravajhala
    • Year: 2018
    • Citations: 104
  • Gaussian mixture model based segmentation methods for brain MRI images
    • Authors: MA Balafar
    • Year: 2014
    • Citations: 95
  • The state-of-the-art in expert recommendation systems
    • Authors: N Nikzad–Khasmakhi, MA Balafar, MR Feizi–Derakhshi
    • Year: 2019
    • Citations: 89
  • Fuzzy C-mean based brain MRI segmentation algorithms
    • Authors: MA Balafar
    • Year: 2014
    • Citations: 85
  • CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation
    • Authors: AG Oskouei, M Hashemzadeh, B Asheghi, MA Balafar
    • Year: 2021
    • Citations: 70
  • CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features
    • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, L Farzinvash, MA Balafar
    • Year: 2022
    • Citations: 48
  • Cy: Chaotic yolo for user intended image encryption and sharing in social media
    • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, N Nikzad-Khasmakhi
    • Year: 2021
    • Citations: 36
  • A new method for MR grayscale inhomogeneity correction
    • Authors: MA Balafar, AR Ramli, S Mashohor
    • Year: 2010
    • Citations: 36

Humam Kourani | Computer Science | Best Researcher Award

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Mr. Humam Kourani | Computer Science | Best Researcher Award

Research Associate at Fraunhofer FIT, Germany

Mr. Humam Kourani is a dedicated and highly skilled researcher with a strong background in Data Science and Computer Science. He holds both a Master’s and Bachelor’s degree from RWTH Aachen University, specializing in process mining, artificial intelligence, and data-driven decision-making. He has gained valuable experience working in research institutions and industry settings, most notably at the Fraunhofer Institute for Applied Information Technology and Fondazione Bruno Kessler in Italy. His research focuses on improving data science methodologies, particularly in process mining and workflow language models. With a solid academic foundation, practical experience, and significant contributions to his field, Humam has proven himself to be a promising and impactful researcher.

Professional Profile

Education

Humam Kourani completed his Master of Science in Data Science from RWTH Aachen University in 2022, with a focus on Computer Science. His master’s thesis explored the improvement of the Hybrid Miner by utilizing causal graph metrics, an area critical for process mining. Prior to that, he earned his Bachelor of Science degree in Computer Science from the same institution in 2019. His Bachelor’s thesis involved the development of a scalable interactive event data visualization tool in Python, further showcasing his technical skills. Humam’s academic journey reflects his dedication to mastering complex data science concepts and his drive to contribute to the field’s advancement through academic research and innovation.

Professional  Experience

Mr. Kourani’s professional experience spans key positions in research and data science. Since May 2022, he has been working as a Research Associate at the Fraunhofer Institute for Applied Information Technology, specializing in Data Science and Artificial Intelligence. In this role, he contributes to research on process mining, artificial intelligence, and data-driven decision-making. Earlier, he held student assistant roles at RWTH Aachen University, including positions at the Chair of Process and Data Science and the Chair of Process and Data Science in 2021. Humam also completed an Erasmus+ internship at Fondazione Bruno Kessler in Italy, where he gained hands-on experience in process and data intelligence. His professional experience reflects a consistent focus on leveraging data science and AI for practical problem-solving and research innovation.

Research Interests

Humam Kourani’s research interests lie primarily in data science, artificial intelligence, and process mining. He is particularly focused on enhancing data-driven methods for analyzing and improving business processes, with an emphasis on process modeling and workflow languages. His recent work has explored innovative approaches, such as large language models for process modeling, and improving existing hybrid mining techniques using causal graph metrics. Through his work, Humam aims to bridge the gap between advanced computational techniques and practical business process applications, enabling more efficient decision-making. His research also delves into the intersection of data science and AI, with a strong interest in developing scalable models that address real-world challenges across various industries.

Awards and Honors

Humam Kourani has received several prestigious awards in recognition of his outstanding research contributions. He won the Best Paper Award at the EMMSAD 2024 conference for his paper on “Process Modeling with Large Language Models”. Additionally, he received the Best Paper Award at the BPM 2023 conference for his work on the “POWL: Partially Ordered Workflow Language”. These awards highlight the significance of his research in the fields of process mining and business process management. Humam was also honored with membership in the PADS Excellence Honors Class at RWTH Aachen University in 2022, further underscoring his academic excellence. These honors attest to his innovative contributions to the research community and his growing influence in the fields of data science and AI.

Conclusion

Humam Kourani is undoubtedly a highly talented researcher with a solid foundation in data science and process mining. His research achievements, international experience, and awards demonstrate that he is already making significant contributions to his field. His multidisciplinary skills, coupled with his passion for continuous learning, make him a standout candidate for the Best Researcher Award. While there are opportunities for growth in areas like expanding his publication base and increasing leadership roles in research initiatives, his strengths far outweigh these minor areas of improvement. Humam Kourani is a promising researcher with the potential for continued excellence and impact in the field of data science and artificial intelligence.

Publications Top Noted

  • Title: Process Modeling With Large Language Models
    Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
    Year: 2024
    Citations: 21
  • Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, Evaluation Strategies, and Future Challenges
    Authors: A. Berti, H. Kourani, H. Hafke, C.Y. Li, D. Schuster
    Year: 2024
    Citations: 8
  • Title: POWL: Partially Ordered Workflow Language
    Authors: H. Kourani, S.J. van Zelst
    Year: 2023
    Citations: 7
  • Title: ProMoAI: Process Modeling with Generative AI
    Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
    Year: 2024
    Citations: 5
  • Title: PM4KNIME: Process Mining Meets the KNIME Analytics Platform
    Authors: H. Kourani, S.J. van Zelst, B.D. Lehmann, G. Einsdorf, S. Helfrich, F. Liße
    Year: 2022
    Citations: 5
  • Title: Scalable Discovery of Partially Ordered Workflow Models with Formal Guarantees
    Authors: H. Kourani, D. Schuster, W. Van Der Aalst
    Year: 2023
    Citations: 4
  • Title: PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks
    Authors: A. Berti, H. Kourani, W.M.P. van der Aalst
    Year: 2024
    Citations: 3
  • Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
    Authors: W.M.P. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
    Year: 2023
    Citations: 3
  • Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies
    Authors: A. Berti, H. Kourani, H. Häfke, C.Y. Li, D. Schuster
    Year: 2024
    Citations: 2
  • Title: Mining for Long-Term Dependencies in Causal Graphs
    Authors: H. Kourani, C. Di Francescomarino, C. Ghidini, W. van der Aalst, S. van Zelst
    Year: 2022
    Citations: 2
  • Title: Bridging Domain Knowledge and Process Discovery Using Large Language Models
    Authors: A. Norouzifar, H. Kourani, M. Dees, W. van der Aalst
    Year: 2024
    Citations: 0 (preprint)
  • Title: Leveraging Large Language Models for Enhanced Process Model Comprehension
    Authors: H. Kourani, A. Berti, J. Hennrich, W. Kratsch, R. Weidlich, C.Y. Li, A. Arslan, et al.
    Year: 2024
    Citations: 0 (preprint)
  • Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
    Authors: W. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
    Year: 2023
    Citations: 0

Shahbaz Gul Hassan | Computer Science | Best Researcher Award

Posted on by ScienceFather

Assoc. Prof. Dr.Shahbaz Gul Hassan | Computer Science | Best Researcher Award

Associat professor at Zhongkai University of Agriculture and Engineering, China

Dr. Shahbaz Gul Hassan is an accomplished Associate Professor at Zhongkai University of Agriculture and Engineering, specializing in agricultural information technology and computer science. With a strong academic background, including a Ph.D. from China Agricultural University, he focuses on machine learning, image processing, and predictive modeling in the context of agricultural and environmental systems. His work has earned significant recognition, including awards for research and innovation in agricultural technology. Dr. Hassan’s numerous high-impact publications in top-tier journals demonstrate his ability to integrate advanced computational techniques into real-world applications in agriculture.

Professional Profile

Education

Dr. Shahbaz Gul Hassan completed his Ph.D. in Agricultural Information Technology at China Agricultural University, Beijing, in 2017. His research during his Ph.D. focused on the integration of information technology with agriculture, particularly in areas such as machine learning and predictive modeling. Prior to his Ph.D., he earned a Master’s in Computer Science from PMAS Arid Agriculture University, Rawalpindi, in 2011, where he developed a deep understanding of computer science applications in agriculture. He completed his Bachelor’s degree in Science from the University of Punjab, Lahore, in 2007. These educational milestones have equipped Dr. Hassan with a solid foundation in both computer science and agricultural technology, enabling him to innovate at the intersection of these two fields. His academic journey reflects a consistent focus on enhancing agricultural practices through advanced technologies, positioning him as a leading figure in agricultural information systems and technology research.

Experience

Dr. Shahbaz Gul Hassan has extensive experience in both academia and industry. He is currently an Associate Professor at Zhongkai University of Agriculture and Engineering, Guangzhou, China, where he has been teaching since 2019. Prior to this, he served as a Postdoctoral Researcher in Agricultural Engineering at South China Agricultural University, Guangzhou, from 2017 to 2019. In this role, he applied his expertise in machine learning and image processing to agricultural engineering projects. Dr. Hassan also worked as a Ph.D. Research Scholar at China Agricultural University, Beijing, from 2013 to 2017, where he focused on applying technology to solve critical problems in agriculture. Earlier, he worked as a Software Engineer at MTBC in Rawalpindi from 2011 to 2012. His diverse professional experience blends research, teaching, and practical applications of technology in agriculture, with a focus on using advanced computing to optimize agricultural processes.

Research Interests

Dr. Shahbaz Gul Hassan’s research focuses on the application of machine learning, image processing, and predictive modeling to solve agricultural challenges. He is particularly interested in developing smart technologies for precision farming and environmental monitoring. One of his key areas of research involves computer vision and machine learning techniques for detecting and predicting behaviors and conditions in agricultural environments, such as water quality and animal health. His work aims to enhance automation in agriculture and improve sustainability by leveraging data-driven technologies. Dr. Hassan also focuses on predictive modeling for environmental variables such as humidity, temperature, and dissolved oxygen levels in aquaculture. These models help optimize farming processes and ensure better resource management. His research not only pushes the boundaries of agricultural technology but also contributes to the development of sustainable practices in farming and aquaculture. Dr. Hassan’s interdisciplinary approach integrates computer science and engineering with practical agricultural needs to drive innovation.

Awards and Honors

Dr. Shahbaz Gul Hassan has received numerous prestigious awards for his outstanding contributions to agricultural research. In December 2023, he was honored with the First Prize in the Guangdong Province Agricultural Technology Promotion Award. He also received the Third Prize from the Guangdong Provincial Science and Technology Department in January 2024. Dr. Hassan’s work on a microservice-based agricultural app earned him the Second Prize in the 16th China University Computer Design Competition in the Guangdong-Hong Kong-Macao Greater Bay Area. Additionally, he was awarded the Excellent Instructor Award in the 13th Blue Bridge Cup Provincial Competition. His work has been recognized by the Guangdong Computer Society, where he received the Second Prize for Outstanding Paper. These awards reflect Dr. Hassan’s innovative approach to integrating advanced technologies in agriculture, as well as his ability to drive real-world impact with his research. His accolades highlight his leadership and dedication to improving agricultural technologies globally.

Conclusion

Dr. Shahbaz Gul Hassan is an outstanding candidate for the Best Researcher Award. His innovative approach to integrating machine learning with agricultural processes, alongside his strong academic qualifications and prolific output, make him a leading figure in his field. His numerous prestigious awards and contributions to practical agricultural technologies demonstrate the significant real-world impact of his work. Dr. Hassan is a researcher who continues to push the boundaries of knowledge and practical application in agricultural engineering and information technology, making him a valuable contender for the award.

Publications Top Noted

Title: Green synthesis of iron oxide nanorods using Withania coagulans extract improved photocatalytic degradation and antimicrobial activity
Authors: S Qasim, A Zafar, MS Saif, Z Ali, M Nazar, M Waqas, AU Haq, T Tariq, …
Citations: 175
Year: 2020

Title: Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO
Authors: H Yu, Y Chen, SG Hassan, D Li
Citations: 158
Year: 2016

Title: Bioinspired synthesis of zinc oxide nano-flowers: A surface enhanced antibacterial and harvesting efficiency
Authors: M Hasan, M Altaf, A Zafar, SG Hassan, Z Ali, G Mustafa, T Munawar, …
Citations: 114
Year: 2021

Title: Models for estimating feed intake in aquaculture: A review
Authors: M Sun, SG Hassan, D Li
Citations: 108
Year: 2016

Title: Phyto-reflexive zinc oxide nano-flowers synthesis: an advanced photocatalytic degradation and infectious therapy
Authors: MS Saif, A Zafar, M Waqas, SG Hassan, A ul Haq, T Tariq, S Batool, …
Citations: 75
Year: 2021

Title: Fractionation of Biomolecules in Withania coagulans Extract for Bioreductive Nanoparticle Synthesis, Antifungal and Biofilm Activity
Authors: M Hasan, A Zafar, I Shahzadi, F Luo, SG Hassan, T Tariq, S Zehra, …
Citations: 66
Year: 2020

Title: Phytotoxic evaluation of phytosynthesized silver nanoparticles on lettuce
Authors: M Hasan, K Mehmood, G Mustafa, A Zafar, T Tariq, SG Hassan, …
Citations: 53
Year: 2021

Title: Green synthesis of Cordia myxa incubated ZnO, Fe2O3, and Co3O4 nanoparticle: Characterization, and their response as biological and photocatalytic agent
Authors: S Batool, M Hasan, M Dilshad, A Zafar, T Tariq, Z Wu, R Chen, …
Citations: 49
Year: 2022

Title: Physiological and anti-oxidative response of biologically and chemically synthesized iron oxide: Zea mays a case study
Authors: M Hasan, S Rafique, A Zafar, S Loomba, R Khan, SG Hassan, MW Khan, …
Citations: 47
Year: 2020

Title: Dissolved oxygen content prediction in crab culture using a hybrid intelligent method
Authors: H Yu, Y Chen, SG Hassan, D Li
Citations: 43
Year: 2016

Title: Cursive handwritten text recognition using bi-directional LSTMs: a case study on Urdu handwriting
Authors: S Hassan, A Irfan, A Mirza, I Siddiqi
Citations: 42
Year: 2019

Title: Green synthesized ZnO-Fe2O3-Co3O4 nanocomposite for antioxidant, microbial disinfection and degradation of pollutants from wastewater
Authors: S Batool, M Hasan, M Dilshad, A Zafar, T Tariq, A Shaheen, R Iqbal, Z Ali, …
Citations: 41
Year: 2022

Title: A hybrid model for short-term dissolved oxygen content prediction
Authors: J Huang, S Liu, SG Hassan, L Xu, C Huang
Citations: 39
Year: 2021

Title: Biological synthesis of bimetallic hybrid nanocomposite: a remarkable photocatalyst, adsorption/desorption and antimicrobial agent
Authors: X Huang, A Zafar, K Ahmad, M Hasan, T Tariq, S Gong, SG Hassan, …
Citations: 36
Year: 2023

Title: Nano-managing silver and zinc as bio-conservational approach against pathogens of the honey bee
Authors: R Hussain, M Hasan, KJ Iqbal, A Zafar, T Tariq, MS Saif, SG Hassan, …
Citations: 33
Year: 2023