Young Won Kim | Engineering | Research Excellence Award

Published on by Popular Scientist

Dr. Young Won Kim | Engineering | Research Excellence Award

Senior Researcher | Korea Institute of Industrial Technology | South Korea

Dr. Young Won Kim is a researcher specializing in advanced manufacturing, smart materials, and energy harvesting technologies, with strong expertise in additive manufacturing, digital twin systems, and nano/micro-fabrication. His research focuses on triboelectric and piezoelectric nanogenerators, sensor development, and AI-driven predictive modeling for smart manufacturing applications. He has contributed extensively to high-impact international journals as both lead and corresponding author, particularly in nanomaterials, flexible electronics, and biomedical scaffolds. With 68 publications, 1,085 citations, and an h-index of 17, his work reflects strong academic impact. His professional experience spans academic and industrial research environments, integrating machine learning, materials science, and mechanical engineering to develop innovative systems for energy, healthcare, and intelligent industrial technologies.

Citation Metrics (Scopus)

1250

1000

750

500

250

0

 

Citations
1085

Documents
68

h-index
17

🟦 Citations    🟥 Documents    🟩 h-index


View Scopus Profile      View Orcid Profile

Featured Publications

Genetic Insights into Avian Influenza Resistance in Jeju Island Chickens

– Journal of Animal Science and Technology, 2025

Bittu Ghosh | Civil Engineering | Research Excellence Award

Published on by Popular Scientist

Dr. Bittu Ghosh | Civil Engineering | Research Excellence Award

Research Scholar | Nit Durgapur | India

Dr. Bittu Ghosh is a civil engineering researcher specializing in construction engineering and management with a strong focus on infrastructure digitalisation. His research centers on Building Information Modeling applications for claims, quality, safety, productivity enhancement, and dispute management in transportation and infrastructure projects. He has contributed extensively through peer-reviewed journal articles, patents, book chapters, and conference papers addressing BIM frameworks, artificial intelligence applications, document management systems, and sustainable construction practices. Professionally, he has experience in infrastructure project execution and management roles, combining academic research insights with practical construction industry implementation and technology-driven decision support.

Citation Metrics (Google Scholar)

80

60

40

20

0

Citations
76

i10-index
4

h-index
6

🟦 Citations   🟥 i10-index   🟩 h-index


View Google Scholar Profile

Featured Publications

3D Printing Technology and Future of Construction: A Review

– International Conference on Creative and Innovative Solutions in Civil Engineering

Digitalized Document Management System for Construction Claims and Disputes Using BIM

– Journal of Legal Affairs and Dispute Resolution in Engineering

Deme Hirko | Civil | Best Researcher Award

Published on by Popular Scientist

Mr. Deme Hirko | Civil | Best Researcher Award

Lecturer | Stellenbosch University | South Africa

Mr. Deme Hirko is a Water Resources Engineer, Climate Researcher, and Lecturer with extensive experience in teaching, research, and project coordination in the fields of hydrology, irrigation, and water systems management. His work focuses on sustainable water resource strategies, integrating advanced hydrological models with machine learning to optimize water allocation and assess the impacts of climate change. As a PhD researcher at Stellenbosch University, he develops Python-based analytical tools and applies data-driven methods to enhance climate impact assessments and water management efficiency. He has taught and supervised numerous students in hydraulic engineering and water resource modelling, combining academic rigor with practical insights from his earlier experience in infrastructure project management. His research explores the intersection of climate science, data analytics, and high-performance computing, aiming to improve resilience in water-scarce regions. He has published several peer-reviewed papers on topics such as machine learning applications in hydrology, water allocation modelling, and irrigation performance evaluation. In addition to his academic work, he actively participates in international conferences and specialized training programs related to climate modelling, Earth observation, and water resource assessment. Committed to interdisciplinary collaboration, he strives to advance research that supports equitable and data-informed water management policies, particularly within the context of climate variability in Africa.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Hirko, D. B., Du Plessis, J. A., & Bosman, A. (2025). Using machine learning and satellite data to analyse climate change in the Upper Awash Sub-basin, Ethiopia. Physics and Chemistry of the Earth, Parts A/B/C.

Hirko, D. B., Du Plessis, J. A., & Bosman, A. (2025). Review of machine learning and WEAP models for water allocation under climate change. Earth Science Informatics.

Hirko, D. B., Gebul, M. A., Du Plessis, J. A., & Emama, W. O. (2023). Assessment of irrigation water allocation, Koftu, Ethiopia. Water Practice & Technology, 18(6).

Hirko, D. B., & Du Plessis, J. A. (2023, April 16). Assessing the effectiveness of Koftu’s small-scale irrigation schemes in enhancing water resource utilization in Ethiopia [Preprint]. ESSOAr.

Hirko, D. B. (2022, July 27). Irrigation land suitability assessment of Sibilu River catchment using geographic information system. Irrigation & Drainage Systems Engineering.

Nazanin Irani | Civil Engineering | Best Researcher Award

Published on by Popular Scientist

Dr. Nazanin Irani | Civil Engineering | Best Researcher Award

Postdoctoral Researcher at  Ruhr University Bochum | Germany

Dr. Nazanin Irani is a postdoctoral researcher at the Chair of Soil Mechanics, Foundation Engineering, and Environmental Geotechnics at Ruhr-University Bochum (RUB), Germany. She holds a Ph.D. in Geotechnical Engineering from Shiraz University of Technology (2023), along with M.Sc. and B.Sc. degrees in Civil and Geotechnical Engineering from Iranian institutions.

Profile:

Educational Background:

Dr. Irani earned her Ph.D. in Geotechnical Engineering from Shiraz University of Technology (2018–2023), preceded by an M.Sc. in Geotechnical Engineering (2015–2017) and a B.Sc. in Civil Engineering (2010–2014) from institutions in Iran. She graduated with top honors and received distinctions throughout her academic career.

Professional Experience:

Dr. Irani has held multiple roles in both academia and industry. She has served as a Scientific Associate and Postdoctoral Researcher at RUB since 2023. Her earlier experience includes research at Shiraz University of Technology and visiting research at RUB’s Chair of Continuum Mechanics. Additionally, she has worked in civil structural design and construction management in Iran.

Research and Teaching Engagement:

Her research focuses on constitutive modeling of soils, finite element simulations, soil improvement using AI, and geothermal energy enhancement. She lectures on specialized topics including Soil Dynamics, Geotechnical Structures, and Earthquake Engineering at RUB. Dr. Irani has supervised multiple Ph.D. and Master’s theses in advanced soil mechanics and AI applications.

Honors and Awards:

Dr. Irani has received multiple prestigious grants and scholarships, including two €1 million research grants from the German Federal Ministry of Education and Research (BMBF) and the Emmy Noether Programme.

Publications and Contributions:

Dr. Irani has authored 13 peer-reviewed publications, including articles in Acta Geotechnica, Computers and Geotechnics, and the Journal of Geotechnical and Geoenvironmental Engineering. Her work bridges experimental, numerical, and AI-driven approaches to solving complex geotechnical problems.

Research Interests:

Her main areas of interest include:

  • Thermodynamically consistent constitutive modeling

  • Particle breakage in soils

  • Micro-to-macro soil behavior analysis

  • AI and machine learning in geotechnics

  • Environmentally sustainable soil improvement techniques

Citation Metrics for Dr. Nazanin Irani:

  • Total Citations: 113

  • Citations Since 2020: 113

  • h-index: 6

  • i10-index: 4

Publication Top Notes:

A state-dependent hyperelastic-plastic constitutive model considering shear-induced particle breakage in granular soils

2022
Citations: 35

Effect of scrap tyre on strength properties of untreated and lime-treated clayey sand
2021
Citations: 18

Effects of over-consolidation history on flow instability of clean and silty sands
2022
Citations: 17

Micro-mechanical response of transversely isotropic samples under cyclic loading
2023
Citations: 10*

Effects of the inclusion of industrial and agricultural wastes on the compaction and compression properties of untreated and lime-treated clayey sand
2020
Citations: 9

Mohammad Azim Eirgash | Civil engineering | Best Researcher Award

Published on by Popular Scientist

Dr. Mohammad Azim Eirgash | Civil engineering | Best Researcher Award

Ph.D at Karadeniz Technical University, Turkey

Dr. Mohammad Azim Eirgash is a Ph.D. researcher in Construction Management at Karadeniz Technical University (Türkiye), with dual citizenship in Afghanistan and Türkiye. He holds a Ph.D. and MSc from KTÜ and a B.Tech in Civil Engineering from the National Institute of Technology Warangal (India). His research focuses on multi-objective optimization, artificial intelligence in construction project scheduling, and time-cost-quality-CO₂ trade-off problems.

Profile:

🎓 Academic Background:

  • Ph.D. in Construction Management – Karadeniz Technical University, Türkiye (2019–2024)

  • M.Sc. in Construction Management – KTÜ, Türkiye (2015–2018)

  • B.Tech. in Civil Engineering – NIT Warangal, India (2010–2014)

  • Scholarships: ICCR (India) 🇮🇳 and Türkiye Bursları 🇹🇷

🧠 Research Focus & Skills:

  • Specializations:

    • Multi-objective Optimization

    • AI & Machine Learning in Project Scheduling

    • Time-Cost-Quality-CO₂ Trade-off Models

    • Metaheuristic Algorithms (e.g., TLBO, Jaya, Rao)

  • Software Tools: Primavera P-7, MATLAB, Python 🐍

📈 Scientific Achievements:

  • h-index: 9

  • Total Citations: 317

    • Scopus: 184

    • Web of Science: 105

  • Publications:

    • 20+ peer-reviewed articles in Q1/Q2 journals

    • 5+ national/international conferences

    • Springer Book Chapter Contributor

🏆 Awards & Recognition:

  • 🥇 Young Scientist Award – International Young Scientist Awards 2022

  • ✍️ Reviewer for top journals:

    • Automation in Construction, Scientific Reports, Applied Soft Computing

🏗️ Professional Roles:

  • Karadeniz Technical University – Researcher

  • Baybars YAPI, Türkiye – Construction Consultant

  • Lord Tech Datus, India – Research Consultant

  • TEOREM LTD, Türkiye – Educator & Engineer

🌍 Global Research Impact:

Dr. Eirgash is a rising expert in integrating artificial intelligence with construction management. His international background and collaborations across India, Türkiye, and Europe highlight his global mindset and practical contributions to smart infrastructure and sustainable project planning.

Publication Top Notes:

  1. Time–cost trade-off optimization in generalized construction projects using an opposition learning-augmented multi-objective Jaya algorithm
    Asian Journal of Civil Engineering, 2025-06-23
    DOI: 10.1007/s42107-025-01401-z

  2. Optimization of time–cost–quality–CO₂ emission trade-off problems via super oppositional TLBO algorithm
    Asian Journal of Civil Engineering, 2025-04
    DOI: 10.1007/s42107-025-01282-2

  3. Influence of jumping rate on opposition-based Jaya algorithm for discrete time–cost trade-off optimization problems
    Uludağ University Journal of The Faculty of Engineering, 2025-04-11
    DOI: 10.17482/uumfd.1561366

  4. Optimizing time–cost in construction projects using modified quasi-opposition learning-based multi-objective Jaya optimizer and multi-criteria decision-making methods
    Asian Journal of Civil Engineering, 2025-03
    DOI: 10.1007/s42107-024-01235-1

  5. A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time–cost–quality–CO₂ emissions of generalized construction projects
    Engineering Computations, 2024-10-10
    DOI: 10.1108/EC-01-2024-0043

  6. A novel oppositional teaching learning strategy based on the golden ratio to solve the time–cost–environmental impact trade-off optimization problems
    Expert Systems with Applications, 2023
    DOI: 10.1016/j.eswa.2023.119995

  7. Modified dynamic opposite learning assisted TLBO for solving time–cost optimization in generalized construction projects
    Structures, 2023
    DOI: 10.1016/j.istruc.2023.04.091

  8. A multi-objective decision-making model based on TLBO for the time–cost trade-off problems
    Structural Engineering and Mechanics, 2019
    DOI: 10.12989/sem.2019.71.2.139

  9. Time–cost trade-off optimization of construction projects using teaching learning-based optimization
    KSCE Journal of Civil Engineering, 2019
    DOI: 10.1007/s12205-018-1670-6

  10. Time–cost trade-off optimization with a new initial population approach
    Teknik Dergi (Technical Journal of Turkish Chamber of Civil Engineers), 2019
    DOI: 10.18400/TEKDERG.410934