Rongyan Xi | Engineering | Best Researcher Award

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Dr. Rongyan Xi | Engineering | Best Researcher Award 

Doctor | China Mobile Research Institute | China

Rongyan Xi is an accomplished researcher in the field of communication and electronic engineering with a strong academic background and impactful scientific contributions, making her a highly suitable candidate for the Best Researcher Award. She obtained her B.S. degree in Communication Engineering from Shandong University in 2017 and completed her Ph.D. in Electronic Engineering from Tsinghua University in 2023, one of China’s most prestigious institutions. Currently, she is affiliated with the Future Research Lab at China Mobile Research Institute, where she focuses on cutting-edge areas such as 6G, integrated sensing and communication (ISAC), MIMO, advanced signal processing, target detection, positioning, and system design. Her research work demonstrates both depth and practical relevance to the next generation of wireless technologies. Rongyan Xi has contributed to several high-impact publications in leading journals including the IEEE Journal on Selected Areas in Communications, IEEE Internet of Things Journal, IEEE Sensors Journal, and Sensors. Her works cover critical topics like cooperative sensing for 6G networks, system design, beam management, target recognition using millimeter-wave sensing, and performance evaluation frameworks such as SensCAP. Her active role in collaborative research projects and contributions to addressing key challenges in ISAC and radar technology reflect her innovative mindset and technical leadership. With publications in top-tier journals and involvement in field trials, she has established herself as a rising leader in the wireless communication research community. Her academic excellence, technological innovation, and industry relevance collectively demonstrate her exceptional potential and make her an outstanding candidate for the Best Researcher Award.

Profiles: Google Scholar | ORCID

Featured Publications

  1. Liu, G., Xi, R., Wang, X., Han, L., Gui, X., Jin, J., Dong, J., Wei, N., He, H., Xia, L., et al. (2025). Cooperative sensing for ISAC: Challenges, system design, beam management, and performance validation. IEEE Journal on Selected Areas in Communications.

  2. He, X., Ding, H., Xi, R., Dong, J., Jin, J., Wang, Q., Shao, C., Dong, X., & Zhang, Y. (2025, September 12). Target recognition based on millimeter-wave-sensed point cloud using PointNet++ model. Sensors.

  3. Liu, G., Xi, R., Han, Z., Han, L., Zhang, X., Ma, L., Wang, Y., Lou, M., Jin, J., Wang, Q., et al. (2024). Cooperative sensing for 6G mobile cellular networks: Feasibility, performance and field trial. IEEE Journal on Selected Areas in Communications.

  4. Liu, G., Ma, L., Xue, Y., Han, L., Xi, R., Han, Z., Wang, H., Dong, J., Lou, M., Jin, J., et al. (2024). SensCAP: A systematic sensing capability performance metric for 6G ISAC. IEEE Internet of Things Journal.

  5. Xi, R., Ma, D., Liu, X., Wang, L., & Liu, Y. (2022, October). Intra-pulse frequency coding design for a high-resolution radar against smart noise jamming. Remote Sensing.

Xingyu Zhou | Engineering | Best Researcher Award

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Prof. Dr. Xingyu Zhou | Engineering | Best Researcher Award 

Assistant Professor | Beijing Institute of Technology | China

Dr. Zhou Xingyu, Assistant Professor at the Beijing Institute of Technology, is an accomplished researcher specializing in renewable energy and electric vehicles. He earned his Ph.D. in Vehicle Engineering from Chongqing University in 2020, following a Bachelor’s degree in Mechanical Design, Manufacturing, and Automation from the same institution. Dr. Zhou has extensive professional experience, including his current role as Assistant Professor at the School of Mechanical Engineering and Vehicle Engineering, Beijing Institute of Technology since March 2023, and a postdoctoral fellowship at the same institute from 2020 to 2023, where he contributed to the National Engineering Research Center for Electric Vehicles. His research interests focus on vehicle powertrain optimization, intelligent energy management, stochastic and data-driven modeling, and electric vehicle motion planning for enhanced energy efficiency. He has demonstrated expertise in multi-objective optimization, machine learning applications for powertrain design, and integration of fuel cell and hybrid electric vehicle systems. Dr. Zhou has led and participated in multiple high-impact research projects, including a National Natural Science Foundation of China Youth Project and key provincial and national projects on electric vehicle energy optimization and system integration. He has published 27 Scopus-indexed documents with 448 citations and an h-index of 11, in reputed journals such as Applied Energy, Journal of Power Sources, Journal of Cleaner Production, and IEEE Transactions on Vehicular Technology, serving frequently as corresponding author. His awards and honors include the Best Student Paper Award at the 2018 Italian Conference on Machines and Mechanisms. In addition, he contributes to the academic community as a reviewer for top journals and Guest Editor of Sustainability. Dr. Zhou Xingyu’s strong technical expertise, leadership in research projects, international collaborations, and commitment to sustainable innovation make him a highly deserving candidate for the Best Researcher Award, reflecting both outstanding academic achievements and meaningful contributions to advancing green mobility and energy-efficient transportation solutions globally.

Profiles: Scopus | ORCID

Featured Publications

Sun, C., Zhang, C., Sun, F., & Zhou, X. (2022). Stochastic co-optimization of speed planning and powertrain control with dynamic probabilistic constraints for safe and ecological driving. Applied Energy, 35, 119874.

Zhou, X., Sun, C., Sun, F., & Zhang, C. (2022). Commuting-pattern-oriented optimal sizing of electric vehicle powertrain based on stochastic optimization. Journal of Power Sources, 545, 23178.

Zhou, X., Sun, F., Zhang, C., & Sun, C. (2022). Stochastically predictive co-optimization of speed planning and powertrain controls for electric vehicles driving in random traffic environment safely and efficiently. Journal of Power Sources, 528, 231200.

Zhou, X., Sun, F., Sun, C., & Zhang, C. (2022). Predictive co-optimization of speed planning and powertrain energy management for electric vehicles driving in traffic scenarios: Combining strengths of simultaneous and hierarchical methods. Journal of Power Sources, 523, 230910.

Zhou, X., Sun, F., & Sun, C. (2021). Machine learning aided methods for reducing the dimensionality of the comprehensive energy economy optimization of fuel cell powertrains. Journal of Cleaner Production, 327, 129250.

Naghi Rostami | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Naghi Rostami | Engineering | Best Researcher Award 

Faculty of Electrical and Computer Engineering | University of Tabriz | Iran

Dr. Naghi Rostami is an accomplished academic and researcher in electrical power engineering, currently serving as Associate Professor and Head of the Electric Power Engineering Department at the University of Tabriz, Iran, where he has held leadership responsibilities from 2018 to 2024. He completed his B.Sc. in Electrical Engineering at K. N. Toosi University of Technology in 2006, his M.Sc. at the University of Tehran in 2008, and earned his Ph.D. from the University of Tabriz in 2013. He also gained international exposure through a research opportunity at Lappeenranta University of Technology, Finland, in 2012 under the supervision of Prof. Juha Pyrhönen. His primary research interests include permanent magnet machine design, particularly axial flux and radial flux configurations, hybrid electric vehicle energy management, modeling and optimization of electrical machines, and the integration of renewable energy systems with electric vehicles and storage technologies. Dr. Rostami’s research skills span analytical and numerical design methods, genetic algorithms, particle swarm optimization, and advanced co-simulation approaches, which he has applied to the design and performance improvement of permanent magnet machines and energy systems. He has an impressive record of publications in reputable journals such as IEEE Transactions on Magnetics, IET Electric Power Applications, Sustainable Cities and Society, and COMPEL, with many works indexed in Scopus and IEEE Xplore. His Google Scholar profile records more than 1,500 citations with an H-index of 19, highlighting the international recognition of his work. While his CV does not list specific awards and honors, his achievements in leadership, international collaborations, and sustained scholarly contributions stand as testaments to his professional excellence. In conclusion, Dr. Rostami is a dedicated scholar whose expertise, impactful publications, and leadership in academia and research make him a strong candidate for recognition through distinguished awards and honors in the field of electrical power engineering.

Profiles: Google Scholar | LinkedIn | ResearchGate

Featured Publications

  1. Jalilzadeh, T., Rostami, N., Babaei, E., & Maalandish, M. (2018). Nonisolated topology for high step-up DC–DC converters. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11(1), 137–150.(Cited 137 times)

  2. Rostami, N., Feyzi, M. R., Pyrhonen, J., Parviainen, A., & Niemela, M. (2012). Lumped-parameter thermal model for axial flux permanent magnet machines. IEEE Transactions on Magnetics, 49(3), 1178–1184.(Cited 136 times)

  3. Zeynali, S., Rostami, N., Ahmadian, A., & Elkamel, A. (2020). Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology. Sustainable Energy Technologies and Assessments, 39, 100722.(Cited 104 times)

  4. Marzang, V., Hosseini, S. H., Rostami, N., Alavi, P., & Mohseni, P. (2020). A high step-up nonisolated DC–DC converter with flexible voltage gain. IEEE Transactions on Power Electronics, 35(10), 10489–10500.(Cited 96 times)

  5. Zeynali, S., Rostami, N., & Feyzi, M. R. (2020). Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering uncertainties. International Journal of Electrical Power & Energy Systems, 119, 105885.(Cited 93 times)

Saurabh Kumar | Engineering | Best Researcher Award

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Dr. Saurabh Kumar | Engineering | Best Researcher Award

Assistant Professor | IIMT University | India

Dr. Saurabh Kumar is an accomplished researcher in Civil and Environmental Engineering with a focus on water quality, corrosion monitoring, wastewater treatment, and sustainable engineering practices. He is currently serving as a Postdoctoral Researcher at Thammasat University, Thailand (July 2024 – June 2025). Prior to this, he worked as an Assistant Professor in the Department of Civil Engineering at IIMT University, Meerut, Uttar Pradesh, India (June 2023 – June 2024). His academic journey began with a B.Tech in Civil Engineering from Darbhanga College of Engineering, Bihar, under Aryabhatta Knowledge University (2013 – 2017), followed by an M.Tech in Environmental Engineering from the National Institute of Technology Patna (2018 – 2020). He pursued a Ph.D. in Environmental Engineering at NIT Patna, Ministry of Education, Government of India (2020 – 2023), where his research focused on “Monitoring and Modelling of Corrosion Rate in Drinking Water Distribution Network.” Dr. Kumar has a strong research profile, with publications in SCI, Scopus, and IEEE-indexed journals such as Environmental Science and Pollution Research, Journal of Materials Engineering and Performance, and Nature’s Scientific Reports. He has authored a book, contributed to book chapters with reputed publishers, and presented papers at multiple international conferences, including IEEE and Springer platforms. His work reflects the integration of conventional engineering with advanced tools such as machine learning for solving environmental challenges.

Profile: Scopus | ORCID

 Publications

  1. Kumar Saurabh, Singh R., Maurya N.S., Water quality analysis and corrosion potential of the distribution network of Patna, Bihar, India. J. Environ. Eng. Sci., 2022, 17(4), 164–174. (ESCI)

  2. Kumar Saurabh, Singh R., Maurya N.S., Assessment of corrosion potential based on water quality index in the distribution network of urban Patna, Bihar, India. J. Nat. Environ. Pollut. Technol., 2022, 21(5), 2117–2127. (Scopus)

  3. Kumar Saurabh, Singh R., Maurya N.S., Raj V., Monitoring of the corrosion in the pipeline of the distribution network using weight loss method & image processing technique. J. Mater. Eng. Perform., 2022. (SCI)

  4. Kumar Saurabh, Singh R., Maurya N.S., Modelling of corrosion rate in the drinking water distribution network using Design Expert 13 software. Environ. Sci. Pollut. Res., 2023. (SCI)

  5. Rajeev R., Kumar Saurabh, Singh R., Monitoring of traffic noise pollution in urban Patna, Bihar, India. Noise Vib. Worldwide, 2023. (Scopus)

  6. Kumar P., Kumar Saurabh, Kumar M., Chaubey A., Nayak J., Groundwater flow and solute transport modelling: A mathematical analysis. Indian J. Environ. Prot., 2024, 44(3), 257–264. (Scopus)

  7. Shivam H., Sharma D., Bansal T., Pande R., Kumar Saurabh, Weesakul U., In-vessel bioconversion of garden waste into compost with an emphasis on process efficiency and compost quality. Eng. Appl. Sci. Res., 2025, 52(1), 105–111. (Scopus)

João Felipe C L Costa | Engineering | Best Research Article Award

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Prof. João Felipe C L Costa | Engineering | Best Research Article Award

Professor at Federal University of Rio Grande do Sul, Brazil

Dr. João Felipe Costa 🎓 is a distinguished Professor of Mining Engineering at the Federal University of Rio Grande do Sul, Brazil, with over four decades of expertise in geostatistics, mineral exploration, and mine planning ⛏️. He holds a PhD in Geostatistics from the University of Queensland and has published 300+ peer-reviewed papers 📚. A respected mentor, he has guided over 110 theses and dissertations and received multiple teaching accolades, including the prestigious John Cedric Griffiths Teaching Award 🏅. As head of the mineral exploration lab for 30+ years and an active member of leading international mining societies 🌍, Dr. Costa has led significant resource estimation projects globally, especially in phosphate deposit modeling. His career exemplifies academic excellence, innovation, and impactful contributions to mining sciences and education 🔍.

Professional Profile

🎓 Education

Dr. João Felipe Costa earned his BSc (1983) and MSc (1992) in Mining Engineering from the Federal University of Rio Grande do Sul 🇧🇷, where he currently serves as Professor. He advanced his academic journey by earning a PhD in Geostatistics from the University of Queensland, Australia 🇦🇺 in 1997. His education bridges deep technical knowledge with applied innovation, particularly in geological modeling and statistical data analysis 📊. His foundation in mining engineering and specialization in geostatistics has positioned him as an expert in both practical and academic settings. Dr. Costa’s education reflects a strong commitment to continuous learning and excellence in the evolving field of mineral resources and spatial data science 🧠.

💼 Professional Experience

Dr. Costa began his career as a mining engineer at a major coal operation in southern Brazil, where he optimized unit operations using early computer applications in the 1980s 🖥️⛏️. He joined the Federal University of Rio Grande do Sul in 1986 and has served as a Professor in the Mining Engineering Department ever since. His professional journey includes roles as Department Head, research lab coordinator, and consultant on numerous mineral resource evaluation projects 🌐. With over 30 years of teaching and field experience, he has balanced academic leadership with applied industrial insight, making significant contributions to both sectors. His dedication to education, project execution, and resource modeling showcases his deep engagement with both theory and practice ⚙️📘.

🔬 Research Interest

Dr. João Felipe Costa’s core research interests lie in geostatistics, mineral resource estimation, mine planning, and phosphate deposit modeling 📈. He is especially known for developing robust techniques for spatial data analysis, resource classification, and geological uncertainty evaluation. His work extends to a variety of geological settings, including sedimentary and carbonatite phosphate formations in Brazil and Peru 🌍. Passionate about data-driven solutions, his research integrates statistical modeling with software tools to improve decision-making in exploration and mining processes 💡. As a leading voice in mathematical geosciences, Dr. Costa’s interdisciplinary research not only enhances mining efficiency but also supports sustainable resource management 🔎🧭.

🏅 Awards and Honors

Dr. Costa has been honored multiple times throughout his career. Most notably, he received the John Cedric Griffiths Teaching Award in 2014 from the International Association for Mathematical Geosciences, recognizing his excellence in geoscience education 🎖️. He has also been named Distinguished Professor by graduating classes over the past 20 years, reflecting his lasting impact on student learning 👨‍🏫. As an esteemed member of professional societies like AusIMM, IAMG, SME (USA), and SAIMM (South Africa), his global contributions have earned widespread recognition 🌐. His leadership in Brazil’s mineral resources committee further reinforces his influence in shaping mining policy and academic standards 🏆.

🛠️ Research Skills

Dr. Costa possesses advanced research skills in geostatistical modeling, orebody evaluation, spatial data interpretation, and mineral resource classification 🔍. He is proficient in using industry-relevant software for data simulation, variography, and risk assessment 🖥️📊. His methodological rigor is evident in over 300 peer-reviewed publications and advisory roles in complex exploration projects worldwide 🌎. As the head of a leading mine planning lab for three decades, he has cultivated a dynamic research environment integrating computational tools with field data. His skills also include thesis supervision, technical writing, and collaborative research management, making him a versatile and highly capable scientific contributor 🔧📘.

Publications Top Note 📝

Title: Localized conditional simulation to integrate production data in grade control models
Authors: R. L. Silva, J. F. C. L. Costa, D. M. Marques
Year: 2021
Source: Computers & Geosciences
Citation: Computers & Geosciences, Vol. 150, 104722

Title: Uncertainty in the modeling of lateritic nickel ores by multiple indicator kriging
Authors: A. L. F. Duarte, J. F. C. L. Costa
Year: 2016
Source: Ore Geology Reviews
Citation: Ore Geology Reviews 73, 223–233

Title: Conditional simulation of iron ore deposit grades using co-simulation with proportional correction
Authors: J. F. C. L. Costa, R. H. Rubio, D. M. Marques
Year: 2018
Source: Revista Escola de Minas
Citation: Rev. Esc. Minas 71(4), 531–538

Title: Application of indicator kriging to define cutoff grades for iron ore
Authors: J. F. C. L. Costa, D. M. Marques
Year: 2013
Source: Revista Escola de Minas
Citation: Rev. Esc. Minas 66(1), 37–43

Title: Geostatistical conditional simulation to define grade control strategy for a bauxite mine
Authors: D. M. Marques, J. F. C. L. Costa
Year: 2015
Source: J. South African Institute of Mining and Metallurgy
Citation: J. SAIMM 115(6), 533–540

Title: Geostatistical simulation of mineral grades using multiple-point statistics
Authors: F. G. da Silva, J. F. C. L. Costa
Year: 2016
Source: Computers & Geosciences
Citation: Computers & Geosciences 94, 1–12

Title: Simulation of grade control based on inverse distance weighting
Authors: D. M. Marques, J. F. C. L. Costa
Year: 2018
Source: Revista Escola de Minas
Citation: Rev. Esc. Minas 71(1), 123–129

Title: Geostatistical modeling in lateritic nickel ore: A comparative study between ordinary kriging and indicator kriging
Authors: A. L. F. Duarte, J. F. C. L. Costa
Year: 2015
Source: Natural Resources Research
Citation: Nat. Resour. Res. 24(2), 213–225

Title: Use of stochastic simulation to support mining strategy selection
Authors: D. M. Marques, J. F. C. L. Costa
Year: 2016
Source: Journal of the Southern African Institute of Mining and Metallurgy
Citation: J. SAIMM 116(7), 669–676

Title: Comparison of multivariate conditional simulation techniques for iron ore grade modeling
Authors: R. H. Rubio, J. F. C. L. Costa
Year: 2017
Source: Computers & Geosciences
Citation: Computers & Geosciences 101, 1–12

✅ Conclusion

Dr. João Felipe Costa is a world-class academic and professional in mining engineering and geostatistics, blending education, research, and leadership with remarkable consistency 🌟. His impact spans over 40 years of scholarly excellence, with hundreds of publications, international collaborations, and influential teaching. A mentor, innovator, and geoscience leader, he continues to shape the future of mineral exploration and resource evaluation 🔬🧭. With his global recognition, research depth, and technical command, Dr. Costa stands as a compelling candidate for top honors in scientific research and academic excellence 🎓🏅.