Shuokang Wang | Environmental Science | Best Researcher Award

Dr. Shuokang Wang | Environmental Science | Best Researcher Award

Department of Mathematics and Physics at Hebei Petroleum University of Technology, China

Dr. Shuokang Wang is a Lecturer at Hebei Petroleum University of Technology, holding a Ph.D. in Mining Engineering. His research primarily focuses on water conservation mining and rock control, addressing the critical balance between coal extraction and ecological preservation in environmentally fragile regions of China such as Inner Mongolia, Shaanxi, and Shanxi. He has published 10 academic papers in reputed journals including Energies and the International Journal of Coal Science & Technology, and holds 8 authorized invention patents. His work has earned him a first and second prize from the Coal Industry Association, and he actively contributes to the scientific community as a reviewer for SCI-indexed journals and as a young editorial board member for Green Mining and Metallurgy. Dr. Wang has also contributed to major national research projects like the β€œ973” Program and the National Natural Science Foundation of China, gaining interdisciplinary experience across geological, environmental, and engineering sciences. His innovations, such as methods for evaluating rock damage and permeability zoning post-mining, have significant implications for sustainable mining practices.

Professional ProfileΒ 

πŸŽ“ Education of Shuokang Wang

Dr. Shuokang Wang earned his Ph.D. in Mining Engineering, laying a solid academic foundation for his specialization in water conservation mining and rock control. His doctoral training equipped him with advanced knowledge in geomechanics, mining-induced stress evolution, and environmental sustainability in coal mining. This rigorous academic background has been instrumental in shaping his research contributions, enabling him to tackle complex challenges related to ecological protection in mining regions through both theoretical and applied innovations.

πŸ’Ό Professional Experience of Shuokang Wang

Dr. Shuokang Wang currently serves as a Lecturer at Hebei Petroleum University of Technology, where he actively engages in teaching, research, and academic mentorship. His professional work is centered on water conservation mining and rock control, with a focus on improving ecological outcomes in coal mining operations. He has participated in major national projects such as the β€œ973” Program and the National Natural Science Foundation of China’s general research initiative, collaborating across disciplines including geological engineering, environmental science, and materials development. In addition to his research roles, Dr. Wang contributes to the academic community as a peer reviewer for SCI-indexed journals like Scientific Reports and Science Progress, and as a young editorial board member of Green Mining and Metallurgy. His work bridges academic research and engineering practice, earning recognition and awards from national industry associations.

πŸ”¬ Research Interest of Shuokang Wang

Dr. Shuokang Wang’s research interests lie at the intersection of sustainable mining practices and environmental protection, with a strong emphasis on water conservation mining and rock control mechanisms. He is particularly focused on developing innovative methods to mitigate the ecological impact of coal mining in fragile regions such as Inner Mongolia, Shanxi, and Shaanxi. His work involves analyzing stress evolution, permeability changes, and damage characteristics of rock strata during and after mining activities. Dr. Wang also explores the application of acoustic emission testing, permeability zoning, and roof collapse prediction to improve mine safety and water resource management. His interdisciplinary approach integrates engineering mechanics, geological analysis, and environmental science to develop practical, science-backed solutions for the mining industry.

πŸ… Awards and Honors of Shuokang Wang

Dr. Shuokang Wang has received notable recognition for his impactful research and contributions to the field of mining engineering. He was honored with a First Prize and a Second Prize by the Coal Industry Association, acknowledging his innovative work in water conservation mining and rock control. His scientific credibility and expertise have also earned him roles as a reviewer for high-impact SCI-indexed journals such as Scientific Reports and Science Progress, and as a young editorial board member of the academic journal Green Mining and Metallurgy. These accolades reflect both his technical excellence and his growing leadership within the academic and professional mining communities.

πŸ“š Publications Top Noted

  1. Title: Study on Creep Characteristics of High-Volume Fly Ash-Cement Backfill Considering Initial Damage
    Authors: Shuokang Wang, Jingjing Yan, Zihui Dong, Hua Guo, Yuanzhong Yang, Naseer Muhammad Khan
    Year: 2025
    Citation: DOI: 10.3390/min15070759

  2. Title: Strategic Recovery of Titanium from Low-Grade Titanium-Bearing Blast Furnace Slag via Hydrothermal-Crystallization Coupling
    Authors: Zihui Dong, Ruichen Yang, Shuokang Wang, Changyong Chen, Mingming Zhao, Nannan Zhou, Peipei Zhang, Yingxin Wang
    Year: 2025
    Citation: DOI: 10.3390/min15050445

  3. Title: Characteristics and Control of Mining Induced Fractures above Longwall Mines Using Backfilling
    Authors: Shuokang Wang, Liqiang Ma
    Year: 2019
    Citation: DOI: 10.3390/en12234604

βœ…Conclusion

Dr. Shuokang Wang is highly suitable for the Best Researcher Award, especially in the domain of Mining Engineering with Environmental Sustainability Focus. His contributions are original, practical, and nationally recognized, with strong academic grounding and real-world impact

saeid shabani | Environmental Science | Best Researcher Award

saeid shabani | Environmental Science | Best Researcher Award

Assist. Prof. Dr saeid shabani, AREEO, Iran

Assist. Prof. Dr. Saeid Shabani is a distinguished forestry researcher specializing in forest monitoring, natural hazards, and sustainable ecosystem development 🌳🌍. He holds a Ph.D. in Forestry from Tarbiat Modares University, Iran (2015), where he developed models for predicting soil and forest stand disturbances caused by logging πŸ—οΈπŸŒ². His research integrates GIS, machine learning, and statistical modeling to assess forest fragmentation, carbon stock monitoring, and climate change impacts πŸ“ŠπŸ›°οΈ. Dr. Shabani has led numerous projects on afforestation, ecosystem assessments, and genetic variations in tree species πŸŒ±πŸ”¬. His publications in high-impact journals, along with his role as a reviewer for esteemed scientific outlets, solidify his reputation as a leading expert in forestry research πŸ“–πŸŒΏ. With expertise in ArcGIS, R, and SPSS, he bridges the gap between technology and environmental conservation πŸ’»πŸƒ. His dedication to sustainable forest management makes him an outstanding candidate for the Best Researcher Award πŸ…πŸ‘.

Publication Profile

Google Scholar

Education

Dr. Saeid Shabani earned his Bachelor of Science in Forestry from the University of Guilan, Iran (2005) 🌲, where he developed a strong foundation in forest management and conservation. He pursued his Master of Science in Forestry at Tarbiat Modares University (2009) πŸ“Š, focusing on the relationship between forest gaps, physiographic factors, and vegetation in Lalis Forest, Nowshahr πŸŒ³πŸ—ΊοΈ. He further advanced his expertise with a Ph.D. in Forestry from the same institution (2015) πŸ—οΈ, specializing in modeling soil and forest stand disturbance caused by logging operations πŸžοΈπŸ”. Dr. Shabani’s academic journey emphasizes sustainable forest ecosystem development, leveraging GIS, machine learning, and data-driven modeling πŸ“ˆπŸŒ. His interdisciplinary research bridges ecological conservation and technological advancements to enhance forestry management strategies πŸ’‘πŸŒ±.

Experience

Dr. Saeid Shabani has extensive experience in forestry research, specializing in forest monitoring, sustainable development, and climate impact assessments 🌍🌿. He has led and collaborated on multiple projects across the Hyrcanian and Arasbaran forests, focusing on afforestation, forest road monitoring, and carbon stock assessment πŸžοΈπŸ“Š. His expertise in GIS, R, and machine learning has enabled him to develop predictive models for forest stand disturbances and susceptibility to environmental threats like snowstorms and windthrow πŸŒͺοΈπŸ›°οΈ. As a scientific reviewer, he contributes to journals such as Scientific Reports, Turkish Journal of Forestry, and Ecology of Iranian Forests πŸ“–πŸ“. He has also been involved in standardizing forestry job competencies and ecosystem differentiation. His impactful work in forest conservation and ecosystem modeling positions him as a leading researcher in environmental sustainability and forestry science

Awards & Honors

Dr. Saeid Shabani has received multiple recognitions for his groundbreaking contributions to forestry research πŸŒΏπŸ†. His work on forest sustainability, ecosystem monitoring, and climate resilience has earned him prestigious awards and funding. He was a recipient of the Chinese Government Scholarship πŸŽ“πŸ‡¨πŸ‡³ and has won Best Paper Awards for high-impact forestry research in journals like the European Journal of Forest Research πŸ“œπŸ…. His expertise as a reviewer has been acknowledged with Reviewer Recognition Awards from Scientific Reports, South African Geographical Journal, and Journal of Forest Research & Development πŸ”πŸ“–. He has secured project grants from environmental organizations for studies on afforestation, soil health, and carbon stock modeling πŸŒ²πŸ’°. His Excellence in Forestry Research Award highlights his innovative use of GIS and machine learning in forest monitoring πŸ—οΈπŸ›°οΈ. Through his dedication to sustainable forestry and advanced modeling techniques, he has cemented his reputation as an award-winning researcher in environmental science.

Research Focus

Dr. Saeid Shabani is a distinguished researcher specializing in forest monitoring, ecosystem sustainability, and climate impact assessment 🌲🌎. His expertise lies in applying GIS, machine learning, and statistical modeling to predict forest disturbances caused by natural hazards and human activities πŸ›°οΈπŸ“Š. His research focuses on forest fragmentation and logging impact modeling πŸ—οΈπŸŒ³, assessing the effects of snowstorms, windthrow, and climate change on forest ecosystems ❄️πŸŒͺ️, and evaluating carbon stock in Hyrcanian and Arasbaran forests πŸŒ±πŸ“ˆ. Additionally, he contributes to afforestation efforts and sustainable forest management strategies πŸŒΏβ™»οΈ while analyzing soil health and biodiversity conservation in forest stands πŸ”¬πŸ‚. Through cutting-edge methodologies, he develops innovative solutions to preserve global forest ecosystems and mitigate environmental risks πŸŒπŸ’‘. His work plays a vital role in policy-making and sustainable forestry development, ensuring the long-term health of natural resources.

Publication Top Notes

  • πŸ“œ Spatial Prediction of Soil Disturbance Caused by Forest Logging Using Generalized Additive Models and GIS – European Journal of Forest Research πŸ—οΈπŸŒ²

  • 🌍 Forest Stand Susceptibility Mapping During Harvesting Using Logistic Regression and Boosted Regression Tree Models – Global Ecology and Conservation πŸ“ŠπŸ›°οΈ

  • πŸ“ Spatial Modeling of Forest Stand Susceptibility to Logging Operations – Environmental Impact Assessment Review πŸžοΈπŸ“‰

  • ❄️ Modeling the Susceptibility of Uneven‑Aged Broad‑Leaved Forests to Snowstorm Damage – Environmental Science and Pollution Research 🌨️🌲

  • 🌑️ How Do Different Land Uses/Covers Contribute to Land Surface Temperature and Albedo? – Sustainability 🏑🌞

  • 🌱 Soil Health Reduction Following Conversion of Primary Vegetation Covers in Semi-Arid Environments – Science of the Total Environment 🏜️🌾

  • πŸ—ΊοΈ Modeling and Mapping of Soil Damage Caused by Harvesting in Caspian Forests Using CART and RF Techniques – Journal of Forest Science πŸ“ˆπŸ“Š

  • πŸŒͺ️ Prediction of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression & Random Forest – Cerne Journal πŸŒ³πŸ’¨