Binghua Liu | Environmental Science | Research Excellence Award

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Dr. Binghua Liu | Environmental Science | Research Excellence Award

Dr. Binghua Liu | Environmental Science | Key Laboratory of Mariculture Ministry of Education | China

Environmental Science Dr. Binghua Liu is an accomplished researcher whose scientific journey reflects deep commitment to advancing sustainable aquatic systems and marine biological sciences. Dr. Binghua Liu earned his academic foundation through rigorous training in marine biology and aquaculture-related disciplines, culminating in advanced degrees that strengthened his expertise in cellular mechanisms, aquatic organism development, and environmental adaptation. Over the years, Dr. Binghua Liu has gained extensive professional experience working at the Key Laboratory of Mariculture, Ministry of Education, Qingdao, China, where he has contributed to high-impact research in fish physiology, molecular regulation, and environmentally responsive biological pathways. His research interests span environmental science, aquatic genomics, non-coding RNA mechanisms, physiological stress responses, molecular regulation of muscle development, and the environmental factors influencing aquaculture species. Demonstrating exceptional scientific skills, Dr. Binghua Liu specializes in molecular biology techniques, transcriptome analysis, environmental impact assessment, marine organism developmental studies, and functional gene expression profiling, along with advanced laboratory methodologies central to modern environmental and biological sciences. His research skills also include quantitative data interpretation, biomolecular pathway mapping, experimental aquaculture design, gene-environment interaction analysis, and integrative evaluation of environmental stress effects on marine species. Throughout his academic and professional career, Dr. Binghua Liu has earned recognition through authoring multiple peer-reviewed publications, contributing to collaborative scientific projects, and receiving honors associated with impactful research outcomes in environmental science, marine biology, and aquaculture systems. His work supports global scientific efforts to improve sustainable fishery practices, understand organismal responses to environmental change, and apply molecular tools to enhance ecological resilience. In conclusion, Dr. Binghua Liu’s professional trajectory demonstrates a strong alignment with the broader goals of Environmental Science, offering valuable contributions to the scientific community through rigorous research, scholarly publications, interdisciplinary collaboration, and continuous advancement of knowledge related to marine organism development and environmental interactions. His ongoing dedication positions him as a significant contributor to the future of marine environmental sustainability, aquaculture innovation, and applied biological sciences.

Academic Profile: Scopus

Featured Publications:

  1. Liu, B. (2025). Circtefb regulates myocytes development by sponging Pol-miR-138 in Japanese flounder (Paralichthys olivaceus). Journal of Ocean University of China.

 

 

Bo Zhang | Environmental Science | Best Research Article Award

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Mr. Bo Zhang | Environmental Science | Best Research Article Award

Mr. Bo Zhang | Environmental Science | Associate Professor at Northwestern Polytechnical University | China

Mr. Bo Zhang is an accomplished researcher whose career reflects a distinguished trajectory in the fields of remote sensing, geoinformation science, and artificial intelligence applications for environmental monitoring, and he has consistently demonstrated excellence through impactful research contributions and global collaborations. Educated with a doctoral degree in Remote Sensing from a leading academic institution, Mr. Bo Zhang has established a solid academic foundation that underpins his expertise in applying advanced computational methods to real-world environmental challenges. His professional experience includes multiple collaborative projects with internationally recognized scholars from institutions such as Tsinghua University, Southeast University, Technical University of Munich, University of Hong Kong, and others, through which he has advanced the use of deep learning techniques, satellite image downscaling, and GIS-based data integration for earth observation and climate-related studies. Mr. Bo Zhang’s research interests lie primarily in satellite remote sensing, super-resolution reconstruction of geospatial datasets, atmospheric and environmental data analysis, epidemiological mapping, and the integration of machine learning for improved predictive accuracy in public health and ecological monitoring, reflecting an interdisciplinary approach that combines computing, earth science, and applied technology. His research skills span deep learning model development, neural network applications for image processing, spatial epidemiology analysis, super-resolution algorithms, and the integration of volunteered geographic information into traditional mapping platforms, enabling him to contribute both theoretically and practically to geospatial sciences.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  • Zhu, B., Liu, J., Fu, Y., Zhang, B., & Mao, Y. (2018). Spatio-temporal epidemiology of viral hepatitis in China (2003–2015): Implications for prevention and control policies. International Journal of Environmental Research and Public Health, 15(4), 661. Citations: 73.

  • Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for the road-network extraction from VHR remote sensing images—Using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Citations: 59.

  • Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646. Citations: 29.

  • Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., Yu, L., Zhang, Q., … (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin, 67(24), 2526–2530. Citations: 25.