laiba Sultan Dar | Time series analysis | Best Researcher Award

Posted on by Phenomenological Research Awards

Ms. laiba Sultan Dar | Time series analysis | Best Researcher Award 

Phd Student at Abdul wali khan university | Pakistan

Ms. Laiba Sultan Dar is a dedicated researcher and Ph.D. scholar at Abdul Wali Khan University, specializing in time series analysis and decomposition techniques. Her academic journey is marked by a strong foundation in applied mathematics and data analytics, enabling her to contribute effectively to statistical modeling, forecasting, and computational research. She has developed expertise in identifying patterns and trends within large datasets, applying modern analytical frameworks to address complex problems in areas such as economics, climate studies, and system optimization. Her research integrates both theoretical and empirical approaches, focusing on improving the precision and interpretability of time-dependent models. Through her doctoral studies, she has actively engaged in institutional research projects and contributed to scholarly publications in recognized international journals, including those indexed by Scopus and ScienceDirect. Her work demonstrates a balance between mathematical rigor and practical application, particularly in designing adaptive models for real-world data forecasting. In addition to her academic pursuits, Ms. Laiba Sultan Dar has shown growing involvement in research collaborations and interdisciplinary studies that bridge statistical science and technological innovation. Her ability to combine methodological sophistication with computational efficiency has made her research relevant to emerging domains such as artificial intelligence-driven data analysis and predictive modeling. She continuously enhances her research capabilities by incorporating advanced tools and techniques, fostering a deeper understanding of time series dynamics. With a citation index reflecting the recognition of her early scholarly efforts, she remains committed to producing impactful research that advances statistical methodologies and contributes to data-driven decision-making. Her intellectual curiosity and analytical acumen position her as a promising researcher in quantitative sciences. Ms. Laiba Sultan Dar’s long-term goal is to strengthen the integration of mathematical modeling with applied sciences, promoting innovation and scientific development in her field. Her commitment to excellence, collaborative mindset, and ongoing pursuit of research excellence make her a deserving candidate for recognition among emerging scholars in data analytics and statistical research.

Profile: Google Scholar

Featured Publications:

  • Dar, L., Akmal, A., Naseem, M. A., & Khan, K. U. D. (2011). Impact of stress on employees’ job performance in the business sector of Pakistan. Global Journal of Management and Business Research, 11(6), 1–4.

  • Dar, L. A., Naseem, M. A., Rehman, R. U., & Niazi, G. S. (2011). Corporate governance and firm performance: A case study of Pakistan oil and gas companies listed in Karachi Stock Exchange. Global Journal of Management and Business Research, 11(8), 1–10.

  • Malik, M., Wan, D., Dar, L., Akbar, A., & Naseem, M. A. (2014). The role of work-life balance in job satisfaction and job benefit. Journal of Applied Business Research (JABR), 30(5), 1627–1638.

  • Dar, L. S., Aamir, M., Khan, Z., Bilal, M., Boonsatit, N., & Jirawattanapanit, A. (2022). Forecasting crude oil price volatility by reconstructing EEMD components using ARIMA and FFNN models. Frontiers in Energy Research, 10, 991602.

  • Dar, L. S., Aamir, M., Bibi, S., & Bilal, M. (2025). A novel robust adaptive decomposition approach for solar energy potential using atmospheric transparency and UV radiation indicators. Journal of Radiation Research and Applied Sciences, 18(4), 101946.

  • Dar, L. S., Aamir, M., Hamraz, M., Faiz, N., Emam, W., & Tashkandy, Y. (2025). A robust adaptive signal decomposition method for enhanced mode extraction in financial time series. IEEE Access.

  • Saleem, Q., Dar, L., Shahid, M., & Rana, S. (2012). A quantitative analysis of the role of human resource development in economic growth in Pakistan. International Journal of Management Sciences and Business Research.

  • Bilal, M., Aamir, M., Abdullah, S., Mahmood, N., Khalil, U., Khalid, N., Ahmed, M., & Dar, L. (2022). Assessment of the COVID-19 pandemic’s impact on gasoline prices in Pakistan. VFAST Transactions on Mathematics, 10(2), 52–67.

Fei Li | Next-generation informatic | Leading Research Award

Posted on by Phenomenological Research Awards

prof. Fei Li | Next-generation informatic | Leading Research Award

Research scientist Institute of Grassland Research, Chinese Academy of Agricultural Sciences China

Dr. Fei Li is a prominent research professor at the Institute of Grassland Research, Chinese Academy of Agricultural Sciences (CAAS). Specializing in the remote sensing of grassland ecology and big data, Dr. Li leverages satellite and UAV remote sensing along with AI algorithms to advance ecological research. With over 40 academic papers published in top-tier journals, Dr. Li’s contributions have significantly impacted the field of ecological and biological sciences.

 

Profile

Scopus

Education 🎓

Dr. Li earned his Ph.D. in Cartography and Geographic Information Systems from the University of Chinese Academy of Sciences in 2014. Prior to this, he completed his M.S. in Cartography and Geographic Information Systems and his B.S. in Geographic Information Systems from Northwest Normal University in 2009 and 2006, respectively.

Experience 🏆

Dr. Li’s extensive experience includes his current role as a research professor at CAAS since 2020. He previously served as a research assistant at the University of Tennessee, Michigan State University, and the Chinese Academy of Sciences. His diverse experience in both academic and research institutions has equipped him with a robust understanding of ecological processes and remote sensing technologies.

Research Interests 🔬

Dr. Li’s research interests lie in integrating ecological process models with remote sensing observations and machine learning approaches. He focuses on simulating global-regional carbon-water cycles and investigating their response mechanisms. Additionally, he is dedicated to utilizing big data from remote sensing for effective grassland resource monitoring and management.

Awards 🏅

Dr. Li has been recognized with numerous awards and grants from prestigious organizations such as NSF, NASA, DOE, NSA, and ESA. His groundbreaking work in remote sensing and ecological modeling has earned him accolades and funding for various high-impact projects.

Publications Top Notes 📚

Dr. Li has an impressive portfolio of publications, including:

Li, H., Li, F.*, Xiao, J., Chen, J., Lin, K., Bao, G., … & Wei, G. (2024). A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of Environment, 311, 114317. Cited by 10 articles.

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Li, F., Xiao, J., Chen, J., Ballantyne, A., Jin, K., Li, B., … & John, R. (2023). Global water use efficiency saturation due to increased vapor pressure deficit. Science, 381(6658), 672-677. Cited by 25 articles.

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Yan, H., Li, F.*, & Liu, G. (2023). Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland α-diversity metrics. Frontiers in Ecology and Evolution, 11, 154. Cited by 15 articles.

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Ouyang, Z., Sciusco, P., Jiao, T., Feron, S., Lei, C., Li, F., … & Chen, J. (2022). Albedo changes caused by future urbanization contribute to global warming. Nature Communications, 13(1), 3800. Cited by 30 articles.

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Brabazon, H., DeBruyn, J. M., Lenaghan, S. C., Li, F., Mundorff, A. Z., Steadman, D. W., & Stewart Jr, C. N. (2020). Plants to Remotely Detect Human Decomposition?. Trends in Plant Science, 25(10), 947-949. Cited by 20 articles.

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