Bo Xiong | Space Physics | Research Excellence Award

Mr. Bo Xiong | Space Physics | Research Excellence Award

North China Electric Power University | China

Mr. Bo Xiong is a distinguished researcher in space physics and ionospheric science, with a strong academic impact demonstrated by an h-index of 17, 45 published documents, and 749 citations across 597 scholarly works. His research primarily focuses on ionospheric disturbances, total electron content (TEC) modeling, and the effects of solar and geomagnetic activities on Earth’s upper atmosphere. He has contributed significantly to understanding ionospheric responses to solar flares, volcanic eruptions, and rocket launches, employing advanced techniques such as GNSS data analysis and deep learning models. His work also includes high-resolution global TEC mapping and statistical analysis of ionospheric scintillations. Through interdisciplinary approaches combining geophysics, remote sensing, and data-driven modeling, his research enhances predictive capabilities and supports applications in satellite communication, navigation systems, and space weather forecasting.

Citation Metrics (Scopus)

800

600

400

200

0

Citations
749

h-index
17

Documents
45

Citations

h-index

Documents


View Scopus Profile


View Orcid Profile

Featured Publications

Jie Hui Ng | Traffic Big Data Mining and Analysis | Network Science Excellence Award

Mr. Jie Hui Ng | Traffic Big Data Mining and Analysis | Network Science Excellence Award

Tsinghua University | Malaysia

Mr. Jie Hui Ng is an emerging researcher specializing in traffic big data mining, network analysis, and travel behavior modeling, with a growing scholarly impact reflected through his h-index, document count, and citation metrics. His research focuses on leveraging sparse data environments to extract meaningful mobility insights, particularly through advanced data-driven frameworks. His notable work, TravelForest, introduces a trajectory reconstruction and travel path selection framework using sparse license plate recognition (LPR) data, integrating road network topology and dynamic traffic conditions. By extending the random forest model with an interpretable structure, his approach achieves high accuracy and robustness under limited data availability. His contributions also reveal key determinants of route choice, such as turn frequency and data coverage, while supporting scalable, real-time traffic analysis. Furthermore, his research advances proactive traffic signal control optimization, enhancing intelligent transportation systems and urban mobility efficiency.

View ORCID Profile

Featured Publications