Jun-Xiu Li

19 papers receiving 628 citations

Peers

Jun-Xiu Li
Comparison fields: 5 of 98
  • Computer Vision and Pattern Recognition 183
  • Automotive Engineering 78
  • Safety, Risk, Reliability and Quality 45
  • Artificial Intelligence 139
  • Biochemistry 21
Replace Xiaobao Liu with:
Xiaobao Liu China
Andrew Taylor United States
Shuming Zhang China
Xi Yang China
Michiharu Maeda Japan
Lixin Wang China
Xiang-Jun Li China
H. Kawakami Japan
Marcel Hoffmann Germany
Jun-Xiu Li relative to Xiaobao Liu China Xiaobao Liu's profile →
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Citations per year

Countries citing papers authored by Jun-Xiu Li

Since Specialization
Citations

This map shows the geographic impact of Jun-Xiu Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun-Xiu Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun-Xiu Li more than expected).

Fields of papers citing papers by Jun-Xiu Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun-Xiu Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun-Xiu Li. The network helps show where Jun-Xiu Li may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun-Xiu Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun-Xiu Li Line = papers co-authored together Jun-Xiu Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019122
2 201998
3 201973
4 201269
5 202045
6 201338
7 201334
8 201330
9 201422
10 201622
11 201322
12 201821
13 202416
14 20159
15 20137
16 20245
17 20144
18 20203
19 20142
20 20250

About Jun-Xiu Li

Jun-Xiu Li is a scholar working on Atomic and Molecular Physics, and Optics, Astronomy and Astrophysics, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 642 indexed citations. Recurring topics across this work include Dust and Plasma Wave Phenomena (8 papers), Ionosphere and magnetosphere dynamics (7 papers), Advancements in Battery Materials (5 papers), Advanced Battery Materials and Technologies (4 papers), Supercapacitor Materials and Fabrication (4 papers), Cold Atom Physics and Bose-Einstein Condensates (3 papers), Advanced Battery Technologies Research (2 papers) and Earthquake Detection and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (183 citations), Automotive Engineering (78 citations), Safety, Risk, Reliability and Quality (45 citations), Artificial Intelligence (139 citations) and Biochemistry (21 citations). Jun-Xiu Li has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Zhi-Qi Cheng, Xiao Wu, Alexander G. Hauptmann, Qi Dai, Jizhou Kong, Fei Zhou, Haifa Zhai, Zhou Tang, Xiaoyan Yang and Chong Ren. Their work appears in journals such as Physics of Plasmas, Journal of Alloys and Compounds, Journal of Solid State Electrochemistry, World Journal of Clinical Oncology and Journal of Surgical Oncology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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