Jun Yan

1.4k citations
101 papers · 816 indexed · h-index 16

Impact in

Papers in

Jun Yan

91 papers receiving 765 citations

Peers

Jun Yan
Comparison fields: 5 of 91
  • Statistical and Nonlinear Physics 249
  • Industrial and Manufacturing Engineering 116
  • Pollution 112
  • Applied Mathematics 97
  • Mathematical Physics 83
Replace V.S. Manoranjan with:
V.S. Manoranjan United States
Qiang Xi China
Kexue Li China
Jitendra Kumar India
A. Cloot South Africa
Yujun Cui China
T. R. Marchant Australia
Konrad Bajer Poland
Yuwen Wang China
Jun Yan relative to V.S. Manoranjan United States V.S. Manoranjan's profile →
Citations per field
00.5×
V.S. Manoranjan · 1×
Citations per year

Countries citing papers authored by Jun Yan

Since Specialization
Citations

This map shows the geographic impact of Jun Yan'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 Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Yan more than expected).

Fields of papers citing papers by Jun Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Yan. 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 Yan. The network helps show where Jun Yan may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Yan, 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 Yan Line = papers co-authored together Jun Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 101 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202173
2 200464
3 202247
4 202345
5 200940
6 201635
7 199832
8 201829
9 201020
10 202020
11 202218
12 202017
13 202217
14 201117
15 200615
16 201615
17 202414
18 202213
19 201912
20 201110

About Jun Yan

Jun Yan is a scholar working on Statistical and Nonlinear Physics, Industrial and Manufacturing Engineering, Mathematical Physics, Control and Systems Engineering and Applied Mathematics, having authored 101 papers that have together received 816 indexed citations. Recurring topics across this work include Quantum chaos and dynamical systems (24 papers), Constructed Wetlands for Wastewater Treatment (10 papers), Industrial Technology and Control Systems (9 papers), Advanced Sensor and Control Systems (7 papers), Cosmology and Gravitation Theories (7 papers), Wastewater Treatment and Nitrogen Removal (7 papers), Physics of Superconductivity and Magnetism (7 papers) and Nonlinear Waves and Solitons (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (249 citations), Industrial and Manufacturing Engineering (116 citations), Pollution (112 citations), Applied Mathematics (97 citations) and Mathematical Physics (83 citations). Jun Yan has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Chong-Qing Cheng, Kaizhi Wang, Lin Wang, Jianqiang Zhu, Yi Chen, Dongliang Qi, Qixia Wu, Sung Kyu Choi, Xuebin Hu and B.G. Zhang. Their work appears in journals such as Bioresource Technology, Journal of Differential Equations, Journal de Mathématiques Pures et Appliquées, The International Journal of Advanced Manufacturing Technology and Frontiers in Plant Science.

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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