Chaoran Wu

23 papers receiving 585 citations

Peers

Chaoran Wu
Comparison fields: 5 of 76
  • Physiology 218
  • Sensory Systems 36
  • Statistical and Nonlinear Physics 64
  • Neurology 39
  • Cellular and Molecular Neuroscience 82
Replace Paul G.A. Volders with:
Paul G.A. Volders Netherlands
James Coromilas United States
Jianmin Liang China
Esther Pueyo Spain
Albert J. Getson United States
Vicente Iragui‐Madoz United States
U. Zwiener Germany
Bernd Walter Germany
Vincent Jacquemet Canada
Jochen Schaefer Germany
Chaoran Wu relative to Paul G.A. Volders Netherlands Paul G.A. Volders's profile →
Citations per field
00.5×2.9×
Paul G.A. Volders · 1×
Citations per year

Countries citing papers authored by Chaoran Wu

Since Specialization
Citations

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

Fields of papers citing papers by Chaoran Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200585
2 200772
3 200861
4 200841
5 201539
6 202039
7 200934
8 202433
9 202232
10 202331
11 201930
12 202422
13 202421
14 201919
15 20166
16 20165
17 20165
18 20154
19 20213
20 20203

About Chaoran Wu

Chaoran Wu is a scholar working on Surgery, Physiology, Molecular Biology, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 26 papers that have together received 591 indexed citations. Recurring topics across this work include Pain Mechanisms and Treatments (4 papers), Chaos control and synchronization (3 papers), Chaos-based Image/Signal Encryption (3 papers), Quantum chaos and dynamical systems (2 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers), Pediatric Pain Management Techniques (2 papers), Nerve injury and regeneration (2 papers) and Neural Networks Stability and Synchronization (2 papers). The work is most often cited by research in Physiology (218 citations), Sensory Systems (36 citations), Statistical and Nonlinear Physics (64 citations), Neurology (39 citations) and Cellular and Molecular Neuroscience (82 citations). Chaoran Wu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Timothy J. Brennan, Ratan K. Banik, Alberto Subieta, Leila M. Boustany, Liang Hong, Fei Yu, Li Song, Narender R. Gavva, Yunxia Zuo and Mark Erickson. Their work appears in journals such as Anesthesiology, Pain, Journal of Clinical Anesthesia, Nonlinear Dynamics and Journal of Cardiothoracic and Vascular Anesthesia.

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.

Explore authors with similar magnitude of impact