Cui-Ping Cheng

13 papers receiving 335 citations

Peers

Cui-Ping Cheng
Comparison fields: 5 of 67
  • Modeling and Simulation 43
  • Endocrinology, Diabetes and Metabolism 139
  • Aquatic Science 60
  • Physiology 34
  • Numerical Analysis 31
Replace Mark Pernarowski with:
Mark Pernarowski United States
Romain Yvinec France
K. Sriram India
Hideki Murakawa Japan
Mackenzie Smith Canada
Fangfang Jiang China
Fusheng Tang United States
Kyle C. A. Wedgwood United Kingdom
Gabriel Stölting Germany
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Citations per field
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Citations per year

Countries citing papers authored by Cui-Ping Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Cui-Ping Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cui-Ping Cheng. 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 Cui-Ping Cheng. The network helps show where Cui-Ping Cheng may publish in the future.

Co-authors

The 16 scholars most cited alongside Cui-Ping Cheng, 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 Cui-Ping Cheng Line = papers co-authored together Cui-Ping Cheng links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 1995160
2
Positive solutions of fractional differential equations with derivative terms
201275
3 200437
4 199524
5 201017
6 200310
7 20085
8 20125
9 20175
10 20173
11 20132
12 20142
13 20161
14 20160

About Cui-Ping Cheng

Cui-Ping Cheng is a scholar working on Public Health, Environmental and Occupational Health, Numerical Analysis, Modeling and Simulation, Computer Networks and Communications and Mathematical Physics, having authored 14 papers that have together received 346 indexed citations. Recurring topics across this work include Mathematical and Theoretical Epidemiology and Ecology Models (8 papers), Differential Equations and Numerical Methods (4 papers), Nonlinear Dynamics and Pattern Formation (3 papers), Stochastic processes and statistical mechanics (3 papers), Mathematical Biology Tumor Growth (2 papers), Growth Hormone and Insulin-like Growth Factors (2 papers), Fractional Differential Equations Solutions (2 papers) and Nonlinear Differential Equations Analysis (2 papers). The work is most often cited by research in Modeling and Simulation (43 citations), Endocrinology, Diabetes and Metabolism (139 citations), Aquatic Science (60 citations), Physiology (34 citations) and Numerical Analysis (31 citations). Cui-Ping Cheng has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Michael J. Shamblott, David Bolt, You-Hui Su, Zhaosheng Feng, Wan‐Tong Li, Zhi‐Cheng Wang, Carolyn A. Bondy, Jianqing Zhou, Cynthia Shannon Weickert and Anne M. Smith. Their work appears in journals such as Nonlinear Analysis Real World Applications, International Journal of Bifurcation and Chaos, Applied Mathematics and Computation, Discrete and Continuous Dynamical Systems - B and Proceedings of the National Academy of Sciences.

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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