Xin-Rong Dai
- Mathematical Physics top 1%
- Mathematical Dynamics and Fractals 13
- Advanced Algebra and Geometry 2
- Applied Mathematics top 1%
- Mathematical Analysis and Transform Methods 15
-
- Image and Signal Denoising Methods 9
-
- Advanced Mathematical Modeling in Engineering 2
-
- Advanced Numerical Analysis Techniques 9
-
- Advanced machining processes and optimization 2
-
- Iterative Methods for Nonlinear Equations 2
- Journals
- Journal of Mathematical Analysis and Applications (2 papers)Advances in Mathematics (3 papers)Journal of Functional Analysis (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xin-Rong Dai
23 papers receiving 652 citations
Peers
Comparison fields: 5 of 34
- Mathematical Physics 569
- Applied Mathematics 595
- Computer Vision and Pattern Recognition 98
- Statistical and Nonlinear Physics 59
- Computational Theory and Mathematics 70
Countries citing papers authored by Xin-Rong Dai
This map shows the geographic impact of Xin-Rong Dai'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 Xin-Rong Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin-Rong Dai more than expected).
Fields of papers citing papers by Xin-Rong Dai
This network shows the impact of papers produced by Xin-Rong Dai. 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 Xin-Rong Dai. The network helps show where Xin-Rong Dai may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Xin-Rong Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 1 | |
| 4 | 2020 | 52 | |
| 5 | 2016 | 12 | |
| 6 | 2016 | 65 | |
| 7 | 2015 | 35 | |
| 8 | 2014 | 132 | |
| 9 | 2014 | 1 | |
| 10 | 2013 | 124 | |
| 11 | 2012 | 1 | |
| 12 | 2012 | 141 | |
| 13 | 2011 | 1 | |
| 14 | 2008 | 3 | |
| 15 | 2007 | 23 | |
| 16 | 2006 | 8 | |
| 17 | 2006 | 14 | |
| 18 | 2005 | 0 | |
| 19 | Compactly supported both "m" and "n" refinable distributions | 2000 | 2 |
| 20 | 1999 | 34 |
About Xin-Rong Dai
Xin-Rong Dai is a scholar working on Mathematical Physics, Applied Mathematics and Computer Vision and Pattern Recognition, having authored 26 papers that have together received 668 indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (15 papers), Mathematical Dynamics and Fractals (13 papers), Image and Signal Denoising Methods (9 papers), Advanced Numerical Analysis Techniques (9 papers), Advanced Algebra and Geometry (2 papers), Advanced machining processes and optimization (2 papers), Advanced Mathematical Modeling in Engineering (2 papers) and Iterative Methods for Nonlinear Equations (2 papers). The work is most often cited by research in Mathematical Physics (569 citations), Applied Mathematics (595 citations) and Computer Vision and Pattern Recognition (98 citations). Xin-Rong Dai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xing‐Gang He, Ka‐Sing Lau, Chun‐Kit Lai, Qiyu Sun, Yang Wang, De‐Jun Feng, Ning Bi, Ding-jiang Huang, Zeyin Zhang and Jun Luo. Their work appears in journals such as Journal of Mathematical Analysis and Applications, Advances in Mathematics and Journal of Functional Analysis.
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.