Qiang Yu

567 total citations
19 papers, 432 citations indexed

About

Qiang Yu is a scholar working on Modeling and Simulation, Numerical Analysis and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Qiang Yu has authored 19 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Modeling and Simulation, 7 papers in Numerical Analysis and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Qiang Yu's work include Fractional Differential Equations Solutions (15 papers), Advanced Neuroimaging Techniques and Applications (6 papers) and Differential Equations and Numerical Methods (5 papers). Qiang Yu is often cited by papers focused on Fractional Differential Equations Solutions (15 papers), Advanced Neuroimaging Techniques and Applications (6 papers) and Differential Equations and Numerical Methods (5 papers). Qiang Yu collaborates with scholars based in Australia, China and United Kingdom. Qiang Yu's co-authors include Ian Turner, Fawang Liu, Viktor Vegh, Tianzeng Li, Yuli Chen, Kevin Burrage, F. Liu, Qianqian Yang, Vo Anh and David C. Reutens and has published in prestigious journals such as PLoS ONE, NeuroImage and Magnetic Resonance in Medicine.

In The Last Decade

Qiang Yu

19 papers receiving 414 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qiang Yu Australia 12 340 208 76 73 61 19 432
M. Pilar Velasco Spain 12 304 0.9× 102 0.5× 41 0.5× 154 2.1× 68 1.1× 23 409
F. Liu Australia 6 353 1.0× 239 1.1× 94 1.2× 111 1.5× 56 0.9× 9 391
Samia Bushnaq Jordan 13 359 1.1× 141 0.7× 22 0.3× 131 1.8× 76 1.2× 33 490
Łukasz Płociniczak Poland 13 226 0.7× 158 0.8× 60 0.8× 162 2.2× 39 0.6× 44 422
Mostafa Bendahmane France 18 268 0.8× 136 0.7× 44 0.6× 360 4.9× 54 0.9× 65 902
Min Cai China 6 302 0.9× 148 0.7× 49 0.6× 95 1.3× 116 1.9× 22 433
A. K. Shukla India 10 364 1.1× 159 0.8× 108 1.4× 282 3.9× 62 1.0× 64 562
Neeraj Dhiman India 11 171 0.5× 143 0.7× 75 1.0× 54 0.7× 75 1.2× 50 334
Fuensanta Andreu-Vaillo Spain 3 145 0.4× 58 0.3× 81 1.1× 343 4.7× 14 0.2× 3 615
M. Hosseininia Iran 14 452 1.3× 223 1.1× 144 1.9× 72 1.0× 197 3.2× 32 497

Countries citing papers authored by Qiang Yu

Since Specialization
Citations

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

Fields of papers citing papers by Qiang Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qiang Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Qiang Yu. A scholar is included among the top collaborators of Qiang Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qiang Yu. Qiang Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Yu, Qiang, Ian Turner, Fawang Liu, & Viktor Vegh. (2022). The application of the distributed-order time fractional Bloch model to magnetic resonance imaging. Applied Mathematics and Computation. 427. 127188–127188. 11 indexed citations
2.
Yu, Qiang, Ian Turner, Fawang Liu, & Timothy J. Moroney. (2022). A study of distributed‐order time fractional diffusion models with continuous distribution weight functions. Numerical Methods for Partial Differential Equations. 39(1). 383–420. 7 indexed citations
3.
Chen, Yuli, Fawang Liu, Qiang Yu, & Tianzeng Li. (2021). Review of fractional epidemic models. Applied Mathematical Modelling. 97. 281–307. 85 indexed citations
4.
Yu, Qiang, David C. Reutens, & Viktor Vegh. (2018). Can anomalous diffusion models in magnetic resonance imaging be used to characterise white matter tissue microstructure?. NeuroImage. 175. 122–137. 15 indexed citations
5.
Yu, Qiang, et al.. (2018). Research on Application of SPOC Flipped Classroom in Computer Basic Courses. 7. 1–3. 2 indexed citations
6.
Liu, Fawang, et al.. (2017). Multi-term time-fractional Bloch equations and application in magnetic resonance imaging. Journal of Computational and Applied Mathematics. 319. 308–319. 47 indexed citations
7.
Liu, Fawang, et al.. (2017). Modelling anomalous diffusion using fractional Bloch–Torrey equations on approximate irregular domains. Computers & Mathematics with Applications. 75(1). 7–21. 18 indexed citations
8.
Yu, Qiang, David C. Reutens, Kieran O’Brien, & Viktor Vegh. (2016). Tissue microstructure features derived from anomalous diffusion measurements in magnetic resonance imaging. Human Brain Mapping. 38(2). 1068–1081. 21 indexed citations
9.
Liu, Fawang, et al.. (2016). Characterization of anomalous relaxation using the time-fractional Bloch equation and multiple echo T2*-weighted magnetic resonance imaging at 7 T. Magnetic Resonance in Medicine. 77(4). 1485–1494. 28 indexed citations
10.
Yu, Qiang, Viktor Vegh, Fawang Liu, & Ian Turner. (2015). A Variable Order Fractional Differential-Based Texture Enhancement Algorithm with Application in Medical Imaging. PLoS ONE. 10(7). e0132952–e0132952. 28 indexed citations
11.
Yu, Qiang, F. Liu, Ian Turner, & Kevin Burrage. (2013). Numerical simulation of the fractional Bloch equations. Journal of Computational and Applied Mathematics. 255. 635–651. 31 indexed citations
12.
Yu, Qiang, Fawang Liu, Ian Turner, Kevin Burrage, & Viktor Vegh. (2013). The use of a Riesz fractional differential-based approach for texture enhancement in image processing. ANZIAM Journal. 54. 590–590. 3 indexed citations
13.
Yu, Qiang, Fawang Liu, Ian Turner, & Kevin Burrage. (2013). Numerical investigation of three types of space and time fractional Bloch-Torrey equations in 2D. Open Physics. 11(6). 39 indexed citations
14.
Song, Jiacheng, Qiang Yu, F. Liu, & Ian Turner. (2013). A spatially second-order accurate implicit numerical method for the space and time fractional Bloch-Torrey equation. Numerical Algorithms. 66(4). 911–932. 33 indexed citations
15.
Yu, Qiang, F. Liu, Ian Turner, & Kevin Burrage. (2012). A computationally effective alternating direction method for the space and time fractional Bloch–Torrey equation in 3-D. Applied Mathematics and Computation. 219(8). 4082–4095. 4 indexed citations
16.
Yu, Qiang, F. Liu, Ian Turner, & Kevin Burrage. (2011). Analytical and Numerical Solutions of the Space and Time Fractional Bloch-Torrey Equation. 201–210. 1 indexed citations
17.
Yu, Qiang, et al.. (2011). The computational simulation of brain connectivity using diffusion tensor MRI. ANZIAM Journal. 51. 18–18. 1 indexed citations
18.
Yu, Qiang, Jiacheng Song, Fawang Liu, Vo Anh, & Ian Turner. (2009). An Approximate Solution for the Rayleigh-Stokes Problem for a Heated Generalized Second Grade Fluid with Fractional Derivative Model Using the Adomian Decomposition Method. Journal of Algorithms & Computational Technology. 3(4). 553–572. 6 indexed citations
19.
Yu, Qiang, et al.. (2007). Solving linear and non‐linear space–time fractional reaction–diffusion equations by the Adomian decomposition method. International Journal for Numerical Methods in Engineering. 74(1). 138–158. 52 indexed citations

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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