Wu Yan

602 total citations
44 papers, 467 citations indexed

About

Wu Yan is a scholar working on Mechanical Engineering, Biomedical Engineering and Polymers and Plastics. According to data from OpenAlex, Wu Yan has authored 44 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Mechanical Engineering, 12 papers in Biomedical Engineering and 11 papers in Polymers and Plastics. Recurrent topics in Wu Yan's work include Phase Equilibria and Thermodynamics (12 papers), Polymer Foaming and Composites (11 papers) and Injection Molding Process and Properties (10 papers). Wu Yan is often cited by papers focused on Phase Equilibria and Thermodynamics (12 papers), Polymer Foaming and Composites (11 papers) and Injection Molding Process and Properties (10 papers). Wu Yan collaborates with scholars based in China, Rwanda and United States. Wu Yan's co-authors include Mengshan Li, Xingyuan Huang, Bingxiang Liu, Hesheng Liu, Lixin Guan, Kai Zhang, Qiang Mao, Xin Wang, Lijiao Wang and Haiyan Shi and has published in prestigious journals such as Scientific Reports, Food Chemistry and RSC Advances.

In The Last Decade

Wu Yan

40 papers receiving 454 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wu Yan China	13	125	105	89	67	61	44	467
Mengshan Li China	14	151 1.2×	120 1.1×	104 1.2×	36 0.5×	65 1.1×	38	475
Jianjun Xu China	15	75 0.6×	92 0.9×	74 0.8×	131 2.0×	37 0.6×	50	550
Jinhua Wang China	11	70 0.6×	63 0.6×	100 1.1×	51 0.8×	51 0.8×	43	623
Ryan Gosselin Canada	17	141 1.1×	129 1.2×	151 1.7×	29 0.4×	17 0.3×	63	903
Jianfeng Guo China	13	135 1.1×	62 0.6×	60 0.7×	321 4.8×	34 0.6×	50	726
Yuhao Zhang China	15	127 1.0×	74 0.7×	44 0.5×	50 0.7×	235 3.9×	56	635
Taichi Nakamura Japan	15	97 0.8×	23 0.2×	75 0.8×	48 0.7×	63 1.0×	70	970
Weidong Tang China	14	130 1.0×	19 0.2×	188 2.1×	104 1.6×	60 1.0×	56	602
Zhiming Huang China	10	56 0.4×	70 0.7×	17 0.2×	52 0.8×	42 0.7×	35	377

Countries citing papers authored by Wu Yan

Since Specialization

Citations

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

Fields of papers citing papers by Wu Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wu Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Wu Yan. A scholar is included among the top collaborators of Wu Yan 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 Wu Yan. Wu Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Yan, Wu, Fujun Yu, Li Tan, et al.. (2025). A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction. BMC Biology. 23(1). 108–108. 1 indexed citations

Qi, Peng, et al.. (2024). Improving ecological protection Redline from the perspective of water quality protection: A case study in the Tianmu lake watershed, China. Ecological Indicators. 166. 112445–112445. 2 indexed citations

Wu, Wei, et al.. (2024). Multi-scale model integrated particle dynamics and evolutionary algorithm with the coarse-grained particle partitioning ratio strategy for calculating the dissolution behaviors of supercritical carbon dioxide in polymer fluids. Journal of Molecular Liquids. 396. 124018–124018. 1 indexed citations

Hu, Wenxing, et al.. (2024). A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding. iScience. 27(6). 110030–110030. 1 indexed citations

Gu, Xiaohong, Yunqing Xu, Yuquan Wu, et al.. (2024). Water Quality Analysis of a 300 Mvar Large-Scale Dual Internal Water Cooling Synchronous Condenser External Cooling System and Exploration of Optimal Water Treatment Agent Dosage at Different Temperatures. Water. 16(22). 3158–3158. 1 indexed citations

Yan, Wu, et al.. (2024). Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine. BMC Chemistry. 18(1). 24–24. 2 indexed citations

Hu, Wenxing, et al.. (2024). Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction. Food Chemistry. 468. 142464–142464. 4 indexed citations

Li, Mengshan, et al.. (2019). Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm. Scientific Reports. 9(1). 17261–17261. 4 indexed citations

Chen, Peng, et al.. (2019). Cognitive Impairments in First-Episode Drug-Naïve Versus Medicated Depressive Patients: RBANS in a Chinese Population. Psychiatric Quarterly. 90(3). 471–480. 15 indexed citations

10.

Li, Mengshan, et al.. (2018). Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods. Scientific Reports. 8(1). 3991–3991. 41 indexed citations

11.

Li, Mengshan, et al.. (2018). A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network. Applied Sciences. 8(7). 1121–1121. 14 indexed citations

12.

Mao, Qiang, et al.. (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health. 11(5). 707–712. 60 indexed citations

13.

Li, Mengshan, Liang Liu, Xingyuan Huang, et al.. (2017). Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory. RSC Advances. 7(78). 49817–49827. 12 indexed citations

14.

Guo, Lin, et al.. (2015). Fluidized Bed Agglomeration Diagnose Based on Wavelet Packet Entropy and Gaussian Test. 84. 54–57. 3 indexed citations

15.

Yan, Wu, et al.. (2014). Fusion of Visible and Infrared Images Based on Non-Sampling Contourlet and Wavelet Transform. Applied Mechanics and Materials. 599-601. 1523–1526. 1 indexed citations

16.

Yan, Wu, et al.. (2013). Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift. Applied Mechanics and Materials. 457-458. 1050–1053. 2 indexed citations

17.

Wang, Xin, et al.. (2012). Combination of Interacting Multiple Models with the Particle Filter for Three‐Dimensional Target Tracking in Underwater Wireless Sensor Networks. Mathematical Problems in Engineering. 2012(1). 38 indexed citations

18.

Ren, Honge, et al.. (2006). The quadratic B-spline curve fitting for the shape of log cross sections. Journal of Forestry Research. 17(2). 150–152. 5 indexed citations

19.

Yan, Wu, et al.. (1998). An initial study of the comprehensive benefits of seven tree species disposition models in shelterbelts in the Ningxia plains.. Beijing Linye Daxue xuebao. 20(4). 48–53. 3 indexed citations

20.

Wen, Yali, et al.. (1992). The Selection and Risk Level in Xinji City's Fruit Trees Planting Planning. Beijing Linye Daxue xuebao. 14(4). 90–92. 1 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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