Mo Yu

1.3k total citations · 1 hit paper
35 papers, 655 citations indexed

About

Mo Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Mo Yu has authored 35 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Mo Yu's work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Advanced Vision and Imaging (5 papers). Mo Yu is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Advanced Vision and Imaging (5 papers). Mo Yu collaborates with scholars based in China, United States and Indonesia. Mo Yu's co-authors include Biqing Huang, Xiu Li, Qianhui Wu, Wei Han, Shiyu Chang, Thomas S. Huang, Michael Witbrock, Ding Liu, Liang Li and Chao Xiao and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and IEEE Access.

In The Last Decade

Mo Yu

31 papers receiving 637 citations

Hit Papers

Remaining useful life estimation via transformer encoder ... 2021 2026 2022 2024 2021 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit
    2021 197 citations Mo Yu, Xiu Li et al. Journal of Intelligent Manufacturing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mo Yu China 10 229 167 160 115 65 35 655
Ting Rui China 16 376 1.6× 102 0.6× 84 0.5× 93 0.8× 71 1.1× 65 638
Aihua Li China 14 110 0.5× 172 1.0× 32 0.2× 68 0.6× 115 1.8× 47 486
Guangle Yao China 11 351 1.5× 35 0.2× 164 1.0× 68 0.6× 22 0.3× 30 591
Donghai Zhai China 11 190 0.8× 30 0.2× 87 0.5× 59 0.5× 90 1.4× 42 537
Ping Han China 11 339 1.5× 31 0.2× 98 0.6× 194 1.7× 25 0.4× 85 707
Bailin Li China 14 236 1.0× 26 0.2× 50 0.3× 122 1.1× 79 1.2× 53 556
Qingwei Gao China 15 248 1.1× 115 0.7× 190 1.2× 87 0.8× 74 1.1× 74 562
Zhiyong An China 12 151 0.7× 29 0.2× 71 0.4× 79 0.7× 39 0.6× 83 576

Countries citing papers authored by Mo Yu

Since Specialization
Citations

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

Fields of papers citing papers by Mo Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mo Yu

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

All Works

20 of 20 papers shown
1.
Wu, Hangbin, et al.. (2025). Semantic-Rearrangement-based Hierarchical Alignment for domain generalized segmentation. Neural Networks. 189. 107567–107567.
2.
Li, Jiahao, Zhen Zhou, Zhaofeng Zhang, et al.. (2025). Indigenous broilers in crossbreeding: impacts on meat quality and candidate gene screening. Poultry Science. 104(7). 105245–105245. 1 indexed citations
3.
Yu, Mo, Jiyue Wu, Wei Liu, et al.. (2025). Ultrafast piezocatalytic organic pollutant degradation enabled by dynamic spin state regulation of cobalt in nano-ferroelectrics. Advanced Powder Materials. 4(4). 100307–100307. 3 indexed citations
4.
Zhou, Zhen, Danfeng Cai, Zhaofeng Zhang, et al.. (2024). Metabolomic, lipidomic and transcriptomic reveal meat quality differences among hybrid, indigenous and commercial broiler. LWT. 209. 116765–116765. 3 indexed citations
5.
Yang, Xin, Bolin Cai, Zhaofeng Zhang, et al.. (2024). Exploring variances in meat quality between Qingyuan partridge chicken and Cobb broiler: Insights from combined multi-omics analysis. Poultry Science. 104(2). 104666–104666. 3 indexed citations
6.
Pang, Liang, Mo Yu, Fandong Meng, et al.. (2024). Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation. 133–145. 3 indexed citations
7.
Yu, Mo, et al.. (2024). DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling. Advanced Engineering Informatics. 62. 102823–102823. 3 indexed citations
8.
Li, Lijuan, Mo Yu, Xingbing Huang, et al.. (2024). Psilocybin for major depressive disorder: a systematic review of randomized controlled studies. Frontiers in Psychiatry. 15. 1416420–1416420. 3 indexed citations
9.
Yu, Mo, Matthew W. Hahn, & Megan L. Smith. (2024). Applications of machine learning in phylogenetics. Molecular Phylogenetics and Evolution. 196. 108066–108066. 14 indexed citations
10.
Han, Yang, et al.. (2024). Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification. Bioengineering. 11(5). 468–468. 7 indexed citations
11.
Yu, Mo, et al.. (2023). Sub-microscale 3D printing based on cold-constrained local electrodeposition. Virtual and Physical Prototyping. 19(1).
12.
Wang, Sijia, Mo Yu, & Lifu Huang. (2023). The Art of Prompting: Event Detection based on Type Specific Prompts. 1286–1299. 6 indexed citations
13.
14.
Wang, Zihao, et al.. (2023). XMR: an explainable multimodal neural network for drug response prediction. SHILAP Revista de lepidopterología. 3. 1164482–1164482. 8 indexed citations
15.
Yu, Mo, et al.. (2023). Explicit Planning Helps Language Models in Logical Reasoning. 11155–11173. 3 indexed citations
16.
Yu, Mo, et al.. (2022). MBTI Personality Prediction for Fictional Characters Using Movie Scripts. 6715–6724. 4 indexed citations
17.
Xu, Ying, Dakuo Wang, Mo Yu, et al.. (2022). Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 447–460. 27 indexed citations
18.
Yu, Mo, Liang Li, Biqing Huang, & Xiu Li. (2022). Few-shot RUL estimation based on model-agnostic meta-learning. Journal of Intelligent Manufacturing. 34(5). 2359–2372. 41 indexed citations
19.
Chang, Shiyu, Yang Zhang, Wei Han, et al.. (2017). Dilated Recurrent Neural Networks. Neural Information Processing Systems. 30. 77–87. 66 indexed citations
20.
Yu, Mo. (2001). Automatic Thresholding of Gray-Level Image Based on Two-Dimensional Correlation Criterion. Journal of Shanghai University (English Edition). 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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