Jinlan Liu

965 total citations · 1 hit paper
87 papers, 659 citations indexed

About

Jinlan Liu is a scholar working on Artificial Intelligence, Computational Mechanics and Plant Science. According to data from OpenAlex, Jinlan Liu has authored 87 papers receiving a total of 659 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 10 papers in Computational Mechanics and 9 papers in Plant Science. Recurrent topics in Jinlan Liu's work include Stochastic Gradient Optimization Techniques (16 papers), Sparse and Compressive Sensing Techniques (9 papers) and Research in Cotton Cultivation (7 papers). Jinlan Liu is often cited by papers focused on Stochastic Gradient Optimization Techniques (16 papers), Sparse and Compressive Sensing Techniques (9 papers) and Research in Cotton Cultivation (7 papers). Jinlan Liu collaborates with scholars based in China, United Kingdom and United States. Jinlan Liu's co-authors include Yin Bai, Dongpo Xu, Sheng Lin, Jinshan Li, Gang Fu, Jiaxin Qi, Dawei Zhang, Ru‐Ping Liang, Jian‐Ding Qiu and Beiwei Zhu and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Analytical Chemistry.

In The Last Decade

Jinlan Liu

75 papers receiving 628 citations

Hit Papers

align trajectories

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.

Ocean wave-driven covalent organic framework/ZnO heterostructure composites for piezocatalytic uranium extraction from seawater

2025 34 citations Jiaxin Qi, Jingwen Gong et al. profile →

Peers

Jinlan Liu

MB

MC

IC

AI

PS

Xiaoye Li China

Yung‐Sheng Chang Taiwan

Yupei Liu China

José Antonio Álvarez Bermejo Spain

Qingpu Zhang China

Rick Mullin Canada

Yanli Zhang China

Chengzhi Zhang China

Joerg Leukel Germany

Xiaoye Li China

Jinlan Liu

61 ×0.5

MB

50 ×0.5

MC

14 ×0.1

IC

21 ×0.3

AI

30 ×0.5

PS

Citations per year, relative to Jinlan Liu Jinlan Liu (= 1×) peers Xiaoye Li

Countries citing papers authored by Jinlan Liu

Since Specialization

Citations

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

Fields of papers citing papers by Jinlan Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinlan Liu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Liu, Jinlan, et al.. (2025). Tea leaf disease recognition using attention convolutional neural network and handcrafted features. Crop Protection. 190. 107118–107118. 5 indexed citations

2.

Liang, Yuqing, et al.. (2025). A robust stochastic quasi-Newton algorithm for non-convex machine learning. Applied Intelligence. 55(7).

3.

Liu, Jinlan, et al.. (2025). SSR1M: A stochastic SR1 method with momentum acceleration for non-Convex optimization. Neural Networks. 195. 108283–108283.

4.

Jiang, Yiming, et al.. (2025). On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients. IEEE Transactions on Signal Processing. 73. 549–558. 1 indexed citations

5.

Liang, Yuqing, Yang Yang, Jinlan Liu, & Dongpo Xu. (2024). Shuffling-type gradient method with bandwidth-based step sizes for finite-sum optimization. Neural Networks. 179. 106514–106514.

6.

Hu, Yang, Guanbo Wang, Jinlan Liu, et al.. (2024). Functional polymers-coated Cu hollow microspheres for CO2 conversion in primary Zn–CO2 batteries. Chemical Engineering Journal. 493. 152856–152856. 1 indexed citations

7.

Cai, Yuan‐Jun, Qiu‐Xia Luo, Qiao‐Qiao Jiang, et al.. (2024). Hydrogen‐Bonded Cocrystals Encapsulating CsPbBr3 Perovskite Nanocrystals with Enhancement of Charge Transport for Photocatalytic Reduction of Uranium. Small. 20(25). e2310672–e2310672. 8 indexed citations

8.

Qi, Jiaxin, Jingwen Gong, Zhihai Peng, et al.. (2024). Defect Engineering Enhancing Piezoelectric Catalytic Activity of Covalent Organic Framework Matrix Composites for Uranium Removal. Small. 21(3). e2409682–e2409682. 4 indexed citations

9.

Jiang, Wenhan, Jinlan Liu, Naimin Zhang, & Dongpo Xu. (2024). DMAdam: Dual averaging enhanced adaptive gradient method for deep neural networks. Knowledge-Based Systems. 309. 112886–112886.

10.

Liu, Jinlan, Zhihai Peng, Jiaxin Qi, et al.. (2024). Redox-active sp-c connected metal covalent organic frameworks for selective detection and reductive separation of uranium. SHILAP Revista de lepidopterología. 3(4). 100124–100124. 4 indexed citations

11.

Lin, Sheng Hsien, et al.. (2024). Factors impacting college students’ collaborative learning intention: a social cognitive theory perspective. Interactive Learning Environments. 1–15. 1 indexed citations

12.

Liu, Jinlan, et al.. (2024). An Iterative Algorithm for Quaternion Eigenvalue Problems in Signal Processing. IEEE Signal Processing Letters. 31. 2505–2509. 2 indexed citations

13.

Liu, Jinlan, et al.. (2023). PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network. Environmental Science and Pollution Research. 30(30). 75104–75115. 11 indexed citations

14.

Liu, Jinlan, et al.. (2023). The relationship between stressors and Chinese employees' innovative work behavior: the role of task crafting and psychological detachment. European Journal of Innovation Management. 27(6). 1864–1884. 21 indexed citations

15.

Fu, Gang, Jinlan Liu, Jinshan Li, Beiwei Zhu, & Dawei Zhang. (2018). Systematic Screening of Optimal Signal Peptides for Secretory Production of Heterologous Proteins in Bacillus subtilis. Journal of Agricultural and Food Chemistry. 66(50). 13141–13151. 71 indexed citations

16.

Liu, Jinlan, et al.. (2010). Effects of live yeast cell derivative on superoxide dismutase activity in hemocytes and muscle of American white shrimp (Litopenaeus vannamei).. 62(2). 63–71. 1 indexed citations

17.

Liu, Jinlan. (2008). A Research on the Outputs Prediction of Medical Wastes. 1 indexed citations

18.

Wang, Zan, et al.. (2007). Probabilistic Latent Semantic Analyses (PLSA) in Bibliometric Analysis for Technology Forecasting. SHILAP Revista de lepidopterología. 3 indexed citations

19.

Liu, Jinlan. (2006). Empirical Study on Pareto/NBD Model and Its Applications. 1 indexed citations

20.

Zuo, Kaijing, Xianlong Zhang, Yichun Nie, Jinlan Liu, & Kexuan Tang. (2002). The relationship between insect resistance and agronomic traits in transgenic Bt cotton varieties. 32(4). 409–413. 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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