Ming Ju

857 citations
56 papers · 531 · h-index 12

Impact in

Papers in

Ming Ju

47 papers receiving 520 citations

Peers

Ming Ju
Comparison fields: 5 of 94
  • Media Technology 56
  • Computational Mechanics 127
  • Plant Science 194
  • Numerical Analysis 24
  • Animal Science and Zoology 45
Replace Shuli Mei with:
Shuli Mei China
Cheng Li-zhi China
Henrique Mohallem Paiva Brazil
Chong–Jun Li China
K. S. P. Amaratunga United States
Qiong Huang China
Kewei Zhang United Kingdom
Michael McLaughlin United States
Rafael Gadea Gironés Spain
Yuan Su United States
Ming Ju relative to Shuli Mei China Shuli Mei's profile →
Citations per field
00.5×10.5×
Shuli Mei · 1×
Citations per year

Countries citing papers authored by Ming Ju

Since Specialization
Citations

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

Fields of papers citing papers by Ming Ju

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ming Ju, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ming Ju Line = papers co-authored together Ming Ju links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201592
2 202177
3 201748
4 202332
5 201831
6 201925
7 202118
8 202018
9 202116
10 201315
11 202214
12 202213
13 202210
14 200910
15 20219
16
Optimization of EMS mutagenesis condition and screening of mutants in sesame.
20178
17 20248
18 20148
19 20197
20 20247

About Ming Ju

Ming Ju is a scholar working on Plant Science, Molecular Biology, Statistics, Probability and Uncertainty, Computational Mechanics and Statistical and Nonlinear Physics, having authored 56 papers that have together received 531 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (11 papers), Sesame and Sesamin Research (11 papers), Plant Genetic and Mutation Studies (7 papers), Protein Hydrolysis and Bioactive Peptides (5 papers), Advanced Mathematical Modeling in Engineering (5 papers), Advanced Numerical Methods in Computational Mathematics (5 papers), Meat and Animal Product Quality (4 papers) and Model Reduction and Neural Networks (4 papers). The work is most often cited by research in Media Technology (56 citations), Computational Mechanics (127 citations), Plant Science (194 citations), Numerical Analysis (24 citations) and Animal Science and Zoology (45 citations). Ming Ju has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiaoming He, Jian Li, Yanping Lin, Hongmei Miao, Jiantao Qu, Yinghui Duan, Guanyi Li, Wei Huang, Qiqiang Chen and Chun Li. Their work appears in journals such as LWT, Food Bioscience, Journal of Scientific Computing, SIAM Journal on Scientific Computing and International Journal for Uncertainty Quantification.

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.

Explore authors with similar magnitude of impact