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您的检索式：作者名="Longbin SHEN"
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| 1 | DTBVis:An interactive visual comparison system for digital twin brain and human brain显示文摘The digital twin brain(DTB)computing model from brain-inspired computing research is an emerging artificial intelligence technique,which is realized by a computational modeling approach of hardware and software.It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain.Given that the task of the DTB is to simulate the functions of the human brain,comparing the similarities and differences between the two is crucial.However,the visualization study of the DTB is still under-researched.Moreover,the complexity of the datasets(multilevel spatiotemporal granularity and different types of comparison tasks)presents new challenges to the analysis and exploration of visualization.Therefore,in this study,we proposed DTBVis,a visual analytics system that supports comparison tasks for the DTB.DTBVis supports iterative explorations from different levels and at different granularities.Combined with automatic similarity recommendation,and high-dimensional exploration,DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain,thus helping them adjust their model and enhance its functionality.The highest level of DTBVis shows an overview of the datasets from the brain,which is used for comparison and exploration of the function and structure of the DTB and the human brain.The medium level is used for the comparison and exploration of a designated brain region.The low level can analyze a designated brain voxel.We worked closely with experts of brain science and held regular seminars with them.Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations. | Yuxiao Li Xinhong Li Siqi Shen Longbin Zeng Richen Liu Qibao Zheng Jianfeng Feng | 2023 | Visual Informatics2023，7，2： | 1 |
| 2 | Genetic Diversity and Correlation Analysis of Main Botanical Traits of Chili Pepper Genetic Resources显示文摘A total of 398 chili pepper germplasms were used as test materials and genetic diversity and correlation analysis were performed on 17 botanical traits.The results of diversity analysis showed that the diversity indexes of the 17 botanical traits ranged from 0. 15 to 5. 97 with the average value of 4. 12. The data distributions of 11 quantitative traits were more dispersed than qualitative traits. The average value of coefficient of variation was 36. 90% and the variation ranges were one to six times larger than the average value. The results of correlation analysis showed that plant height had significantly positive correlations with plant breadth,leaf length,leaf width,petiole length,the first flower node and carpopodium length. The first flower node was significantly negatively correlated with fruit length,fruit width,flesh thickness and weight per fruit and significantly positively correlated with plant height and plant breadth. The flesh thickness was significantly positively correlated with leaf length,leaf width,petiole length,fruit length,fruit width and carpopodium length. The weight per fruit was significantly positively correlated with leaf length,leaf width,petiole length,fruit length,fruit width,carpopodium length and flesh thickness. Materials with low first flower node,moderate plant height and width and large fruit should be selected for the breeding of early-maturing and high-yield chili pepper varieties. The genetic distances between chili pepper traits were calculated based on the genotypic values of the 11 quantitative traits. The genetic distances between different traits ranged from 14. 26 to 32. 99.The 11 quantitative traits were divided into seven groups when the rescaled distance was ten,which further clarified the relationships between different traits. The research results laid a solid foundation for the new variety breeding of chili pepper. | Ziji LIU Longbin SHEN Yan YANG Zhenmu CAO | 2015 | Agricultural Biotechnology2015，4，4： | 0 |
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