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您的检索式：作者名="Hongming Fei"
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| 1 | Ultra-wide tuning single channel filter based on one-dimensional photonic crystal with an air cavity显示文摘By inserting an air cavity into a one-dimensional photonic crystal of LiF/GaSb, a tunable filter covering the whole visible range is proposed. Following consideration of the dispersion of the materials, through modulating the thickness of the air cavity, we demonstrate that a single resonant peak can shift from 416.1 to 667.3 nm in the band gap at normal incidence by means of the transfer matrix method. The research also shows that the transmittance of the channel can be maximized when the number of periodic Li F/Ga Sb layers on one side of the air defect layer is equal to that of the other side. When adding a period to both sides respectively, the full width at half maximum of the defect mode is reduced by one order of magnitude. This structure will provide a promising approach to fabricate practical tunable filters in the visible region with ultra-wide tuning range. | Xiaodan Zhao Yibiao Yang Zhihui Chen Yuncai Wang Hongming Fei Xiao Deng | 2017 | Journal of Semiconductors2017，38，2： | 1 |
| 2 | Coupling climate change with hydrological dynamic in Qinling Mountains, China显示文摘 | Hongming He Quanfa Zhang Jie Zhou Jie Fei Xiuping Xie | 2009 | Climatic Change2009，，3： | 1 |
| 3 | Geometry Flow-Based Deep Riemannian Metric Learning显示文摘Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness. | Yangyang Li Chaoqun Fei Chuanqing Wang Hongming Shan Ruqian Lu | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，9： | 0 |
| 4 | Tumor-suppressive function and mechanism of HOXB13 in right-sided colon cancer显示文摘Right-sided colon cancer(RCC)and left-sided colon cancer(LCC)differ in their clinical and molecular features.An investigation of differentially expressed genes(DEGs)between RCC and LCC could contribute to targeted therapy for colon cancer,especially RCC,which has a poor prognosis.Here,we identified HOXB13,which was significantly less expressed in RCC than in LCC and associated with prognosis in RCC,by using 5 datasets from the Gene Expression Omnibus(GEO).Tissue sample analysis showed that HOXB13 was differentially expressed between normal and only RCC tumor tissues.HOXB13 inhibited colon cancer cell proliferation and induced apoptosis both in vitro and in vivo.Furthermore,we found that HOXB13 might be regulated by DNMT3B and suppress C-myc expression to exert antitumor effects viaβ-catenin/TCF4 signals in RCC.In conclusion,the current study is the first to demonstrate that HOXB13 has a tumor-suppressive effect in RCC.High expression levels of HOXB13 are associated with prolonged overall survival in patients with RCC.The DNMT3B-HOXB13-C-myc signaling axis might be a molecular target for the treatment of RCC. | Binbin Xie Bingjun Bai Yuzi Xu Yunlong Liu Yiming Lv Xing Gao Fei Wu Zhipeng Fang Ying Lou Hongming Pan Weidong Han | 2019 | Signal Transduction and Targeted Therapy2019，4，1： | 0 |
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