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Institution

Shanghai University

EducationShanghai, Shanghai, China
About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.


Papers
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Journal ArticleDOI
TL;DR: The authors demonstrate a heterostructured NiFe LDH-NS@DG10 hybrid catalyst by coupling of exfoliated Ni-Fe layered double hydroxide (LDH) nanosheet (NS) and defective graphene (DG).
Abstract: Herein, the authors demonstrate a heterostructured NiFe LDH-NS@DG10 hybrid catalyst by coupling of exfoliated Ni-Fe layered double hydroxide (LDH) nanosheet (NS) and defective graphene (DG). The catalyst has exhibited extremely high electrocatalytic activity for oxygen evolution reaction (OER) in an alkaline solution with an overpotential of 0.21 V at a current density of 10 mA cm-2, which is comparable to the current record (≈0.20 V in Fe-Co-Ni metal-oxide-film system) and superior to all other non-noble metal catalysts. Also, it possesses outstanding kinetics (Tafel slope of 52 mV dec-1) for the reaction. Interestingly, the NiFe LDH-NS@DG10 hybrid has also exhibited the high hydrogen evolution reaction (HER) performance in an alkaline solution (with an overpotential of 115 mV by 2 mg cm-2 loading at a current density of 20 mA cm-2) in contrast to barely HER activity for NiFe LDH-NS itself. As a result, the bifunctional catalyst the authors developed can achieve a current density of 20 mA cm-2 by a voltage of only 1.5 V, which is also a record for the overall water splitting. Density functional theory calculation reveals that the synergetic effects of highly exposed 3d transition metal atoms and carbon defects are essential for the bifunctional activity for OER and HER

794 citations

Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper proposed a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation, which mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module.
Abstract: Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, etc. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations lead to the loss of some spatial information. In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module. We use pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution (DAC) block and residual multi-kernel pooling (RMP) block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation and retinal optical coherence tomography layer segmentation.

788 citations

Journal ArticleDOI
TL;DR: This work proposes a novel reversible data hiding scheme for encrypted image, where the additional data can be embedded into the image by modifying a small proportion of encrypted data.
Abstract: This work proposes a novel reversible data hiding scheme for encrypted image. After encrypting the entire data of an uncompressed image by a stream cipher, the additional data can be embedded into the image by modifying a small proportion of encrypted data. With an encrypted image containing additional data, one may firstly decrypt it using the encryption key, and the decrypted version is similar to the original image. According to the data-hiding key, with the aid of spatial correlation in natural image, the embedded data can be successfully extracted and the original image can be perfectly recovered.

768 citations

Journal ArticleDOI
TL;DR: The intravenous transplantation of MSCs was safe and effective for treatment in patients with COVID-19 pneumonia, especially for the patients in critically severe condition.
Abstract: A coronavirus (HCoV-19) has caused the novel coronavirus disease (COVID-19) outbreak in Wuhan, China. Preventing and reversing the cytokine storm may be the key to save the patients with severe COVID-19 pneumonia. Mesenchymal stem cells (MSCs) have been shown to possess a comprehensive powerful immunomodulatory function. This study aims to investigate whether MSC transplantation improves the outcome of 7 enrolled patients with COVID-19 pneumonia in Beijing YouAn Hospital, China, from Jan 23, 2020 to Feb 16, 2020. The clinical outcomes, as well as changes of inflammatory and immune function levels and adverse effects of 7 enrolled patients were assessed for 14 days after MSC injection. MSCs could cure or significantly improve the functional outcomes of seven patients without observed adverse effects. The pulmonary function and symptoms of these seven patients were significantly improved in 2 days after MSC transplantation. Among them, two common and one severe patient were recovered and discharged in 10 days after treatment. After treatment, the peripheral lymphocytes were increased, the C-reactive protein decreased, and the overactivated cytokine-secreting immune cells CXCR3+CD4+ T cells, CXCR3+CD8+ T cells, and CXCR3+ NK cells disappeared in 3-6 days. In addition, a group of CD14+CD11c+CD11bmid regulatory DC cell population dramatically increased. Meanwhile, the level of TNF-α was significantly decreased, while IL-10 increased in MSC treatment group compared to the placebo control group. Furthermore, the gene expression profile showed MSCs were ACE2- and TMPRSS2- which indicated MSCs are free from COVID-19 infection. Thus, the intravenous transplantation of MSCs was safe and effective for treatment in patients with COVID-19 pneumonia, especially for the patients in critically severe condition.

765 citations

Journal ArticleDOI
TL;DR: In this paper, the stability of an n-dimensional linear fractional differential equation with time delays was studied, where the delay matrix is defined in (R+n×n).
Abstract: In this paper, we study the stability of n-dimensional linear fractional differential equation with time delays, where the delay matrix is defined in (R+)n×n. By using the Laplace transform, we introduce a characteristic equation for the above system with multiple time delays. We discover that if all roots of the characteristic equation have negative parts, then the equilibrium of the above linear system with fractional order is Lyapunov globally asymptotical stable if the equilibrium exist that is almost the same as that of classical differential equations. As its an application, we apply our theorem to the delayed system in one spatial dimension studied by Chen and Moore [Nonlinear Dynamics29, 2002, 191] and determine the asymptotically stable region of the system. We also deal with synchronization between the coupled Duffing oscillators with time delays by the linear feedback control method and the aid of our theorem, where the domain of the control-synchronization parameters is determined.

748 citations


Authors

Showing all 59993 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Yang Yang1712644153049
Yang Liu1292506122380
Zhen Li127171271351
Xin Wang121150364930
Jian Liu117209073156
Xin Li114277871389
Wei Zhang112118993641
Jianjun Liu112104071032
Liquan Chen11168944229
Jin-Quan Yu11143843324
Jonathan L. Sessler11199748758
Peng Wang108167254529
Qian Wang108214865557
Wei Zhang104291164923
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023182
2022741
20216,318
20205,569
20195,063
20184,235