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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 & Catalysis. 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: Zhang et al. as mentioned in this paper examined the relationship between knowledge acquisition from social media, two forms of market orientation (proactive and reactive), social media strategic capability, and brand innovation strategy in the context of China's online technology industry.

173 citations

Journal ArticleDOI
Bing Zhao1, Peng Liu1, Hua Zhuang1, Zheng Jiao1, Tao Fang1, Weiwen Xu1, Bo Lu1, Yong Jiang1 
TL;DR: In this article, a leaf-like porous CuO-graphene nanostructure is synthesized by a hydrothermal method, and the as-prepared composite is characterized using XRD, Raman, SEM, TEM and nitrogen adsorption-desorption.
Abstract: In this paper, a leaf-like porous CuO–graphene nanostructure is synthesized by a hydrothermal method. The as-prepared composite is characterized using XRD, Raman, SEM, TEM and nitrogen adsorption–desorption. The growth mechanism is discussed by monitoring the early growth stages. It is shown that the CuO nanoleaves are formed through oriented attachment of tiny Cu(OH)2 nanowires. Electrochemical characterization demonstrates that the leaf-like CuO–graphene are capable of delivering specific capacitances of 331.9 and 305 F g−1 at current densities of 0.6 and 2 A g−1, respectively. A capacity retention of 95.1% can be maintained after 1000 continuous charge–discharge cycles, which may be attributed to the improvement of electrical contact by graphene and mechanical stability by the layer-by-layer structure. The method provides a facile and straightforward approach to synthesize CuO nanosheets on graphene and may be readily extended to the preparation of other classes of hybrids based on graphene sheets for technological applications.

173 citations

Journal ArticleDOI
TL;DR: It is determined that circ_104075 was highly expressed in HCC and its upstream and downstream regulatory mechanisms are elucidated and it has the potential to serve as a new diagnostic biomarker in H CC.
Abstract: Some types of circular RNA (circRNA) are aberrantly expressed in human diseases including hepatocellular carcinoma (HCC). However, its regulation mechanism and diagnostic roles are largely unknown. Here, we identified that circRNA_104075 (circ_104075) was highly expressed in HCC tissues, cell lines and serum. Mechanistically, HNF4a bound to the −1409 to −1401 region of the circ_104075 promoter to stimulate the expression of circ_104075. Moreover, circ_104075 acted as a ceRNA to upregulate YAP expression by absorbing miR-582-3p. Interestingly, an N6-methyladenosine (m6A) motif was identified in the 353–357 region of YAP 3′UTR, and this m6A modification was essential for the interaction between miR-582-3p and YAP 3′UTR. Further, the diagnostic performance of circ_104075 was evaluated. The area under the receiver operating characteristic (AUC-ROC) for circ_104075 was 0.973 with a sensitivity of 96.0% and a specificity of 98.3%. Collectively, we determined that circ_104075 was highly expressed in HCC and elucidated its upstream and downstream regulatory mechanisms. circ_104075 additionally has the potential to serve as a new diagnostic biomarker in HCC. Targeting circ_104075 may provide new strategies in HCC diagnosis and therapy.

173 citations

Journal ArticleDOI
TL;DR: In this paper, the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining was discussed. And the hybridization approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods.
Abstract: This paper discusses the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining. The hybridisation approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods. Based on an experimental data, the model was tested with satisfactory results. The developed methodology with the model is highly beneficial to manufacturing industries, such as aerospace, automobile and tool making industries.

173 citations

Journal ArticleDOI
L. Xia, W. H. Li, S. S. Fang, B. C. Wei1, Y. D. Dong 
TL;DR: In this paper, the glass formation mechanism for binary Ni-Nb alloys was studied from the thermodynamic point of view and a parameter γ* was proposed to approach the ability of glass formation against crystallization.
Abstract: We studied the glass forming ability of Ni–Nb binary alloys and found that some of the alloys can be prepared into bulk metallic glasses by a conventional Cu-mold casting. The best glass former within the compositional range studied is off-eutectic Ni62Nb38 alloy, which is markedly different from those predicted by the multicomponent and deep eutectic rules. The glass formation mechanism for binary Ni–Nb alloys was studied from the thermodynamic point of view and a parameter γ* was proposed to approach the ability of glass formation against crystallization.

173 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
2022742
20216,322
20205,569
20195,063
20184,235