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Wei Zheng
Researcher at Xiamen University
Publications - 123
Citations - 3320
Wei Zheng is an academic researcher from Xiamen University. The author has contributed to research in topics: Chemistry & Scheduling (computing). The author has an hindex of 28, co-authored 78 publications receiving 2756 citations. Previous affiliations of Wei Zheng include Ministry of Health (New South Wales) & Shandong University.
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Electrospinning of PLGA/gelatin randomly-oriented and aligned nanofibers as potential scaffold in tissue engineering
TL;DR: Cell culture results showed that the elongation of the osteoblast on the aligned nanofibrous scaffold was parallel to the fiber arrangement and the cell number was similar to that of randomly-oriented scaffold, indicating that the aligning scaffold provide a beneficial approach for the bone regeneration.
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Physical Properties of 5 Root Canal Sealers
Hui-min Zhou,Hui-min Zhou,Ya Shen,Wei Zheng,Li Li,Yufeng Zheng,Yufeng Zheng,Markus Haapasalo +7 more
TL;DR: The tested sealers were pseudoplastic according to their viscosities according to the ISO 6876/2001 recommendations as determined in this study.
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Preparation and characterization of electrospun PLGA/gelatin nanofibers as a potential drug delivery system
Z.X. Meng,Xiaoxue Xu,Wei Zheng,Hui-min Zhou,Lingyu Li,Yufeng Zheng,Yufeng Zheng,Xia Lou,Xia Lou +8 more
TL;DR: Drug (Fenbufen, FBF)-loaded poly(D,L-lactide-co-glycolide) (PLGA) and PLGA/gelatin nanofibrous scaffolds were fabricated via electrospinning technique and the release rate of FBF was found to be increased with the increment of gelatin content for all the composite samples.
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Fabrication and characterization of three-dimensional nanofiber membrance of PCL-MWCNTs by electrospinning
TL;DR: In this paper, the uniform and highly smooth nanofibers of poly(e-caprolactone) (PCL) composited with different multi-walled carbon nanotubes (MWCNTs) content were successfully prepared by electrospinning method without the occurrence of bead defects.
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A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
TL;DR: This paper presents a new fuzzy time series model combined with ant colony optimization (ACO) and auto-regression and shows that the proposed model outperforms other existing models.