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Your search keyword '"Xia, Bin"' showing total 247 results
Start Over Author "Xia, Bin" Publisher elsevier b.v.
247 results on '"Xia, Bin"'

46. Effect of MoO3 addition on fly ash based porous and high-strength mullite ceramics: In situ whisker growth and self-enhancement mechanism.

Author

Huo, Xiangtao, Xia, Bin, Hu, Tianzhuo, Zhang, Mei, and Guo, Min

Subjects
*FLY ash, *CRYSTAL whiskers, *MULLITE, *CERAMICS, *ALUMINUM oxide
Abstract

In this paper, using only high alumina fly ash as raw material and MoO 3 as additive agent, the high-strength porous mullite ceramics were achieved by in situ mullite whisker growth via a calcination method. The influence of MoO 3 addition on pore structure and mechanical properties of as-prepared mullite ceramics was investigated. The mechanism of MoO 3 promoting the formation of mullite whiskers and improving the strength of matrix was elucidated. The porous mullite ceramic reached its best performance with a high porosity of 49.4% and an improved compressive strength of 13.03 MPa with 10 wt% MoO 3 addition calcinated at 1300 °C. Specifically, MoO 3 facilitated the mullite whisks growth mainly by reducing melting point of amorphous SiO 2 /quartz and changing crystal structure of Al 2 O 3. The larger aspect ratio of mullite grain at 1300 °C was easier to form an interlocking structure, which enhanced the strength of mullite ceramic while maintaining high porosity. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Novel Radiomics-Clinical Model for the Noninvasive Prediction of New Fractures After Vertebral Augmentation.

Author

Liu, Jin, Tang, Jing, Xia, Bin, Gu, Zuchao, Yin, Hongkun, Zhang, Huiling, Yang, Haosen, and Song, Bin

Abstract

To investigate the noninvasive prediction model for new fractures after percutaneous vertebral augmentation (PVA) based on radiomics signature and clinical parameters. Data from patients who were diagnosed with osteoporotic vertebral compression fracture (OVCF) and treated with PVA in our hospital between May 2014 and April 2019 were retrospectively analyzed. Radiomics features were extracted from T1-weighted magnetic resonance imaging (MRI) of the T11-L5 segments taken before PVA. Different radiomics models was developed by using linear discriminant analysis (LDA), multilayer perceptron (MLP), and stochastic gradient descent (SGD) classifiers. A nomogram was constructed by integrating clinical parameters and Radscore that calculated by the best radiomics model. The model performance was quantified in terms of discrimination, calibration and clinical usefulness. Four clinical parameters and 16 selected radiomics features were used for model development. The clinical model showed poor discrimination capability with area under the curves (AUCs) yielding of 0.522 in the training dataset and 0.517 in the validation dataset. The LDA, MLP and SGD classifier-based radiomics model had achieved AUCs of 0.793, 0.810, and 0.797 in the training dataset, and 0.719, 0.704, and 0.725 in the validation dataset, respectively. The nomogram showed the best performance with AUCs achieving 0.810 and 0.754 in the training and validation datasets, respectively. The decision curve analysis demonstrated the net benefit of the nomogram was higher than that of other models. Our findings indicate that combining clinical features with radiomics features from pre-augmentation T1-weighted MRI can be used to develop a nomogram that can predict new fractures in patients after PVA. [ABSTRACT FROM AUTHOR]

Published
2023
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