Institution
Xuzhou Institute of Technology
Education•Xuzhou, China•
About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.
Papers published on a yearly basis
Papers
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TL;DR: Some new sufficient conditions are obtained for the existence of at least single, twin, triple or arbitrary odd positive pseudo-symmetric solutions of p-Laplacian dynamic equations on time scales by using pseudo-Symmetric technique and fixed-point theorems in cone.
21 citations
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TL;DR: In this article, a new Lax pair is introduced for a perturbed Kaup-Newell equation and used to construct two series of conservation laws through a Riccati equation that a ratio of eigenfunctions satisfies.
Abstract: A new Lax pair is introduced for a perturbed Kaup–Newell equation and used to construct two series of conservation laws through a Riccati equation that a ratio of eigenfunctions satisfies. Both series of conservation laws are defined recursively, and the first two in each series are presented explicitly.
21 citations
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21 citations
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TL;DR: In this paper, a simple and cost-effective route was developed for the low temperature (700°C) synthesis of K 2 Ti 6 O 13 powder, using lowmelting-point KNO 3 and high-reactive-activity P25 TiO 2 nanocrystals as the reactants.
21 citations
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TL;DR: A new deep learning model based on an improved LeNet-5 model of convolutional neural network and does not require the extraction of the diseased tissue to reduce the recurrence of meningioma is presented.
Abstract: Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.
21 citations
Authors
Showing all 1711 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peng Wang | 108 | 1672 | 54529 |
Qiong Wu | 51 | 316 | 12933 |
Wenping Cao | 34 | 176 | 4093 |
Bin Hu | 30 | 213 | 3121 |
Syed Abdul Rehman Khan | 29 | 131 | 2733 |
Jingui Duan | 29 | 93 | 3807 |
Vivian C.H. Wu | 25 | 105 | 2566 |
Lei Chen | 16 | 99 | 1062 |
Chao Wang | 16 | 74 | 741 |
Wenbin Gong | 16 | 27 | 953 |
Jing Li | 16 | 40 | 1025 |
Chao Liu | 15 | 43 | 737 |
Qinglin Wang | 14 | 72 | 595 |
Yaocheng Zhang | 14 | 54 | 566 |
Chao Wang | 13 | 25 | 774 |