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Institution

Xuzhou Institute of Technology

EducationXuzhou, 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Peng Wang108167254529
Qiong Wu5131612933
Wenping Cao341764093
Bin Hu302133121
Syed Abdul Rehman Khan291312733
Jingui Duan29933807
Vivian C.H. Wu251052566
Lei Chen16991062
Chao Wang1674741
Wenbin Gong1627953
Jing Li16401025
Chao Liu1543737
Qinglin Wang1472595
Yaocheng Zhang1454566
Chao Wang1325774
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20237
202228
2021328
2020181
2019121
201873