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Your search keyword '"Liang, Zhiyu"' showing total 11 results
Start Over Author "Liang, Zhiyu" Publisher elsevier b.v.
11 results on '"Liang, Zhiyu"'

5. Association of Cerebral Hemodynamics and Cognitive Function in Adult Patients with Moyamoya Disease: A Three-Dimensional Pseudo-Continuous Arterial Spin Labeling Study.

Author

Zha, Xiangyue, Liang, Zhiyu, Zheng, Liyun, and Xu, Yikai

Subjects
*MOYAMOYA disease, *SPIN labels, *COGNITIVE ability, *CEREBRAL infarction, *HEMODYNAMICS, *ANTERIOR cerebral artery
Abstract

Cognitive dysfunction is a serious complication of moyamoya disease (MMD) in adults, and reduced cerebral blood flow (CBF) might be the potential cause. We aimed to explore the correlation between cerebral hemodynamics and cognitive function in adults with MMD by using three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL). A total of 24 MMD patients with a history of cerebral infarction, 25 asymptomatic MMD patients, and 25 healthy controls were prospectively enrolled in this study. All participants were performed 3D-pCASL, and cognitive function was evaluated with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment Scale (MoCA), and the Trail Making Test Part A (TMTA). The correlation between cerebral hemodynamics and cognitive function was explored in the region of interest-based analysis. Compared with healthy controls, both CBF and cognition decreased in adult MMD. In the infarction group, the MMSE and MoCA scores correlated with CBF of the right anterior cerebral artery (P = 0.037 and 0.010, respectively) and the left middle cerebral artery (MCA) cortical territories (P = 0.002 and 0.001, respectively), and the TMTA time-consuming has a negative correlation with CBF of the right and left MCA cortical territories (P = 0.044 and 0.010, respectively); in the asymptomatic group, the MMSE and MoCA scores correlated with CBF of the left MCA cortical territory (P = 0.032 and 0.029, respectively). The 3D-pCASL can find the hypoperfusion area of CBF in adults with MMD, and hypoperfusion in specific brain regions may cause cognitive dysfunction even in asymptomatic patients. [ABSTRACT FROM AUTHOR]

Published
2023
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6. Heavy metals leaching in bricks made from lead and zinc mine tailings with varied chemical components

Author

Li, Chong, Wen, Qingjuan, Hong, Mingzhu, Liang, Zhiyu, Zhuang, Zanyong, and Yua, Yan

Subjects
Heavy metals -- Research -- Composition, Leaching -- Research, Zinc mining -- Research, Business, Construction and materials industries
Abstract

GRAPHICAL ABSTRACT ABSTRACT The stabilization/solidification approach has received considerable attentions for the disposal of hazardous metal-bearing mine tailings. A poor understanding of the immobilization mechanism of heavy metals in final [...]

Published
2017
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9. Efficient class-specific shapelets learning for interpretable time series classification.

Author

Liang, Zhiyu and Wang, Hongzhi

Subjects
*TIME series analysis, *MAGNITUDE (Mathematics), *CLASSIFICATION
Abstract

In the last decade, time series classification approaches based on time-independent shapelets, have received considerable attention due to their high prediction accuracy and intuitive interpretability. However, most existing shapelets discovery approaches find shapelets by evaluating the discriminatory power of all subsequences of the series, which is computationally expensive even with certain speed-up techniques. Even though some shapelet learning approaches learn the near-to-optimal shapelets from the training series rather than searching from numerous segments, they still have significant drawbacks in their performance regarding the accuracy, efficiency, and interpretability due to the numerous class-shared shapelets with fixed lengths. Thus, we propose a new shapelet learning approach that can learn as few as possible class-specific and variable-length shapelets. Extensive experiments demonstrate that our proposed method is competitive about classification accuracy over 18 baselines on 25 datasets, outperforms 2 orders of magnitude about efficiency, and is more interpretable than existing classifiers. [ABSTRACT FROM AUTHOR]

Published
2021
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10. Highly stable lead-free Cs2AgBiI6-GO composite photocatalysts for efficient organic pollutant degradation.

Author

Huang, Qiang, Liu, Jiaxin, Qi, Fei, Pu, Yayun, Zhang, Nan, Yang, Jiayu, Liang, Zhiyu, and Tian, Changqing

Subjects
PHOTOCATALYSTS, POLLUTANTS, PRECIPITATION (Chemistry), ELECTRON-hole recombination, LEAD halides, PEROVSKITE, METAL halides
Abstract

The use of halide perovskites as photocatalysts has attracted more and more attention. However, the instability and toxicity of the lead halide perovskites have hindered their practical large-scale application. Herein, lead-free Cs 2 AgBiI 6 double perovskite was synthesized by a simple antisolvent recrystallization approach for photocatalysis. Meanwhile, in order to solve the limited photocatalytic performance problem of the pure metal halide perovskites, for the first time, we designed and prepared highly stable Cs 2 AgBiI 6 -Graphene oxide (GO) composite photocatalyst. The photodegradation rate of Sudan Red III by Cs 2 AgBiI 6 -GO was found to be as high as 85% after 100 min of light irradiation, which was twice that of the pure Cs 2 AgBiI 6 and also more efficient than recently reported photocatalysts under comparable conditions. This work demonstrated that the GO with unique physicochemical properties could prevent the photocatalyst agglomeration and provide charge carrier transport channel to inhibit electron-hole recombination to improve the overall photocatalytic performance. • For the first time, the Cs 2 AgBiI 6 -GO composites were synthesized and used for the photodegradation of organic pollutant. • The Cs 2 AgBiI 6 -GO could degrade 85% of Sudan Red Ⅲ in 100 min, which was twice as high as that of pure Cs 2 AgBiI 6. • The lead-free environmentally friendly Cs 2 AgBiI 6 -GO exhibited high stability during the recycling photodegradation test. • This work provides deep insight into the rational design of photocatalysts with efficient surface/interface. [ABSTRACT FROM AUTHOR]

Published
2023
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11. TodyNet: Temporal dynamic graph neural network for multivariate time series classification.

Author

Liu, Huaiyuan, Yang, Donghua, Liu, Xianzhang, Chen, Xinglei, Liang, Zhiyu, Wang, Hongzhi, Cui, Yong, and Gu, Jun

Subjects
*GRAPH neural networks, *TIME series analysis, *DEEP learning, *REPRESENTATIONS of graphs, *DATA mining
Abstract

Multivariate time series classification (MTSC) is a crucial data mining task that can be effectively tackled using prevalent deep learning technology. However, current methods often overlook hidden dependencies across dimensions and struggle to capture dynamic time series features adequately, leading to poor accuracy. To tackle this challenge, we propose TodyNet, a novel dynamic temporal graph neural network. TodyNet extracts latent spatio-temporal dependencies without predefined graph structures, facilitating information flow among isolated but implicitly interdependent variables. It captures associations between time slots through dynamic graphs, boosting classification performance. Furthermore, to address the limitation that the hierarchical representations of graphs are challenging to learn with graph neural networks (GNNs), we introduce a temporal graph pooling layer for obtaining a global graph-level representation. The dynamic graph, graph information propagation, and temporal convolution are jointly learned within an end-to-end framework. Experimental results on 26 UEA benchmark datasets indicate that compared to current state-of-the-art deep learning methods in the MTSC task, the proposed Todynet achieved a performance improvement of 5.5%. [ABSTRACT FROM AUTHOR]

Published
2024
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