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  1. Article ; Online: Effects of oxygen adsorption on the corrosion behavior of the Ti(0001) surface: a DFT investigation.

    Wang, Xiaoting / Xie, Dong / Liu, Huaiyuan / Li, Yantao / Jing, Fengjuan / Leng, Yongxiang

    Physical chemistry chemical physics : PCCP

    2024  Volume 26, Issue 9, Page(s) 7794–7807

    Abstract: The electrochemical corrosion of Ti surfaces is significantly affected by O adsorption, yet the underlying mechanisms remain unexplored. Herein, density functional theory calculations are employed to examine the adsorption energies, structural properties, ...

    Abstract The electrochemical corrosion of Ti surfaces is significantly affected by O adsorption, yet the underlying mechanisms remain unexplored. Herein, density functional theory calculations are employed to examine the adsorption energies, structural properties, electronic structures, and thermodynamic stability of atomic O on Ti(0001) surfaces during initial oxidation. Additionally, the impact of O adsorption on Ti dissolution is assessed by introducing a Ti vacancy on the Ti(0001) surface. The passivation of the Ti(0001) surface is predominantly ascribed to the robust adsorption of O atoms. The thermodynamic results reveal that bulk TiO
    Language English
    Publishing date 2024-02-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 1476244-4
    ISSN 1463-9084 ; 1463-9076
    ISSN (online) 1463-9084
    ISSN 1463-9076
    DOI 10.1039/d3cp05758e
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: WER-Net: A New Lightweight Wide-Spectrum Encoding and Reconstruction Neural Network Applied to Computational Spectrum.

    Ding, Xinran / Yang, Lin / Yi, Mingyang / Zhang, Zhiteng / Liu, Zhen / Liu, Huaiyuan

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 16

    Abstract: The computational spectrometer has significant potential for portable in situ applications. Encoding and reconstruction are the most critical technical procedures. In encoding, the random mass production and selection method lacks quantitative designs ... ...

    Abstract The computational spectrometer has significant potential for portable in situ applications. Encoding and reconstruction are the most critical technical procedures. In encoding, the random mass production and selection method lacks quantitative designs which leads to low encoding efficiency. In reconstruction, traditional spectrum reconstruction algorithms such as matching tracking and gradient descent demonstrate disadvantages like limited accuracy and efficiency. In this paper, we propose a new lightweight convolutional neural network called the wide-spectrum encoding and reconstruction neural network (WER-Net), which includes optical filters, quantitative spectral transmittance encoding, and fast spectral reconstruction of the encoded spectral information. The spectral transmittance curve obtained by WER-net can be fabricated through the inverse design network. The spectrometer developed based on WER-net experimentally demonstrates that it can achieve a 2-nm high resolution. In addition, the spectral transmittance encoding curve trained by WER-Net has also achieved good performance in other spectral reconstruction algorithms.
    MeSH term(s) Algorithms ; Neural Networks, Computer
    Language English
    Publishing date 2022-08-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22166089
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: TodyNet

    Liu, Huaiyuan / Liu, Xianzhang / Yang, Donghua / Liang, Zhiyu / Wang, Hongzhi / Cui, Yong / Gu, Jun

    Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

    2023  

    Abstract: Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different ... ...

    Abstract Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy. To address this problem, we propose a novel temporal dynamic graph neural network (TodyNet) that can extract hidden spatio-temporal dependencies without undefined graph structure. It enables information flow among isolated but implicit interdependent variables and captures the associations between different time slots by dynamic graph mechanism, which further improves the classification performance of the model. Meanwhile, the hierarchical representations of graphs cannot be learned due to the limitation of GNNs. Thus, we also design a temporal graph pooling layer to obtain a global graph-level representation for graph learning with learnable temporal parameters. The dynamic graph, graph information propagation, and temporal convolution are jointly learned in an end-to-end framework. The experiments on 26 UEA benchmark datasets illustrate that the proposed TodyNet outperforms existing deep learning-based methods in the MTSC tasks.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-04-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Enhancing voriconazole therapy in liver dysfunction: exploring administration schemes and predictive factors for trough concentration and efficacy.

    Zhao, Yichang / Liu, Huaiyuan / Xiao, Chenlin / Hou, Jingjing / Zhang, Bikui / Li, Jiakai / Zhang, Min / Jiang, Yongfang / Sandaradura, Indy / Ding, Xuansheng / Yan, Miao

    Frontiers in pharmacology

    2024  Volume 14, Page(s) 1323755

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2024-01-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587355-6
    ISSN 1663-9812
    ISSN 1663-9812
    DOI 10.3389/fphar.2023.1323755
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces.

    Zhao, Yi-Chang / Sun, Zhi-Hua / Xiao, Ming-Xuan / Li, Jia-Kai / Liu, Huai-Yuan / Cai, Hua-Lin / Cao, Wei / Feng, Yu / Zhang, Bi-Kui / Yan, Miao

    Environmental research

    2023  Volume 245, Page(s) 117995

    Abstract: Background: The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.: Methods: We collected data on QnR eco resistance rates and detection frequencies from ... ...

    Abstract Background: The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.
    Methods: We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.
    Result: Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences.
    Conclusion: The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.
    MeSH term(s) Escherichia coli ; Quinolones ; Escherichia coli Proteins ; China/epidemiology ; Drug Resistance, Bacterial ; Anti-Bacterial Agents/pharmacology
    Chemical Substances Quinolones ; Escherichia coli Proteins ; Anti-Bacterial Agents
    Language English
    Publishing date 2023-12-23
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 205699-9
    ISSN 1096-0953 ; 0013-9351
    ISSN (online) 1096-0953
    ISSN 0013-9351
    DOI 10.1016/j.envres.2023.117995
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Magnitude and Spatial Distribution Control of the Supercurrent in Bi

    Ying, Jianghua / He, Jiangbo / Yang, Guang / Liu, Mingli / Lyu, Zhaozheng / Zhang, Xiang / Liu, Huaiyuan / Zhao, Kui / Jiang, Ruiyang / Ji, Zhongqing / Fan, Jie / Yang, Changli / Jing, Xiunian / Liu, Guangtong / Cao, Xuewei / Wang, Xuefeng / Lu, Li / Qu, Fanming

    Nano letters

    2020  Volume 20, Issue 4, Page(s) 2569–2575

    Abstract: Many proposals for exploring topological quantum computation are based on superconducting quantum devices constructed on materials with strong spin-orbit coupling (SOC). For these devices, full control of both the magnitude and the spatial distribution ... ...

    Abstract Many proposals for exploring topological quantum computation are based on superconducting quantum devices constructed on materials with strong spin-orbit coupling (SOC). For these devices, full control of both the magnitude and the spatial distribution of the supercurrent is highly demanded, but has been elusive up to now. We constructed a proximity-type Josephson junction on nanoplates of Bi
    Language English
    Publishing date 2020-03-26
    Publishing country United States
    Document type Journal Article
    ISSN 1530-6992
    ISSN (online) 1530-6992
    DOI 10.1021/acs.nanolett.0c00025
    Database MEDical Literature Analysis and Retrieval System OnLINE

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