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  1. Article ; Online: Acid treatment for enhancing Hg

    Zhang, Yiwen / Wang, Hui / Yang, Kang / Zeng, Qingshan / Le, Lingyan / Ran, Hengyuan / Liu, Dong

    Environmental science and pollution research international

    2023  Volume 31, Issue 3, Page(s) 4897–4909

    Abstract: Adsorbents modified solely with chlorine have limited effectiveness in removing mercury at high temperatures. This study aims to investigate the influence of various acid ( ... ...

    Abstract Adsorbents modified solely with chlorine have limited effectiveness in removing mercury at high temperatures. This study aims to investigate the influence of various acid (HNO
    MeSH term(s) Chlorine/analysis ; Kinetics ; Hydrogen Peroxide ; Charcoal/chemistry ; Oxygen/analysis ; Mercury/analysis ; Adsorption ; Water Pollutants, Chemical/analysis
    Chemical Substances biochar ; Chlorine (4R7X1O2820) ; Hydrogen Peroxide (BBX060AN9V) ; Charcoal (16291-96-6) ; Oxygen (S88TT14065) ; Mercury (FXS1BY2PGL) ; Water Pollutants, Chemical
    Language English
    Publishing date 2023-12-18
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-023-31522-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 5915: Show issues

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  2. Article ; Online: Local Built-In Field at the Sub-nanometric Heterointerface Mediates Cascade Electrochemical Conversion of Lithium-sulfur Batteries.

    Ding, Chenfeng / Niu, Mang / Cassidy, Cathal / Kang, Hyung-Been / Ono, Luis K / Wang, Hengyuan / Tong, Guoqing / Zhang, Congyang / Liu, Yuan / Zhang, Jiahao / Mariotti, Silvia / Wu, Tianhao / Qi, Yabing

    Small (Weinheim an der Bergstrasse, Germany)

    2023  Volume 19, Issue 37, Page(s) e2301755

    Abstract: Heterogeneous catalytic mediators have been proposed to play a vital role in enhancing the multiorder reaction and nucleation kinetics in multielectron sulfur electrochemistry. However, the predictive design of heterogeneous catalysts is still ... ...

    Abstract Heterogeneous catalytic mediators have been proposed to play a vital role in enhancing the multiorder reaction and nucleation kinetics in multielectron sulfur electrochemistry. However, the predictive design of heterogeneous catalysts is still challenging, owing to the lack of in-depth understanding of interfacial electronic states and electron transfer on cascade reaction in Li-S batteries. Here, a heterogeneous catalytic mediator based on monodispersed titanium carbide sub-nanoclusters embedded in titanium dioxide nanobelts is reported. The tunable catalytic and anchoring effects of the resulting catalyst are achieved by the redistribution of localized electrons caused by the abundant built-in fields in heterointerfaces. Subsequently, the resulting sulfur cathodes deliver an areal capacity of 5.6 mAh cm
    Language English
    Publishing date 2023-05-05
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2168935-0
    ISSN 1613-6829 ; 1613-6810
    ISSN (online) 1613-6829
    ISSN 1613-6810
    DOI 10.1002/smll.202301755
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.

    Kang, Hengyuan / Xia, Liming / Yan, Fuhua / Wan, Zhibin / Shi, Feng / Yuan, Huan / Jiang, Huiting / Wu, Dijia / Sui, He / Zhang, Changqing / Shen, Dinggang

    IEEE transactions on medical imaging

    2020  Volume 39, Issue 8, Page(s) 2606–2614

    Abstract: Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and ... ...

    Abstract Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.
    MeSH term(s) Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Betacoronavirus ; COVID-19 ; Child ; Coronavirus Infections/diagnostic imaging ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnostic imaging ; Radiography, Thoracic ; SARS-CoV-2 ; Tomography, X-Ray Computed/methods ; Young Adult
    Keywords covid19
    Language English
    Publishing date 2020-05-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2020.2992546
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning

    Kang, Hengyuan / Xia, Liming / Yan, Fuhua / Wan, Zhibin / Shi, Feng / Yuan, Huan / Jiang, Huiting / Wu, Dijia / Sui, He / Zhang, Changqing / Shen, Dinggang

    IEEE Transactions on Medical Imaging

    2020  Volume 39, Issue 8, Page(s) 2606–2614

    Keywords Electrical and Electronic Engineering ; Radiological and Ultrasound Technology ; Software ; Computer Science Applications ; covid19
    Publisher Institute of Electrical and Electronics Engineers (IEEE)
    Publishing country us
    Document type Article ; Online
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/tmi.2020.2992546
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article: Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning

    Kang, Hengyuan / Xia, Liming / Yan, Fuhua / Wan, Zhibin / Shi, Feng / Yuan, Huan / Jiang, Huiting / Wu, Dijia / Sui, He / Zhang, Changqing / Shen, Dinggang

    IEEE Trans Med Imaging

    Abstract: Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and ... ...

    Abstract Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #216713
    Database COVID19

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  6. Book ; Online: Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning

    Kang, Hengyuan / Xia, Liming / Yan, Fuhua / Wan, Zhibin / Shi, Feng / Yuan, Huan / Jiang, Huiting / Wu, Dijia / Sui, He / Zhang, Changqing / Shen, Dinggang

    2020  

    Abstract: Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and ... ...

    Abstract Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; covid19
    Subject code 006
    Publishing date 2020-05-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  7. Book ; Online: AIM 2020 Challenge on Efficient Super-Resolution

    Zhang, Kai / Danelljan, Martin / Li, Yawei / Timofte, Radu / Liu, Jie / Tang, Jie / Wu, Gangshan / Zhu, Yu / He, Xiangyu / Xu, Wenjie / Li, Chenghua / Leng, Cong / Cheng, Jian / Wu, Guangyang / Wang, Wenyi / Liu, Xiaohong / Zhao, Hengyuan / Kong, Xiangtao / He, Jingwen /
    Qiao, Yu / Dong, Chao / Luo, Xiaotong / Chen, Liang / Zhang, Jiangtao / Suin, Maitreya / Purohit, Kuldeep / Rajagopalan, A. N. / Li, Xiaochuan / Lang, Zhiqiang / Nie, Jiangtao / Wei, Wei / Zhang, Lei / Muqeet, Abdul / Hwang, Jiwon / Yang, Subin / Kang, JungHeum / Bae, Sung-Ho / Kim, Yongwoo / Qu, Yanyun / Jeon, Geun-Woo / Choi, Jun-Ho / Kim, Jun-Hyuk / Lee, Jong-Seok / Marty, Steven / Marty, Eric / Xiong, Dongliang / Chen, Siang / Zha, Lin / Jiang, Jiande / Gao, Xinbo / Lu, Wen / Wang, Haicheng / Bhaskara, Vineeth / Levinshtein, Alex / Tsogkas, Stavros / Jepson, Allan / Kong, Xiangzhen / Zhao, Tongtong / Zhao, Shanshan / S, Hrishikesh P / Puthussery, Densen / C V, Jiji / Nan, Nan / Liu, Shuai / Cai, Jie / Meng, Zibo / Ding, Jiaming / Ho, Chiu Man / Wang, Xuehui / Yan, Qiong / Zhao, Yuzhi / Chen, Long / Sun, Long / Wang, Wenhao / Liu, Zhenbing / Lan, Rushi / Umer, Rao Muhammad / Micheloni, Christian

    Methods and Results

    2020  

    Abstract: This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples ... ...

    Abstract This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2020-09-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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