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11
Fog Computing and the Internet of Things: A Review
Fog Computing and the Internet of Things: A Review
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Fog Computing and the Internet of Things: A Review

Atlam, Hany ; Walters, Robert ; Wills, Gary

Big data and cognitive computing, 2018-06, Vol.2 (2), p.10 [Periódico revisado por pares]

Basel: MDPI AG

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12
Engineering 2D Architectures toward High‐Performance Micro‐Supercapacitors
Engineering 2D Architectures toward High‐Performance Micro‐Supercapacitors
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Engineering 2D Architectures toward High‐Performance Micro‐Supercapacitors

Da, Yumin ; Liu, Jinxin ; Zhou, Lu ; Zhu, Xiaohui ; Chen, Xiaodong ; Fu, Lei

Advanced materials (Weinheim), 2019-01, Vol.31 (1), p.e1802793-n/a [Periódico revisado por pares]

Germany: Wiley Subscription Services, Inc

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13
Predicting disruptive instabilities in controlled fusion plasmas through deep learning
Predicting disruptive instabilities in controlled fusion plasmas through deep learning
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Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Kates-Harbeck, Julian ; Svyatkovskiy, Alexey ; Tang, William

Nature (London), 2019-04, Vol.568 (7753), p.526-531 [Periódico revisado por pares]

England: Nature Publishing Group

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14
The development of a competency framework for architectural engineering graduates: Perspectives by the construction industry in Indonesia
The development of a competency framework for architectural engineering graduates: Perspectives by the construction industry in Indonesia
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The development of a competency framework for architectural engineering graduates: Perspectives by the construction industry in Indonesia

Nurtanto, Muhammad ; Hidayat, Nur ; Fu'adi, Athok ; Daryono, Rihab Wit

Journal of technology and science education, 2024, Vol.14 (2), p.274-290 [Periódico revisado por pares]

OmniaScience

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15
Synergistic Engineering of Defects and Architecture in Binary Metal Chalcogenide toward Fast and Reliable Lithium–Sulfur Batteries
Synergistic Engineering of Defects and Architecture in Binary Metal Chalcogenide toward Fast and Reliable Lithium–Sulfur Batteries
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Synergistic Engineering of Defects and Architecture in Binary Metal Chalcogenide toward Fast and Reliable Lithium–Sulfur Batteries

Luo, Dan ; Li, Gaoran ; Deng, Ya‐Ping ; Zhang, Zhen ; Li, Jingde ; Liang, Ruilin ; Li, Matthew ; Jiang, Yi ; Zhang, Weiwei ; Liu, Yangshuai ; Lei, Wen ; Yu, Aiping ; Chen, Zhongwei

Advanced energy materials, 2019-05, Vol.9 (18), p.n/a [Periódico revisado por pares]

Weinheim: Wiley Subscription Services, Inc

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16
Implementing Smart Factory of Industrie 4.0: An Outlook
Implementing Smart Factory of Industrie 4.0: An Outlook
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Implementing Smart Factory of Industrie 4.0: An Outlook

Wang, Shiyong ; Wan, Jiafu ; Li, Di ; Zhang, Chunhua Qiu, Meikang

International journal of distributed sensor networks, 2016-01, Vol.2016 (1), p.3159805 [Periódico revisado por pares]

London, England: Hindawi Publishing Corporation

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17
Anode Interface Engineering and Architecture Design for High‐Performance Lithium–Sulfur Batteries
Anode Interface Engineering and Architecture Design for High‐Performance Lithium–Sulfur Batteries
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Anode Interface Engineering and Architecture Design for High‐Performance Lithium–Sulfur Batteries

Zhao, Yuanyuan ; Ye, Yusheng ; Wu, Feng ; Li, Yuejiao ; Li, Li ; Chen, Renjie

Advanced materials (Weinheim), 2019-03, Vol.31 (12), p.e1806532-n/a [Periódico revisado por pares]

Germany: Wiley Subscription Services, Inc

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18
Improving fatty acids production by engineering dynamic pathway regulation and metabolic control
Improving fatty acids production by engineering dynamic pathway regulation and metabolic control
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Improving fatty acids production by engineering dynamic pathway regulation and metabolic control

Xu, Peng ; Li, Lingyun ; Zhang, Fuming ; Stephanopoulos, Gregory ; Koffas, Mattheos

Proceedings of the National Academy of Sciences - PNAS, 2014-08, Vol.111 (31), p.11299-11304 [Periódico revisado por pares]

United States: National Academy of Sciences

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19
Remote Sensing Image Scene Classification Using CNN-CapsNet
Remote Sensing Image Scene Classification Using CNN-CapsNet
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Remote Sensing Image Scene Classification Using CNN-CapsNet

Zhang, Wei ; Tang, Ping ; Zhao, Lijun

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.494 [Periódico revisado por pares]

Basel: MDPI AG

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20
Large-scale transportation network congestion evolution prediction using deep learning theory
Large-scale transportation network congestion evolution prediction using deep learning theory
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Large-scale transportation network congestion evolution prediction using deep learning theory

Ma, Xiaolei ; Yu, Haiyang ; Wang, Yunpeng ; Wang, Yinhai Gomez-Gardenes, Jesus

PloS one, 2015-03, Vol.10 (3), p.e0119044-e0119044 [Periódico revisado por pares]

United States: Public Library of Science

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