Artificial intelligence: A powerful paradigm for scientific research
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yongjun Xu [verfasserIn] Xin Liu [verfasserIn] Xin Cao [verfasserIn] Changping Huang [verfasserIn] Enke Liu [verfasserIn] Sen Qian [verfasserIn] Xingchen Liu [verfasserIn] Yanjun Wu [verfasserIn] Fengliang Dong [verfasserIn] Cheng-Wei Qiu [verfasserIn] Junjun Qiu [verfasserIn] Keqin Hua [verfasserIn] Wentao Su [verfasserIn] Jian Wu [verfasserIn] Huiyu Xu [verfasserIn] Yong Han [verfasserIn] Chenguang Fu [verfasserIn] Zhigang Yin [verfasserIn] Miao Liu [verfasserIn] Ronald Roepman [verfasserIn] Sabine Dietmann [verfasserIn] Marko Virta [verfasserIn] Fredrick Kengara [verfasserIn] Ze Zhang [verfasserIn] Lifu Zhang [verfasserIn] Taolan Zhao [verfasserIn] Ji Dai [verfasserIn] Jialiang Yang [verfasserIn] Liang Lan [verfasserIn] Ming Luo [verfasserIn] Zhaofeng Liu [verfasserIn] Tao An [verfasserIn] Bin Zhang [verfasserIn] Xiao He [verfasserIn] Shan Cong [verfasserIn] Xiaohong Liu [verfasserIn] Wei Zhang [verfasserIn] James P. Lewis [verfasserIn] James M. Tiedje [verfasserIn] Qi Wang [verfasserIn] Zhulin An [verfasserIn] Fei Wang [verfasserIn] Libo Zhang [verfasserIn] Tao Huang [verfasserIn] Chuan Lu [verfasserIn] Zhipeng Cai [verfasserIn] Fang Wang [verfasserIn] Jiabao Zhang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: The Innovation - Elsevier, 2020, 2(2021), 4, Seite 100179- |
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| Übergeordnetes Werk: |
volume:2 ; year:2021 ; number:4 ; pages:100179- |
| Links: |
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| DOI / URN: |
10.1016/j.xinn.2021.100179 |
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| Katalog-ID: |
DOAJ075379503 |
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| 245 | 1 | 0 | |a Artificial intelligence: A powerful paradigm for scientific research |
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| 520 | |a Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. | ||
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| 650 | 4 | |a deep learning | |
| 650 | 4 | |a information science | |
| 650 | 4 | |a mathematics | |
| 650 | 4 | |a medical science | |
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| 700 | 0 | |a Xin Cao |e verfasserin |4 aut | |
| 700 | 0 | |a Changping Huang |e verfasserin |4 aut | |
| 700 | 0 | |a Enke Liu |e verfasserin |4 aut | |
| 700 | 0 | |a Sen Qian |e verfasserin |4 aut | |
| 700 | 0 | |a Xingchen Liu |e verfasserin |4 aut | |
| 700 | 0 | |a Yanjun Wu |e verfasserin |4 aut | |
| 700 | 0 | |a Fengliang Dong |e verfasserin |4 aut | |
| 700 | 0 | |a Cheng-Wei Qiu |e verfasserin |4 aut | |
| 700 | 0 | |a Junjun Qiu |e verfasserin |4 aut | |
| 700 | 0 | |a Keqin Hua |e verfasserin |4 aut | |
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| 700 | 0 | |a Yong Han |e verfasserin |4 aut | |
| 700 | 0 | |a Chenguang Fu |e verfasserin |4 aut | |
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| 700 | 0 | |a Miao Liu |e verfasserin |4 aut | |
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| 700 | 0 | |a Sabine Dietmann |e verfasserin |4 aut | |
| 700 | 0 | |a Marko Virta |e verfasserin |4 aut | |
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| 700 | 0 | |a Ze Zhang |e verfasserin |4 aut | |
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| 700 | 0 | |a Liang Lan |e verfasserin |4 aut | |
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| 700 | 0 | |a Bin Zhang |e verfasserin |4 aut | |
| 700 | 0 | |a Xiao He |e verfasserin |4 aut | |
| 700 | 0 | |a Shan Cong |e verfasserin |4 aut | |
| 700 | 0 | |a Xiaohong Liu |e verfasserin |4 aut | |
| 700 | 0 | |a Wei Zhang |e verfasserin |4 aut | |
| 700 | 0 | |a James P. Lewis |e verfasserin |4 aut | |
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| 700 | 0 | |a Chuan Lu |e verfasserin |4 aut | |
| 700 | 0 | |a Zhipeng Cai |e verfasserin |4 aut | |
| 700 | 0 | |a Fang Wang |e verfasserin |4 aut | |
| 700 | 0 | |a Jiabao Zhang |e verfasserin |4 aut | |
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10.1016/j.xinn.2021.100179 doi (DE-627)DOAJ075379503 (DE-599)DOAJf012a0d028854bffb505c4676fd1987e DE-627 ger DE-627 rakwb eng Q1-390 Yongjun Xu verfasserin aut Artificial intelligence: A powerful paradigm for scientific research 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Xin Liu verfasserin aut Xin Cao verfasserin aut Changping Huang verfasserin aut Enke Liu verfasserin aut Sen Qian verfasserin aut Xingchen Liu verfasserin aut Yanjun Wu verfasserin aut Fengliang Dong verfasserin aut Cheng-Wei Qiu verfasserin aut Junjun Qiu verfasserin aut Keqin Hua verfasserin aut Wentao Su verfasserin aut Jian Wu verfasserin aut Huiyu Xu verfasserin aut Yong Han verfasserin aut Chenguang Fu verfasserin aut Zhigang Yin verfasserin aut Miao Liu verfasserin aut Ronald Roepman verfasserin aut Sabine Dietmann verfasserin aut Marko Virta verfasserin aut Fredrick Kengara verfasserin aut Ze Zhang verfasserin aut Lifu Zhang verfasserin aut Taolan Zhao verfasserin aut Ji Dai verfasserin aut Jialiang Yang verfasserin aut Liang Lan verfasserin aut Ming Luo verfasserin aut Zhaofeng Liu verfasserin aut Tao An verfasserin aut Bin Zhang verfasserin aut Xiao He verfasserin aut Shan Cong verfasserin aut Xiaohong Liu verfasserin aut Wei Zhang verfasserin aut James P. Lewis verfasserin aut James M. Tiedje verfasserin aut Qi Wang verfasserin aut Zhulin An verfasserin aut Fei Wang verfasserin aut Libo Zhang verfasserin aut Tao Huang verfasserin aut Chuan Lu verfasserin aut Zhipeng Cai verfasserin aut Fang Wang verfasserin aut Jiabao Zhang verfasserin aut In The Innovation Elsevier, 2020 2(2021), 4, Seite 100179- (DE-627)1747737119 26666758 nnns volume:2 year:2021 number:4 pages:100179- https://doi.org/10.1016/j.xinn.2021.100179 kostenfrei https://doaj.org/article/f012a0d028854bffb505c4676fd1987e kostenfrei http://www.sciencedirect.com/science/article/pii/S2666675821001041 kostenfrei https://doaj.org/toc/2666-6758 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 4 100179- |
| spelling |
10.1016/j.xinn.2021.100179 doi (DE-627)DOAJ075379503 (DE-599)DOAJf012a0d028854bffb505c4676fd1987e DE-627 ger DE-627 rakwb eng Q1-390 Yongjun Xu verfasserin aut Artificial intelligence: A powerful paradigm for scientific research 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Xin Liu verfasserin aut Xin Cao verfasserin aut Changping Huang verfasserin aut Enke Liu verfasserin aut Sen Qian verfasserin aut Xingchen Liu verfasserin aut Yanjun Wu verfasserin aut Fengliang Dong verfasserin aut Cheng-Wei Qiu verfasserin aut Junjun Qiu verfasserin aut Keqin Hua verfasserin aut Wentao Su verfasserin aut Jian Wu verfasserin aut Huiyu Xu verfasserin aut Yong Han verfasserin aut Chenguang Fu verfasserin aut Zhigang Yin verfasserin aut Miao Liu verfasserin aut Ronald Roepman verfasserin aut Sabine Dietmann verfasserin aut Marko Virta verfasserin aut Fredrick Kengara verfasserin aut Ze Zhang verfasserin aut Lifu Zhang verfasserin aut Taolan Zhao verfasserin aut Ji Dai verfasserin aut Jialiang Yang verfasserin aut Liang Lan verfasserin aut Ming Luo verfasserin aut Zhaofeng Liu verfasserin aut Tao An verfasserin aut Bin Zhang verfasserin aut Xiao He verfasserin aut Shan Cong verfasserin aut Xiaohong Liu verfasserin aut Wei Zhang verfasserin aut James P. Lewis verfasserin aut James M. Tiedje verfasserin aut Qi Wang verfasserin aut Zhulin An verfasserin aut Fei Wang verfasserin aut Libo Zhang verfasserin aut Tao Huang verfasserin aut Chuan Lu verfasserin aut Zhipeng Cai verfasserin aut Fang Wang verfasserin aut Jiabao Zhang verfasserin aut In The Innovation Elsevier, 2020 2(2021), 4, Seite 100179- (DE-627)1747737119 26666758 nnns volume:2 year:2021 number:4 pages:100179- https://doi.org/10.1016/j.xinn.2021.100179 kostenfrei https://doaj.org/article/f012a0d028854bffb505c4676fd1987e kostenfrei http://www.sciencedirect.com/science/article/pii/S2666675821001041 kostenfrei https://doaj.org/toc/2666-6758 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 4 100179- |
| allfields_unstemmed |
10.1016/j.xinn.2021.100179 doi (DE-627)DOAJ075379503 (DE-599)DOAJf012a0d028854bffb505c4676fd1987e DE-627 ger DE-627 rakwb eng Q1-390 Yongjun Xu verfasserin aut Artificial intelligence: A powerful paradigm for scientific research 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Xin Liu verfasserin aut Xin Cao verfasserin aut Changping Huang verfasserin aut Enke Liu verfasserin aut Sen Qian verfasserin aut Xingchen Liu verfasserin aut Yanjun Wu verfasserin aut Fengliang Dong verfasserin aut Cheng-Wei Qiu verfasserin aut Junjun Qiu verfasserin aut Keqin Hua verfasserin aut Wentao Su verfasserin aut Jian Wu verfasserin aut Huiyu Xu verfasserin aut Yong Han verfasserin aut Chenguang Fu verfasserin aut Zhigang Yin verfasserin aut Miao Liu verfasserin aut Ronald Roepman verfasserin aut Sabine Dietmann verfasserin aut Marko Virta verfasserin aut Fredrick Kengara verfasserin aut Ze Zhang verfasserin aut Lifu Zhang verfasserin aut Taolan Zhao verfasserin aut Ji Dai verfasserin aut Jialiang Yang verfasserin aut Liang Lan verfasserin aut Ming Luo verfasserin aut Zhaofeng Liu verfasserin aut Tao An verfasserin aut Bin Zhang verfasserin aut Xiao He verfasserin aut Shan Cong verfasserin aut Xiaohong Liu verfasserin aut Wei Zhang verfasserin aut James P. Lewis verfasserin aut James M. Tiedje verfasserin aut Qi Wang verfasserin aut Zhulin An verfasserin aut Fei Wang verfasserin aut Libo Zhang verfasserin aut Tao Huang verfasserin aut Chuan Lu verfasserin aut Zhipeng Cai verfasserin aut Fang Wang verfasserin aut Jiabao Zhang verfasserin aut In The Innovation Elsevier, 2020 2(2021), 4, Seite 100179- (DE-627)1747737119 26666758 nnns volume:2 year:2021 number:4 pages:100179- https://doi.org/10.1016/j.xinn.2021.100179 kostenfrei https://doaj.org/article/f012a0d028854bffb505c4676fd1987e kostenfrei http://www.sciencedirect.com/science/article/pii/S2666675821001041 kostenfrei https://doaj.org/toc/2666-6758 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 4 100179- |
| allfieldsGer |
10.1016/j.xinn.2021.100179 doi (DE-627)DOAJ075379503 (DE-599)DOAJf012a0d028854bffb505c4676fd1987e DE-627 ger DE-627 rakwb eng Q1-390 Yongjun Xu verfasserin aut Artificial intelligence: A powerful paradigm for scientific research 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Xin Liu verfasserin aut Xin Cao verfasserin aut Changping Huang verfasserin aut Enke Liu verfasserin aut Sen Qian verfasserin aut Xingchen Liu verfasserin aut Yanjun Wu verfasserin aut Fengliang Dong verfasserin aut Cheng-Wei Qiu verfasserin aut Junjun Qiu verfasserin aut Keqin Hua verfasserin aut Wentao Su verfasserin aut Jian Wu verfasserin aut Huiyu Xu verfasserin aut Yong Han verfasserin aut Chenguang Fu verfasserin aut Zhigang Yin verfasserin aut Miao Liu verfasserin aut Ronald Roepman verfasserin aut Sabine Dietmann verfasserin aut Marko Virta verfasserin aut Fredrick Kengara verfasserin aut Ze Zhang verfasserin aut Lifu Zhang verfasserin aut Taolan Zhao verfasserin aut Ji Dai verfasserin aut Jialiang Yang verfasserin aut Liang Lan verfasserin aut Ming Luo verfasserin aut Zhaofeng Liu verfasserin aut Tao An verfasserin aut Bin Zhang verfasserin aut Xiao He verfasserin aut Shan Cong verfasserin aut Xiaohong Liu verfasserin aut Wei Zhang verfasserin aut James P. Lewis verfasserin aut James M. Tiedje verfasserin aut Qi Wang verfasserin aut Zhulin An verfasserin aut Fei Wang verfasserin aut Libo Zhang verfasserin aut Tao Huang verfasserin aut Chuan Lu verfasserin aut Zhipeng Cai verfasserin aut Fang Wang verfasserin aut Jiabao Zhang verfasserin aut In The Innovation Elsevier, 2020 2(2021), 4, Seite 100179- (DE-627)1747737119 26666758 nnns volume:2 year:2021 number:4 pages:100179- https://doi.org/10.1016/j.xinn.2021.100179 kostenfrei https://doaj.org/article/f012a0d028854bffb505c4676fd1987e kostenfrei http://www.sciencedirect.com/science/article/pii/S2666675821001041 kostenfrei https://doaj.org/toc/2666-6758 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 4 100179- |
| allfieldsSound |
10.1016/j.xinn.2021.100179 doi (DE-627)DOAJ075379503 (DE-599)DOAJf012a0d028854bffb505c4676fd1987e DE-627 ger DE-627 rakwb eng Q1-390 Yongjun Xu verfasserin aut Artificial intelligence: A powerful paradigm for scientific research 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Xin Liu verfasserin aut Xin Cao verfasserin aut Changping Huang verfasserin aut Enke Liu verfasserin aut Sen Qian verfasserin aut Xingchen Liu verfasserin aut Yanjun Wu verfasserin aut Fengliang Dong verfasserin aut Cheng-Wei Qiu verfasserin aut Junjun Qiu verfasserin aut Keqin Hua verfasserin aut Wentao Su verfasserin aut Jian Wu verfasserin aut Huiyu Xu verfasserin aut Yong Han verfasserin aut Chenguang Fu verfasserin aut Zhigang Yin verfasserin aut Miao Liu verfasserin aut Ronald Roepman verfasserin aut Sabine Dietmann verfasserin aut Marko Virta verfasserin aut Fredrick Kengara verfasserin aut Ze Zhang verfasserin aut Lifu Zhang verfasserin aut Taolan Zhao verfasserin aut Ji Dai verfasserin aut Jialiang Yang verfasserin aut Liang Lan verfasserin aut Ming Luo verfasserin aut Zhaofeng Liu verfasserin aut Tao An verfasserin aut Bin Zhang verfasserin aut Xiao He verfasserin aut Shan Cong verfasserin aut Xiaohong Liu verfasserin aut Wei Zhang verfasserin aut James P. Lewis verfasserin aut James M. Tiedje verfasserin aut Qi Wang verfasserin aut Zhulin An verfasserin aut Fei Wang verfasserin aut Libo Zhang verfasserin aut Tao Huang verfasserin aut Chuan Lu verfasserin aut Zhipeng Cai verfasserin aut Fang Wang verfasserin aut Jiabao Zhang verfasserin aut In The Innovation Elsevier, 2020 2(2021), 4, Seite 100179- (DE-627)1747737119 26666758 nnns volume:2 year:2021 number:4 pages:100179- https://doi.org/10.1016/j.xinn.2021.100179 kostenfrei https://doaj.org/article/f012a0d028854bffb505c4676fd1987e kostenfrei http://www.sciencedirect.com/science/article/pii/S2666675821001041 kostenfrei https://doaj.org/toc/2666-6758 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 4 100179- |
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Yongjun Xu @@aut@@ Xin Liu @@aut@@ Xin Cao @@aut@@ Changping Huang @@aut@@ Enke Liu @@aut@@ Sen Qian @@aut@@ Xingchen Liu @@aut@@ Yanjun Wu @@aut@@ Fengliang Dong @@aut@@ Cheng-Wei Qiu @@aut@@ Junjun Qiu @@aut@@ Keqin Hua @@aut@@ Wentao Su @@aut@@ Jian Wu @@aut@@ Huiyu Xu @@aut@@ Yong Han @@aut@@ Chenguang Fu @@aut@@ Zhigang Yin @@aut@@ Miao Liu @@aut@@ Ronald Roepman @@aut@@ Sabine Dietmann @@aut@@ Marko Virta @@aut@@ Fredrick Kengara @@aut@@ Ze Zhang @@aut@@ Lifu Zhang @@aut@@ Taolan Zhao @@aut@@ Ji Dai @@aut@@ Jialiang Yang @@aut@@ Liang Lan @@aut@@ Ming Luo @@aut@@ Zhaofeng Liu @@aut@@ Tao An @@aut@@ Bin Zhang @@aut@@ Xiao He @@aut@@ Shan Cong @@aut@@ Xiaohong Liu @@aut@@ Wei Zhang @@aut@@ James P. Lewis @@aut@@ James M. Tiedje @@aut@@ Qi Wang @@aut@@ Zhulin An @@aut@@ Fei Wang @@aut@@ Libo Zhang @@aut@@ Tao Huang @@aut@@ Chuan Lu @@aut@@ Zhipeng Cai @@aut@@ Fang Wang @@aut@@ Jiabao Zhang @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ075379503</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503065618.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.xinn.2021.100179</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ075379503</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf012a0d028854bffb505c4676fd1987e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">Q1-390</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yongjun Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence: A powerful paradigm for scientific research</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">information 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Artificial intelligence: A powerful paradigm for scientific research |
| abstract |
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. |
| abstractGer |
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. |
| abstract_unstemmed |
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. |
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| title_short |
Artificial intelligence: A powerful paradigm for scientific research |
| url |
https://doi.org/10.1016/j.xinn.2021.100179 https://doaj.org/article/f012a0d028854bffb505c4676fd1987e http://www.sciencedirect.com/science/article/pii/S2666675821001041 https://doaj.org/toc/2666-6758 |
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Xin Liu Xin Cao Changping Huang Enke Liu Sen Qian Xingchen Liu Yanjun Wu Fengliang Dong Cheng-Wei Qiu Junjun Qiu Keqin Hua Wentao Su Jian Wu Huiyu Xu Yong Han Chenguang Fu Zhigang Yin Miao Liu Ronald Roepman Sabine Dietmann Marko Virta Fredrick Kengara Ze Zhang Lifu Zhang Taolan Zhao Ji Dai Jialiang Yang Liang Lan Ming Luo Zhaofeng Liu Tao An Bin Zhang Xiao He Shan Cong Xiaohong Liu Wei Zhang James P. Lewis James M. Tiedje Qi Wang Zhulin An Fei Wang Libo Zhang Tao Huang Chuan Lu Zhipeng Cai Fang Wang Jiabao Zhang |
| author2Str |
Xin Liu Xin Cao Changping Huang Enke Liu Sen Qian Xingchen Liu Yanjun Wu Fengliang Dong Cheng-Wei Qiu Junjun Qiu Keqin Hua Wentao Su Jian Wu Huiyu Xu Yong Han Chenguang Fu Zhigang Yin Miao Liu Ronald Roepman Sabine Dietmann Marko Virta Fredrick Kengara Ze Zhang Lifu Zhang Taolan Zhao Ji Dai Jialiang Yang Liang Lan Ming Luo Zhaofeng Liu Tao An Bin Zhang Xiao He Shan Cong Xiaohong Liu Wei Zhang James P. Lewis James M. Tiedje Qi Wang Zhulin An Fei Wang Libo Zhang Tao Huang Chuan Lu Zhipeng Cai Fang Wang Jiabao Zhang |
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Q - General Science |
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10.1016/j.xinn.2021.100179 |
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2024-07-03T14:34:33.702Z |
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| score |
7.4007673 |