Deep Learning and COVID-19: Two Pathways to Scientific Evolution
文献类型：期刊论文
| 作者 | Kang, Huquan2; Dong, Hanyan3; Ding, Yuang2; Jin, Zhouyang2; Fu, Luoyi2; Ding, Jiaxin2; Wang, Xinbing2; Zhou, Lei4; Zhou, Chenghu1 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2025-08-13 |
| 卷号 | 15期号:16页码:8912 |
| 关键词 | deep learning COVID-19 interdisciplinary research science of science sci-entropy knowledge diffusion knowledge integration |
| DOI | 10.3390/app15168912 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | COVID-19 and deep learning have each marked pivotal milestones in the evolution of modern science. Since the onset of the pandemic, researchers from diverse disciplines have converged to address urgent, real-world challenges, while deep learning has catalyzed methodological innovation across fields. These two phenomena exemplify distinct scientific paradigms: spread-out science, which propagates novel ideas and methods, and merge-in science, which synthesizes existing knowledge to solve complex problems. We introduce the concept of sci-entropy, defined as the difference between the semantic entropy of a paper's citations and references. Positive sci-entropy reflects the diffusion of new ideas (spread-out), whereas negative values indicate knowledge consolidation (merge-in). Our analysis, spanning deep learning, COVID-19, and 19 additional disciplines, reveals that scientific progress is governed by the dynamic interplay between these two forces. Excessively high sci-entropy may fragment research, while overly low values can stifle innovation. Our findings suggest that the balance between innovation and synthesis is fundamental to the trajectory of scientific development, offering a new framework for understanding interdisciplinary research and knowledge integration. |
| URL标识 | 查看原文 |
| WOS关键词 | INTERDISCIPLINARY RESEARCH ; CHALLENGES ; IMPACT |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001557275300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216032]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Fu, Luoyi |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Shanghai Jiao Tong Univ, Sch Comp Sci, Shanghai 200240, Peoples R China; 3.Shanghai Jiao Tong Univ, SJTU Paris Elite Inst Technol, Shanghai 200240, Peoples R China; 4.Shanghai Jiao Tong Univ, Sch Oceanog, Shanghai 200240, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Kang, Huquan,Dong, Hanyan,Ding, Yuang,et al. Deep Learning and COVID-19: Two Pathways to Scientific Evolution[J]. APPLIED SCIENCES-BASEL,2025,15(16):8912. |
| APA | Kang, Huquan.,Dong, Hanyan.,Ding, Yuang.,Jin, Zhouyang.,Fu, Luoyi.,...&Zhou, Chenghu.(2025).Deep Learning and COVID-19: Two Pathways to Scientific Evolution.APPLIED SCIENCES-BASEL,15(16),8912. |
| MLA | Kang, Huquan,et al."Deep Learning and COVID-19: Two Pathways to Scientific Evolution".APPLIED SCIENCES-BASEL 15.16(2025):8912. |
入库方式： OAI收割
来源：地理科学与资源研究所
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