Nature旗下SCI期刊征收AI方向论文, 56%录用率, 5个月出录用_scientific reports 录用率

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Nature旗下的开源期刊Scientific Reports现征收AI方向论文，具体征稿内容包括以下三个特辑，大家可以根据对应方向进行关注。

期刊信息：

期刊名：Scientific Reports

影响因子：2-year impact factor (2021): 4.996，5-year impact factor (2021): 5.516（信息来自官网）

审稿周期：Submission to first decision - 56 days，Submission to accept - 133 days（来自官网声明）

中科院SCI分区：基础版三区，升级版三区。

单年录用率：2018年56%（信息来自知乎）

费用：USD2190（信息来自letpub）

征稿特辑一：基于物理的机器学习研究及应用

Physics-informed machine learning and its real-world applications

截稿时间：2022.11.30

Advances in machine learning (ML) and deep learning (DL) are undoubtedly enabling significant breakthroughs in all areas of science and technology. ML/DL models, however, do not necessarily obey the fundamental governing laws of physical systems and often fail to describe and predict scenarios beyond the ones they have been trained on. In addition, training deep neural networks requires a huge amount of quality data, which is not always available for scientific problems. To solve these challenges, a new paradigm that integrates physical principles into ML models is emerging: physics-informed machine learning. Incorporating physics into ML models makes it possible to build physically consistent predictive models which are faster to train, more generalizable, interpretable, and trustworthy.

This Collection aims to gather the latest advances in physics-informed machine learning applications in sciences and engineering. Submissions that provide evidence of scalable, robust, and reliable physics-informed machine learning approaches for large-scale, real-world applications are particularly welcome.

网址：

https://www.nature.com/collections/hdjhcifhad

征稿特辑二：机器学习在医学图像分析中的应用

Machine learning applications in medical image analysis

截稿时间：2022.11.30

Significant breakthroughs in the capabilities of machine learning (ML) algorithms in recent years coupled with advancements in imaging tools have precipitated a revolution in automated medical image analysis. ML-based methods are increasingly being used to extract data from diverse imaging modalities and guide clinical decision making in a range of specialties including radiology, ophthalmology, neurology, respiratory medicine and cancer.

This Collection aims to bring together original research on all aspects of ML-based medical image analysis, including but not limited to technological developments and new clinical applications.

网址：

https://www.nature.com/collections/gfbjhfjfgg

征稿特辑三：仿生机器人运动

Bioinspired robotic locomotion

截稿时间：2022.11.30

Robotic locomotion has always been inspired by nature, as we strive to achieve the same levels of efficiency, fluidity and grace often observed when animals move through air, water and on land. On the other hand, developing bioinspired robots allows us to better understand highly intricate biological systems, in both living and extinct species. Whether employing insect leg structures, streamlined body shapes of fish or the flapping motions of birds, by using state of the art technology the possibilities are almost endless.

This Collection aims to facilitate interdisciplinary collaboration and gather original research focused on the development of robotic locomotion systems inspired by animal movement, as well as studies using robots to provide insights into biological locomotion.

网址：

https://www.nature.com/collections/hfgibaebbf

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