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Updated: Feb 10, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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[2013年至2023年青岛PM2.5的关键驱动因素使用可解释的机器学习模型]

Jian-Bin Huang1,2, Jian-Hua Qi1,2

  • 1Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China.

Huan jing ke xue= Huanjing kexue
|February 9, 2026
PubMed
概括

青岛的空气质量从2013年到2023年有46.3%的改善,主要是由于减少了二次气溶. 2017年以后的减速是由于前体失衡造成的,这凸显了协调排放控制的必要性.

关键词:
对于PM2.5来说,这是一个很好的例子.关键的驱动因素 关键的驱动因素长期变化的变化.机器学习是机器学习.政策 政策 政策 政策

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