yuhan cheng

Abstract Particle number size distribution (PNSD) is fundamental for characterizing aerosols and quantifying aerosol–cloud interactions, while simulations remain uncertain due to strong spatiotemporal variability during air-parcel transport. We developed a framework that combines air-parcel location history with co-located aerosol, cloud, meteorological, and gas variables into deep learning (DL) models to reconstruct PNSD.

We thank Lars Hansen, Kewei Hou, Leonid Kogan, Sai Ma (discussant), Stefan Nagel, Andrew Patton (discussant), Monika Piazzesi, Dacheng Xiu, Guofu Zhou (discussant), and participants at the NBER Conference on Big Data, Artificial Intelligence, and Financial Economics, ASSA, Econometric Society Summer School in Lausanne, CICF, AMES, SIF, MIT Sloan, Chicago Booth, Stanford GSB, UC Irvine, Tsinghua University, HKUST, Central University of Finance and Economics, Renmin University of China, and...

1 Introduction With the continuous deepening of population aging and the increasing diversity of healthcare demands, nursing has become an increasingly vital component of China’s healthcare system. It plays a critical role in addressing aging-related challenges, improving healthcare quality, and promoting the realization of national health goals (1, 2). However, current studies reveal that nursing in China still faces multiple challenges.