Spatial Pattern Evolution and Influencing Factors on Industrial Agglomeration: Evidence from Pearl River Delta Urban Agglomeration.

In order to clarify the spatial pattern and influencing factors on industrial agglomeration in urban agglomerations, based on the data of prefecture-level cities from 2006 to 2018, this paper uses spatial standard deviation ellipse to analyze the spatial pattern evolution of manufacturing, producer services, consumer services, and foreign-invested industries and takes a dynamic spatial Durbin model to empirically test the influencing factors of industrial agglomeration in Pearl River Delta (PRD) urban agglomeration. The main conclusions are as follows: 1) the degree of industrial agglomeration is at a low level and the difference in the industrial agglomeration level between cities is mainly manifested in the service industries; 2) manufacturing and foreign-invested industries have entered the stage of industrial diffusion, and all types of industries show an east (by south)-to-west (by north) pattern, with a trend of expansion to the south and north; 3) the agglomeration level of service industries and foreign-invested industries on the east bank of the Pearl River is higher than that on the west; and 4) from the empirical results, there is a general inertia effect in the industrial agglomeration and a siphon effect in the manufacturing agglomeration. Economic scale, transportation infrastructure, government intervention, opening up, and urban environment can all positively influence the agglomeration in some industries, with the apparent spatial spillover effects of each influencing factor.

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

Background: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis.

Methods: We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level.