No evidence is available on whether cardiovascular mortality is affected by the ambient temperatures in Yinchuan, which is located in the northwestern region of China, with a typical continental semi-humid semi-arid climate. Daily data on cardiovascular mortality and meteorological factors was collected from Yinchuan city for the period of 2010-2015. A distributed lag non-linear model with quasi-Poisson link was used to assess the association between daily temperatures and cardiovascular deaths, after controlling for seasonality, day of the week, atmospheric pressure, humidity, sunshine duration, and wind speed. The relationship between ambient temperature and cardiovascular mortality was non-linear, with a U-shaped exposure-response curve. For all cardiovascular mortality, the effects of high temperatures appeared at lag 2-5 days, with the largest hot effect at lag 3 day (RR 1.082, 95% CI 1.021-1.146), while the effects of cold temperatures were insignificant. Both cold and high temperatures have more serious influence on the elderly (age ≥ 65) and males than the youth and females, respectively. The study has shown that both cold and high temperatures affect cardiovascular mortality. The findings may be helpful to identify the susceptible subgroups of cardiovascular mortality induced by temperatures, and to provide useful information for establishing public health programs that would better protect local population health from ambient temperatures.
The prevention and control of navel orange pests and diseases is an important measure to ensure the yield of navel oranges. Aiming at the problems of slow speed, strong subjectivity, high requirements for professional knowledge required, and high identification costs in the identification methods of navel orange pests and diseases, this paper proposes a method based on DenseNet and attention. The power mechanism fusion (DCPSNET) identification method of navel orange diseases and pests improves the traditional deep dense network DenseNet model to realize accurate and efficient identification of navel orange diseases and pests. Due to the difficulty in collecting data of navel orange pests and diseases, this article uses image enhancement technology to expand. The experimental results show that, in the case of small samples, compared with the traditional model, the DCPSNET model can accurately identify different types of navel orange diseases and pests images and the accuracy of identifying six types of navel orange diseases and pests on the test set is as high as 96.90%. The method proposed in this paper has high recognition accuracy, realizes the intelligent recognition of navel orange diseases and pests, and also provides a way for high-precision recognition of small sample data sets.
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