Digital economy has become an important driving force for green economic growth in China. Based on the province-level data of China from 2003 to 2018, this paper constructed the Total-factor Nonradial Directional Distance Function (TNDDF) model to measure the carbon emission efficiency of industrial sector and discussed the impact of digital economy on carbon emission efficiency. Empirical analysis shows that the carbon emission efficiency of China’s industrial sector is low, and there is obvious regional heterogeneity where the carbon emission efficiency of eastern China is higher than that of central and western China. Areas with high level of digital economy development have higher carbon emission efficiency, and digital economy is conducive to promoting energy conservation and pollution reduction in China’s industrial sector. The optimal threshold interval of digital economy for promoting carbon emission efficiency is explored by means of threshold model. In view of this, the Chinese government should vigorously develop the digital economy, promote industrial enterprises to networking and digital evolution, and improve the efficiency of carbon emission as well.
ObjectivesThis meta-analysis was prepared to synthesize published data on the association of two polymorphisms (T45G and G276T) in adiponectin-encoding gene (ADIPOQ) with hypertension risk and the changes of circulating adiponectin and blood pressure.Methodology and Major FindingsData were collected and corrected by two authors, and were managed with Stata software. In total, 12 articles were synthesized, including 12 studies (3358 cases and 5121 controls) for the association of two study polymorphisms with hypertension risk and 11 studies (3053 subjects) for the between-genotype changes of adiponectin and/or blood pressure. Based on all qualified studies, the risk prediction for hypertension was nonsignificant for both polymorphisms, with significant heterogeneity for G276T polymorphism (I2 = 53.8%). Overall changes in adiponectin and blood pressure were also nonsignificant for T45G, while contrastingly 276GT genotype was associated with significantly higher levels of adiponectin (weighted mean difference [WMD] = 0.72 μg/mL, 95% confidence interval [CI]: 0.04 to 1.41, P = 0.038), systolic (WMD = 5.15 mm Hg, 95% CI: 0.98 to 9.32, P = 0.016) and diastolic (WMD = 3.45 mm Hg, 95% CI: 0.37 to 6.53, P = 0.028) blood pressure with evident heterogeneity (I2 = 72.0%, 78.3% and 80.0%, respectively), and these associations were more obvious in hypertensive patients. Publication bias was a low probability event for overall comparisons.ConclusionsOur findings suggested that in spite of the nonsignificant association between ADIPOQ T45G or G276T polymorphism and hypertension, the heterozygous mutation of G276T was observed to account for increased levels of circulating adiponectin and blood pressure, especially in hypertensive patients.
In this paper, we propose a novel deep convolutional neural network (DCNN) for removing snowflakes from light field (LF) images. We observe that snowflakes in LF images always interrupt slopes in background scenes in epipolar plane images (EPIs), which means that snowflakes may be easily detected in EPIs. Our method takes 3D EPI volumes (i.e., stacked subaperture views along the same row or column of an LF image) as input. In this way, our snowflake detector based on a 3D residual network with a convolutional long short-term memory (ResNet-ConvLSTM) can utilize both contextual information and 3D scene structural information to effectively detect snowflakes of different sizes in LF images. Then, an encoderdecoder-based LF image restoration network is proposed to restore the background image. Finally, extensive experiments for comparison with the state-of-the-art methods demonstrate the effectiveness of our method for challenging scenes. INDEX TERMS snow removal, light field (LF) image, deep convolutional neural network, 3D EPI volume.
A layered structure model is proposed for microwave dielectric properties of nonhomogeneous hydrogen plasma in carbon nanotubes (CNTs) film. Using the transfer matrix method for solving electromagnetic wave propagation equation, the microwave attenuation of the film is calculated in the range of 0–30 GHz under different conditions. It is found theoretically that with the increase of hydrogen plasma nonhomogeneity, the frequency bandwidth of strong microwave absorption by the film increases markedly. The application of a moderate static magnetic field can effectively improve microwave attenuation properties of hydrogen plasma in CNTs. The numerical results are in good agreement with the available experimental data.
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