Income inequality of destination countries and trade patterns: Evidence from Chinese firm-level data

In this paper, we investigate the relation between the export patterns and the income inequality of the destination countries using the Chinese firm-level data. Our empirical analysis finds two main results: (i) export price decreases in the income inequality of the destination countries; while (ii) the exporting firm number and export value will increase in the inequality level. With a conventionally theoretical framework, we discuss the potential influencing mechanism. A higher income inequality leads to higher share of poor consumers in a country, which will lower the quality threshold for Chinese exporters. In this case, the firms with less competitive and producing low quality products are able to enter this market. As a result, we observe that in response to a higher income inequality, more firms enter the market while the exporting price decreases in this market

Income inequality of destination countries and trade patterns: Evidence from Chinese firm-level data

In this paper, we investigate the relation between the export patterns and the income inequality of the destination countries using the Chinese firm-level data. Our empirical analysis finds two main results: (i) export price decreases in the income inequality of the destination countries; while (ii) the exporting firm number and export value will increase in the inequality level. With a conventionally theoretical framework, we discuss the potential influencing mechanism. A higher income inequality leads to higher share of poor consumers in a country, which will lower the quality threshold for Chinese exporters. In this case, the firms with less competitive and producing low quality products are able to enter this market. As a result, we observe that in response to a higher income inequality, more firms enter the market while the exporting price decreases in this market

N,N′-[(2,3,5,6-Tetra­methyl-p-phenyl­ene)dimethyl­ene]bis­[2-chloro-N-(2-chloro­ethyl)ethanamine]

The title mol­ecule, C20H32Cl4N2, lies on an inversion center. A weak intra­molecular C—H⋯N hydrogen bond may, in part, influence the conformation of the mol­ecule

Revisiting Stereo Triangulation in UAV Distance Estimation

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the stereo triangulation cannot stand for UAV scenes. The core reason is the position deviation issue due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones and then perform compensation in stereo triangulation calculation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 9.8%), which demonstrates its effectiveness and superiority. The code and dataset are available at https://github.com/duanyuan13/PCM.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient

Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation. In this work, we propose a conditioning mechanism utilizing Gaussian mixture models (GMMs) as feature conditioning to guide the denoising process. Based on set theory, we provide a comprehensive theoretical analysis that shows that conditional latent distribution based on features and classes is significantly different, so that conditional latent distribution on features produces fewer defect generations than conditioning on classes. Two diffusion models conditioned on the Gaussian mixture model are trained separately for comparison. Experiments support our findings. A novel gradient function called the negative Gaussian mixture gradient (NGMG) is proposed and applied in diffusion model training with an additional classifier. Training stability has improved. We also theoretically prove that NGMG shares the same benefit as the Earth Mover distance (Wasserstein) as a more sensible cost function when learning distributions supported by low-dimensional manifolds

Variation in Bacterial Community Structure Under Long-Term Fertilization, Tillage, and Cover Cropping in Continuous Cotton Production

Agricultural practices alter the structure and functions of soil microbial community. However, few studies have documented the alterations of bacterial communities in soils under long-term conservation management practices for continuous crop production. In this study, we evaluated soil bacterial diversity using 16S rRNA gene sequencing and soil physical and chemical properties within 12 combinations of inorganic N fertilization, cover cropping, and tillage throughout a cotton production cycle. Soil was collected from field plots of the West Tennessee Agriculture Research and Education Center in Jackson, TN, United States. The site has been under continuous cotton production for 38 years. A total of 38,038 OTUs were detected across 171 soil samples. The dominant bacterial phyla were Proteobacteria, Acidobacteria, Actinobacteria, Verrucomicrobia, and Chloroflexi, accounting for ~70% of the total bacterial community membership. Conventional tillage increased alpha diversity in soil samples collected in different stages of cotton production. The effects of inorganic N fertilization and conventional tillage on the structure of bacterial communities were significant at all four sampling dates (p \u3c 0.01). However, cover cropping (p \u3c 0.05) and soil moisture content (p \u3c 0.05) only showed significant influence on the bacterial community structure after burn-down of the cover crops and before planting of cotton (May). Nitrate-N appeared to have a significant effect on the structure of bacterial communities after inorganic fertilization and at the peak of cotton growth (p \u3c 0.01). Structural equation modeling revealed that the relative abundances of denitrifying and nitrifying bacteria were higher when conventional tillage and vetch cover crop practices were applied, respectively. Our results indicate that long-term tillage and fertilization are key factors increasing the diversity and restructuring the composition of bacterial communities, whereas cover cropping may have shorterterm effects on soil bacteria community structure. In this study, management practices might positively influence relative abundances of bacterial functional groups associated with N cycling. The bacteria functional groups may build a network for providing N and meet microbial N needs in the long term

Increased human pressures on the alpine ecosystem along the Qinghai-Tibet Railway

Construction of the Qinghai-Tibet Railway (QTR) increased the links between inland China and the Qinghai-Tibet Plateau (QTP). The QTR accelerated surrounding tourism, boosted the local economy and led to rapid development of livestock raising. To assess how distance from the railway and different regions has influenced the impact of the QTR on the alpine ecosystem, human footprint maps were produced to indicate human pressures, and the normalized difference vegetation index (NDVI), an index of vegetation greenness, was used to characterize the growth of alpine vegetation. The construction and operation of the QTR have increased human pressures, while the establishment of nature reserves has effectively reduced human pressures. The QTR contributes significantly to the increased human pressures in the Tibetan region compared with the Qinghai region and exerts negative impacts on alpine vegetation. Although the warmer and wetter climate trend has proven beneficial in enhancing alpine vegetation greenness, the declining trend of alpine vegetation has been stronger in regions with more intensive human pressures, especially in the grazing areas and the tourist areas around Lhasa. These results suggest that the impact of the QTR on alpine vegetation in Tibet is greater than that in Qinghai and that the spatial extent of the indirect impact of the QTR in Tibet is confined to approximately 30 km from the railway. These results will provide guidance and a theoretical basis for the protection of the alpine environment on the QTP under intensified anthropogenic influence