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Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U2-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U2-Net. These results indicate that U2-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

Efforts have been made to find a better therapeutic approach with fewer side effects in treating chronic obstructive pulmonary disease (COPD). This study investigated the effect of Buyuan decoction (BYD) on autophagy in COPD rats. An experimental model with Sprague-Dawley rats was established by lipopolysaccharide (LPS) injection and cigarette smoke exposure. Rats were randomly allocated into blank control (normal control), experimental model, low-dose BYD (8.0 g/kg/day), medium-dose BYD (16.0 g/kg/day), high-dose BYD (32.0 g/kg/day) and 3-MA (methyladenine) groups (6 rats/group). Cell and tissue morphology were observed using hematoxylin and eosin staining. Autophagic vesicles were examined with a transmission electron microscope. Protein expression of LC3-II/I, BNIP- 1, ATG7, p62, PI3K and p-PI3K in lung tissue was detected by Western blotting. Compared with the experimental model group, the inflammatory infiltrate in lung tissue was reduced, the nuclei of the pulmonary epithelial cells were restored to normal, and the expression of LC3, BNIP1, ATG7 and p-PI3K was significantly downregulated, while p62 expression was significantly upregulated after treatment with the BYD. The effect was most significant in the low-dose BYD group (P

Here, we report a Na-promoted FeCu-based catalyst with excellent liquid hydrocarbon selectivity and catalytic activity. The physiochemical properties of the catalysts were comprehensively characterized by various characterization techniques. The characterization results indicate that the catalytic performance of the catalysts was closely related to the nature of the metal promoters. The Na-AlFeCu possessed the highest CO2 conversion due to enhanced CO2 adsorption of the catalysts by the introduction of Al species. The introduction of excess Mg promoter led to a strong methanation activity of the catalyst. Mn and Ga promoters exhibited high selectivity for light hydrocarbons due to their inhibition of iron carbides generation, resulting in a lack of chain growth capacity. The Na-ZnFeCu catalyst exhibited the optimal C5+ yield, owing to the fact that the Zn promoter improved the catalytic activity and liquid hydrocarbon selectivity by modulating the surface CO2 adsorption and carbide content. Carbon dioxide (CO2) hydrogenation to liquid fuel is considered a method for the utilization and conversion of CO2, whereas satisfactory activity and selectivity remains a challenge. This method provides a new idea for the catalytic hydrogenation of CO2 and from there the preparation of high-value-added products. [ABSTRACT FROM AUTHOR]