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The hawthorns (Crataegus spp.) are widely distributed and famous for their edible and medicinal values. There are ~18 species and seven varieties of hawthorn in China distributed throughout the country. We now report the chloroplast genome sequences from C. scabrifolia, C. chungtienensis and C. oresbia, from the southwest of China and compare them with the previously released six species in Crataegus and four species in Rosaceae. The chloroplast genome structure of Crataegus is typical and can be divided into four parts. The genome sizes are between 159,654 and 159,898bp. The three newly sequenced chloroplast genomes encode 132 genes, including 85 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Comparative analysis of the chloroplast genomes revealed six divergent hotspot regions, including ndhA, rps16-trnQ-UUG, ndhF-rpl32, rps16-psbK, trnR-UCU-atpA and rpl32-trnL-UAG. According to the correlation and cooccurrence analysis of repeats with indels and SNPs, the relationship between them cannot be ignored. The phylogenetic tree constructed based on the complete chloroplast genome and intergenic region sequences indicated that C. scabrifolia has a different origin from C. chungtienensis and C. oresbia. We support the placement of C. hupehensis, C. cuneata, C. scabrifolia in C. subg. Crataegus and C. kansuensis, C. oresbia, C. kansuensis in C. subg. Sanguineae. In addition, based on the morphology, geographic distribution and phylogenetic relationships of C. chungtienensis and C. oresbia, we speculate that these two species may be the same species. In conclusion, this study has enriched the chloroplast genome resources of Crataegus and provided valuable information for the phylogeny and species identification of this genus. [ABSTRACT FROM AUTHOR]

Along with the promotion of China's green development strategy and the policy of financial services to the real economy, how financialization affects corporate green innovation becomes an urgent issue to be explored. Using Chinese A-share listed enterprises from 2007 to 2020, this study empirically analyzes the impact of financialization on corporate green innovation and its underlying mechanism, and the moderating effect of heterogeneous environmental regulation is examined. Results show that financialization significantly inhibits corporate green innovation, and the command-and-control environmental regulation is more effective in weakening the inhibiting effect of financialization on corporate green innovation than market-incentivized and public-participation environmental regulation. Moreover, mechanism analyses show that financialization inhibits corporate green innovation mainly by affecting innovation input and financing constraints. Extended analyses show that the inhibiting effects of financialization on strategic and substantive green innovation of enterprises are not significantly different, but the inhibiting effect of financialization on independent green innovation is significantly higher than that on joint green innovation. In addition, the inhibiting effect of financialization on green innovation is more significant in non-heavy polluting industries and regions with high regulatory pressure. This study reveals the negative effects of financialization on corporate green innovation from a micro-finance perspective, which not only helps to improve the internal investment decisionmaking mechanism of companies and the regulatory system of corporate financialization, but also has some insights to guide the practice of green innovation of real enterprises and promote the green transformation of economic development mode. [ABSTRACT FROM AUTHOR]

Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km2 and 3729.19 km2 during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km2 (with a change rate of 35%) and 197.58 km2 (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake. [ABSTRACT FROM AUTHOR]

Ancient documents are historical evidence of cultural inheritance, and the damage brought by natural and human factors to ancient documents is inevitable, resulting in the collected images of ancient Chinese characters containing a large amount of noise, which seriously affects the accuracy of subsequent image recognition and thus creates a great obstacle to the digitization of ancient documents. To address the complexity of ancient text structure, this paper proposes a Chinese ancient text image denoising method based on the Chinese character writing standard model. The method firstly adds four additional local branches based on the global branching, and uses the supplementary character detail information to weaken the phenomenon of strokes adhering to noise due to the lack of local details; secondly, it introduces the simulation noise of ancient documents to simulate the real ancient character image morphology, which can be used for the adversarial training of this method. In the training process, the minimum absolute value deviation, smoothing loss, structural consistency loss and the refined loss function formed by the adversarial loss are used to iteratively optimize the parameters. Finally, experiments prove that the model in this paper can increase the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image by at least 23.8% and 11.4%, and the user evaluation index (UV) has also reached more than 80%., (© 2022. The Author(s).)