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This paper explores the relationship between peasant entrepreneurship and financial inclusion throughout China. Chinese data for the period 2006–2013 and global data from the Global Entrepreneurship Monitor were analyzed by using descriptive and coupling model. Our results suggest that the degree of coupling between financial inclusion and peasant entrepreneurship is higher in regions of financial underdevelopment than in other regions. This indicates that two distinct types of peasant employment exist as a result of different investment priorities in credit markets. In this paper, we provide new insights on financial breadth in supporting peasant entrepreneurship. [ABSTRACT FROM AUTHOR]

Ecological environmental worsening and water-soil loss occur frequently in mountainous areas in recent years. As an important index of land deterioration and degree of drought, soil moisture can influence vegetation growth, hydrological cycle and water resource distribution in mountainous areas significantly. However, topographic effect of soil moisture under complicated mountainous terrain conditions still remains unknown. In the present study, a quantitative inversion on surface soil moisture content in a typical mountainous area of Dengfeng city, Henan province (China) in 2013 and 2017 was carried out by using temperature vegetation dryness index method based on Landsat-8 OLI (Operational Land Imager) remote sensing images and digital elevation model (DEM). Results were conducive to analyze effects of terrain factors on spatial-temporal distribution of soil moisture and improve ecological environment in mountainous area. Besides, a quantitative analysis on topographic effect (e.g. elevation, slope and slope orientation) on soil moisture in mountainous area was performed based on DEM data. Results demonstrate that topographic factors (e.g. elevation, slope and slope orientation) can cause violent variations of soil moisture in mountainous areas. Soil moisture is positively correlated with elevation and slope. In view of slope orientations, shady slope generally presents the highest soil moisture, followed by semi-shady slope, sunny slope and semi-sunny slope successively. In 2017, the heat island effect in the study area is stronger and the arid area is significantly larger than those in 2013. Additionally, soil moisture in the region with elevation lower than 750 m and slope smaller than 25° is lower in relative to that in 2013, but it is higher in the region with elevation higher than 750 m and slope larger than 25°. This study discloses the influencing law of terrain factors on spatial-temporal distribution of soil moisture in mountainous areas. Research conclusions provide some scientific references to water conservation and ecological environmental protection in mountainous areas. [ABSTRACT FROM AUTHOR]

To deepen financial reform, China's financial policy displays more regional characteristics and the expansion of financial innovation to the regions has a far-reaching impact. This paper aims to evaluate the performance of financial innovation in 30 provinces in China. By using the panel data of 30 provinces in China, we apply the three-stage data envelopment analysis (Three-stage DEA) to regional financial innovation with environmental constraints taken into account. The application of Three-stage DEA that involves environmental factors and statistical noise enables us to assess the innovation disparity in the financial industry of each province in terms of technology. The conclusions are as follows: (1) Technical efficiency and scale efficiency are significantly reduced except for pure technical efficiency in the intermediary-oriented model compared with that before adjustment. The main reason for the decrease in overall efficiency is that the real level of financial innovation declined after the external environmental factors have been excluded. And the overall level in the market-oriented model is lower than that in the intermediary-oriented model. (2) External environment variables, including the regional economic basis, government loan intervention, the founding of the regional financial market, will affect the input of regional financial industry in accounting technical efficiency. Firstly, with a higher level of economic development, the quantity of personnel employed in the financial industry tends to rise. Secondly, the intervention from the local government is positive in the slack model of capital investment input, indicating that more government intervention in one area will lead to the inefficient use of capital investment in the financial industry. The estimated value of personnel input is negative, implying that personnel redundancy is negatively correlated with government intervention. Finally, the effect of the regional dummy variable is significantly negative in the slack of capital input, indicating that the regional financial market can improve the efficiency of capital investment in the financial sector. (3) The efficiency gap between provinces and districts still exists. It is revealed in our finding that there are efficiency disparities between coastal and interior regions, and between eight economic zones as well. It is suggested that fostering a good external environment is beneficial not only to promoting the efficiency of regional financial innovation, but also to bridging the gap between developed and developing provinces. Expanding the perspective of financial innovation research, this paper extends efficiency evaluation from traditional manufacturing to financial industry. Specifically, in this paper, regional financial innovation refers to the ability to reduce costs and increase the efficiency of input factors in financial industry. However, there is still plenty of room for research in this field, such as the characteristics of financial innovation in organizational structure, the external environment of financial competition and cooperation, and the growth effect of financial innovation. These are the directions for further research. [ABSTRACT FROM AUTHOR]

The green fraction (GF), which is the fraction of green vegetation in a given viewing direction, is closely related to the light interception ability of the crop canopy. Monitoring the dynamics of GF is therefore of great interest for breeders to identify genotypes with high radiation use efficiency. The accuracy of GF estimation depends heavily on the quality of the segmentation dataset and the accuracy of the image segmentation method. To enhance segmentation accuracy while reducing annotation costs, we developed a self-supervised strategy for deep learning semantic segmentation of rice and wheat field images with very contrasting field backgrounds. First, the Digital Plant Phenotyping Platform was used to generate large, perfectly labeled simulated field images for wheat and rice crops, considering diverse canopy structures and a wide range of environmental conditions (sim dataset). We then used the domain adaptation model cycle-consistent generative adversarial network (CycleGAN) to bridge the reality gap between the simulated and real images (real dataset), producing simulation-to-reality images (sim2real dataset). Finally, 3 different semantic segmentation models (U-Net, DeepLabV3+, and SegFormer) were trained using 3 datasets (real, sim, and sim2real datasets). The performance of the 9 training strategies was assessed using real images captured from various sites. The results showed that SegFormer trained using the sim2real dataset achieved the best segmentation performance for both rice and wheat crops (rice: Accuracy = 0.940, F1-score = 0.937; wheat: Accuracy = 0.952, F1-score = 0.935). Likewise, favorable GF estimation results were obtained using the above strategy (rice: R 2  = 0.967, RMSE = 0.048; wheat: R 2  = 0.984, RMSE = 0.028). Compared with SegFormer trained using a real dataset, the optimal strategy demonstrated greater superiority for wheat images than for rice images. This discrepancy can be partially attributed to the differences in the backgrounds of the rice and wheat fields. The uncertainty analysis indicated that our strategy could be disrupted by the inhomogeneity of pixel brightness and the presence of senescent elements in the images. In summary, our self-supervised strategy addresses the issues of high cost and uncertain annotation accuracy during dataset creation, ultimately enhancing GF estimation accuracy for rice and wheat field images. The best weights we trained in wheat and rice are available: https://github.com/PheniX-Lab/sim2real-seg., (Copyright © 2023 Yangmingrui Gao et al.)

A Gram-negative, aerobic, rod-shaped bacterium, designated strain JA3-B52(T), was isolated from the roots of fresh rice plants (Oryza sativa). The cells were motile by means of polar single or lateral flagella. The colonies were non-pigmented. On the basis of 16S rRNA gene sequence comparisons, the strain was phylogenetically related to species of the genus Rheinheimera, having the greatest level of sequence similarity with respect to Rheinheimera texasensis A62-14B(T) (97.16 %). The bacterium grew at temperatures from 10 to 37 degrees C, with an optimum at 30 degrees C. The strain exhibited growth with 0-3.0 % (w/v) NaCl and at pH 6.0-8.5. The main cellular fatty acids were C(16 : 1)omega7c, C(17 : 1)omega8c, C (16 : 0), C(18 : 1)omega7c and C(12 : 0) 3-OH. The DNA G+C content was 47.0 mol%. The levels of similarity between the 16S rRNA gene sequence of strain JA3-B52(T) and those of the type strains of Rheinheimera species ranged from 95.38 to 97.16 %. The mean level of DNA-DNA relatedness between strain JA3-B52(T) and R. texasensis A62-14B(T), the strain most closely related to the isolate, was 20.4 %. On the basis of physiological and biochemical characteristics and genotypic data obtained in this work, strain JA3-B52(T) represents a novel species of the genus Rheinheimera, for which the name Rheinheimera tangshanensis sp. nov. is proposed. The type strain is JA3-B52(T) (=CGMCC 1.6362(T) =DSM 19460(T)).