At present, research on inclusive finance issues at home and abroad has made considerable progress, but as China is currently facing epidemics and other geological disasters, the financial industry is facing huge challenges. As the transitional development of China’s electronic commerce has dealt a severe blow to China’s physical manufacturing industry, the people’s economy has been hit hard. The establishment of inclusive finance and its system has broadened the scope of traditional financial system services and has had a greater positive impact on disadvantaged groups and small and micro enterprises. This article uses data on inclusive finance, the development of rural financial institutions, and poverty alleviation of rural households in parts of China’s flood-affected areas shown by satellite images in 2020 as a sample, and builds the level of inclusive finance development from two aspects: the depth and breadth, the measurement index system. On this basis, a model for inclusive finance promotion using satellite data combined with AI algorithms was designed, and then a static panel measurement model for rural household poverty, inclusive finance and rural financial institutions was constructed from the perspective of big data. According to two measurement models, it examines the effects of the development of different types of rural financial institutions on poverty alleviation and proposes the necessary conditions for achieving inclusive finance. The results of the study show that the growth rate of inclusive finance has increased by 19.8% in seven years. Therefore, although the financial demand of various cities is increasing year by year, the growth is still relatively slow. This is mainly caused by the imbalance of the supply side of inclusive finance, because as the financial demand of consumers continues to increase, the supply side cannot meet. The corresponding financial demand will inevitably lead to a decrease in demand growth rate.
Abstract N−N Atropisomers are a common motif in natural products and represent a significant dimension for exploration in modern pharmaceutical and medicinal chemistry. However, the catalytic atroposelective synthesis of such molecules remains challenging, hampering meaningful development. In particular, an enantioselective synthesis of N−N bisindole atropisomers is unprecedented. Herein, the first enantioselective synthesis of N−N bisindole atropisomers via the palladium‐catalyzed de novo construction of one indole skeleton is presented. A wide variety of N−N axially chiral bisindoles were generated in good yields with excellent enantioselectivities via a cascade condensation/N‐arylation reaction. Structurally diverse indole‐pyrrole, indole‐carbazole, and non‐biaryl‐indole atropisomers possessing a chiral N−N axis were accessed using this protocol. Moreover, investigations using density functional theory (DFT) calculations provided insight into the reaction mechanism and enantiocontrol.
Abstract Purpose The goal of this study is to explore whether deep learning based embedded models can provide a better visualization solution for large citation networks. Design/methodology/approach Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters. Findings The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps’ layout has very high stability. Research limitations The computational and time costs of training are very high for network embedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested. Practical implications This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliometric analysis tasks. Originality/value This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer, more stable science map. We also designed a practical evaluation method to investigate and compare maps.
The mole ratio r(r = [I(-)](0)/[ClO(2)](0)) has great influence on ClO(2)-I(-)-H(2)SO(4) closed reaction system. By changing the initiate concentration of potassium iodide, the curve of absorbance along with the reaction time was obtained at 350 nm and 297 nm for triiodide ion, and 460 nm for iodine. The changing point of the absorbance curve's shape locates at r = 6.00. For the reaction of ClO(2)-I(-) in the absence of H(2)SO(4), the curve of absorbance along with the reaction time can be obtained at 350 nm for triiodide ion, 460 nm for iodine. The mole ratio r is equal to 1.00 is the changing point of the curve's shape no matter at which wavelength to determine the reaction. For the reaction of ClO(2)-I(-)-H(+) in different pH buffer solution, the curve of absorbance along with the reaction time was recorded at 460 nm for iodine. When r is greater than 1.00, the transition point of the curve's shape locates at pH 2.0, which is also the point of producing chlorite or chloride for chlorine dioxide at different pH. When r is less than 1.00, the transition point locates at pH 7.0.
Abstract With the change of dynasties and historical changes, the specific routes of the Shu Road and the cultural and geographical environment also changed. Richthofen’s field trip from Qin to Shu recorded more detailed geographical science knowledge along the way, and provided important historical data for scholars to study the geographical science knowledge of Shu Road in the late Qing Dynasty. The article mainly uses Richthofen’s diary of the Shu Road investigation as the basis of historical data, from the perspective of residential history and geography, to study the geographical landscape culture of the Shu Road villages in the late Qing Dynasty. First, clarify the specific description of the village residential landscape along the Shu Road in the diary; secondly, use the typological method to classify and compare the distribution of the villages along the Shu Road, the types of dwellings, and the specific characteristics; finally, use the phenomenological method to analyze the village dwellings.The main factors for the formation of the landscape, and then the establishment of the cultural core model of the residential landscape of the Shudao village.
ABSTRACT This paper focuses on the linguistic evolution of the Tianjin speech community in Sabah, Malaysia. From the perspective of restructuring of speech community, the paper integrates both micro and macro levels of language change into the analysis. Several methods were adopted in this study. Interviews were conducted with community leaders and various families. Besides, an ethnographic approach is taken to observe language use in different activities within the community. In total, 17 Tianjiners were interviewed who were from four generations (G3, G4, G5, and G6) and several community activities were participated. Despite all the social changes within the community, the Tianjin people (also known as northern Chinese) still manifest a strong group identity which differentiate themselves from southern Chinese, such as Hakka in Sabah. The strong group identification goes well with its diversity, which is one of the characteristics of the Tianjin speech community.
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