China＇s geographical regionalization in Chinese secondary school curriculum （1902-2012）

AS geographic literacy training is an education, national geographical education is essential content for Chinese citizens＇ literacy one of the indispensable key aspects of that training. Because of China＇s vast land area and the significance of the regional differences, it is difficult for students to receive a solid education regarding China＇s geography. In 1902, the government established the geography curriculum and began to incorporate the concept of geographical regionalization to facilitate the teaching of China＇s geography by publishing the first syllabi, which was modified later and put into use in 1904. Since 1904, numerous geog raphy textbooks have attempted to find an appropriate way to present China＇s geographical regionalization, as it is important for secondary students to have a clear understanding and appreciation for the different regional characteristics. Accordingly, the geographical region alization content is divided into four main phases： 1902 to 1927; 1928 to 1948; 1949 to 1986; and 1986 to the present. During these phases, the content related to China＇s geographical regionalization in secondary middle school geography textbooks has been gradually simplified and has adopted a more scientific foundation, thus leading to the present time where there are now four geographical regional areas. From this historical study, we find that while China＇s geographical regionalization has gradually matured, there are still some problems. For example, dividing the study into four regions to describe the panorama of China is too simplified, and the learning therefore becomes too scattered and haphazard, particularly qiven the new curriculum reform.

The Homogeneity of Distributional Patterns of the Global Terrestrial Animal,Plant and Microorganism under the Influence of Ecological Conditions

The world animal geographical regionalization scheme and the plant geographical regionalization scheme have been formulated by zoologists and botanists respectively since the biogeography has been established.This research team initially confirmed the homogeneity of Chinese animal and plant geography.To explore the relationship between the distribution pattern of global animals,plants,and microorganisms,global 141,814 genera of terrestrial animals,17,526 genera of plants,21,321 genera of microorganisms,and their major taxa were analyzed using their proposed SGF(Similarity General Formula)and a new multivariate similarity clustering analysis method.Almost identical analytical results were obtained,meeting the requirements of statistics,geography,ecology and biology respectively.The expected consistency of their distribution pattern was achieved for the first time.We prove that the earth’s ecological conditions affect the homogeneity and accumulation of the distribution of animals,plants and microorganisms.Homogeneity determines the distribution pattern of global kinds of biological consistency,accumulation determines the impact of the evolutionary period on the breadth of distribution,microorganisms appear earliest,plants second,animals later,and their average distribution domain decreases in turn,reflecting these differences.Therefore,this study not only provides a theoretical basis and quantitative basis for the establishment of geographical regionalization scheme but also advances the development of biogeography to a new stage and raises the theory of biogeographic analysis to a new height.

Region-aware neural graph collaborative filtering for personalized recommendation

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion.Especially,personalized recommendation models based on graph structure have advanced greatly in predicting user preferences.However,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized recommendation.This study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user preference.The approach first characterizes the relationships between items and users with a user-item-region graph.And,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and regions.Finally,the model fuses region and item vectors to infer user preferences.Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations.This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.