A Study on the Chess Culture of Ethnic Minority Groups in Gansu Province

As a region inhabited by large populations of ethnic minority groups, Gansu province boasts rich ethnic cultural resources which reflect traditional culture and intangible cultural heritage that had taken shape thousands of years ago. Among these are various ethnic folk chess games. They are time-honored with many types such as the “Fang” chess of the Hui ethnic group, the “Jiu” chess of the Tibetans, the “King and Ministers” chess, the Mongolian chess, the Mongolian military chess, etc. These games embody distinctive ethnic and regional characteristics, as well as the cultural elements of a particular ethnic minority group. It is of great significance, therefore, to study the ethnic chess by collecting, documenting and comparing different types of related chess. The paper explores ways to standardize, protect and promote them scientifically in joint efforts, and draws a conclusion that examining their significance in the present era and the ethnic characteristics is the key to understanding the profound culture behind the chess.

A fundamental theorem for eco-environmental surface modelling and its applications

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

新型冠状病毒消退阈值建模方法

On 21 December of 2019,four lower respiratory tract samples were collected from patients with pneumonia of unknown cause in Wuhan,China[1].This disease was later diagnosed as a coronavirus disease.The World Health Organization(WHO)named the coronavirus disease as COVID-19 on 11 February and declared a pandemic on 11 March 2020.The COVID-19 is a previously unknown disease with no tools available for pharmaceutical management at the early stage of its outbreak.