ABSTRACT
【Objective】 To explore the geographical environment factors that may affect serum uric acid (UA) of healthy people and explore the change trend of UA reference value at the national scale. 【Methods】 The UA reference values of 607905 healthy people from 565 loci in China were collected, and the correlation between 25 geographical environment factors and UA reference values was analyzed by correlation analysis. CatBoost model was constructed and SHAP value interpretation model was applied to predict the UA reference values of healthy people in counties and cities in China, and the geographical distribution map of UA reference values of healthy people in China was drawn by using ordinary Kriging. 【Results】 A total of 20 indicators, namely, latitude, altitude, annual average temperature, annual average relative humidity, annual precipitation, air temperature annual range, annual average wind speed, percentage of surface soil silt, surface soil bulk density, surface soil gravel content, surface soil organic matter content, surface soil PH, surface soil (clay) cation exchange capacity, surface soil (silt) cation exchange capacity, surface soil base saturation, total surface soil exchange capacity, T-CaCO3, T-CaSO4, surface soil alkalinity, and surface soil salt showed their correlation with UA reference value of healthy people nationwide. The spatial distribution of UA reference values of healthy people across the country differed, manifested as the changing trend of higher in high altitude regions, higher in coastal regions than in inland regions, lower in the mid-eastern region, and higher in Southwest China at similar altitudes. 【Conclusion】 This study lays a foundation for further studies on the mechanism of different influencing factors on UA reference value. CatBoost model was established to provide the basis for establishing reference standards using UA reference values as prognostic factors for hyperuricemia and related chronic diseases in different regions.
ABSTRACT
Objective: To review the physical characteristics in modern Chinese populations, and to exploring the relationships between physical traits and the geo-environmental factors. Methods: This paper took the principal component and cluster analysis of the craniofacial physical characters of 134 populations in China (65 Han nationalities, 59 minority nationalities and 10 unidentified populations) with 15 measurements. We also focused the linear relationship between the craniofacial measurements and 8 geo-environmental factors. Results: The physical characteristics of Modern Chinese was divided into the northern and southern groups: North Han was close to north minority nationalities; Han nationalities were close to each other in southern region, as well as the minority nationalities. In addition, the physiognomic ear length and physiognomic ear breadth decreased when the annual mean temperature increases, which was not consistent with Allen's rule. Conclusion: The differentiation of northern and southern Chinese population is a result of the physical adaptation which may be caused by the selective pressures under different regional environments. And the north-south demarcation zone (Qinling mountain - Huaihe river) can be considered as the most suitable boundary between the southern and northern Chinese.
ABSTRACT
The service of sensor device in Emerging Sensor Networks (ESNs) is the extension of traditional Web services. Through the sensor network, the service of sensor device can communicate directly with the entity in the geographic environment, and even impact the geographic entity directly. The interaction between the sensor device in ESNs and geographic environment is very complex, and the interaction modeling is a challenging problem. This paper proposed a novel Petri Nets-based modeling method for the interaction between the sensor device and the geographic environment. The feature of the sensor device service in ESNs is more easily affected by the geographic environment than the traditional Web service. Therefore, the response time, the fault-tolerant ability and the resource consumption become important factors in the performance of the whole sensor application system. Thus, this paper classified IoT services as Sensing services and Controlling services according to the interaction between IoT service and geographic entity, and classified GIS services as data services and processing services. Then, this paper designed and analyzed service algebra and Colored Petri Nets model to modeling the geo-feature, IoT service, GIS service and the interaction process between the sensor and the geographic enviroment. At last, the modeling process is discussed by examples.
