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Urban villages represent informal residential areas emerging since China's rapid urbanization process. Scientific map generalization of urban villages with scientific maps aids readers in discerning their distribution and making informed decisions concerning them. However, there is still a scarcity of research on the automatic extraction and generalization of urban villages from vector data, which needs to be studied to further improve the expression of maps. To address this problem, this paper presents a methodology for the extraction and generalization of urban villages from Digital Line Graphics. Firstly, a heuristic approach is employed to analyze the atypical morphological characteristics of urban villages. Then, indices based on computational geometry and the modified Hausdorff distance are utilized to quantify these traits. Lastly, an automatic generalization principle for urban villages is offered. The approach was tested in experimental blocks and proved to be effective. It offers a novel method for the automatic extraction and cartography of urban villages. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

Hyperuricemia has received increasing attention as a major public health problem. This study aims to investigate the risk factors for hyperuricemia and to explore the relationship between changes in biochemical variables and incident hyperuricemia.A cross-sectional and subsequently prospective study was performed among adults who took their health checkups at Zhejiang University Hospital. The participants who were free of hyperuricemia at baseline received annual follow-up examinations during a 6-year period. Cox proportional hazards regression analyses were conducted to calculate the risks for incident hyperuricemia.Of the 9238 participants enrolled, 1704 (18.4%) were diagnosed as hyperuricemia. During 21,757 person-years of follow-up, 1492 incident hyperuricemia cases were identified. The incidence of hyperuricemia was 68.58 cases per 1000 person-year of follow-up in the overall participants. The prevalence and the incidence of hyperuricemia increased greatly in female older than 50 years. High levels of BMI, SBP, FPG, TG, LDL-C, ALT, BUN, and creatinine increased the risk of hyperuricemia. Suffering fatty liver also increased the risk of hyperuricemia. Subjects with increasing DBP, TG, BUN, creatinine, or decreasing HDL-C were more likely to incident hyperuricemia.This study revealed that the change of diastolic blood pressure (DBP), serum triglycerides (TG), blood urea nitrogen (BUN), creatinine, and high-density lipoprotein cholesterol (HDL-C) level were independently associated with incident hyperuricemia.