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Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China. [ABSTRACT FROM AUTHOR]

Backgrounds. Diabetes mellitus (DM) is one of the most common comorbidities in patients with coronavirus disease (COVID-19). We aim to summarize the clinical features of DM patients with COVID-19 and find out potential factors associated with severe disease. Methods. In this retrospective, single-center study, the medical records of patients with COVID-19 in Changsha, Hunan, China, from January 21, 2020, to February 19, 2020, were reviewed. Epidemiological information, clinical features, and outcomes were compared between DM patients admitted to the intensive care unit (ICU) or not. Results. A total of 241 patients confirmed with COVID-19 were enrolled, including 19 DM patients. There were more patients in DM group admitted to the ICU than non-DM group (36.8% vs. 15.8%, P = 0.045). Compared with non-DM group in the ICU, there were more female patients from DM group in the ICU (85.7% vs. 31.4%, P = 0.024). On admission, the mean level of glycated hemoglobin A1c (HbA1c) was higher in the ICU DM patients than that in the non-ICU DM patients (8.5% vs. 7.1%). There were more DM patients with proteinuria in the ICU group than the non-ICU group (57.1% vs. 33.3%). Twelve DM patients (63.2%) changed diabetic therapy during hospitalization, and all DM patients admitted to the ICU used insulin. As of March 14, all 19 DM patients have been discharged, and no death occurred. Conclusions. DM patients with COVID-19 are vulnerable to severe disease, especially for female patients. High levels of HbA1c and proteinuria could be potential risk factors for severe COVID-19 in DM patients. In addition to timely systemic therapy, the control of blood glucose and proper diabetic therapy is essential to improve the prognosis of severe DM patients with COVID-19. [ABSTRACT FROM AUTHOR]