Search

To meet the requirement of the wind energy resource assessment and utilization, the statistical characteristics of wind climate in the China mainland were analyzed in this study by using observation wind dataset for 1971–2017 from the China Meteorological Administration (CMA). A series of key factors closely related to wind power development were analyzed, systematically including the spatial and temporal characteristic of wind speed, the distribution of wind speed at different density grades, the density of the cumulative distribution, strong wind days and weak wind days. The characteristics of wind patterns in different regions are also compares. Results indicate that: (1) the regions with the highest wind speed in China centrally locate in Northwest China, North China and Northeast China with the highest wind speed occurs in spring. The annual variation of wind speed in northern China is bimodal while in southern China it is unimodal. (2) The wind speed probability density distribution shows a significant spatial–temporal difference. The wind speed probability distribution is the most concentrated in spring for almost all regions. For each season, the concentrated and stable wind speed occurs in Xinjiang region in spring. While it appears in the region from Northwest China to the northern part of North China for summer and autumn, and in the southern part of North China and Central China for winter. In the case of equal cumulative probability, the highest wind speed locates in Northeast China and the lowest in Sichuan Basin. (3) For 1971–2017, the wind speed in all regions of China showed a decreasing trend on the whole. But this trend slowed down since 1990s. It is​ worth noting that the wind speed in some regions even increased since 2000. (4) The numbers of strong wind day have been decreasing in all regions, but the numbers of weak wind day demonstrate an evolutionary feature of a growth followed by decrease. In most regions, the numbers of weak wind day reached the maximum in the vicinity of 2000 and then decreased rapidly.

Abstract Background Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is threatening the world with the symptoms of seasonal influenza. This study was conducted to investigate the patient characteristics and clinical value of blood markers to assess the severity of coronavirus disease 2019 (COVID-19). Methods 187 patients, diagnosed with COVID-19 (non-severe and severe cases) and admitted to hospital between January 27th and March 8th of 2020, were enrolled in the present study. Results A higher proportion of clinical symptoms, including cough, expectoration, myalgia, and fatigue were observed in the non-severe group. The level of white blood cell count, neutrophils, CRP, IL-6 and IL-8 were significantly increased, while the platelet count was remarkedly decreased in the severe group. The risk model based on lymphocyte, IL-6, IL-8, CRP and platelet counts had the highest area under the receiver operator characteristic curve (AUROC). The baseline of IL-6, IL-8 and CRP was positively correlated with other parameters except in the cases of lymphocyte, hemoglobin and platelet counts. The baseline of the platelet count was negatively correlated with other parameters except in the lymphocyte and hemoglobin counts. Additionally, there was no connection between the severity of COVID-19 and cultures of blood, sputum or catheter secretion. Conclusions The present study suggested that high leucocyte and low platelets counts were independent predictive markers of the severity of COVID-19.

In the Lancang‒Mekong River basin (LMRB), agriculture, dominating the local economy, faces increasing challenges in water supply under climate change. The projection of future precipitation in this basin is essential for understanding the challenges. In this study, the Weather Research and Forecasting (WRF) model was applied to project the LMRB precipitation. Comparing with the historical period (1986–2005), we analyzed the changes of both the projected precipitation amount and the frequency of rainless (100 mm d−1) for three periods, namely the near-term (2016–2035), mid-term (2046–2065), and long-term (2080–2099). The results indicate that the precipitation amount during the wet season (April–October) is expected to increase in most areas of the basin for the three periods. As for the precipitation during the dry season (November–March), an increase is projected in most areas for the near-term, while an increase in the lower reach of the basin and a decrease in the upper and middle reach for the mid-term and long-term. The precipitation reduction is expected to be greatest in Myanmar, Laos, Thailand, and Yunnan province of China for the mid-term. The frequency of precipitation in different intensities has prominent regional and temporal differences. During the wet season, the days of rainless and light rain are expected to decrease in the middle reach, whereas the days of rainstorm and heavy rainstorm increase. This feature is especially strong in southern Thailand, southern Laos and Cambodia in the near-term and in Laos and Thailand for the mid-term and long-term. During the dry season, there are projected increasing rainless days and decreasing days of precipitation for the other intensities in the middle reach, and opposite in the rest area of the basin. These projected precipitation changes have potential various impact in different parts of the basin. The middle reach would likely face increasing flood risks because of more days of rainstorm and heavy rainstorm, as well as more precipitation. Yunnan, Myanmar, Thailand and Laos would probably be the center of drought threatens during the dry season due to the increment of rainless days and the precipitation reduction. Besides, the seawater intrusion during the dry season in the near-term and mid-term would be more serious as a result of the precipitation decrease in southern Vietnam.

The climatic variation of offshore wind energy has a close relationship with the long-term plan of energy utilization. However, the work on this aspect is scarce and mainly focuses on the variation of wind power density (WPD). There is little research on the climatic trends of effective wind speed occurrence (EWSO) and occurrence of energy level greater than 200 W/m2 (rich level occurrence, RLO), which are directly related to the available rate and richness of wind energy. Based on the ERA-Interim wind product from the ECMWF, this study calculated the climatic trends of series of key factors of wind energy in the global oceans, including the WPD, EWSO, and RLO. The results show that the wind energy exhibits a positive trend globally for the past 36 years, with overall annual increasing trends in WPD, EWSO, and RLO, of 0.698 (W/m2)/yr, 0.076%/yr, and 0.090%/yr separately. The annual trend exhibits evident regional differences. The areas with significant increasing trends are mainly distributed in the mid- low-latitude waters of global oceans and part of the southern hemisphere westerlies. The annual increasing trend of WPD is strongest in the southern westerlies, especially in the extratropical South Pacific (ETSP), of about 1.64 (W/m2)/yr. The annual increasing trends of EWSO and RLO are strongest in the tropical waters, especially the tropical Pacific Ocean (TPO), of 0.17%/yr and 0.19%/yr separately. The annual and seasonal WPD, EWSO, and RLO in most global oceans have significant increasing trends or no significant variation, meaning that the wind energy trends are rich or stable, which is beneficial for energy development. The climatic trends of wind energy are dominated by different time periods. There is no evident abrupt change of wind energy in the extratropical waters globally and tropical Atlantic Ocean (TAO). The abrupt periods of wind energy in the TIO and TPO occurred in the end of the 20th century and the beginning of the 21st century. The wind energy of the South China Sea, Arabian Sea, and the Bay of Bengal and nino3 index share a common period of approximately 5 years. The offshore wind energy was controlled by an oscillating phenomenon.

The quantity of human intestinal microbiota is large and diverse, its changes with age. Intestinal microbiota is closely related to the frailty of the elderly, and can be potential risk factor in the occurrence and development of frailty with different mechanisms. Therefore, the study of the relationship between frailty and intestinal microbiota in elderly patients is of great significance to the regulation of intestinal microbiota in elderly patients with frailty, so that the elderly at risk of frailty can get timely and necessary intervention and help.