The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accur...The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.展开更多
In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through ...In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann-Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5-10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.展开更多
In recent years, the phenomenon of a critical slowing down has demonstrated its major potential in discovering whether a complex dynamic system tends to abruptly change at critical points. This research on the Pacific...In recent years, the phenomenon of a critical slowing down has demonstrated its major potential in discovering whether a complex dynamic system tends to abruptly change at critical points. This research on the Pacific decadal oscillation(PDO) index has been made on the basis of the critical slowing down principle in order to analyze its early warning signal of abrupt change. The chaotic characteristics of the PDO index sequence at different times are determined by using the largest Lyapunov exponent(LLE). The relationship between the regional sea surface temperature(SST) background field and the early warning signal of the PDO abrupt change is further studied through calculating the variance of the SST in the PDO region and the spatial distribution of the autocorrelation coefficient, thereby providing the experimental foundation for the extensive application of the method of the critical slowing down phenomenon. Our results show that the phenomenon of critical slowing down, such as the increase of the variance and autocorrelation coefficient, will continue for six years before the abrupt change of the PDO index. This phenomenon of the critical slowing down can be regarded as one of the early warning signals of an abrupt change. Through calculating the LLE of the PDO index during different times, it is also found that the strongest chaotic characteristics of the system occurred between 1971 and 1975 in the early stages of an abrupt change(1976), and the system was at the stage of a critical slowing down, which proves the reliability of the early warning signal of abrupt change discovered in 1970 from the mechanism. In addition, the variance of the SST,along with the spatial distribution of the autocorrelation coefficient in the corresponding PDO region, also demonstrates the corresponding relationship between the change of the background field of the SST and the change of the PDO.展开更多
Hong Kong is often affected by tropical cyclones.The Hong Kong observatory issues warning signals based on the impact of tropical cyclones on the region.The joint frequency analysis of tropical cyclones in Hong Kong c...Hong Kong is often affected by tropical cyclones.The Hong Kong observatory issues warning signals based on the impact of tropical cyclones on the region.The joint frequency analysis of tropical cyclones in Hong Kong can provide a scientific basis for disaster reduction and prevention and post-disaster reconstruction of tropical cyclones.First,the maximum hourly mean wind speed(W),warning signal duration(D),maximum sea level(L),and total rainfall(R)of each tropical cyclone that affected Hong Kong from 1985 to 2019 are selected and fitted using the Gumbel,Weibull,Pearson type 3,and lognormal distributions.Then,bivariate copula functions,such as the Clayton,Frank,Gumbel-Hougaard,and Gaussian copulas,are applied to construct the joint probability models of W,D,L,and R,respectively.The joint return periods of W and D and those of L and R are defined as the meteorological and hydrological intensities of tropical cyclones,respectively.The results show that the joint return periods are good indicators of the comprehensive effect of the meteorological and hydrological intensities of tropical cyclones.No necessary correlation between meteorological and hydrological intensities of tropical cyclones exists.The meteorological and hydrological intensities of tropical cyclones show an upward trend in recent years.展开更多
文摘The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.
基金Project supported by the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)the National Natural Science Foundation of China(Grant Nos.41175067,41275074,and 41105033)the Special Scientific Research Project for Public Interest,China(Grant No.GYHY201106015)
文摘In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann-Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5-10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.
基金supported by the National Natural Science Foundation of China(Grant Nos.41175067 and 41305056)the National Basic Research Program of China(Grant No.2012CB955901)+1 种基金the Special Scientific Research Project for Public Interest of China(Grant No.GYHY201506001)the Special Fund for Climate Change of China Meteorological Administration(Grant No.CCSF201525)
文摘In recent years, the phenomenon of a critical slowing down has demonstrated its major potential in discovering whether a complex dynamic system tends to abruptly change at critical points. This research on the Pacific decadal oscillation(PDO) index has been made on the basis of the critical slowing down principle in order to analyze its early warning signal of abrupt change. The chaotic characteristics of the PDO index sequence at different times are determined by using the largest Lyapunov exponent(LLE). The relationship between the regional sea surface temperature(SST) background field and the early warning signal of the PDO abrupt change is further studied through calculating the variance of the SST in the PDO region and the spatial distribution of the autocorrelation coefficient, thereby providing the experimental foundation for the extensive application of the method of the critical slowing down phenomenon. Our results show that the phenomenon of critical slowing down, such as the increase of the variance and autocorrelation coefficient, will continue for six years before the abrupt change of the PDO index. This phenomenon of the critical slowing down can be regarded as one of the early warning signals of an abrupt change. Through calculating the LLE of the PDO index during different times, it is also found that the strongest chaotic characteristics of the system occurred between 1971 and 1975 in the early stages of an abrupt change(1976), and the system was at the stage of a critical slowing down, which proves the reliability of the early warning signal of abrupt change discovered in 1970 from the mechanism. In addition, the variance of the SST,along with the spatial distribution of the autocorrelation coefficient in the corresponding PDO region, also demonstrates the corresponding relationship between the change of the background field of the SST and the change of the PDO.
基金The study was supported by the National Natural Science Foundation of China-Shandong Joint Fund(No.U1706226)the National Natural Science Foundation of China(No.52171284).
文摘Hong Kong is often affected by tropical cyclones.The Hong Kong observatory issues warning signals based on the impact of tropical cyclones on the region.The joint frequency analysis of tropical cyclones in Hong Kong can provide a scientific basis for disaster reduction and prevention and post-disaster reconstruction of tropical cyclones.First,the maximum hourly mean wind speed(W),warning signal duration(D),maximum sea level(L),and total rainfall(R)of each tropical cyclone that affected Hong Kong from 1985 to 2019 are selected and fitted using the Gumbel,Weibull,Pearson type 3,and lognormal distributions.Then,bivariate copula functions,such as the Clayton,Frank,Gumbel-Hougaard,and Gaussian copulas,are applied to construct the joint probability models of W,D,L,and R,respectively.The joint return periods of W and D and those of L and R are defined as the meteorological and hydrological intensities of tropical cyclones,respectively.The results show that the joint return periods are good indicators of the comprehensive effect of the meteorological and hydrological intensities of tropical cyclones.No necessary correlation between meteorological and hydrological intensities of tropical cyclones exists.The meteorological and hydrological intensities of tropical cyclones show an upward trend in recent years.