This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate m...This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers su...In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers survival in older adults[2].A decline in physical function can also be observed in older adults with increasing age.Grip strength has been shown to be a marker of overall physiological function in older adults.展开更多
跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统...跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统对M-LAG技术的迫切需求。将M-LAG技术与传统堆叠技术进行比较,证实M-LAG技术具有更高的灵活性及故障恢复能力、更简化网络管理和更低成本,在提高网络性能和可靠性方面具备巨大潜力。以实际应用场景为例,表明M-LAG技术可实现铁路各站、各设备间的高速、可靠、实时的数据传输,为铁路运输智能化、自动化提供有力技术支持。展开更多
The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ...The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.展开更多
A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions...A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.展开更多
基金supported by the National Natural Science Foundation of China(No.52174184)。
文摘This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金supported by the Shanghai New Three-year Action Plan for Public Health(Grant No.GWV-10.1-XK16)the US National Institute on Aging(RO1-AGO34479).
文摘In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers survival in older adults[2].A decline in physical function can also be observed in older adults with increasing age.Grip strength has been shown to be a marker of overall physiological function in older adults.
文摘跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统对M-LAG技术的迫切需求。将M-LAG技术与传统堆叠技术进行比较,证实M-LAG技术具有更高的灵活性及故障恢复能力、更简化网络管理和更低成本,在提高网络性能和可靠性方面具备巨大潜力。以实际应用场景为例,表明M-LAG技术可实现铁路各站、各设备间的高速、可靠、实时的数据传输,为铁路运输智能化、自动化提供有力技术支持。
文摘The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences[grant numbers XDA23090102]the National Natural Science Foundation of China[grant numbers 42175078 and 42075040]+1 种基金the Health Meteorological Project of Hebei Province[grant number FW202150]the National Key Research and Development Program of China[grant number 2018YFA0606203].
基金supported by the National Basic Research Program of China (973 Program: Grant No. 2010CB951902)the Special Program for China Meteorology Trade (Grant No. GYHY201306020)the Technology Support Program of China (Grant No. 2009BAC51B03)
文摘A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.
