目的:分析广州市免费避孕药具在线领取的时空演变特征,并构建时间序列预测模型,为免费避孕药具“互联网+服务”的资源优化与政策调整提供量化依据。方法:基于2020—2024年广东省免费提供基本避孕药具服务管理系统308419人次在线领取记录...目的:分析广州市免费避孕药具在线领取的时空演变特征,并构建时间序列预测模型,为免费避孕药具“互联网+服务”的资源优化与政策调整提供量化依据。方法:基于2020—2024年广东省免费提供基本避孕药具服务管理系统308419人次在线领取记录,结合常住育龄妇女数据,运用多时间维度趋势分析、空间自相关季节性自回归积分滑动平均模型(seasonal autoregressive integrated moving average model,SARIMA)、自回归积分滑动平均模型(autoregressive integrated moving average model,ARIMA)、指数平滑状态空间模型(exponential smoothing state space model,ETS)、三角季节性Box-Cox变换ARMA误差趋势季节性分量模型(trigonometric,box-cox transformation,ARMA errors,trend and seasonal components model,TBATS)、神经网络自回归模型(neural network autoregression model,NNAR)以及Prophet模型等6种时间序列模型,对广州市11个行政区域的免费避孕药具在线领取进行时空特征分析,并预测2025—2026年月度需求。结果:(1)时序特征:2020—2024年年领取量由40.9万只增至370.2万只(年度复合增长率达65.2%),呈阶梯式攀升;月度分布呈“3月主峰、9月和12月次峰”的季节性;周一因在线领取平台的公众号推文触发,形成周内高峰。(2)空间特征:2020—2024年各区人均领取量均上升,白云区、天河区最高;全局Moran's I持续为负(-0.416~-0.360,P>0.05),无显著聚集,提示药具在线发放能打破地域限制,促进服务均等化。(3)预测模型中SARIMA(1,1,1)(1,1,1)[12]在6种模型中精度最高(MAPE=10.85%);广州市的免费避孕药具在线月度申领数量预计从2025年1月的31.68万只逐渐增加到2026年12月的47.69万只,2025年和2026年的预测同比增速分别为20.14%和19.01%,各区均呈稳健增长。结论:广州市在线药具需求呈一定的季节性与持续上升态势,呈现显著的空间分散特征;SARIMA模型适配性强,研究揭示的时空规律为避孕药具精准供应、数字化服务优化提供科学支撑。展开更多
Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.展开更多
With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the m...With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.展开更多
The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system sta...The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.展开更多
A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derive...A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derived from the momentum equation and the flow continuity equation under the condition of quasi-uniform flow. A series of experiments are conducted in a large-scale meandering compound channel. Based on the experimental data, a magnitude analysis is carried out for the governing equation, and two lower-order shear stress terms are ignored. Four groups of experimental data from different sources are used to verify the predictive capability of this model, and good predictions are obtained. Finally, the determination of the velocity parameter and the limitation of this model are discussed.展开更多
文摘目的:分析广州市免费避孕药具在线领取的时空演变特征,并构建时间序列预测模型,为免费避孕药具“互联网+服务”的资源优化与政策调整提供量化依据。方法:基于2020—2024年广东省免费提供基本避孕药具服务管理系统308419人次在线领取记录,结合常住育龄妇女数据,运用多时间维度趋势分析、空间自相关季节性自回归积分滑动平均模型(seasonal autoregressive integrated moving average model,SARIMA)、自回归积分滑动平均模型(autoregressive integrated moving average model,ARIMA)、指数平滑状态空间模型(exponential smoothing state space model,ETS)、三角季节性Box-Cox变换ARMA误差趋势季节性分量模型(trigonometric,box-cox transformation,ARMA errors,trend and seasonal components model,TBATS)、神经网络自回归模型(neural network autoregression model,NNAR)以及Prophet模型等6种时间序列模型,对广州市11个行政区域的免费避孕药具在线领取进行时空特征分析,并预测2025—2026年月度需求。结果:(1)时序特征:2020—2024年年领取量由40.9万只增至370.2万只(年度复合增长率达65.2%),呈阶梯式攀升;月度分布呈“3月主峰、9月和12月次峰”的季节性;周一因在线领取平台的公众号推文触发,形成周内高峰。(2)空间特征:2020—2024年各区人均领取量均上升,白云区、天河区最高;全局Moran's I持续为负(-0.416~-0.360,P>0.05),无显著聚集,提示药具在线发放能打破地域限制,促进服务均等化。(3)预测模型中SARIMA(1,1,1)(1,1,1)[12]在6种模型中精度最高(MAPE=10.85%);广州市的免费避孕药具在线月度申领数量预计从2025年1月的31.68万只逐渐增加到2026年12月的47.69万只,2025年和2026年的预测同比增速分别为20.14%和19.01%,各区均呈稳健增长。结论:广州市在线药具需求呈一定的季节性与持续上升态势,呈现显著的空间分散特征;SARIMA模型适配性强,研究揭示的时空规律为避孕药具精准供应、数字化服务优化提供科学支撑。
基金financially supported by the Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
文摘With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.
文摘The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.
基金Project supported by the National Natural Science Foundation of China(Nos.11171238,51279117,and 11072161)the Program for New Century Excellent Talents in University of China(No.NCET-13-0393)the National Science and Technology Ministry of China(No.2012BAB05B02)
文摘A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derived from the momentum equation and the flow continuity equation under the condition of quasi-uniform flow. A series of experiments are conducted in a large-scale meandering compound channel. Based on the experimental data, a magnitude analysis is carried out for the governing equation, and two lower-order shear stress terms are ignored. Four groups of experimental data from different sources are used to verify the predictive capability of this model, and good predictions are obtained. Finally, the determination of the velocity parameter and the limitation of this model are discussed.
