Objective:Correctly understanding and evaluating the level of public risk perception toward public health emergencies not only helps experts and decision-makers understand the public’s preventative health behaviors t...Objective:Correctly understanding and evaluating the level of public risk perception toward public health emergencies not only helps experts and decision-makers understand the public’s preventative health behaviors to these emergencies but also enhances their risk information communication with the public.The aim of this study was to develop a risk perception scale for public health emergencies and test its validity and reliability during the coronavirus disease 2019(COVID-19)pandemic.Methods:Guided by the theoretical model of risk perception,an initial scale was generated through literature review,group meetings,resident interviews,and expert consultation.A pretest and item screening were then conducted to develop a formal risk perception scale for public health emergencies.Finally,the reliability and validity of the scale were validated through a questionnaire survey of 504 Chinese adults.Results:The final scale had 9 items.The content validity index of the scale was 0.968,and the content validity index of individual items ranged from 0.83 to 1.00.Three common factors,dread risk perception,severe risk perception,and unknown risk perception,were extracted for exploratory factor analysis,and together they explained 66.26%of the variance in the score.Confirmatory factor analysis showed that the model had a satisfactory fit,whereχ^(2)/df=1.384,the goodness-of-fit index(GFI)=0.989,root mean square error of approximation(RMSEA)=0.028,root mean square residual(RMR)=0.018,comparative fit index(CFI)=0.995,normed fit index(NFI)=0.982,and non-normed fit index(NNFI)=0.990.The correlations between dimensions ranged from 0.306 to 0.483(P<0.01).Cronbach’s a was 0.793 for the total scale and ranged between 0.687 and 0.801 for the individual dimensions.The split-half coefficient was 0.861 for the total scale and ranged from 0.727 to 0.856 for induvial dimensions.The test-retest coefficient was 0.846 for the total scale and ranged from 0.843 to 0.868 for induvial dimensions.Conclusion:The developed scale for the risk perception of public health emergencies showed acceptable levels of reliability and validity,suggesting that it is suitable for evaluating residents’risk perception of public health emergencies.展开更多
This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） thr...This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.展开更多
文摘Objective:Correctly understanding and evaluating the level of public risk perception toward public health emergencies not only helps experts and decision-makers understand the public’s preventative health behaviors to these emergencies but also enhances their risk information communication with the public.The aim of this study was to develop a risk perception scale for public health emergencies and test its validity and reliability during the coronavirus disease 2019(COVID-19)pandemic.Methods:Guided by the theoretical model of risk perception,an initial scale was generated through literature review,group meetings,resident interviews,and expert consultation.A pretest and item screening were then conducted to develop a formal risk perception scale for public health emergencies.Finally,the reliability and validity of the scale were validated through a questionnaire survey of 504 Chinese adults.Results:The final scale had 9 items.The content validity index of the scale was 0.968,and the content validity index of individual items ranged from 0.83 to 1.00.Three common factors,dread risk perception,severe risk perception,and unknown risk perception,were extracted for exploratory factor analysis,and together they explained 66.26%of the variance in the score.Confirmatory factor analysis showed that the model had a satisfactory fit,whereχ^(2)/df=1.384,the goodness-of-fit index(GFI)=0.989,root mean square error of approximation(RMSEA)=0.028,root mean square residual(RMR)=0.018,comparative fit index(CFI)=0.995,normed fit index(NFI)=0.982,and non-normed fit index(NNFI)=0.990.The correlations between dimensions ranged from 0.306 to 0.483(P<0.01).Cronbach’s a was 0.793 for the total scale and ranged between 0.687 and 0.801 for the individual dimensions.The split-half coefficient was 0.861 for the total scale and ranged from 0.727 to 0.856 for induvial dimensions.The test-retest coefficient was 0.846 for the total scale and ranged from 0.843 to 0.868 for induvial dimensions.Conclusion:The developed scale for the risk perception of public health emergencies showed acceptable levels of reliability and validity,suggesting that it is suitable for evaluating residents’risk perception of public health emergencies.
文摘This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.
