Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

Let{X_(ni),F_(ni);1≤i≤n,n≥1}be an array of R^(d)martingale difference random vectors and{A_(ni),1≤i≤n,n≥1}be an array of m×d matrices of real numbers.In this paper,the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th(1<p<2)moments.Moreover,the complete convergence and strong law of large numbers are established under some mild conditions.An application to multivariate simple linear regression model is also provided.

Spatio-temporal variation prediction on Cd content in the rice grains from Northern Zhejiang Plain during 2014–2019 based on high-precision soil geochemical data

In recent years,Cadmium(Cd)pollution has been found in many soil geochemical surveys in Northern Zhejiang Plain,a crucial rice production area in East China,located in the lower Yangtze River.To more scientifically predict the effect of soil Cd on rice safety,data including 348 local rhizosphere soil-rice samples obtained in 2014 were used in this study.Meanwhile,we extracted 90% of random samples as variables based on soil Cd content(Cd_(soil)),soil organic matter(SOM),pH,and other indicators.In addition,a multivariate linear model for rice Cd content(Cd_(rice))prediction based on the indicators including the soil Cd content(Cd_(soil)),the soil organic matter(SOM),and the pH value.The remaining 10%of random samples were used for the significance test.Based on the 2014 soil Cd content(Cd_(soil14))and the 2019 soil Cd content(Cd_(soil19)),this study predicted Cd content in 2019 rice grains(Cd_(p-rice19)).The spatio-temporal variation of Cdrice was contrasted in the five years from 2014 to 2019,and the risk areas of rice safety production were analyzed using the Geographical Information System(GIS).The results indicated that compared with the actual Cd content in 2014 rice grains(Cdrice14),the proportion of Cd_(p-rice19),which exceeded the standard food level in China(GB2762-2017),increased dramatically.Moreover,the high-value areas of Cdrice distributed greatly coincidentally in these two years.By contrast,both Cdrice and Cdsoil show very different spatial scales.The dominant reason is the distribution of the local canal systems,indicating that economic activities and agricultural irrigation may aggravate the risk of soil Cd pollution,thus threatening safe rice production.

The relationship between time to a high COVID-19 response level and timing of peak daily incidence:an analysis of governments’Stringency Index from 148 countries

Background The transmission dynamics and severity of coronavirus disease 2019(COVID-19)pandemic is different across countries or regions.Differences in governments’policy responses may explain some of these differences.We aimed to compare worldwide government responses to the spread of COVID-19,to examine the relationship between response level,response timing and the epidemic trajectory.Methods Free publicly-accessible data collected by the Coronavirus Government Response Tracker(OxCGRT)were used.Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1,2020.The sub-indicators were scored and were aggregated into a common Stringency Index(SI,a value between 0 and 100)that reflects the overall stringency of the government’s response in a daily basis.Group-based trajectory modelling method was used to identify trajectories of SI.Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.Results Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated:before January 13,from January 13 to February 12,from February 12 to March 11,and the last stage—from March 11(the day WHO declared a pandemic of COVID-19)on going.Governments’responses were upgraded with further spread of COVID-19 but varied substantially across countries.After the adjustment of SI level,geographical region and initiation stages,each day earlier to a high SI level(SI>80)from the start of response was associated with 0.44(standard error:0.08,P<0.001,R2=0.65)days earlier to the peak number of daily new case.Also,each day earlier to a high SI level from the date of first reported case was associated with 0.65(standard error:0.08,P<0.001,R2=0.42)days earlier to the peak number of daily new case.Conclusions Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases.This may help to reduce the delays in flattening the epidemic curve to the low spread level.