Highlights Higher step count is inversely associated with the risk of premature death and cardiovascular events. As measured by accelerometers, 8959 steps/day (Q3) had a 40.36% lower risk of all-cause mortality than 4183 steps/day (Q1). As measured by accelerometers, 9500 steps/day (Q3) had a 35.05% lower risk of cardiovascular events than 3500 steps/day(Q1). These associations were in nonlinear dose–response patterns.

No abstract

No abstract

No abstract

In order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities, the layout of charging facilities has higher requirements. This paper collects travel data in the form of a traffic travel questionnaire for electric vehicle users. Based on the vehicle parking demand model of the queuing theory and Monte Carlo simulation, the paper gives the number of stopping vehicles and the time of vehicles stopping in different places such as residential areas, workplaces, supermarket parking and roadside. In addition, based on the Bass prediction model, the main parameters are modeled in the model, and the price correction coefficient is introduced. The improved Bass model is used to predict the growth trend of electric vehicles in different regions in different years and in different incentive sites. By predicting the ownership of urban electric vehicles and accurately grasping the distribution and operation of electric vehicles, this paper can provide guidance and suggestions for the planning and construction of charging facilities in different regions, effectively reduce the investment cost of charging facilities and guide local governments to formulate reasonable planning schemes.