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The introduction of electric vehicles(EVs) alleviates greenhouse gases emission.Its application has huge potential in the attempt to achieve green transportation today.However,the long charging time and charging congestion greatly affect the travel experience of EVs.To optimize EV charging,the charging station(CS) selection scheme(where to charge) and the charging scheduling strategy(when to charge) become the core of solving the problem of urban EV charging.In this paper,the preemptive charging scheduling strategy considering the charging priority(CP) is proposed.This strategy allows the preemptive charging of EVs with high urgency of charging(calculated from the charging demand and the remaining parking duration).Based on the CP charging scheduling strategy,a CS selection scheme that further combines reservation information is optimized.This scheme selects the CS with the shortest charging travel time(including one-time charging process) for EVs.Meanwhile,EVs are required to report their charging reservation information to accurately predict the congestion status of CSs,so as to efficiently allocate charging resources.The charging network is simulated through the urban traffic scene of Helsinki.The results show that the charging management scheme,CP scheduling strategy and reservation-based CS selection scheme proposed in this paper,can effectively shorten the average charging travel time of EVs and provide fully charging service for more EVs within a limited parking duration.

Objective To evaluate the accuracy of the reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in community or primary-care settings. Method We systematically searched the Web of Science, Embase, PubMed, and Cochrane Library databases. We conducted quality evaluation using ReviewManager software (version 5.0). We then used MetaDisc software (version 1.4) and Stata software (version 12.0) to build forest plots, along with a Deeks funnel plot and a bivariate boxplot for analysis. Result Overall, the sensitivity, specificity, and diagnostic odds ratio were 0.79, 0.97, and 328.18, respectively. The sensitivity for the subgroup with RNA extraction appeared to be higher, at 0.88 (0.86–0.90), compared to the subgroup without RNA extraction, at 0.50 (0.45–0.55), with no significant difference in specificity. Conclusion RT-LAMP assay exhibited high specificity regarding current SARS-CoV-2 infection. However, its overall sensitivity was relatively moderate. Extracting RNA was found to be beneficial in improving sensitivity. [ABSTRACT FROM AUTHOR]