Spatial Autocorrelation Analysis of Genetic Structure of Zelkova schneideriana in Mailing Town,Guangxi

We analyzed the fine-scale spatial genetic structure of the individuals of Zelkova schneideriana , which were classified by age using the spatial autocorrelation method, to quantify spatial patterns of genetic variation within the population and to explore potential mechanisms that determine genetic variation in population. The spatial autocorrelation coefficient （ r ） at 13 distance classes was determined on the basis of both geographical distance and genetic distance matrix which was derived from co-dominant SSR data using GenAlEx software. The results showed that all the individuals of Z. schneideriana exhibited significantly positive spatial genetic structure at distance less than 40 m （the X -intercept was 53.568）, indicating that the average length of the smallest genetic patch for the same genotype clustering of the Z. schneideriana Mailing population was about 50 m. Limited seed dispersal is the main factor that leads to the spatial genetic variation within populations. The individuals in age Class II showed significantly positive spatial genetic structure at distance less than 30 m （the X -intercept was 47.882）, while the individuals in age Class I and age Class III showed no significant spatial genetic structure in any of the spatial distance classes. Z. schneideriana is a long-lived perennial plant; the self-thinning resulted from the cohort competition between individuals in the growing process may lead to this certain spatial structure in age Class III of Z. schneideriana population.

Reliability of electric vehicle charging infrastructure:A cross-lingual deep learning approach

Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector.Deployment of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions.In this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese.We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available.We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest.This evidence contrasts with predictions in the U.S.and European markets,where the performance is closer to parity.We also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.