RESUMEN
COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82â¼63.56 ‱ and 3.00â¼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951â¼181.261. The adjusted R2 in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.
RESUMEN
The bulk tin selenide (SnSe) is the best thermoelectric material currently with the highest figure-of-merit due to strong phonon-phonon interactions. We investigate the effect of electron-phonon coupling (EPC) on the transport properties of a two-dimensional (2D) SnSe sheet. We demonstrate that EPC plays a key role in the scattering rate when the constant relaxation time approximation is deficient. The EPC strength is especially large in contrast to that of pristine graphene. The scattering rate depends sensitively on the system temperatures and the carrier densities when the Fermi energy approaches the band edge. We also investigate the magnetothermoelectric effect of the 2D SnSe. It is found that at low temperatures there is enormous magnetoelectrical resistivity and magnetothermal resistivity above 200%, suggesting possible potential applications in device design. Our results agree qualitatively well with the experimental data.