Interpersonal distance perception during the normalization of an pandemic situation: Effects of mask-wearing and vaccination.

This study aimed to evaluate the effect of anti-pandemic measures, including wearing a face mask and receiving vaccinations, on interpersonal distance (IPD) during the normalization stage of the COVID-19 pandemic. Virtual reality (VR) technology was used to simulate the experimental environment and a confederate in different conditions. Thirty-one participants were asked to approach the virtual confederate, who could exhibit three vaccination states and two mask-wearing conditions, actively and passively in both indoor and outdoor environments. ANOVA results showed that the participants kept a smaller IPD from the confederate wearing a face mask (IPD = 125.6 cm) than from the one without a face mask (IPD = 154.2 cm). The effects of vaccination states were significant, with the largest distance for an unvaccinated confederate (IPD = 182.3 cm) and the smallest distance for the confederate who had received a booster vaccine (IPD = 111.5 cm). Significant effects of environment were also found, with the participants maintaining a larger IPD in an outdoor environment (IPD = 143.4 cm) than in an indoor room (IPD = 136.4 cm). Additionally, the IPD collected when the participants were passively approached (IPD = 149.6 cm) was significantly larger than that obtained when they actively approached the confederate (IPD = 130.3 cm). Moreover, when the participants faced a confederate who had received a booster vaccine and wore a mask, the IPD was not significantly different from that collected before the COVID-19 pandemic in both the active and passive patterns. These findings help us to better understand the nature of IPD and human behaviors during the normalization stage of the pandemic and provide scientific suggestions for policymakers to develop pandemic-prevention measures.

Explaining the GeV Antiproton Excess, GeV γ-Ray Excess, and W-Boson Mass Anomaly in an Inert Two Higgs Doublet Model.

For the newly discovered W-boson mass anomaly, one of the simplest dark matter (DM) models that can account for the anomaly without violating other astrophysical and experimental constraints is the inert two Higgs doublet model, in which the DM mass (m_{S}) is found to be within ∼54-74 GeV. In this model, the annihilation of DM via SS→bb[over ¯] and SS→WW^{*} would produce antiprotons and gamma rays, and may account for the excesses identified previously in both particles. Motivated by this, we reanalyze the AMS-02 antiproton and Fermi-LAT Galactic center γ-ray data. For the antiproton analysis, the novel treatment is the inclusion of the charge-sign-dependent three-dimensional solar modulation model as constrained by the time-dependent proton data. We find that the excess of antiprotons is more distinct than previous results based on the force-field solar modulation model. The interpretation of this excess as the annihilation of SS→WW^{*} (SS→bb[over ¯]) requires a DM mass of ∼40-80 (40-60) GeV and a velocity-averaged cross section of O(10^{-26}) cm^{3} s^{-1}. As for the γ-ray data analysis, besides adopting the widely used spatial template fitting, we employ an orthogonal approach with a data-driven spectral template analysis. The fitting to the GeV γ-ray excess yields DM model parameters overlapped with those to fit the antiproton excess via the WW^{*} channel. The consistency of the DM particle properties required to account for the W-boson mass anomaly, the GeV antiproton excess, and the GeV γ-ray excess suggests a common origin of them.

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data.

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images.

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.