Social distancing is one of the most recommended policies worldwide to reduce diffusion risk during the COVID-19 pandemic. Based on a risk management perspective, this study explores the mechanism of the risk perception effect on social distancing in order to improve individual physical distancing behavior. The data for this study were collected from 317 Chinese residents in May 2020 using an internet-based survey. A structural equation model (SEM) and hierarchical linear regression (HLR) analyses were conducted to examine all the considered research hypotheses. The results show that risk perception significantly affects perceived understanding and social distancing behaviors in a positive way. Perceived understanding has a significant positive correlation with social distancing behaviors and plays a mediating role in the relationship between risk perception and social distancing behaviors. Furthermore, safety climate positively predicts social distancing behaviors but lessens the positive correlation between risk perception and social distancing. Hence, these findings suggest effective management guidelines for successful implementation of the social distancing policies during the COVID-19 pandemic by emphasizing the critical role of risk perception, perceived understanding, and safety climate.

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PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.