Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching

Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress detection model using the expanded training data. The new image generated based on SICAP does not change the pixel values of the original image. Thus, there is little loss of information, and the generated images are effective in constructing a robust model for various subway tunnel lines. We conducted experiments with some comparative methods. The experimental results show that the detection performance can be improved by our data augmentation

Saturation time of exposure interval for cross-neutralization response to SARS-CoV-2: Implications for vaccine dose interval

Summary: Evaluating the serum cross-neutralization responses after breakthrough infection with various SARS-CoV-2 variants provides valuable insight for developing variant-proof COVID-19 booster vaccines. However, fairly comparing the impact of breakthrough infections with distinct epidemic timing on cross-neutralization responses, influenced by the exposure interval between vaccination and infection, is challenging. To compare the impact of pre-Omicron to Omicron breakthrough infection, we estimated the effects on cross-neutralizing responses by the exposure interval using Bayesian hierarchical modeling. The saturation time required to generate saturated cross-neutralization responses differed by variant, with variants more antigenically distant from the ancestral strain requiring longer intervals of 2–4 months. The breadths of saturated cross-neutralization responses to Omicron lineages were comparable in pre-Omicron and Omicron breakthrough infections. Our results highlight the importance of vaccine dosage intervals of 4 months or longer, regardless of the antigenicity of the exposed antigen, to maximize the breadth of serum cross-neutralization covering SARS-CoV-2 Omicron lineages