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Author(s): Yin Du [sup.1], Long Wang [sup.1], Gang Wu [sup.1], Xiaoming Lei [sup.1], Wei Li [sup.1], Jianrui Lv [sup.1] Author Affiliations: (1) grid.452672.0, Department of Anesthesiology, The Second Affiliated Hospital [...], An outbreak of novel coronavirus pneumonia occurred worldwide since December 2019, which had been named COVID-19 subsequently. It is extremely transmissive that infection in pregnant women were unavoidable. The delivery process will produce large amount of contaminated media, leaving a challenge for medical personnel to ensure both the safety of the mother and infant and good self-protection. Only rare cases of pregnant women with COVID-19 are available for reference. Here, we report a 30-year-old woman had reverse transcription polymerase chain reaction-confirmed COVID-19 at 36 weeks 2 days of gestation. Significant low and high variability of fetal heart rate baseline and severe variable decelerations were repeated after admission. An emergency cesarean section at 37 weeks 1 day of gestation under combined spinal and epidural anesthesia was performed with strict protection for all personnel. Anesthesia and operation went uneventfully. None of the participants were infected. We can conclude that when confronted with cesarean section in parturient with COVID-19, careful planning and detailed preparation can improve the safety of the mother and infant and reduce the risk of infection for medical staff to help preventing and controlling the epidemic.

Most of vision‐based methods for structural damage detection rely on supervised learning, requiring a substantial number of labeled images for model training, which is labor‐intensive and time‐consuming. To address these challenges, this study introduces a vision‐based structural anomaly detection and localization approach using unsupervised learning and reverse knowledge distillation. The proposed model incorporates a teacher model, a student model, and a trainable one‐class bottleneck embedding module. The asymmetrical architecture of the teacher and student models forms an encoder‐decoder structure for parameter transfer and feature extraction. The student network receives a specific embedding from the teacher network as input and target, facilitating the recovery of multiscale information from the teacher. Training images only contain the undamaged structures, and the teacher model, a pretrained model, instructs the student model to remember their undamaged features to detect and localize damages in unseen testing images. Through experiments, including a comparison among five candidate backbones for pretrained teacher models based on the residual network and testing across various structural damage types, the optimal model is identified, demonstrating good performance in both anomaly detection and localization. Furthermore, the model's generalization performance is thoroughly validated, confirming its efficacy across diverse scenarios. [ABSTRACT FROM AUTHOR]

Bridges play a significant role in urban areas, and their performance and safety are highly related to the carbon emissions of infrastructure systems. Previous studies have mainly offered maintenance policies that balance structural safety with overall costs. Considering the goal of achieving near-zero global carbon emissions by 2050, this study proposes a policymaking agent based on a convolutional autoencoder–structured deep-Q network (ConvAE-DQN) for managing deteriorating bridges at the network level while considering sustainability performance. This agent considers environmental, economic, and safety metrics, including spatially correlated structural failure probability, traffic volume, bridge size, and others, which are transformed into a multiattribute utility model to form the reward function. Reinforcement learning is employed to optimize the life-cycle maintenance planning to minimize the total carbon emissions and economic costs while maximizing regional safety performance. The proposed method is substantiated by developing sustainable life-cycle maintenance policies for an existing bridge network in Northern China. It is found that the proposed ConvAE-DQN policymaking agent could output efficient and sustainable life-cycle maintenance policies, which are annually stable and easy to schedule. The utility-based reward function enhances the stability and convergence efficiency of the algorithm. This study also assesses the impact of budget levels on network-level bridge safety and carbon footprint. [ABSTRACT FROM AUTHOR]

Displacement is an essential indicator of the functioning and safety of long-span cable-supported bridges under operational conditions. However, displacement estimation is challenging as these bridges are simultaneously subjected to various loading conditions such as temperature, wind, and vehicles. This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. Monitoring data of a cable-stayed bridge are collected to validate the proposed approach, including comprehensive measurements of the various loads and the displacement responses. Characteristics of temperature, wind, and vehicle loads are taken as the input, and the displacement responses at the mid-span of the main girder and top of the two pylons are taken as the output. The results showed the effectiveness of the proposed approach with an accuracy higher than 95%, which clearly outperformed other models such as long short-term memory networks in accuracy and efficiency. The effects of different types of loads are also investigated, and the wind load is found to be the most influential. Furthermore, multistep ahead prediction is carried out using the proposed approach, and good accuracy is achieved even 5 min ahead. The proposed approach can shed light on early warning of the malfunction of the bridge. [ABSTRACT FROM AUTHOR]