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Due to the spread of COVID-19 worldwide, a large number of universities had to close their campuses. To maintain teaching and learning during this disruption to the traditional teaching, most universities have adopted online teaching model. The current study aimed at investigating the efficacy of various online teaching modes as well as comparing a proposed combined model of online and flipped learning to other online and traditional models. The Learning under COVID-19questionnaire was designed and administered to undergraduate engineering students at Chengdu University of Information Technology (CUIT). The questionnaire included five parts: demographic questions, frequencies of online courses, types of online courses, the communication and Q&A in online classes and the effect of online classes, as well as the effect of combined model learning. The results of the study showed that, students were dissatisfied with online learning in general, and they were especially dissatisfied with the communication and Q&A modes. In addition, the combined model of online teaching with the flipped learning improved students' learning, attention, and evaluation of courses. [ABSTRACT FROM AUTHOR]

Rail transit plays a significant role in the operation of an efficient and effective urban public transportation system. Safety and capacity are some of the most crucial objectives in railway operations. The communication-based train control (CBTC) system is a continuous and automatic train control system that realizes constant and high-capacity train ground two-way communication. In this study, a dynamic headway model of the 'softwall' moving-block approach is proposed for CBTC to increase the track capacity and improve dispatching efficiency based on the train trajectory prediction. For this precise trajectory prediction task, we introduce a hybrid trajectory prediction model to combine Long Short-term memory (LSTM) and Kalman Filter (KF) to extract the train's local data features and learn the long-term dependencies, respectively. Then we present a dynamic headway model to maximize the train headway and reduce the track distance. The leading trains' information is used to construct the iterative learning control strategy, and the predicted trajectory is input into the algorithm of the headway model. We use a simulation model of the rail network in Chengdu to demonstrate the effectiveness of our proposed approach. The results show the Mean Absolute Error (MAE) of the predicted trajectory retreated to 93.97 cm and reductions in operation headway of at least 64.33% under the dynamic headway model versus the traditional moving-block model. [ABSTRACT FROM AUTHOR]