Background Physical activity intervention and watching natural environment videos have been proven to improve young children’s attention levels. However, evidence comparing the improvement effects of different combinations of the two activities has rarely been reported. By comparing the differences in the improvement effects of four combinations of physical activities and watching natural environment videos on young children’s attention levels, this study can enrich the evidence in this research field and also provide a reference for arranging effective intervention methods for children’s attention recovery between classes. Method A total of 152 children aged 4 to 6 years were recruited and randomly divided into four intervention groups: (1) physical activity intervention first and thereafter watching a natural environment video group (activity + video group), (2) watching a natural environment video first and thereafter the physical activity intervention group (video + activity group), (3) physical activity-based group, and (4) natural environment video-based group. Physical activity involved 4 min of moderate-intensity basic physical fitness combination training. The subjects wore the Pico Neo pioneer version of the VR glasses all-in-one machine to watch a natural environment video. Thereafter, population sociological variables and daily physical activity levels were investigated. Auditory and visual sustained attention tests were performed before and after intervention in each group. Result The auditory attention post-test scores of the four groups showed an improvement trend compared with the pretest scores. In particular, the activity + video group (F = 10.828; ɳp2 = 0.226; p = 0.002) and natural environment video-based group (F = 9.452; ɳp2 = 0.203; p = 0.004) have the best improvement effect. For visual attention, only the activity + video group showed a significant improvement trend (F = 4.287; ɳp2 = 0.104; p = 0.045), while the other three groups showed a downward trend in scores. Conclusions Among the different intervention combinations, the physical activity intervention first and watching natural environment videos thereafter group has the best effect on improving children’s attention during recess. Physical activity interventions at the end of recess adversely affect young children’s visual attention levels at the beginning of the class. Therefore, this study recommends that children should not engage in physical activity interventions in the second half of the class break. Lastly, the current research recommends presenting the content of physical activity interventions first and further improving their attention thereafter by watching natural environment videos.

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.

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The civil aviation industry is undergoing rapid development. However, the on-time rate of airport flights and passenger service quality are not particularly satisfying. The cause of the above problems is the contradiction between the limited operational support capability and the continuous growth of passenger traffic volume. Therefore, the key to solving these problems is achieving situation awareness of airport operation. Many situation awareness algorithms, typically categorized into modeling and machine learning, have been proposed in the past years. However, existing models lack flexibility and their prediction accuracy is unstable. Machine learning's results cannot be timely and effective when external conditions are suddenly changed although some related algorithms have higher accuracy because of the retraining of artificial neural network (ANN). This paper proposes a situation awareness method based on Petri nets (PNs). This method introduces the queuing theory and perceptual parameters into the existing PN and constructs the perceptual PNs' model for general service systems so that it can quickly model different scene service systems. In combination with the ANN, this paper proposes a complete situation awareness algorithm to realize a sustained and accurate situation awareness prediction of the service system by solving point estimations of the macroscopic and microscopic situation in this model, which helps to address some challenges faced by current civil aviation airports. By experimenting on-ground support in civil aviation airports and the access of website as well as comparing the situation separately with Airport Collaborative Decision Making and stochastic PNs, the validity and accuracy of the algorithm proposed in this paper are well verified. INDEX TERMS Artificial neural network, modeling, Petri nets, situation awareness.

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