In the post-epidemic era, balancing epidemic prevention and control with sustainable economic development has become a serious challenge for all countries around the world. In China, a range of interventions include detection policies, clinical treatment policies, and most notably, traffic policies have been carried out for epidemic prevention and control. It has been widely confirmed that massive traffic restriction policies effectively brought the spread of the pandemic under control. However, restrictions on the use of transportation infrastructure undermine the smooth functioning of the economy. Particularly, China has a vast territory, with provinces differing in economic development, leading industries and transportation infrastructure; economic shock varies from region to region. In this case, targeted policies are the key to sustainable development. This paper sets forth advice for the Chinese government on its measures to boost the economy by analyzing regional differences in the impact of massive traffic restriction policies, based on large-scale human mobility data. After applying the Data Envelopment Analysis model, we classify Chinese provinces into different regions from the perspective of economic gradient, degree of internationalization and level of traffic convenience, respectively. Classification results are matched with the indicators of New Venues Created and the weekly Volumes of Visits to Venues from Baidu Maps. We find that the regional differences in the recovery of investment and consumption levels are striking. Based on the findings, we suggest that the government should adjust the intensity of traffic restrictions and economic stimulus policies dynamically according to regional differences to achieve sustainable economic development.

Violent interaction detection is a hot topic in computer vision. However, the recent research works on violent interaction detection mainly focus on the traditional hand-craft features, and does not make full use of the research results of deep learning in computer vision. In this paper, we propose a new robust violent interaction detection framework based on multi-stream deep learning in surveillance scene. The proposed approach enhances the recognition performance of violent action in video by fusing three different streams: attention-based spatial RGB stream, temporal stream, and local spatial stream. The attention-based spatial RGB stream learns the spatial attention regions of persons that have high probability to be action region through soft-attention mechanism. The temporal stream employs optical flow as input to extract temporal features. The local spatial stream learns spatial local features using block images as input. Experimental results demonstrate the effectiveness and reliability of the proposed method on three violent interactive datasets: hockey fights, movies, violent interaction. We also verify the proposed method on our own elevator surveillance video dataset and the performance of the proposed method is satisfied.

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

The underestimation of fuel consumption impacts various aspects. In the vehicle market, manufacturers often advertise fuel economy for marketing. In fact, the fuel consumption reference value provided by the manufacturer is quite different from the real-world fuel consumption of the vehicles. The divergence between reference fuel consumption and real-world fuel consumption also has negative effect on the aspects of policy and environment. In order to effectively promote the sustainable development of transport, it is urged to recognize the real-world fuel consumption of vehicles. The gaps in previous studies includes small sample size, single data dimension, and lack of feature weight evaluation. To fill the research gap, in this study, we conduct a comparative analysis through building five regression models to forecast the real-world fuel consumption rate of light-duty gasoline vehicles in China based on big data from the perspectives of vehicle factors, environment factors, and driving behavior factors. Results show that the random forest regression model performs best among the five candidate models, with a mean absolute error of 0.630 L/100 km, a mean absolute percentage error of 7.5%, a mean squared error of 0.805, an R squared of 0.776, and a 10-fold cross-validation score of 0.791. Further, we capture the most important features affecting fuel consumption among the 25 factors from the above three perspectives. According to the relative weight of each factor in the most optimal model, the three most important factors are brake and accelerator habits, engine power, and the fuel economy consciousness of vehicle owners in sequence.

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