Scenario-based assessment of emergency management of urban infectious disease outbreaks.

Infectious diseases pose a severe threat to human health and are accompanied by significant economic losses. Studies of urban outbreaks of infectious diseases are diverse. However, previous studies have neglected the identification of critical events and the evaluation of scenario-based modeling of urban infectious disease outbreak emergency management mechanisms. In this paper, we aim to conduct an empirical analysis and scenario extrapolation using a questionnaire survey of 18 experts, based on the CIA-ISM method and scenario theory, to identify the key factors influencing urban infectious disease outbreaks. Subsequently, we evaluate the effectiveness of urban infectious disease outbreak emergency management mechanisms. Finally, we compare and verify the actual situation of COVID-19 in China, drawing the following conclusions and recommendations. (1) The scenario-based urban infectious disease emergency management model can effectively replicate the development of urban infectious diseases. (2) The establishment of an emergency command center and the isolation and observation of individuals exposed to infectious diseases are crucial factors in the emergency management of urban outbreaks of infectious disease.

Saliency Guided Deep Neural Network for Color Transfer With Light Optimization.

Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization. First, a saliency detection module is designed to separate the foreground regions from the background regions for images. Then a dual-branch module is introduced to implement color transfer for images. Finally, a brightness optimization operation is designed during the fusion of foreground and background regions for color transfer. Experimental results show that the proposed method can implement the color transfer for images while keeping the color consistency well. Compared with other existing studies, the proposed method can obtain significant performance improvement. The source code and pre-trained models are available at https://github.com/PlanktonQAQ/SCTNet.

Study on the evolution of green innovation city network and its carbon emission effect in Yellow River Basin cities.

Against the background of increasing urbanization rate and intensifying global warming, conflicts between humans and the natural environment continue to arise, and regionalized forms of spatial organization have become an important research direction. This paper constructs a green innovation city network. It empirically tests the evolution process of the green innovation city network and its carbon emission effect by combining the social network approach and the spatial Durbin model. The conclusions are as follows: (1) the strong ties among green innovation city networks are mainly distributed in and around the provincial capital cities and the middle and lower reaches of the Yellow River Basin; the density of green innovation city networks has been strengthened, and the degree centrality and closeness centrality have been improved. (2) The carbon emissions of cities in the Yellow River Basin are generally increasing. Still, the rate of increase is slowing down. The carbon emissions from liquefied petroleum gas show a decreasing trend yearly, and the energy structure tends to improve. (3) The impact of the green innovation city network on carbon emissions mainly comes from its externality's direct and indirect effects; the increase of degree centrality will reduce the total carbon emissions in the region and the network-associated regions.