Since the outbreak of the novel coronavirus disease (COVID-19) at the beginning of December 2019, there have been more than 28.69 million cumulative confirmed cases worldwide as of 12^th September 2020, affecting over 200 countries and regions with more than 920,463 deaths. The COVID-19 pandemic has been sweeping worldwide with unexpected rapidity. In this paper, a hybrid modelling strategy based on tessellation structure- (TS-) configured SEIR model is adopted to estimate the scale of the pandemic spread. Building on the data pertaining to the global pandemic transmission over the last six months around the world, key impact factors in the transmission and control procedure have been analysed, including isolation rate, number of the infected cases before taking prevention measures, degree of contact scope, and medical level, so as to capture the fundamental factor influencing the pandemic. The quantitative evaluation allowed us to illustrate the magnitude of risks of pandemic and to recommend appropriate national health policy of prevention measures for effectively controlling both intra- and interregional pandemic spread. Our modelling results clearly indicate that the early-stage preventive measures are the most effective action to be taken to contain the pandemic spread of the highly contagious nature of the COVID-19.

There were 27 novel coronavirus pneumonia cases found in Wuhan, China in December 2019, named as 2019-nCoV temporarily and COVID-19 formally by the World Health Organization (WHO) on the 11 February 2020. In December 2019 and January 2020, COVID-19 has spread on a large scale among the population, which brought terrible disaster to the life and property of the Chinese people. In this paper, we analyze the features and pattern of the virus transmission. Considering the influence of indirect transmission, a conscious-based Susceptible-Exposed-Infective-Recovered (SEIR) (C-SEIR) model is proposed, and the difference equation is used to establish the model. We simulated the C-SEIR model and key important parameters. The results show that (1) increasing people’s awareness of the virus can effectively reduce the spread of the virus; (2) as the capability and possibility of indirect infection increases, the proportion of people being infected will also increase; (3) the increased cure rate can effectively reduce the number of infected people. Then, the virus transmission can be modelled and used for the inflexion and extinction period of pandemic development so as to provide theoretical support for the Chinese government in the decision-making of pandemic prevention and recovery of economic production. Further, this study has demonstrated the effectiveness of the prevention measures taken by the Chinese government such as multi-level administrative district isolation and public health awareness.

With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

Much research has been conducted to improve the defect-detection rate and detection accuracy of the imaging technology used in terahertz nondestructive testing. Due to the power limit of light sources and noise interference in terahertz equipment, images have low resolution and fuzzy defect edges. Hence, improving the resolution is crucial for detecting defects. We designed an edge detection network structure based on a traditional deep neural network. Besides, we devised a node-fusing strategy to train the network. It demonstrates significant improvement of the resolution of the terahertz defect contour. A quartz fiber composites with embedded defects was tested with our network. The results showed that the proposed super-resolution reconstruction algorithm improves resolution, particularly on the edges of defect contours.