Protein–Protein interactions (PPI) networks are protein complexes arranged in networks that are created by biochemical processes or electrostatic forces to carry out biological functions. PPI research is a challengin...
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With the development of the economy and the accumulation of social wealth, urban residents have begun to give more attention to quality of life than to material needs. Consequently, environmental factors that affect h...
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With the development of the economy and the accumulation of social wealth, urban residents have begun to give more attention to quality of life than to material needs. Consequently, environmental factors that affect human health, such as air quality, have become a new focus when traveling. A travel scheme with relatively low pollutant exposure to travelers can not only improve their health and satisfy their goals but also benefit social stability and sustained progress. However, low spatiotemporal resolution and coarse spatial details of the distribution of PM2.5 (particles with an aerodynamic diameter of 2.5 mu m or less) educe the success rate of short distance healthy travel route planning. This paper proposes a short-distance healthy route planning approach that is based on PM2.5 retrieval with high spatiotemporal resolution and a dynamic dijkstra algorithm. First, fine spatial resolution images, meteorological data, and socioeconomic data are used to retrieve the spatial distribution of PM2.5 concentration in hourly intervals via a back-propagation neural network (BPNN). Second, a PM2.5 concentration value is obtained for each road section, and the harm degree to the human body is calculated as the weight of each road section. Then, the healthiest route is obtained based on the dijkstraalgorithm. Finally, the route planning effectiveness is verified by comparing the PM2.5 potential dose descending rate between the healthy route and the shortest route. The results show that the coefficient of determination (R2) of the PM2.5 retrieval approach that is based on multisource data and BPNN is 0.85, which can ensure the accuracy of the PM2.5 data at the street level. On this basis, the potential dose reduction rate of the healthy route can reach up to 20%, which proves that our approach can perform well. It can effectively improve the safety of travel and alleviate the anxiety that is caused by air pollution. In addition, it provides an easy implementation strategy f
An electric vehicle-routing problem (EVRP) is developed to settle some operation distribution troubles such as battery energy limitations and difficulties in finding charging stations for electric vehicles (EVs). Mean...
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An electric vehicle-routing problem (EVRP) is developed to settle some operation distribution troubles such as battery energy limitations and difficulties in finding charging stations for electric vehicles (EVs). Meanwhile, in view of realistic traffic conditions and features of EVs, energy consumption with travel speed and cargo load is considered in the EVRP model. Moreover, to avoid the depletion of all battery power and ensure safe operation, EVs with insufficient battery power can be recharged at charging stations many times in transit. In conclusion, a large, realistic case study with the road network of Beijing urban, 100 customers and 30 charging stations is conducted to test the performance of the model and obtain an optimal operation scheme consisted of the routes, charging plan and driving paths. The EVRP model is solved based on the hybrid genetic algorithm to get the routes and charging plan. The dynamic dijkstra algorithm with some improvements over the classical dijkstraalgorithm is applied to find the driving paths called the most energy efficient paths between any two adjacent visited nodes in the routes.
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