Two stages kinetics of municipal solid waste inoculation composting processes

In order to understand the key mechanisms of the composting processes, the municipal solid waste(MSW) composting processes were divided into two stages, and the characteristics of typical experimental scenarios from the viewpoint of microbial kinetics was analyzed. Through experimentation with advanced composting reactor under controlled composting conditions, several equations were worked out to simulate the degradation rate of the substrate. The equations showed that the degradation rate was controlled by concentration of microbes in the first stage. The degradation rates of substrates of inoculation Run A, B, C and Control composting systems were 13 61 g/(kg·h), 13 08 g/(kg·h), 15 671 g/(kg·h), and 10 5 g/(kg·h), respectively. The value of Run C is around 1 5 times higher than that of Control system. The decomposition rate of the second stage is controlled by concentration of substrate. Although the organic matter degradation rates were similar to all Runs, inoculation could reduce the values of the half velocity coefficient K \-m and could be more efficient to make the composting stable. Particularly, for Run C, the degradation rate is high in the first stage, and K \-m is low in the second stage. The results indicated that the inoculation was efficient for the composting processes.

Congestion warning method based on the Internet of vehicles and community discovery of complex networks

The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles（IOV） and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman（FN） algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.