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Based on the online travel notes released by tourists, using social network analysis, this article analyses the spatial structure characteristics of tourist flow network under the county-level administrative units in Guangxi. The results show that:(1) The density of tourist flow network in Guangxi is sparse, and the tourist flow is dominated by landscape sightseeing tours and coastal holiday tours. (2) Nanning, Guilin, Beihai and Yangshuo are the core nodes of tourism system in Guangxi, which not only have great tourism attraction, but also have strong resource control capabilities. (3) Guangxi tourism presents a significant clustering phenomenon. The tourist flow is extremely uneven. The popularity of scenic spots has an important impact on the node flows. The level of the regional transportation network determines the central position of the node in the network to a large extent.

China is facing serious heavy metal pollution in farmland soil, which is a major pressing concern for food safety and human health. This research delivers an integrated methodology for pollution source apportionment and a soil-rice-human model to predict heavy metal transfer in the farmland soil, rice grain and human blood chain. The source identification integrated positive matrix factorization (PMF), cluster analysis (CA) and the life cycle assessment (LCA) survey of agricultural and industrial production and consumption. Based on the case analysis of Shaoxing, this method showed very good performance through the illustration of the source contributions by PMF and LCA at county level and the identification of the pollution sources using CA and LCA at field scale. According to the overall evaluation, the integrated method was superior for the farmland metals pollution source identification comparing to existing source apportionment methods. To predict metal transformation in soil-rice-human chain, a set of models of metals (As, Pb, Cd, Hg, Cr) accumulation ability in rice grain and human blood has been established by literature review and monitoring data. The models showed adequate predictability for the metal content of rice grains at both the field and regional scale, and plausible simulation of the metal concentration in human blood throughout the whole study region. Therefore, this study provides valuable tools for farmland soil heavy metal pollution source identification and for the prediction of heavy metal transformation in soil-rice-human chain; and it can highlight the need to take mitigating action to reduce farmland metal pollution risks in specific regions.

This paper presents a framework for decentralized control of self-organizing swarm systems based on the artificial potential functions (APFs). In this scheme, multiple agents in a swarm self-organize to flock and achieve formation control through attractive and repulsive forces among themselves using APFs. In particular, this paper presents a set of analytical guidelines for designing potential functions to avoiding local minima for a number of representative scenarios. Specifically the following cases are addressed: 1) a case that the potential of the goal is overwhelmed by the potential of an obstacle, 2) a case that the potential of the obstacle is overwhelmed by the potential of the goal, 3) a case that the potential of the obstacle is overwhelmed by potential of other robots in a group formation and 4) a case that the potential of the robot in a formation is overwhelmed by potential of the goal.

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by integrating stream function based path planning methods with novel artificial potential based flocking methods to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as flexible and agile formation, obstacle avoidance and inter-agent collision. In particular, the DA algorithms employ a so-called singular association (SA) rule for inter-agent interaction, i.e., each agent associates itself with only one other judiciously chosen agent. The resulting algorithms enjoy two important interrelated benefits. First, the SA rule greatly simplifies the interactions in swarm systems thus it is more readily to pursue rigorous analysis of such systems, some of which are included in this paper. Secondly, the simplicity of the SA rule is advantageous for practical implementations. Extensive simulation as well as preliminary experimental results are presented to illustrate the viability of the proposed framework.