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This paper presents a coastal migration index (CMI) useful for decision-making in the current scenario of sea-level rise (SLR) due to climate change. The CMI includes coastal human population density, degree of urbanization, and coastal-flooding penetration. Quantitative and qualitative statistical techniques and the geographic information system ArcGIS View 9.0 were used. Further, a panel of fifteen international experts in coastal management issues was consulted to establish and validate the CMI. Results led to three index components based on 22 indicators. CMI was applied in the state of Tamaulipas, Mexico and in Santiago de Cuba province, Cuba. According to CMI estimates, the risk levels associated with SLR for human settlements analyzed in Mexico and Cuba were 5.3% and 11.0%, respectively. The most severely affected communities will require resettlement. Meanwhile, the CMI determined that 15.8% of the Mexican territory studied will be able to withstand the effects of SLR through the management of engineering works that will protect human settlements. The CMI determined that 79.0%, in the case of Tamaulipas, as well as 89.0% of the Cuban territory, will not require new policies or guidelines to promote conservation and protection of coastal natural resources. Lastly, the method used allowed for creation of a CMI stoplight map useful to coastal decision-makers to adopt sound management actions.

"In this paper, we present a new algorithm to route text queries within a P2P network, called Neighboring-Ant Search (NAS) algorithm. The algorithm is based on the Ant Colony System metaheuristic and the SemAnt algorithm. More so, NAS is hybridized with local environment strategies of learning, characterization, and exploration. Two Learning Rules (LR) are used to learn from past performance, these rules are modified by three new Learning Functions (LF). A Degree-Dispersion-Coefficient (DDC) as a local topological metric is used for the structural characterization. A variant of the well-known one-step Lookahead exploration is used to search the nearby environment. These local strategies make NAS self-adaptive and improve the performance of the distributed search. Our results show the contribution of each proposed strategy to the performance of the NAS algorithm. The results reveal that NAS algorithm outperforms methods proposed in the literature, such as Random-Walk and SemAnt."