RESUMEN
Ceiba speciosa (Malvaceae), also called silk floss tree, is a beautiful and deciduous tree native to tropical and subtropical forests of South America. Its phylogenetic position remains unclear. In this study, the complete chloroplast genome sequence of C. speciosa was reported. Its chloroplast genome size was 160,360 bp, which contains a small single copy (SSC) region of 19,947 bp and a large single copy region (LSC) of 89,393 bp, and two inverted repeats (IRs) of 25,510 bp each. In total, 129 genes were annotated for the chloroplast genome, including 86 protein-coding genes, 37 tRNA genes and 8 rRNA genes. Phylogenetic analysis showed that C. speciosa was sister to Bombax ceiba.
RESUMEN
The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.