Soil biochemistry and microbial activity in vineyards under conventional and organic management at Northeast Brazil.

The São Francisco Submedium Valley is located at the Brazilian semiarid region and is an important center for irrigated fruit growing. This region is responsible for 97% of the national exportation of table grapes, including seedless grapes. Based on the fact that orgThe São Francisco Submedium Valley is located at the Brazilian semiarid region and is an important center for irrigated fruit growing. This region is responsible for 97% of the national exportation of table grapes, including seedless grapes. Based on the fact that organic fertilization can improve soil quality, we compared the effects of conventional and organic soil management on microbial activity and mycorrhization of seedless grape crops. We measured glomerospores number, most probable number (MPN) of propagules, richness of arbuscular mycorrhizal fungi (AMF) species, AMF root colonization, EE-BRSP production, carbon microbial biomass (C-MB), microbial respiration, fluorescein diacetate hydrolytic activity (FDA) and metabolic coefficient (qCO2). The organic management led to an increase in all variables with the exception of EE-BRSP and qCO2. Mycorrhizal colonization increased from 4.7% in conventional crops to 15.9% in organic crops. Spore number ranged from 4.1 to 12.4 per 50 g-1 soil in both management systems. The most probable number of AMF propagules increased from 79 cm-3 soil in the conventional system to 110 cm-3 soil in the organic system. Microbial carbon, CO2 emission, and FDA activity were increased by 100 to 200% in the organic crop. Thirteen species of AMF were identified, the majority in the organic cultivation system. Acaulospora excavata, Entrophospora infrequens, Glomus sp.3 and Scutellospora sp. were found only in the organically managed crop. S. gregaria was found only in the conventional crop. Organically managed vineyards increased mycorrhization and general soil microbial activity

Observational constraint on generalized Chaplygin gas model

We investigate observational constraints on the generalized Chaplygin gas (GCG) model as the unification of dark matter and dark energy from the latest observational data: the Union SNe Ia data, the observational Hubble data, the SDSS baryon acoustic peak and the five-year WMAP shift parameter. It is obtained that the best fit values of the GCG model parameters with their confidence level are $A_{s}=0.73^{+0.06}_{-0.06}$ ($1\sigma$) $^{+0.09}_{-0.09}$ $(2\sigma)$, $\alpha=-0.09^{+0.15}_{-0.12}$ ($1\sigma$) $^{+0.26}_{-0.19}$ $(2\sigma)$. Furthermore in this model, we can see that the evolution of equation of state (EOS) for dark energy is similar to quiessence, and its current best-fit value is $w_{0de}=-0.96$ with the $1\sigma$ confidence level $-0.91\geq w_{0de}\geq-1.00$.Comment: 9 pages, 5 figure

Local Search is Underused in Genetic Programming

Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.authorsversionpublishe