Foraminifera, thecamoebians and palynomorphs as hydrodynamic indicators in Araguari Estuary, Amazonian Coast, Amapá State - Brazil

O regime de macromaré e a pororoca controlam a dinâmica estuarina do Araguari, na qual é muito diferente dos modelos tradicionais dos estuários sob este regime de maré. Objetivando estabelecer zonas estuarinas no Araguari com base em assembléias de foraminíferos, tecamebas, palinomorfos e parâmetros físico-químicos, foram estabelecidas dezesseis estações amostrais ao longo do estuário. A turbidez e a temperatura foram os parâmetros ambientais que permitiram determinar gradiente estuarino. Dezoito espécies de foraminíferos, dez espécies de tecamebas e quatorze espécies de palinomorfos foram identificadas. A análise de agrupamento em modo-R mostrou a existência de assembléias de foraminíferos e quatro de palinomorfos. A análise em CCA demonstrou que a turbidez e a matéria orgânica como os parâmetros de maior influência na distribuição dos foraminíferos tecamebas no Araguari. A análise de agrupamento em Modo-Q usando todos os dados formou quarto grupos de estações que sugerem a existência de três zonas estuarinas: Zona I - composta por tecamebas e palinomorfos continentais; Zona II - compostas por foraminíferos de manguezal, acritacos e todas as assembléias de palinomorfos; e Zona III - composta por foraminíferos de manguezal e estuarinos e por todas as assembléias de palinomorfos.

Cryptic Genetic Diversity Is Paramount in Small-Bodied Amphibians of the Genus Euparkerella (Anura: Craugastoridae) Endemic to the Brazilian Atlantic Forest

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Maintenance of Manioc Diversity by Traditional Farmers in the State of Mato Grosso, Brazil: A 20-Year Comparison

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Perceptions of environmental change and use of traditional knowledge to plan riparian forest restoration with relocated communities in Alcantara, Eastern Amazon

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Establishing erosion susceptibility: analytical hierarchical process and traditional approaches

This study evaluated alternatives for producing erosion susceptibility maps, considering different weight combinations for an environment's attributes, according to four different points of views. The attributes considered were landform, steepness, soils, rocks and land occupation. Considered alternatives were: (1) equal weights, more traditional approach, (2) different weights, according to a previous study in the area, (3) different weights, based on other works in the literature, and (4) different weights based on the analytical hierarchical process. The area studied included the Prosa Basin located in Campo Grande-Mato Grosso do Sul State, Brazil. The results showed that the assessed alternatives can be used together or in different stages of studies aiming at urban planning and decision-making on the interventions to be applied.

Sensory profile and acceptability of traditional and double-distilled cachaca aged in oak casks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Static Video Summarization through Optimum-Path Forest Clustering

This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.

Pseudechis guttatus venom proteome: Insights into evolution and toxin clustering

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Automatic visual dictionary generation through optimum-path forest clusteringautomatic visual dictionary generation through optimum-path forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Metrics for Association Rule Clustering Assessment

Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.

Multiphase feeding program for broilers can replace traditional system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Energy values of traditional ingredients and sugarcane yeast for laying hens

An experiment was conducted to determine the chemical composition and apparent metabolizable energy (AME) and apparent metabolizable energy corrected for nitrogen balance (AMEn) values of corn, soybean meal (SBM), soybean oil (SO) and sugarcane yeast (SY) (Saccharomyces cerevisiae). A metabolism trial was performed with 120 Dekalb White laying hens at 65 weeks of age, using the method of total excreta collection. Birds were housed in metabolism cages and distributed according to a completely randomized design into five treatments with, six replicates of four birds each. The experimental period consisted of four days of adaptation and four days of excreta collection. The experimental diets included: a reference diet based on corn and SBM and four test diets containing 40% corn, 30% SBM, 10% SO or 30 % SY. The chemical compositions of the tested ingredients, expressed on "as-is" basis were: 86.9, 87.29, 87.32 and 99.5% dry matter; and 3.51, 2.08, 99.31 and 0.03 ether extract for corn, SBM, SO and SY, respectively. Corn, SBM, and SO presented 7.33, 43.61 and 24.64% crude protein, and 0.58, 5.07 and 6.77% ash, respectively; and crude fiber contents of corn and SBM were, respectively, 2.24% and 3.56%. The following AME and AMEn (kcal/kg dry matter) values were obtained: 3,801 and 3,760 kcal/kg for corn, 2,640 and 2,557 kcal/kg for SBM, 8,952 and 8,866 kcal/kg for SO, and 1,023 and 925 kcal/kg for sugarcane yeast, respectively.

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.