Otimização da modificação de cadeia do PHB com anidrido maleico e estudo de comportamento térmico

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Abordagens biossintéticas em piper tuberculatum auxiliada pela síntese de precursores fenilpropanoídicos

Pós-graduação em Química - IQ

Ti/TiO2 nanotubes enhance Mycobacterium fortuitum, Mycobacterium chelonae and Mycobacterium abscessus inactivation in water

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

Formulação de extrato aquoso de tremoço branco (Lupinus albus L.) adicionado de suco de pitanga utilizando metodologia de superfície de resposta

The aim of the present study was to verify, through the response surface methodology, the optimum conditions for the combined use of aqueous extract of white lupine (Lupinus albus L.) and tropical pitanga juice (Eugenia unifl ora L.) in the development of a drink. Therefore, the independent variables were represented by the volume of aqueous extract of lupine (mL) and by the volume of pitanga juice (mL). The dependent variables (responses) were obtained through sensory test of acceptance (“appearance”, “aroma”, “taste” and “overall acceptability”). The combined optimization of the variables pointed the second formulation (50mL of aqueous extract of lupine and 30mL of pitanga juice) of the experimental project as the most adequate. In conclusion it was possible to get a drink with attractive sensory characteristics using aqueous extract of white lupine and tropical pitanga juice. The usage of response surface methodology made possible the determination of regions of maximum acceptance for each attribute evaluated in the formulation of a drink with aqueous extract of white lupine and pitanga juice, with a minimum of tests.

Cassava root husks as a sorbent material for the uptake and pre-concentration of cadmium(II) from aqueous media

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

Obtenção de consórcio de bactérias fotoheterotróficas geradoras de H2 visando sua produção em reator anaeróbio em batelada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudos experimentais e teórico-computacionais de novos complexos nanozeólitas-enzimas empregados na produção de biocombustíveis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Determining the Optimum Processing Parameters of Glass-like Carbon Using Weibull Analysis of its Fracture Behaviour

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

Comparison of the univariate and multivariate methods in the optimization of experimental conditions for determining Cu, Pb, Ni and Cd in biodiesel by GFAAS

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

Germination of Ocotea pulchella (Nees) Mez (Lauraceae) seeds in laboratory and natural restinga environment conditions

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

Influence of the processing conditions and corona poling on the morphology of β-PVDF

The α → β phase transformation of PVDF through the stretching process at different temperatures was investigated. The optimum stretching conditions were studied and characterised by infrared spectroscopy and differential scanning calorimetry. The maximum β-phase content was achieved at 80°C and a stretch ratio of 5. These samples were poled at several electric fields by the corona charge method. The effect of the electric field on the phase transformation was studied by infrared spectroscopy.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.