Electrical conductivity testing of corn seeds as influenced by temperature and period of storage

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

Electrical conductivity as an indicator of pea seed aging of stored at different temperatures

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

Electrical conductivity and soybean seedling emergence

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Electrical conductivity and mineral composition of the imbibition solution of bean seeds during storage

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

Electrical conductivity test in the evaluation of physiological quality in seeds of Zeyheria tuberculosa

The electrical conductivity test is still an excellent tool to evaluate the effect of seeds of various forest and agricultural species and recent studies have been conducted aiming at verifying its application in forest seeds. The objective of this study was to establish a specific methodology to test the electrical conductivity of forest seeds of Zeyheria tuberculosis. Four lots of seeds were used, which were submitted to the germination test, evaluating the percentage of germination, germination speed index and dry mass of seedlings. For the electrical conductivity test, five replicates of 20 seeds installed in three volumes of deionized water (75, 100 and 125 mL) were used and eight periods for seed imbibitions (2, 4, 6, 12, 18, 24, 48 and 72 hour) at 25 degrees C were allowed. The statistical design used was completely random; the comparison of means was performed by Tukey test at 5 % probability. Lot II showed higher germination percentage and speed. The electrical conductivity test allows discrimination of the same batch by the germination test under laboratory conditions. It was possible to separate the seed lot presenting better physiological quality (lot II) from among the other lots. It was recommended the use of 75 or 125 mL of deionized water at a temperature of 25 degrees C to perform the electrical conductivity test.

Efficiency of a constructed wetland for wastewaters treatment

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

Automated system of seedling image analysis (SVIS) and electrical conductivity to assess sun hemp seed vigor

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

Avaliação do contato pneu-solo em três modelos de pneus agrícolas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Dependence of the electrical conductivity and elastomeric properties on sample preparation of blends of polyaniline and natural rubber

Blend films (free-standing) containing 20% in volume of polyaniline (PANI) in 80% of natural rubber (NR) were fabricated by casting in three different ways: (1) adding PANI-EB (emeraldine base) dissolved in N-methyl-2-pyrrolidone (NMP) to the latex (NRL), (2) adding PANI-EB dissolved in in-cresol to NR dissolved in xylol (NRD), (3) overlaying the surface of a pure NR cast film with a PANI layer grown by in situ polymerization (NRO). All the films were immersed into HCl solution to achieve the primary doping (protonation) of PANI before the characterization. The main goal here was to investigate the elastomeric and electrical conductivity properties for each blend, which may be applied as pressure and deformation sensors in the future. The characterization was carried out by optical microscopy, dc conductivity, vibrational spectroscopy (infrared absorption and Raman scattering), thermogravimetry analysis (TGA), differential scanning calorimetry (DSC), dynamic mechanical thermal analysis (DMTA), and tensile stress-strain curves. The results suggest that the NRL blend is the most suitable in terms of mechanical and electrical properties required for applications in pressure and deformation sensors: a gain of conductivity without losing the elastomeric property of the rubber. (c) 2005 Wiley Periodicals, Inc.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Magnetic field effects on the conductivity of organic bipolar and unipolar devices at room temperature

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

Influence of pH of Colloidal Suspension on the Electrical Conductivity of SnO2 thin Films Deposited Via Sol-Gel-Dip-Coating

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Radial Plasma Dynamic in Sequential Pinches

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determination of fluoroacetate and fluoride in blood serum by capillary zone electrophoresis using capacitively coupled contactless conductivity detection

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

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.