Guandu: planta forrageira para a produção de proteína.

O guandu (Cajanus cajan), leguminosa de origem africana, adaptada a região Tropical, é enfocada no documento apenas quanto a seu emprego na alimentação de bovinos. São apresentadas recomendações de adubação; época de plantio; cálculo de produção; utilização em pastejo e na produção de forragem. O emprego do guandu, introduzido em pastagens de gramíneas ja existentes, é recomendado para a produção de forragem especial e recuperação de solo.bitstream/item/138535/1/COT-21.pdfCNPG

Cosmic shear requirements on the wavelength dependence of telescope point spread functions

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational point spread function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent; therefore, the differences between the spectral energy distribution of the observed objects introduce further complexity. In this paper, we investigate the effect of the wavelength dependence of the PSF, focusing on instruments in which the PSF size is dominated by the diffraction limit of the telescope and which use broad-band filters for shape measurement. We first calculate biases on cosmological parameter estimation from cosmic shear when the stellar PSF is used uncorrected. Using realistic galaxy and star spectral energy distributions and populations and a simple three-component circular PSF, we find that the colour dependence must be taken into account for the next generation of telescopes. We then consider two different methods for removing the effect: (i) the use of stars of the same colour as the galaxies and (ii) estimation of the galaxy spectral energy distribution using multiple colours and using a telescope model for the PSF. We find that both of these methods correct the effect to levels below the tolerances required for per cent level measurements of dark energy parameters. Comparison of the two methods favours the template-fitting method because its efficiency is less dependent on galaxy redshift than the broad-band colour method and takes full advantage of deeper photometr

ARCADE: Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission

The Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission (ARCADE) is a balloon-borne instrument designed to measure the temperature of the cosmic microwave background at centimeter wavelengths. ARCADE searches for deviations from a blackbody spectrum resulting from energy releases in the early universe. Long-wavelength distortions in the CMB spectrum are expected in all viable cosmological models. Detecting these distortions or showing that they do not exist is an important step for understanding the early universe. We describe the ARCADE instrument design, current status, and future plans.Comment: 12 pages, 6 figures. Proceedings of the Fundamental Physics With CMB workshop, UC Irvine, March 23-25, 2006, to be published in New Astronomy Review

Influence of supramolecular forces on the linear viscoelasticity of gluten

Stress relaxation behavior of hydrated gluten networks was investigated by means of rheometry combined with μ-computed tomography (μ-CT) imaging. Stress relaxation behavior was followed over a wide temperature range (0–70 °C). Modulation of intermolecular bonds was achieved with urea or ascorbic acid in an effort to elucidate the presiding intermolecular interactions over gluten network relaxation. Master curves of viscoelasticity were constructed, and relaxation spectra were computed revealing three relaxation regimes for all samples. Relaxation commences with a well-defined short-time regime where Rouse-like modes dominate, followed by a power law region displaying continuous relaxation concluding in a terminal zone. In the latter zone, poroelastic relaxation due to water migration in the nanoporous structure of the network also contributes to the stress relief in the material. Hydrogen bonding between adjacent protein chains was identified as the determinant force that influences the relaxation of the networks. Changes in intermolecular interactions also resulted in changes in microstructure of the material that was also linked to the relaxation behavior of the networks

Cosmic shear requirements on the wavelength-dependence of telescope point spread functions

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational Point Spread Function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent, therefore the differences between the spectral energy distribution of the observed objects introduces further complexity. In this paper we investigate the effect of the wavelength-dependence of the PSF, focusing on instruments in which the PSF size is dominated by the diffraction-limit of the telescope and which use broad-band filters for shape measurement. We first calculate biases on cosmological parameter estimation from cosmic shear when the stellar PSF is used uncorrected. Using realistic galaxy and star spectral energy distributions and populations and a simple three-component circular PSF we find that the colour-dependence must be taken into account for the next generation of telescopes. We then consider two different methods for removing the effect (i) the use of stars of the same colour as the galaxies and (ii) estimation of the galaxy spectral energy distribution using multiple colours and using a telescope model for the PSF. We find that both of these methods correct the effect to levels below the tolerances required for per-cent level measurements of dark energy parameters. Comparison of the two methods favours the template-fitting method because its efficiency is less dependent on galaxy redshift than the broad-band colour method and takes full advantage of deeper photometry.Comment: 10 pages, 8 figures, version accepted for publication in MNRA

Effect of Fourier filters in removing periodic systematic effects from CMB data

We consider the application of high-pass Fourier filters to remove periodic systematic fluctuations from full-sky survey CMB datasets. We compare the filter performance with destriping codes commonly used to remove the effect of residual 1/f noise from timelines. As a realistic working case, we use simulations of the typical Planck scanning strategy and Planck Low Frequency Instrument noise performance, with spurious periodic fluctuations that mimic a typical thermal disturbance. We show that the application of Fourier high-pass filters in chunks always requires subsequent normalisation of induced offsets by means of destriping. For a complex signal containing all the astrophysical and instrumental components, the result obtained by applying filter and destriping in series is comparable to the result obtained by destriping only, which makes the usefulness of Fourier filters questionable for removing this kind of effects.Comment: 10 pages, 8 figures, published in Astronomy & Astrophysic

Planck pre-launch status: calibration of the Low Frequency Instrument flight model radiometers

The Low Frequency Instrument (LFI) on-board the ESA Planck satellite carries eleven radiometer subsystems, called Radiometer Chain Assemblies (RCAs), each composed of a pair of pseudo-correlation receivers. We describe the on-ground calibration campaign performed to qualify the flight model RCAs and to measure their pre-launch performances. Each RCA was calibrated in a dedicated flight-like cryogenic environment with the radiometer front-end cooled to 20K and the back-end at 300K, and with an external input load cooled to 4K. A matched load simulating a blackbody at different temperatures was placed in front of the sky horn to derive basic radiometer properties such as noise temperature, gain, and noise performance, e.g. 1/f noise. The spectral response of each detector was measured as was their susceptibility to thermal variation. All eleven LFI RCAs were calibrated. Instrumental parameters measured in these tests, such as noise temperature, bandwidth, radiometer isolation, and linearity, provide essential inputs to the Planck-LFI data analysis.Comment: 15 pages, 18 figures. Accepted for publication in Astronomy and Astrophysic

Peptide-based microcapsules obtained by self-assembly and microfluidics as controlled environments for cell culture

Funding for this study was provided by the Portuguese Foundation for Science and Technology (FCT, grant PTDC/EBB-BIO/ 114523/2009). D. S. Ferreira gratefully acknowledges FCT for the PhD scholarship (SFRH/BD/44977/2008)

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio