Kiln dry probes verification for maritime pine and eucalyptus wood

Comunicação apresentada na 8th International IUFRO Wood Drying Conference, que decorreu de 28 a 29 de Agosto em Brasov na Roménia.Proper technical drying is a condition for processing sawn timber into high quality products. The drying process is a phase with an extreme importance in wood transformation process, because it gives a significant improvement to the characteristics of workability, prevents damage during transport and insects or fungi attack (Tsoumis, 1991; Walker, 1993). One of the most critical and important phases in the kiln dry is the perfect knowledge of moisture evolution in wood that is drying and is given by the probes measures. Therefore, it became necessary estimate frequently the wood moisture content, which should be at the same time accurate and practical. The objective of the present study is to verify the fiability of the data given by different probes types, and between the values given by this and the ones obtains by laboratory. This work was done in 2 different industrial dryers and in a laboratory dryer. There were executed several simple regression analysis to evaluated the existent relation between this ones and the ones obtain in laboratory; it was observed that to values until 30% of moisture it exist a strong correlation between this two parameters but the same it wasn’t observed to higher moisture contents. It was made the confrontation between the result obtain in Maritime pine (Pinus pinaster) and Eucalyptus (Eucaliptus globulus), and it was observed that the probes can evaluated the moisture content with higher accuracy in pine comparing with eucalyptus. So, it is necessary have some caution when the probes are disconnected, during the kiln dry process, specially to moisture content values are higher then 30%, because the values given by them are some times liable to error

Thermal performance of a solar hybrid dryer for Conilon coffee (Coffea canephora)

The study was aimed at design and development of an energy efficient hybrid solar dryer suitable for drying of organic Conilon coffee placed in the town of Seropédica, Rio de Janeiro, Brazil. The energy efficiency and the drying efficiency were the evaluation criteria for thermal performance of the hybrid solar dryer during the coffee drying. Temperature and relative humidity (RH) of the drying and ambient air, solar radiation intensity and coffee weight loss were monitored during the drying process. The process occurred over six consecutive days; the drying time was from 07:00 to 17:00 h, totalling 120 h of operation with an intermittent period (at night) of 14 h. During intermittence, the exhaust system kept off and solar collector and drying chamber sealed. The effective drying period took 60 h, with temperature and RH, respectively, of 38.3 °C and 60.6% outlet of the solar collector, 32.7 °C and 72.2% outlet drying chamber and 27.8 °C and 74.5% ambient air. The maximum temperature in the solar collector and drying chamber reached 54 and 47.7 °C, respectively, with an ambient air temperature of 32 °C at 12:00 h. These values showing temperature increase 22.2 °C in solar collector and 10 ºC drying chamber. The mean variation for the reduction in RH between the drying air inside the solar collector and the ambient air was 28%, while in the chamber obtained in a range of 10.5% at 13:00 h. The solar collector and dryer chamber efficiency were 29.1 and 40.8%, respectively, while the overall dryer efficiency 39.7%

Spin relaxation time, spin dephasing time and ensemble spin dephasing time in nn-type GaAs quantum wells

We investigate the spin relaxation and spin dephasing of $n$-type GaAs quantum wells. We obtain the spin relaxation time $T_1$, the spin dephasing time $T_2$ and the ensemble spin dephasing time $T_2^{\ast}$ by solving the full microscopic kinetic spin Bloch equations, and we show that, analogous to the common sense in an isotropic system for conduction electrons, $T_1$, $T_2$ and $T_2^{\ast}$ are identical due to the short correlation time. The inhomogeneous broadening induced by the D'yakonov-Perel term is suppressed by the scattering, especially the Coulomb scattering, in this system.Comment: 4 pages, 2 figures, to be published in Phys. Lett.

Matrix Model and Time-like Linear Dilaton Matter

We consider a matrix model description of the 2d string theory whose matter part is given by a time-like linear dilaton CFT. This is equivalent to the c=1 matrix model with a deformed, but very simple fermi surface. Indeed, after a Lorentz transformation, the corresponding 2d spacetime is a conventional linear dilaton background with a time-dependent tachyon field. We show that the tree level scattering amplitudes in the matrix model perfectly agree with those computed in the world-sheet theory. The classical trajectories of fermions correspond to the decaying D-branes in the time-like linear dilaton CFT. We also discuss the ground ring structure. Furthermore, we study the properties of the time-like Liouville theory by applying this matrix model description. We find that its ground ring structure is very similar to that of the minimal string.Comment: 30 pages, harvmac, typos corrected, acknowledgements and comments added(v2), published version (v3

Research of the optical communications groups at University of Aveiro and Institute of Telecommunications - Aveiro Pole

This paper summarizes the research activities of the optical communications group at University of Aveiro and Institute of Telecommunications – Aveiro pole. Several activities like clock recovery systems, both electrical and all optical, electrical equalizers for very high bit rate DST systems, post-detection filters for multigigabit optical receivers, soliton systems, simulation work on WDM, DST, EDFA and short pulse generation for high bit rate systems are presented

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio