Electron Beam Adjustment in PLATO RTS 2 Including the Effect of Air Gaps

Background and Purpose: Beam characterization for electron dose calculations in PLATO RTS 2 treatment planning system requires the tuning of two adjustment parameters: sqx (the initial angular spread) and FMCS (a "fudge" multiple Coulomb scattering parameter). This work provides a set of suggestions to optimise electron dose calculations with PLATO, taking into account the effect of air gaps between the electron applicator and the patient skin. Material and Methods: Two adjustment criteria have been followed: one which uses just one input data set corresponding to the standard (null) air gap and another one that takes into account the whole range of clinically used distances between the electron applicator and the patient surface. The adjusted values of sqx were compared with experimental data and GEANT3 Monte Carlo code results. A systematic study has been carried out of the effect of both adjustment parameters on electron dose calculations in water. Comparisons of dose distributions and point dose values have been done between PLATO RTS2, GEANT3 Monte Carlo code and experimental data. Also the dependence on field size has been assessed. The values of sqx for the different electron energies obtained through the different approaches are discussed. Results and conclusions: The first adjustment criteria yield unrealistic dose distributions whenever the air gap is different from the standard one. A sqx balanced with a proper FMCS parameter leads to reasonably good dose distributions and point dose values that agree with experimental results within less than 1%

Uso de modelos de simulação sócio-bio-econômico integrado como ferramenta para o desenvolvimento agrário na região sudoeste do Rio Grande Sul.

Suporte tecnológico tem sido oferecido aos produtores com a finalidade de aumentar a eficiência produtiva e fornecer subsídios para as suas tomadas de decisões; entretanto, os métodos tradicionais de pesquisa e extensão estão sendo cada vez mais questionados, principalmente quanto ao custo e tempo necessário para oferecer soluções aos problemas enfrentados pelos produtores.bitstream/item/109814/1/USO-DE-MODELOS-DE-SIMULACAO.pd

Does it still hold?

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Quality of different tropical fruit cultivars produced in the Lower Basin of the São Francisco Valley.

The present study evaluated the physical, physico-chemical and chemical characteristics of fruit from commercial cultivars of the mango, acerola, guava, atemoya and custard apple, produced in the Lower Basin of the São Francisco Valley. Fruit harvested in commercial areas of the region were evaluated for weight, length, diameter, colouration of the peel and pulp, firmness, pH, titratable acidity (TA), soluble solids (SS), SS to TA ratio, and levels of total soluble sugars, reducing sugars, starch and pectic substances. The data were subjected to descriptive statistical analysis. Fruits from cultivars of the guava (Paluma, Rica and Pedro Sato), the custard apple and atemoya display a high level of pectic substances, a characteristic which favours industrial use. In the mango, a high level of pectic substances was noted in fruit of the cultivars Kent, Espada, Tommy Atkins and Van Dyke. Fruits of the acerola cultivar Costa Rica show high SS content and a low AT, favouring consumption in natura

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA

Incidência de couro de sapo em mandioca (Manihot esculenta Crantz) no Estado do Pará.

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