Association of radio polar cap brightening with bright patches and coronal holes

Radio-bright regions near the solar poles are frequently observed in Nobeyama Radioheliograph (NoRH) maps at 17 GHz, and often in association with coronal holes. However, the origin of these polar brightening has not been established yet. We propose that small magnetic loops are the source of these bright patches, and present modeling results that reproduce the main observational characteristics of the polar brightening within coronal holes at 17 GHz. The simulations were carried out by calculating the radio emission of the small loops, with several temperature and density profiles, within a 2D coronal hole atmospheric model. If located at high latitudes, the size of the simulated bright patches are much smaller than the beam size and they present the instrument beam size when observed. The larger bright patches can be generated by a great number of small magnetic loops unresolved by the NoRH beam. Loop models that reproduce bright patches contain denser and hotter plasma near the upper chromosphere and lower corona. On the other hand, loops with increased plasma density and temperature only in the corona do not contribute to the emission at 17 GHz. This could explain the absence of a one-to-one association between the 17 GHz bright patches and those observed in extreme ultraviolet. Moreover, the emission arising from small magnetic loops located close to the limb may merge with the usual limb brightening profile, increasing its brightness temperature and width.Comment: 8 pages, 6 figures, 1 table. Accepted for publication in The Astrophysical Journa

Guidelines for Machine Tool Sensing and Smart Manufacturing Integration

30th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2021) 15-18 June 2021, Athens, Greece.Nowadays the Industry is becoming increasingly competitive with the emergence of even more advanced technologies. This environment leads the companies to look for a bigger availability of the assets, a higher quality of the products and consequently less costs. Thus, is because of this purpose that Maintenance is becoming even more fundamental. The focus of this paper was to develop a strategy of Predictive Maintenance on a Machine Tool with the aim of reducing the unplanned stops, increasing the productivity and creating the bases for an Industry 4.0 environment in the short term. Thus, a model has been created in order to fulfil this goal. The first step was the selection of the critical component of the machine tool that would be studied. In the next phase the variables that will be monitored were selected and their trigger limits. Finally, the necessary components to monitor this system were chosen. In order to reach the objective, a system of condition-based maintenance where the acoustic emissions and vibration of the bearing of a machine tool were monitor was proposed.info:eu-repo/semantics/publishedVersio

Desempenho produtivo da Bananeira Pacovan ken Na chapada do Apodi.

A bananeira é cultivada em todos os estados brasileiros, sendo que a maior parte da produção provém da Região Nordeste do país, onde são produzidos 34% do volume total. O Brasil vem destacando com o quinto lugar no ranking mundial na produção de banana, mas apresentando produtividade média de apenas 19 toneladas/ha/ano (4)

Characterization of phenolic composition in tropical wines of altitude in the Northeast of Brazil.

The objective of this study was to evaluate the phenolic composition of red wines made from grapes cultivated in tropical region of altitude at 1,100 meters, in the Northeast of Brazil.BIO Web of Conferences, v. 7, p. 02014, 2016

What are the Best Hierarchical Descriptors for Complex Networks?

This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.Comment: 9 pages, 4 figure