The Southern Photometric Local Universe Survey (S-PLUS) is imaging ∼9300 deg2 of the celestial sphere in 12 optical bands using a dedicated 0.8 m robotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 $\rm {\mu m}$ pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel−1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30°, 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, H δ, G band, Mg b triplet, H α, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than $1\, (\mathrm{Gpc}/h)^3$. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ∼336 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.
Há larga evidência da efetividade do uso de métodos ágeis no desenvolvimento profissional de software, sendo essencial sua incorporação na formação de egressos de cursos da área de Computação. No entanto, métodos ágeis exigem experiência e disciplina das equipes e sua adoção na universidade deve levar em conta a falta de maturidade e de experiência natural aos profissionais iniciantes. Esse relato apresenta a experiência do Núcleo de Práticas em Informática do Campus da UFC em Quixadá na adoção de práticas baseadas em métodos ágeis. As práticas relatadas neste trabalho demonstram que seu uso em ambientes acadêmicos trazem benefícios para a qualidade dos produtos e da formação dos alunos, porém devem ser adotadas e adaptadas de acordo com necessidades e restrições acadêmicas.
Purpose – The purpose of this paper is to present a four-level architecture that aims at integrating, publishing and retrieving ecological data making use of linked data (LD). It allows scientists to explore taxonomical, spatial and temporal ecological information, access trophic chain relations between species and complement this information with other data sets published on the Web of data. The development of ecological information repositories is a crucial step to organize and catalog natural reserves. However, they present some challenges regarding their effectiveness to provide a shared and global view of biodiversity data, such as data heterogeneity, lack of metadata standardization and data interoperability. LD rose as an interesting technology to solve some of these challenges. Design/methodology/approach – Ecological data, which is produced and collected from different media resources, is stored in distinct relational databases and published as RDF triples, using a relational-Resource Description Format mapping language. An application ontology reflects a global view of these datasets and share with them the same vocabulary. Scientists specify their data views by selecting their objects of interest in a friendly way. A data view is internally represented as an algebraic scientific workflow that applies data transformation operations to integrate data sources. Findings – Despite of years of investment, data integration continues offering scientists challenges in obtaining consolidated data views of a large number of heterogeneous scientific data sources. The semantic integration approach presented in this paper simplifies this process both in terms of mappings and query answering through data views. Social implications – This work provides knowledge about the Guanabara Bay ecosystem, as well as to be a source of answers to the anthropic and climatic impacts on the bay ecosystem. Additionally, this work will enable evaluating the adequacy of actions that are being taken to clean up Guanabara Bay, regarding the marine ecology. Originality/value – Mapping complexity is traded by the process of generating the exported ontology. The approach reduces the problem of integration to that of mappings between homogeneous ontologies. As a byproduct, data views are easily rewritten into queries over data sources. The architecture is general and although applied to the ecological context, it can be extended to other domains.
Hot spot policing involves the deployment of police patrols to places where high levels of crime have previously concentrated. The creation of patrol routes in these hot spots is mainly a manual process that involves using the results from an analysis of spatial patterns of crime to identify the areas and draw the routes that police officers are required to patrol. In this article we introduce a computational approach for automating the creation of hot spot policing patrol routes. The computational techniques we introduce created patrol routes that covered areas of higher levels of crime than an equivalent manual approach for creating hot spot policing patrol routes, and were more efficient in how they covered crime hot spots. Although the evidence on hot spot policing interventions shows they are effective in decreasing crime, the findings from the current research suggest that the impact of these interventions can potentially be greater when using the computational approaches that we introduce for creating hot spot policing patrol routes.
Irrigated agriculture is the most water-consuming sector in Brazil, representing one of the main challenges for the sustainable use of water. This study proposes and experimentally evaluates univariate time series models that predict the value of reference evapotranspiration, a metric of the water loss from crop to the environment. Reference evapotranspiration plays an essential role in irrigation management since it can be used to reduce the amount of water that will not be absorbed by the crop. The experiments performed under the meteorological dataset generated by a weather station. Moreover, the results show that the approach is a viable and lower cost solution for predicting ET0, since only a variable needs to be monitored.
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