Institution
Federal Institute of Pernambuco
Education•Recife, Brazil•
About: Federal Institute of Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Salinity. The organization has 719 authors who have published 791 publications receiving 3662 citations. The organization is also known as: Pernambuco Federal Institute of Education, Science, and Technology.
Papers
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TL;DR: In this paper, the authors present the results of an opinion poll conducted in Brazil on the perception of social isolation during the COVID-19 pandemic, with questions about the socioeconomic profile and factors associated with isolation.
Abstract: This paper presents the results of an opinion poll conducted in Brazil on the perception of social isolation during the COVID-19 pandemic. The questionnaire was prepared on Google Forms, disseminated through social networks, with questions about the socioeconomic profile and factors associated with isolation. A non-probabilistic sample was obtained with 16,440 respondents. Data were analyzed using the Stata 13 software. Social interaction was the most affected aspect among people with higher education and income (45.8%), and financial problems caused a more significant impact (35%) among people with low income and education. Those who practice some physical activity showed lower levels of stress 13%, as well as greater normality in sleep 50.3%. People who reported living in worse habitability conditions reported willingness to remain isolated for less time, 73.9%. Among non-isolated people (10.7% of the total sample), 75.8% believe that social isolation will reduce the number of victims of COVID-19. We conclude, based on this sample, that the perception about social isolation as a pandemic mitigation action varies by income, education, age, and gender. However, most believe that it is the most appropriate control measure and are willing to wait as long as necessary to contribute to the fight against COVID-19.
235 citations
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TL;DR: A review of studies in the Caatinga, focusing on four main fields: vertebrate ecology, plant ecology, human ecology, and ethnobiology, to help define actions and strategies for the conservation of the biological diversity of the Caatesa.
Abstract: Besides its extreme climate conditions, the Caatinga (a type of tropical seasonal forest) hosts an impressive faunal and floristic biodiversity. In the last 50 years there has been a considerable increase in the number of studies in the area. Here we aimed to present a review of these studies, focusing on four main fields: vertebrate ecology, plant ecology, human ecology, and ethnobiology. Furthermore, we identify directions for future research. We hope that the present paper will help defining actions and strategies for the conservation of the biological diversity of the Caatinga.
214 citations
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TL;DR: This paper evaluates eight different concept drift detectors and performs tests using artificial datasets affected by abrupt and gradual concept drifts, with several rates of drift, with and without noise and irrelevant attributes, and also using real-world datasets.
Abstract: In data stream environments, drift detection methods are used to identify when the context has changed. This paper evaluates eight different concept drift detectors ( ddm , eddm , pht , stepd , d o f , adwin , Paired Learners, and ecdd ) and performs tests using artificial datasets affected by abrupt and gradual concept drifts, with several rates of drift, with and without noise and irrelevant attributes, and also using real-world datasets. In addition, a 2 k factorial design was used to indicate the parameters that most influence performance which is a novelty in the area. Also, a variation of the Friedman non-parametric statistical test was used to identify the best methods. Experiments compared accuracy, evaluation time, as well as false alarm and miss detection rates. Additionally, we used the Mahalanobis distance to measure how similar the methods are when compared to the best possible detection output. This work can, to some extent, also be seen as a research survey of existing drift detection methods.
132 citations
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TL;DR: The Reactive Drift Detection Method (RDDM) is proposed, which is based on DDM and, among other modifications, discards older instances of very long concepts aiming to detect drifts earlier, improving the final accuracy.
Abstract: Concept drift detectors are online learning software that mostly attempt to estimate the drift positions in data streams in order to modify the base classifier after these changes and improve accuracy. This is very important in applications such as the detection of anomalies in TCP/IP traffic and/or frauds in financial transactions. Drift Detection Method (DDM) is a simple, efficient, well-known method whose performance is often impaired when the concepts are very long. This article proposes the Reactive Drift Detection Method (RDDM) , which is based on DDM and, among other modifications, discards older instances of very long concepts aiming to detect drifts earlier, improving the final accuracy. Experiments run in MOA, using abrupt and gradual concept drift versions of different dataset generators and sizes (48 artificial datasets in total), as well as three real-world datasets, suggest RDDM beats the accuracy results of DDM, ECDD, and STEPD in most scenarios.
106 citations
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University of Barcelona1, University of Lisbon2, University of Freiburg3, Carnegie Mellon University4, University of North Carolina at Chapel Hill5, Foundation for Research & Technology – Hellas6, University of Crete7, New York University8, Federal Institute of Pernambuco9, University of Edinburgh10, University of Auvergne11, Université Paris-Saclay12, University of Porto13
TL;DR: In this article, a coarse-to-fine search for new stellar open clusters in the direction of Perseus using precise and accurate astrometry from Gaia DR2 was performed.
Abstract: Context. Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disc. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter claim to be challenged.Aims. We perform a systematic search for new OCs in the direction of Perseus using precise and accurate astrometry from Gaia DR2.Methods. We implemented a coarse-to-fine search method. First, we exploited spatial proximity using a fast density-aware partitioning of the sky via a k -d tree in the spatial domain of Galactic coordinates, (l , b ). Secondly, we employed a Gaussian mixture model in the proper motion space to tag fields quickly around OC candidates. Thirdly, we applied an unsupervised membership assignment method, UPMASK, to scrutinise the candidates. We visually inspected colour-magnitude diagrams to validate the detected objects. Finally, we performed a diagnostic to quantify the significance of each identified over-density in proper motion and in parallax space.Results. We report the discovery of 41 new stellar clusters. This represents an increment of at least 20% of the previously known OC population in this volume of the Milky Way. We also report on the clear identification of NGC 886, an object previously considered an asterism. This study challenges the previous claim of a near-complete sample of OCs up to 1.8 kpc. Our results reveal that this claim requires revision, and a complete census of nearby OCs is yet to be found.
97 citations
Authors
Showing all 720 results
Name | H-index | Papers | Citations |
---|---|---|---|
João Rocha | 93 | 1521 | 49472 |
Thiago R.L.C. Paixão | 30 | 116 | 2813 |
William R. de Araujo | 17 | 43 | 1342 |
Eduardo L. Canedo | 17 | 67 | 916 |
A.A.V. Ochoa | 13 | 25 | 401 |
Andrey Brito | 13 | 82 | 680 |
João G. Alves | 11 | 17 | 297 |
Alexandre Nascimento dos Santos | 10 | 17 | 247 |
Frederico Duarte de Menezes | 10 | 35 | 271 |
Amanda Reges de Sena | 9 | 25 | 136 |
Ronaldo R. B. de Aquino | 9 | 38 | 223 |
Ricardo Lima | 9 | 48 | 249 |
Aida A. Ferreira | 9 | 44 | 356 |
Helber E. P. de Souza | 8 | 23 | 268 |
Thiago Matheus Guimarães Selva | 8 | 13 | 181 |