Atividade de extensão antirracista: educação contra o preconceito e discriminação na escola

This experience report seeks to reflect on a workshop given to 4th grade students at Elementary School Elze Lima Verde Montenegro, located in Iguatu, Ceará. The workshop was part of the curricularization activity for the extension of the discipline "Education and Ethnic-Racial Relations" of the Pedagogy course at the Faculty of Education, Sciences and Letters of Iguatu, and aimed to work with children on the knowledge of their characteristics , respect and fight against racism, prejudice and discrimination. Activities included dynamics, storytelling and solving activities, building knowledge for an anti-racist education. The realization of this workshop as an extension activity contributed to the construction of a more inclusive and anti-racist education, promoting the appreciation of ethnic relations and respect for diversity.Este relato de experiência busca refletir sobre uma oficina ministrada junto aos alunos do 4º ano da Escola de Ensino Fundamental Elze Lima Verde Montenegro, situada em Iguatu, Ceará. A oficina fez parte da atividade de curricularização da extensão da disciplina "Educação e Relações Étnico-Raciais" do curso de Pedagogia da Faculdade de Educação, Ciências e Letras de Iguatu, e teve o objetivo de trabalhar com as crianças sobre o conhecimento de suas características, respeito e combate ao racismo, preconceito e discriminação. As atividades incluíram dinâmicas, contação de histórias e resolução de atividades, construindo conhecimentos para uma educação antirracista. A realização dessa oficina como atividade de extensão contribuiu para a construção de uma educação mais inclusiva e antirracista, promovendo a valorização das relações étnicas e respeito a diversidade

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

New approach to evaluate a non-grain oriented electrical steel electromagnetic performance using photomicrographic analysis via digital image processing

The growing global demand for energy makes it necessary to adopt measures ranging from the exploration of new energy sources to the development of technology for machinery and equipment with greater energy efficiency. Non-grain oriented electrical steels are widely used in the construction of rotors and stators that form the core of electric motors, and their microstructures are directly related to its electromagnetic performance. This paper presents a new, fast and efficient method for the classification of non-grain oriented electrical steel microstructural states and their electromagnetic performance using photomicrographic analysis. The study was performed on non-grain oriented electrical steel samples with 1.28% silicon, cold-rolled with reductions of 50% and 70%, annealed in box at 730 °C for 12 h, and subjected to a subsequent annealing heat treatment for grain growth at 620 °C, 730 °C, 840 °C and 900 °C for 1, 10, 100 and 1000 min at each temperature. A total of 32 samples were used to form a database with 192 images. Our approach used a combination of extractor features (GLCM, LBP and moments) with the classifiers (Bayes, K-NN, K-means, MLP and SVM), also combined with two data partitioning, and the hold out and leave one out. KNN with 1 neighbor using the GLCM extractor showed the highest accuracy rate of 97.44%, and values greater than 96.0% for the other validation methods. The time required for the test was only 15.4 ms. The results obtained with this proposed approach, generate a new approach to evaluate a non-grain oriented electrical steel electromagnetic performance. Keywords: Electrical steel, Electromagnetic performance, Digital image processing, Pattern recognitio

Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

This article discusses a fast and efficient classification of non-grain oriented electrical steel and its electromagnetic efficiency based on the analysis of the images of the Crystalline Orientation Distribution Function (CODF). The study was carried out on samples of a non-grain oriented electrical steel, semi-processed with 1.28% silicon, cold rolled with thickness reductions of 50.0% and 70.0%, and annealed at 730 °C for 12 h. The material was also subjected to annealing heat treatment for grain growth at temperatures of 620 °C, 730 °C, 840 °C and 900 °C for 1, 10, 100 and 1000 min at each temperature. The database used was comprised of 32 images. The extractors Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Central Moments, Statistical Moments, and Hu's Moments were combined with the following classifiers: Bayes, k-Nearest Neighbor (kNN) with 1, 3, and 5 nearest neighbors, MultiLayer Perceptron (MLP) with two configurations, Support Vector Machines (SVM) with four different kernel types (linear, polynomial, radial basis function (RBF) and sigmoid). For all the using cases the method of partitioning data Hold Out. Measurements of precision, sensitivity, specificity and positive predictive values, as well as the confusion matrix were used to evaluate the classifiers. The SVM with polynomial using the GLCM extractor had the highest accuracy rate of 89.00%, specificity of 86.93%, sensitivity of 80.69% and positive predictive values of 80.34%. The time required for this combination, which was the best, was only 0.6 ms. The results showed that this approach generated a new methodology for the analysis of non-grain oriented electrical steels. Keywords: Electrical steel, Crystalline Orientation Distribution Function, Digital image processing, Pattern recognitio

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data