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Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing. Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment chest organ Sample Characteristic - Location Brazil [ABSTRACT FROM AUTHOR]

Copyright of Organon (01026267) is the property of Revista Organon and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anuario de la Sociedad Argentina de Historia de la Educación is the property of Argentinean Society of History of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geosaberes: Revista de Estudos Geoeducacionais is the property of GEOSABERES - Revista de Estudos Geoducacionais and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• aIRT equipment equipped with a RGB camera improves inspection. • Post-flight image analysis provides a faster aIRT inspection. • The most common problem detected were hot spots caused by soiling and vegetation. • The most common defect in the power plants were disconnected cell substrings. • The defect with higher impact on the PV plant performance are disconnected strings. The uptime of utility-scale solar photovoltaic (PV) power plants is of utmost importance for meeting contractual energy yields and expected capacity factors. Aerial Infrared Thermography (aIRT) is a non-destructive, no-downtime, fast and cost-effective method for monitoring large-scale PV power plants and assisting in fault detection. The use of aIRT techniques aims at increasing the quality and service life of PV plants especially in sunny and developing countries such as Brazil, where there is a shortfall of specialised workforce and the costs for a detailed supervisory system of utility-scale PV power plants are high. This paper presents an analysis of an aIRT flight campaign over four utility-scale PV plants in the northeast of Brazil. Two types of measurement equipment have been tested and compared, resulting in more stability and efficiency using a commercially available solution. This solution was also equipped with a RGB camera that accelerated the inspections, since it helped to differentiate defects from hot spots caused by soiling and vegetation over the modules, which were common. Different methods for fault detection were also tested and it was concluded that post-flight image analysis provides faster results. The most common faults that can happen in the early operation of PV power plants and how operators should address and prevent them were also discussed. The most common defect detected during the campaign were disconnected cell substrings. However, disconnected strings had the most impact on the power plants energy performance. [ABSTRACT FROM AUTHOR]

Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the decades, a variety of mapping approaches have been employed for the detection of landslides using Earth observation (EO) data. Object-based image analysis (OBIA) is a widely recognized method for mapping landslides and other morphological features. In Brazil, despite the high frequency of landslides, methods for inventory construction are poorly developed. The aim of this study is to semi-automatically recognize shallow landslides in Itaóca (Brazil) and evaluate the transferability of the approach within different areas in Brazil. RapidEye satellite images (5 m) and the derived normalized difference vegetation index (NDVI), as well as a digital elevation model (DEM) (12.5 m) and morphological data, were integrated into the classification. The results show that the method is suitable for the recognition of this type of hazard in Brazil. The overall accuracy was 89%. The main challenges were the identification of small landslides and the exact delineation of scars. The findings validate the applicability of the approach in Brazil, although additional adjustments to the primary rule set might lead to better results. [ABSTRACT FROM AUTHOR]

Abstract: Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye–brain combination does. The latter uses the object''s color (spectral information), size, texture, shape and occurrence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object''s properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA. [Copyright &y& Elsevier]

The article talks about Institute of Electrical & Electronics Engineers ComSoc Student Chapter in Brazil founded in 2015, Aldebaro Klautau as the adviser of the chapter, hosting the seminar on signal processing for MIMO systems and on hyper-spectral image analysis. It talks about the Forum for European ICT and Media Professionals exploring the e delivery and consumption of digital media and the International conference of Computer Network held in Poland.

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A novel strategy using only sample points to delineate polygons of soil classes. • GEOBIA is a promising approach for digital soil mapping. • The GEOBIA approach improves soil class prediction. The use of techniques to calibrate soil prediction models based on legacy maps is restricted to areas where conventional survey and soil mapping were performed. It is necessary to seek alternatives to calibrate soil predictive models without a legacy soil map. The problem is the difficulty to delineate polygons of soil classes based on soil sampling points. This paper presents a novel digital soil mapping strategy using only georeferenced points of soil profiles to delineate detailed polygons of soil classes by the Geographic Object-Based Image Analysis (GEOBIA) approach and Artificial Neural Networks (ANN) models. The main objective was to evaluate the integration of the GEOBIA approach at different segmentation levels with ANN's models and sampling points to produce a digital soil map of the Vale dos Vinhedos region, in Rio Grande do Sul, Brazil. From a Digital Terrain Model, 10 predictive variables were extracted. From the RapidEye remote sensing image the following spectral variables were extracted: Normalized Difference Vegetation Index and Normalized Difference Water Index. A new set of variables was produced using principal component analysis after standardization of the original variables. The multiresolution segmentation algorithm was used to create image objects at different segmentation levels. Four segmentation levels were tested with scale parameter (SP) 1, 2, 5 and 10; and the Cheesboard segmentation (CS) was used to transform the original pixels into polygons. Information on types of soil was obtained from 163 georeferenced soil profiles. Five ANN's structures were implemented: four for the segmented data (SP01, SP02, SP05 and SP10) and one for the per-pixel approach (CS); 16 repetitions of each ANN were performed. With the best models for each segmentation level, extrapolations of soil classes were performed for the entire study area and validated with a detailed legacy soil map. The per-pixel approach model obtained the worst result. The best result in the extrapolation of soil types found was with the object-based approach (SP1). In our understanding, the scientific community can profit from the proposed methodology particularly when it only has georeferenced points of soil classes and not a legacy map, i.e., immediately after a field survey without the need for photointerpretation for delineation of the soil mapping units. The use of GEOBIA for digital mapping of soil classes seems to be a promising approach for future research. [ABSTRACT FROM AUTHOR]

Brazil's decade of mega sporting events, which ended in 2016, presented a series of unexpected difficulties. The country hosted the events with the goal of changing its international status. This article aims to identify the effects of the Olympic Games of Rio de Janeiro on Brazil's international image through an analysis of articles that were published during the event in nine international media vehicles. Methodologically, this article makes use of media framing to develop five categories: Political and Economic Situation, Unequal Brazil, Culture and Tourism, Event Organization and Urban improvements, and Mediatic Pleonasm or Lochte Affair. It can be concluded that during the Olympic Games, while the news coverage centred around the host city, the scope of reports was limited and themes were replicated across newspapers, and the domestic situation influenced the media source's depiction of the host country. [ABSTRACT FROM AUTHOR]

Soapstone is an abundant mineral in Ouro Preto ‐ Minas Gerais, Brazil and its main destination is in the production of craftsmanship. Rock recovery in those activities is low and the waste disposal is done with little control, which can be hazardous to the environment. This work proposes an alternative use of such potentially harmful waste as reinforcement in a novel polymer matrix composite, which can be particularly attractive to the automotive industry and of which very little information is available elsewhere in the literature. Firstly, the characterization of the waste was performed. Particle size and shape parameters were determined by automated image analysis and the mineralogical composition was determined by X‐ray diffraction, infrared, and Raman spectroscopy. High‐density polyethylene was used as matrix and the composites were made in three matrix/filler ratios: 90/10, 80/20, and 70/30 by weight. Tensile and rheological properties were measured in order to determine the influence of the particles on the polymer mechanical behavior and processing conditions. The materials showed a pseudoplastic behavior and the filler's influence was more pronounced in the 70/30 composites, which showed higher viscosities than the neat polymer. The addition of particles resulted in more brittle and rigid composites, with higher values of tensile strength. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Agricultural Sciences / Revista Brasileira de Ciências Agrárias is the property of Universidade Federal Rural de Pernambuco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study analyses some elements of the historical and social nature of Capoeira, such as the juridical-political, the clothing, the symbolic and the identity, but, essentially, those of a physical and linguistic nature, from the caricatures produced by Calixto Cordeiro, which illustrated the work of Lima Campos in 1906. In this work, a multi-method process was applied, represented by the presuppositions of Historical Archeology, the interpretation of images indicated by Panofsky, and the documental analysis of various sources. The use of these presuppositions and the confrontation of sources of diverse natures allowed us to interpret the signs, symbols, and meanings from the facts and data described by Cordeiro, about Capoeira presented by different literates. The results of these analyses showed substantial information about the blows/moves of this struggle, as well as its different names, slang and expressive forms, often associated with different groups of practitioners from different Brazilian cities. [ABSTRACT FROM AUTHOR]

Tropical coastal wetlands form complex and dynamic ecosystems based on a mixture of vegetation, soil, and water components. Optical remotely sensed data have often been used to characterize and monitor these ecosystems, which are among the environments most threatened by climate change and anthropogenic activity worldwide. The present study sought to evaluate the spectral response of Landsat-5 Thematic Mapper (TM) images for the interpretation of different wetlands and associated environments at the mouth of the Amazon River, including mangroves, saltmarshes, beaches, and dunes, as well as secondary vegetation, water with different levels of sediment suspension, and human occupation. A Spectral Angle Mapper (SAM) classifier was applied to the analysis of Landsat-5 TMsatellite imagery to evaluate the potential for the mapping of these coastal wetland land cover classes. The characterization and comparison of the different spectral classes were obtained through the collection of at least 20 polygonal samples (5 × 5 pixels) for each class, with a total of 4,544 points. Spectral separability indices for each pair of classes were based on an Analysis of Variance, with Tukey post-test. The results indicated that most land cover classes could be separated spectrally with Landsat-5 TM. The overall accuracy and Kappa indices for the results of the classification were 86.1 and 0.84 %, respectively. The results of this spectral analysis demonstrated the potential of the SAM classifier for the classification of the different tropical wetlands in a typical Amazon coastal setting from optical remotely sensed data. [ABSTRACT FROM AUTHOR]