Potential for reuse of effluent from fish-processing industries.

The most common problems in the fish processing industry relate to high water consumption and the generation of effluents with concentrated organic loads. Given that reuse can represent an alternative for sustainable development, this study sought to assess the potential for recycling effluents produced in a fish-processing plant. In order to do so, the final industrial effluent was analyzed using the American Public Health Association (APHA) standard effluent-analysis method (2005). In addition, the study assessed treatments which produce effluents meeting the requirements prescribed by different countries' regulations for reuse and recycling. The results found that effluents with smaller organic loads, such as those from health barriers and monoblock washing, can be treated in order to remove nutrients and solids so that they can be subsequently reused. For effluents produced by the washing and gutting cylinders, it is recommended that large fragments of solid waste be removed beforehand. Effluents can in this way attain a quality compatible with industrial reuse. This study further highlights the possibility of treating effluents so as comply with drinking water standards. This would potentially allow them to be used within the actual fish-processing procedure; in such a case, a revision of standards and measures for controlling use should be considered to prevent microbiological damage to products and risks to handlers and final consumers

Alternativas para construção de classificadores de solos brasileiros.

Este trabalho avalia os algoritmos J48, JRip e PART como alternativas para obtenção de modelos de classificação de solos, todos baseados em regras de classificação. Foram utilizadas observações das classes Nitossolos Brunos e Latossolos Brunos, extraídas do Sistema de Informação de Solos Brasileiros, Embrapa Solos. O algoritmo J48 apresentou modelos com maior acurácia e número de regras, em contraste com o JRip, que apresentou menor acurácia, com menor número de regras, o algoritmo PART se manteve no nível intermediário em ambas as métricas

The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including typical, indeterminate, and atypical appearance for COVID-19, or negative for pneumonia, adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use

The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use