Abstract
A main problem for predicting cell line interactions with chemical compounds is the lack of a computational representation for cell lines. We describe a method for characterizing cell lines from scientific literature. We retrieve and process cell line-related scientific papers, perform a document classification algorithm, and then obtain a description of the information space of each cell line. We have successfully characterized a set of 300+ cell lines. © 2020 Owner/Author.

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Abstract
In the competitive automotive market, where extremely high-quality standards must be ensured independently of the growing product and manufacturing complexity brought by customization, reliable and precise detection of any non-conformities before the vehicle leaves the assembly line is paramount. In this paper we propose a wearable solution to aid quality control workers in the detection, visualization and relay of any non-conformities, while also reducing known performance issues such as skill gaps and fatigue, and improving training methods. We also explore how the reliability, precision and validity tests of the visualization module of our framework were performed, guaranteeing a 0% chance occurrence of undesired non-conformities in the following usability tests and training simulator. © 2021, Springer Nature Switzerland AG.

Abstract
Este artigo tem como objetivo identificar os desafios, as barreiras e as aprendizagens com a atividade de remanufatura. Por meio de uma busca realizada na internet foram encontradas empresas de diferentes regiões do país que operam no setor. Desse modo, como procedimento de coleta, foi adotado o estudo de casos múltiplos e, como técnica de coleta, foi utilizada a entrevista semiestruturada, uma vez que a intenção era obter dos entrevistados o relato sobre o seu dia a dia e o seu ambiente de negócio na remanufatura. Os resultados mostraram que a atividade é carente de incentivos por parte do poder público. Outro ponto que prejudica é o custo da logística reversa. Há, ainda, a falta de locais apropriados na fase de descarte dos materiais, cujas condições de reaproveitamento no processo não são mais viáveis. Destarte, o estudo permitiu conhecer um pouco mais da remanufatura por meio do que as empresas contatadas vivenciam no mercado.

Abstract
Noting recent advances in the field of image classification, where convolutional neural networks (CNNs) are used to classify images with high precision. This paper proposes a method of classifying breathing sounds using CNN, where it is trained and tested. To do this, a visual representation of each audio sample was made that allows identifying resources for classification, using the same techniques used to classify images with high precision.For this we used the technique known as Mel Frequency Cepstral Coefficients (MFCCs). For each audio file in the dataset, we extracted resources with MFCC which means we have an image representation for each audio sample. The method proposed in this article obtained results above 74%, in the classification of respiratory sounds used in the four classes available in the database used (Normal, crackles, wheezes, Both).

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