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This paper presents an overview of the work under development within MAESTRI EU-funded collaborative project. The MAESTRI Total Efficiency Framework (MTEF) aims to advance the sustainability of manufacturing and process industries by providing a management system in the form of a flexible and scalable platform and methodology. The MTEF is based on four pillars: (a) an effective management system targeted at process continuous improvement; (b) Efficiency assessment tools to support improvements, optimisation strategies and decision support; (c) Industrial Symbiosis paradigm to gain value from waste and energy exchange; (d) an Internet-of-Things infrastructure to support easy integration and data exchange among shop-floor, business systems and tools.

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Este trabalho teve por objetivo avaliar a eficiência técnica dos cursos de Administração no Brasil, por meio da Análise Envoltória de Dados (DEA), uma técnica não paramétrica introduzida por Charnes, Cooper e Rhodes (1978). Foram utilizados como variáveis do modelo os oito indicadores que compõem o Conceito Preliminar de Cursos (CPC). A amostra foi composta de 1229 cursos de Administração, com base nos resultados do Exame Nacional de Desempenho dos Estudantes (ENADE) de 2009. Como resultado, constatou-se que apenas 1,2% destes cursos podem ser considerados eficientes. Após esta aferição, uma etapa de recomendações foi realizada a fim de propor algumas metas reparatórias aos cursos ineficientes, trazendo-se assim à tona a possibilidade de alcançarem melhores resultados, dados os insumos já disponíveis pelos cursos de Administração.

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Wine authenticity methods are in increasing demand mainly in Denomination of Origin designations. The DNA-based methodologies are a reliable means of tracking food/wine varietal composition. The main aim of this work was the study of High Resolution Melting (HRM) application as a screening method for must and wine authenticity. Three sample types (leaf, must and wine) were used to validate the three developed HRM assays (Vv1-705 bp; Vv2-375 bp; and. Vv3-119 bp). The Vv1 HRM assay was only successful when applied to leaf and must samples. The Vv2 HRM assay successfully amplified all sample types, allowing genotype discrimination based on melting temperature values. The smallest amplicon, Vv3, produced a coincident melting curve shape in all sample types (leaf and wine) with corresponding genotypes. This study presents sensitive, rapid and efficient HRM assays applied for the first time to wine samples suitable for wine authenticity purposes.

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Deep Learning (DL) has been proven as a powerful recognition method as evidenced by its success in recent computer vision competitions. The most accurate results have been obtained by ensembles of DL models that pool their results. However, such ensembles are computationally costly, making them inapplicable to real-time applications. In this paper, we apply model compression techniques to the problem of semantic segmentation, which is one of the most challenging problems in computer vision. Our results suggest that compressed models can approach the accuracy of full ensembles on this task, combining the diverse strengths of networks of very different architectures, while maintaining real-time performance. (C) 2017 Published by Elsevier Inc.