In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost–benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.

Purpose of the study: The objective of this study is to identify the benefits and challenges of smart industry concept to the human factor, based on the concept of Industry 4.0.Methodology/approach: A systematic literature review was elaborated, based on structured protocols for the selection of a bibliographic portfolio of articles. A bibliometric analysis of the data and content analysis was performed.Originality/relevance: The article discusses human work, focusing on theoretical and practical contributions of international literature. The focus scenario is smart industry, a concept in constant improvement, which currently has acquired influences from Industry 4.0.Main results: The discussions lead us to ponder on human factor in smart industries in the categories physical and mental health at work, human performance and professional career in general. The conclusions points to the need to ensure adequate working conditions in cognitive and psychic aspects, among others.Theoretical and methodological contributions: We present major literature articles, smart industry definitions, main technologies, and grouping benefits and challenges to the human factor.

The technological inference in procedures applied to healthcare is frequently investigated in order to understand the real contribution to decision-making and clinical improvement. In this context, the theoretical field of machine learning has suitably presented itself. The objective of this research is to identify the main machine learning algorithms used in healthcare through the methodology of a systematic literature review. Considering the time frame of the last twenty years, 173 studies were mined based on established criteria, which allowed the grouping of algorithms into typologies. Supervised Learning, Unsupervised Learning, and Deep Learning were the groups derived from the studies mined, establishing 59 works employed. We expect that this research will stimulate investigations towards machine learning applications in healthcare.

A presente pesquisa, por meio de estudo de caso no setor agrícola, tem como objetivo analisar como ocorre a relação Universidade-Empresa (U-E) quanto ao contexto para a criação e transferência do conhecimento. Pretende-se analisar esse tipo de relação que acontece por intermédio da Estação Experimental denominada Fazenda-Escola de propriedade da Universidade Estadual de Ponta Grossa, onde está localizado um Campo Experimental Avançado sob responsabilidade da Empresa BASF S. A. A relação Universidade-Empresa tem se intensificado e, dentro desse contexto, as instituições de ensino e pesquisa podem contribuir de forma eficiente com a geração de novas tecnologias e conhecimentos, manifestando-se como uma alternativa de inovação juntamente com a iniciativa privada. Nesse sentindo, foram identificadas as formas, os fatores intervenientes e seus fatores estruturais no âmbito da criação e transferência do conhecimento em entrevistas com o pesquisador da universidade, o administrador da fazenda-escola da universidade e o responsável técnico pelo campo experimental da empresa. Optou-se, portanto, por uma pesquisa qualitativa, baseada em estudo de caso visando explorar a percepção das pessoas envolvidas sobre o fenômeno em análise. Os resultados indicam novos fatores intervenientes no processo de criação e transferência do conhecimento, considerados importantes para a gestão do processo e a relação com a empresa.

Hélice Tríplice: Um ensaio teórico sobre a relação Universidade-Empresa-Governo em busca da inovação