Abstract Introduction: to predict work related diseases is a challenge for organizations and the governmental authorities. By means of machine learning (ML) techniques it is possible to identify factors that determine the occurrence of an occupational disease, aiming at taking more effective actions to protect workers. Objective: to predict, by comparing ML techniques, the factors which highly influence the occurrence of occupational dermatitis. Methods: we developed a code in R language and a descriptive analysis of the data and identified the influence factors according to the ML technique that presented the best performance. The database was made available by the Occupational Dermatology Service of Oswaldo Cruz Foundation and assembles information of the workers who experienced cutaneous alterations suggestive of occupational dermatitis between 2000-2014. Results: the techniques which presented the best performance were: neural network, random forest, support vector machine, and naive Bayes. Sex, schooling, and profession were the most adequate variables for the occupational dermatitis prediction models. Conclusion: ML techniques allowed to predict the factors that influence the workers’ safety and health, as well as the parameters that subsidize the procedures implementation, and the most effective policies to prevent occupational dermatitis.

Resumo Introdução: realizar a predição de doenças relacionadas ao trabalho é um desafio às organizações e ao poder público. Com as técnicas de aprendizado de máquina (AM), é possível identificar fatores determinantes para a ocorrência de uma doença ocupacional, visando direcionar ações mais efetivas à proteção dos trabalhadores. Objetivo: predizer, a partir da comparação de técnicas de AM, os fatores com maior influência para a ocorrência de dermatite ocupacional. Métodos: desenvolveu-se um código em linguagem R e uma análise descritiva dos dados e identificaram-se os fatores de influência de acordo com a técnica de AM que demonstrou melhor desempenho. O banco de dados foi disponibilizado pelo Serviço de Dermatologia Ocupacional da Fundação Oswaldo Cruz e contém informações de trabalhadores que apresentaram alterações cutâneas sugestivas de dermatite ocupacional no período de 2000-2014. Resultados: as técnicas com melhor desempenho foram: neural network, random forest, support vector machine e naive Bayes. As variáveis sexo, escolaridade e profissão foram as mais adequadas para os modelos de previsão de dermatite ocupacional. Conclusão: as técnicas de AM possibilitam predizer os fatores que influenciam a segurança e a saúde dos trabalhadores, os parâmetros que subsidiam a implantação de procedimentos e as políticas mais efetivas para prevenir a dermatite ocupacional.

Abstract Paper aims This research presents a literature overview in relation to data mining and machine learning applications in the area of occupational health and safety. Originality A summary of main insights obtained from the analysis of systematic mapping is presented at the end, as well as a roadmap with recommendations for directing future research on the topic. Research method This article carries out a thorough descriptive research of the scientific literature on the topic through a systematic mapping covering the period between the years 2008 and 2019 and 12 scientific databases, which at the end presents 68 selected records. Main findings Around 84% of the selected records were of total significance for the research, with the majority of them being classified in the areas of civil construction and steel industry. Implications for theory and practice Through this study it is possible to understand the way research has been developed on this theme, as well as point to the guidelines for future studies. Other contribution is the indication of studies in OSH 4.0 concept, based on monitoring workers full-time.

Abstract Paper aims This paper aims to assist and facilitate decision-making in health management and work safety by the identification and prioritization of occupational health and safety indicators. Originality This research contributes to the literature by demonstrating the applicability of multicriteria tools to solve health and safety-related issues on organizations and also some implications related to this method. Research method The methodological approach is the application of the Fuzzy AHP method for prioritization of categories of occupational health and safety indicators. Main findings The results show that the category of indicators called “Occupational Health and Safety Management” was considered the most relevant, followed by the categories “Accidents and Diseases” and “Risk Assessment”. Implications for theory and practice For the application of the Fuzzy AHP method it is necessary to compare the level of importance between objects. Collecting these assessments can be difficult. In this paper, more than forty companies were reached but only six responded and only two responses were valid.

ABSTRACT The geographical, temporal distances and socio-cultural differences are the biggest challenges for Global Software Development (GSD). In GSD, it is necessary to deal with behaviors, values and skills of the team members, conflicts, rework, delays, etc. can influence a software production process. The objective of this paper is to identify the sociocultural aspects and propose a classification structure under different analytical perspectives in order to offer adequate support to the management of GSD teams. A systematic mapping and proposal an approach that classifies aspects in three perspectives: collaboration, people and external. The analysis allows the project manager to make decisions about the performance of human resources and mitigate problems arising from communication, cooperation and coordination, generating better results in indicators of productivity, quality and efficiency of GSD teams.

Resumen En este trabajo se presenta la optimización de la mezcla de agregados en la producción de bloques de adoquines, mediante la formulación de un modelo de programación lineal. El objetivo es utilizar un modelo de optimización para reducción de los costos de fabricación, respetando el cumplimiento de los requisitos mínimos de calidad y las características mecánicas / físicas requeridas por las normas técnicas. Los resultados obtenidos con el modelo se utilizaron para producir un lote de productos, que fue sometido a un proceso de inspección visual de las pruebas de calidad y de laboratorio. Los resultados demuestran la viabilidad del uso de la programación lineal para definir las cantidades requeridas de materias primas para el proceso de fabricación de bloques de concreto para pavimentos de concreto, que genera beneficios económicos en la industria y permite el control de la calidad del producto.

Abstract This paper presents the optimization of aggregate mixture in the production of paver blocks through the formulation of a linear programming model. The proposed model aims at reducing manufacturing costs, respecting the compliance with the minimum quality requirements and mechanical/physical characteristics required by the technical standards. The results obtained with the model were used to produce a batch of products, which underwent a process of visual inspection of the quality and laboratory tests. The results showed the viability of using a linear programming to define the required quantities of raw materials to the manufacturing process of Concrete Paver Blocks, which generates economic benefits for the industry and allows the product quality control as well.