This article characterizes the problem of violence against health professionals in the workplace (VAHPITWP) in selected settings in Portugal. It addresses the questions of what types of violence are most frequent and who are the most affected health professionals.Three methodological approaches were followed: (i) documentary studies, (ii) a questionnaire-based hospital and health centre (HC) complex case study and (iii) semi-structured interviews with stakeholders.Of the different types of violence, all our study approaches confirm that verbal violence is the most frequent. Discrimination, not infrequent in the hospital, seems to be underestimated by the stakeholders interviewed. Violence seems much more frequent in the HC than in the hospital. In the HC, all types of violence are also most frequently directed against female health workers and, in the hospital, against male workers.These studies allow us to conclude that violence is frequent but underreported.

To accelerate the energy transition, the EU “Clean Energy for all Europeans” package aims to strengthen the involvement of end consumers in the energy market. To this end, together with so-called “active consumers” and provisions for individual and collective renewable energy self-consumption, two types of energy communities were introduced. The EU framework, however, leaves many details of the transposition process to the national level. The corresponding directives were supposed to be transposed by the end of December 2020 (recast Electricity Market Directive, defining active consumers and citizen energy communities) and by the end of June 2021 (Renewable Energy Directive, defining renewables self-consumption and renewable energy communities). In this paper, we critically discuss major developments of the transposition, including questions of the general distinction of the different concepts, governance and ownership, physical expansion, administrative barriers and the overall integration of energy communities into the energy system. The analysis builds on country case studies as well as on previous work by the authors on the status of the transposition process throughout the EU. The paper shows that the national approaches differ greatly and are at very different stages. While basic provisions are in place in most Member States to meet the fundamental EU requirements, the overall integration into the energy system and market is only partly addressed. This concerns, for instance, the analysis of system impacts of energy communities and measures that would allow and support energy system-friendly behaviour. In addition, several practical hurdles need to be overcome. These often relate to administrative requirements such as complex registration and licensing procedures, the need for the involvement of several institutions, or difficult procedures for access to relevant data. The paper concludes that discussed barriers will need to be carefully addressed if the high expectations for the role of energy communities are to be met.

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Cardiovascular disease is a worldwide problem and is the main cause of mortality when coronary heart disease leads to a heart attack. Hence, it is important to evaluate how to prevent this disease considering the symptoms description and physical examinations. This study points out the application and comparison of different performance measures for the classification of heart disease. Firstly, a feedforward neural network was applied to classify heart disease risk, using the well-known Framingham database. Feature selection optimization was performed to identify the most important variables to take into consideration, minimizing the Type II error and maximizing the accuracy. In addition, a multi-objective optimization algorithm was carried out to simultaneously optimize both performance measures. A set of non-dominated solutions representing the trade-offs between objectives were obtained, and gender, age, systolic blood pressure, and glucose level emerged as the principal factors to take into consideration to predict heart disease. The results obtained are promising and show the importance of considering more than one criterion to identify the most important variables.

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Nowadays, manufacturing companies are characterized by complex systems with multiple products being manufactured in multiple assembly lines. In such situations, traditional costing systems based on deterministic cost models cannot be used. This paper focuses on developing a stochastic approach to costing systems that considers the variability in the process cycle time of the different workstations in the assembly line. This approach provides a range of values for the product costs, allowing for a better perception of the risk associated to these costs instead of providing a single value of the cost. The confidence interval for the mean and the use of quartiles one and three as lower and upper estimates are proposed to include variability and risk in costing systems. The analysis of outliers and some statistical tests are included in the proposed approach, which was applied in a tier 1 company in the automotive industry. The probability distribution of the possible range of values for the bottleneck’s cycle time showcase all the possible values of product cost considering the process variability and uncertainty. A stochastic cost model allows a better analysis of the margins and optimization opportunities as well as investment appraisal and quotation activities.

The main objective of this project, carried out in an industrial context, was to apply a multivariate analysis to variables related to the specifications required for the production of an agricultural tire and the dimensional test results. With the exploratory data analysis, it was possible to identify strong correlations between predictor variables and with the response variables of each test. In this project, the principal component analysis (PCA) serves to eliminate the effects of multicollinearity. The use of regression analysis was intended to predict the behavior of the agricultural tire considering the selected variables of each test. In the case of Test 1, when applying the Stepwise methods to select the variables, the model with the lowest value of Akaike Information Criterion (AIC) was achieved with the technique “Both”. However, the lowest value of AIC for Test 2 was achieved with “Backward”. Regarding the validation of assumptions, both Test 1 and Test 2 were validated. Therefore, all the quantitative variables are important, both in Test 1 and Test 2, because they are a linear combination that determines the principal components. In order to make it easier to compute predictions for future agricultural tires, an application that was developed in Shiny allows the company to know the behavior of the tire before it was produced. Using the application, it is possible to reduce the industrialization time, materials and resources, thus increasing efficiency and profits.