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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Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

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