Abstract
Economic and Food Safety Authority receives on a daily basis reports and complaints regarding infractions, delicts and possible food and economic crimes. These reports and complaints can be in different forms, such as e-mails, online forms, letters, phone calls and complaint books present in every establishment. This paper aims to apply text mining and classification algorithms to textual data extracted from these reports and complains in order to help identify if the responsible entity to analyze the content is, in fact, the Economic and Food Safety Authority. The paper describes text preprocessing and feature extraction procedures applied to Portuguese text data. Supervised multi-class classification methods such as Naïve Bayes and Support Vector Machine Classifiers are employed in the task. We show that a non-semantical text mining approach can achieve good results, scoring around 70% of accuracy.

Abstract
Industry 4.0 is the movement towards a fourth industrial revolution that will consist in the digitization and integration of all value chain. In Europe, this movement is led by the German RAMI 4.0 (Reference Architecture for Industry 4.0) proposal, which is attracting a lot of attention from industry, academia and other practitioners. Under the RAMI 4.0 scope there is an Administration Shell proposal to abstract physical and logical assets in a standardized way. Once abstracted, assets become Industry 4.0 Components and can be fully integrated in the Cyber Physical Production System or value chain. This work focuses on the utilization of software components within the Administration Shell. There is a necessity to represent software components and their relation to industrial asset. Therefore, control and monitoring applications involving software components and other assets can be represented in compliance with the I4.0 Component Model. To address this necessity the Smart Object Self Description information model is proposed and applied to a real case study scenario.

Abstract

Abstract
This research analyses the degree of maturity of project management practices. The study object were the organizations that develop their main activity in manufacturing machines for the mining and quarrying and construction industries, in the region of Tamega e Sousa (Portugal). The theoretical background is the Organizational Project Management Maturity Model (OPM3 (R)). The methodological approach adopted was case study research (multiple case study). The results give us cues to believe that the degree of maturity of this industry is very low. The three companies analysed revealed levels of maturity around 15%. The reasons behind the low level of maturity are related with the lack of adoption of organizational enablers and the low level use of project management practices: 48% of project management processes are not used, and 28% are classified into an ad-hoc stage. In a management context this research can be a starting point to improve project management in organizations. Yet, one can conclude that these organizations develop their daily operations within a project management perspective but this is not recognized by them. Even identifying processes that are part of recognized good project management practices, these organizations score for a relatively low maturity level. This research brought important practical contributions, by raising at least awareness of the project management value in the organizations studied.

Abstract
Nowadays, Lean Manufacturing, Industry 4.0, and Sustainability are important concerns for the companies and in a general way for the society, principally, the influence of the two production philosophies, Lean Manufacturing and Industry 4.0, in the three main pillars of sustainability: economic, environmental, and social. According to the literature review done in this work, these relations are not well known and are dispersed by different sustainability's criteria. To address this gap, this research proposes a structural equation model, with six hypotheses, to quantitatively measure the effects of Lean Manufacturing and Industry 4.0, in Sustainability. To statistically validate such hypotheses, we collected 252 valid questionnaires from industrial companies of Iberian Peninsula (Portugal and Spain). Results show that: (1) it is not conclusive that Lean Manufacturing is correlated with any of the sustainability pillars; and (2) Industry 4.0 shows a strong correlation with the three sustainability pillars. These results can contribute as an important decision support for the industrial companies and its stakeholders, even because not all the results are in line with other opinions and studies.

Abstract
This paper proposes a "grey-box" dynamic equivalent model for medium voltage active distribution networks, taking into account a heterogeneous fleet of generation technologies alongside the latest European grid codes requirements. It aims to properly represent the transient behavior of the system upon large voltage disturbances in the transmission side. The proposed equivalent model is composed by four main components: two equivalent generation units, one for converter-connected units' representation, and another accounting for the synchronous generation units' portfolio; an equivalent composite load model; and a battery energy storage system, also converter-connected to the grid. The model's parameters are estimated by an evolutionary particle swarm optimization algorithm, by comparing a fully-detailed model of a medium voltage distribution network with the equivalent model's frequency domain's responses of active and reactive power flows, at the boundary of distribution-transmission interface substation.