Abstract
Laser-induced breakdown spectroscopy allows fast chemical analysis of light elements without significant sample preparation, turning it into a promising technique for on-site mining operations. Still, the performance for quantification purposes remains its major caveat, obstructing a broader application of the technique. In this work, we present an extensive comparison of the performances of distinct algorithms for quantification of Lithium in a mining prospection stage, using spectra acquired with both a commercial handheld device and a laboratory prototype. Covering both linear and non-linear methodologies, the results show that, when covering a wide range of concentrations typical on a mining operation, non-linear methodologies manage to achieve errors compatible with a semi-quantitative performance, offering performances better than those obtained with linear methods, which are more affected by saturation and matrix effects. The findings enclosed offer support for future applications in the field and may possibly be generalized for other elements of interest in similar mining environments.

Abstract
The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.

Abstract
Teaching project management to engineering students demands realworld experiences in which they can apply and develop work-ready skills, such as critical thinking, empathy, and teamwork. While a shortage of these skills in new graduates is frequently claimed by engineering companies and educational bodies, there is still a lack of higher education research studies on how to foster them through teaching practice. This paper intends to contribute to filling this gap by presenting an exploratory case study research of a Project-Based Learning (PjBL) experience aimed at designing and implementing a professional (re)integration plan for social and economic deprived people (e.g., long/short-term unemployed), who depend on external food supply provided by a non-profit organization called REFOOD. The experience was carried out in Portugal, from February to June 2021, with 7 MSc mechanical engineering students from the University of Trasos-Montes and Alto Douro (UTAD). We firstly describe the PjBL experience in terms of the key driving question, the learning goals, the educational activities, the collaboration among students and stakeholders, the scaffolding activities, and the tangible learning artefacts produced. We further discuss the preliminary results of the study from data collected through documental analysis, participant observation, and self-completion questionnaires on students' perceptions of the PjBL experience. Data analysis shows that this experience positively impacted the development of students' project management, empathy, critical thinking, and team-working skills, by mainly having challenged their personal belief systems and biases related to the real-world scenarios they dealt with. Finally, we outline implications for the teaching practice concerning the development of similar PjBL experiences, as well as future research directions.

Abstract
Both logic programming in general and Prolog in particular have a long and fascinating history, intermingled with that of many disciplines they inherited from or catalyzed. A large body of research has been gathered over the last 50 years, supported by many Prolog implementations. Many implementations are still actively developed, while new ones keep appearing. Often, the features added by different systems were motivated by the interdisciplinary needs of programmers and implementors, yielding systems that, while sharing the classic core language, in particular, the main aspects of the ISO-Prolog standard, also depart from each other in other aspects. This obviously poses challenges for code portability. The field has also inspired many related, but quite different languages that have created their own communities. This article aims at integrating and applying the main lessons learned in the process of evolution of Prolog. It is structured into three major parts. First, we overview the evolution of Prolog systems and the community approximately up to the ISO standard, considering both the main historic developments and the motivations behind several Prolog implementations, as well as other logic programming languages influenced by Prolog. Then, we discuss the Prolog implementations that are most active after the appearance of the standard: their visions, goals, commonalities, and incompatibilities. Finally, we perform a SWOT analysis in order to better identify the potential of Prolog and propose future directions along with which Prolog might continue to add useful features, interfaces, libraries, and tools, while at the same time improving compatibility between implementations.

Abstract
The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country's history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.

Abstract
Doing internships is increasingly common in Portugal, often being regarded as a prerequisite for entering the labour market. This trend reinforces the need to understand the interns' perception of the ideal characteristics for this type of experience, in order to stop (or at least slow down) the brain drain that is currently felt. This exploratory research aims at reflecting on how organisations can contribute to successful internships. Themes such as the leadership and organisational culture of the host entity are addressed, areas which have been debated less often in the current literature. Our quantitative research was based on a survey, which obtained 143 responses from individuals with internship experience. Data were analysed using descriptive, reliability, inferential, and multiple linear regression analysis. The results from this study showcased a tendency toward servant leadership (in detriment to paternalistic or autocratic leadership) as being the most appropriate leadership style for an internship. It was also possible to emphasise some important points in an internship experience, including remuneration (desired by the interns) and the tasks performed (the interns want to be given responsibilities and meaningful work during their internship). Some of the results obtained are according to the state-of-the-art, however, others diverge.