Abstract
Computer Science degrees are often seen as challenging by students, especially in what concerns subjects such as programming, data structures or algorithms. Many reasons can be pointed out for this, some of which related to the abstract nature of these subjects and the lack of previous related knowledge by the students. In this paper we tackle this challenge using gamification in the teaching/learning process, with two main goals in mind. The first is to increase the intrinsic motivation of students to learn, by making the whole process more fun, enjoyable and competitive. The second is to facilitate the learning process by providing intuitive tools for the visualization of data structures and algorithmic output, together with a tool for automated assessment that decreases the dependence on the teacher and allows them to work more autonomously. We validated this approach over the course of three academic years in a Computer Science degree of the Polytechnic of Porto, Portugal, through the use of a questionnaire. Results show that the effects of using games and game elements have a generally positive effect on motivation and on the overall learning process.

Abstract
Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data or incorporating human feedback into existing datasets and models. In these dynamic scenarios, data change over time and models must adapt. However, new data do not necessarily mean new patterns. The main goal of this paper is to devise a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth it to train it or not. That is, will the model hold significantly better results than the current one? To address this issue, we propose the use of meta-learning. Specifically, we evaluate two different meta-models, one built for a specific machine learning problem, and another built based on many different problems, meant to be a generic meta-model, applicable to virtually any problem. In this paper, we focus only on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re-training models between 60% and 98%, depending on the problem and on the threshold used.

Abstract
The expectations of Machine Learning systems are becoming increasingly demanding, namely in what concerns the diversity of applications, the expected accuracy, and the pressure for results. However, there are cases in which Human experts are needed to label the data, which may have a significant cost in terms of human resources and time. In these cases, it is often best to learn on-the-fly, without expecting for the whole data to be labeled. Often, it is desirable to guide the Human annotators into focusing on the more relevant instances: this constitutes the so-called active learning. In this paper we propose an approach in which a clustering algorithm is used to find groups of similar instances. Then, the procedure is guided with the objective of favoring the annotation of the groups that are under-represented in the labeled dataset. Results show that this approach leads to models that are, over time, more accurate and reliable.

Abstract
Solid-phasetemplate-assisted mechanosynthesis of Fe-N-C,featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine(TPTZ), and NaCl is reported. Efficient and sustainable synthesis of performant metal/nitrogen-dopedcarbon (M-N-C) catalysts for oxygen reduction and evolutionreactions (ORR/OER) is vital for the global switch to green energytechnologies-fuel cells and metal-air batteries. Thisstudy reports a solid-phase template-assisted mechanosynthesis ofFe-N-C, featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine (TPTZ), and NaCl. ANaCl-templated Fe-TPTZ metal-organic material was formed usingfacile liquid-assisted grinding/compression. With NaCl, the Fe-TPTZtemplate-induced stability allows for a rapid, thus, energy-efficientpyrolysis. Among the produced materials, 3D-FeNC-LAG exhibits remarkableperformance in ORR (E (1/2) = 0.85 V and E (onset) = 1.00 V), OER (E ( j=10) = 1.73 V), and in the zinc-airbattery test (power density of 139 mW cm(-2)). Themultilayer stream mapping (MSM) framework is presented as a tool forcreating a sustainability assessment protocol for the catalyst productionprocess. MSM employs time, cost, resource, and energy efficiency astechnoeconomic sustainability metrics to assess the potential upstreamimpact. MSM analysis shows that the 3D-FeNC-LAG synthesis exhibits90% overall process efficiency and 97.67% cost efficiency. The proposedsynthetic protocol requires 2 times less processing time and 3 timesless energy without compromising the catalyst efficiency, superiorto the most advanced methods.

Abstract
In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools.

Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.