Abstract
The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources.

Abstract
Food waste is responsible for severe environmental, social, and economic issues and therefore it is imperative to prevent or at least minimize its generation. The main cause of food waste is poor demand forecasting and so it is essential to improve the accuracy of the tools tasked with these forecasts. The present work proposes four models meant to help food catering services predict food demand accurately and thus avoid overproducing or underproducing. Each model is based on a different machine learning technique. Two baseline models are also proposed to mimic how food catering services estimate future demand and to infer the added value of employing machine learning in this context. To verify the impact of the proposed models, they were tested on data from the three different canteens chosen as case studies. The results show that the models based on the random forest algorithm and the long short-term memory neural network produced the best forecasts, which would lead to a 14% to 52% reduction in the number of wasted meals. Furthermore, by basing their decisions on these forecasts, the food catering services would be able to reduce unmet demand by 3% to 16% when compared with the forecasts of the baseline models. Thus, employing machine learning to forecast future demand can be very beneficial to food catering services. These forecasts can increase the service level of food services and reduce food waste, mitigating its environmental, social, and economic consequences.

Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

Abstract
Citizen engagement with Sustainable Energy Solutions (SES) is considered essential for the current energy transition, since decarbonization requires individuals to shift from passive consumers to citizens actively involved with the energy system. However, citizen engagement research has remained peripheral and scattered, particularly in what regards the drivers of engagement behaviors. To address this challenge, this study examines how different forms of perceived value of SES (utilitarian, social, and environmental) influence different types of citizen engagement behaviors (information seeking, proactive managing, sharing feedback, helping other users, and advocating). To this end, we developed a quantitative study in the context of a H2020 EU project, with a sample of 456 citizens from the city of Alkmaar (the Netherlands). Our findings show that the utilitarian value of SES has a significant effect on all the engagement behaviors, except for sharing feedback. Social value has a significant influence on the more socially related engagement behaviors, such as sharing feedback, helping other users, and advocating. Finally, environmental value has an indirect effect on information seeking, proactive managing, and advocating, but only when mediated through awareness of consequences. The implications of this study should allow SES providers to design more relevant offerings and policymakers to develop better citizen engagement strategies.

Abstract
The water supply and sewage sector (WSS) is essential for promoting health and providing the population with drinking water and the adequate disposal of effluents. Assessing the evolution of performance in WSS allows for highlighting the best and worst results achieved, identifying benchmarks, and pinpointing sources of improvement for water services. Brazil has a large population and immense freshwater reserves that are unevenly distributed throughout the territory. This situation emanates a challenge that requires the efficient management of water resources. This study develops a composite indicator framework based on the robust Benefit-of-the-Doubt (BoD) approach to estimate the performance of municipalities of the Brazilian State of Santa Catarina from 2009 to 2021, considering financial, operational, and quality dimensions associated with the provision of WSS services. Subsequently, the Global Malmquist Index (GMI) is applied to assess the performance evolution of the municipalities over time. The BoD results enable the quantification of the relative contribution of each sub-indicator to the performance score, allowing the assessment of the strengths and weaknesses of each municipality. The GMI results show an average performance loss of 3.3% in Santa Catarina state and considerable variability among municipalities, with scores ranging from losses of 54.2% to gains of 109.3% in the period analysed.

Abstract
In 2010, the European Commission set out the development of an economy based on knowledge and innovation as one of the priorities of its Europe 2020 strategy for smart, sustainable, and inclusive growth. This culminated in the 'Youth on the Move' flagship initiative, aimed at enhancing the performance and international attractiveness of Europe's higher education institutions and raising the Union's overall education and training levels. Therefore, it is relevant to assess the performance of the 'Youth on the Move' initiative via the creation of composite indicators (CIs) and, ultimately, monitor the progress made by European countries in creating a positive environment supporting learner mobility. For this reason, we make use of the CI-building 'Benefit-of-the-Doubt' approach, in its robust and conditional setting to account for outliers and the human development of those nations, to exploit the European Commission's Mobility Scoreboard framework between 2015/2016 and 2022/2023. Furthermore, we incorporate the value judgements of experts in the sector to construct utility scales and compute weight restrictions through multi-criteria decision analysis. This enables the conversion of ordinal scales into interval ones based on knowledgeable information about reality in higher education. In the end, the results point to a slight performance improvement, but highlight the need to improve the 'Recognition of learning outcomes', 'Foreign language preparation', and 'Information and guidance'.