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- Engineering students education in sustainability: The moderating role of emotional intelligencePublication . Nogueira, Teresa; Castro, Rui; Magano, JoséIn the context of a lack of quantitative research approaching an engineering education in sustainability, this cross-sectional study aims to investigate whether efforts to promote sustainability education contribute to shaping the beliefs, attitudes, and intentions towards sustainability in a sample of Portuguese engineering schools students; in addition, this study investigates whether emotional intelligence impacts the students’ motivation to learn more about sustainability and whether it plays a role in moderating the relationships between those variables. A survey was carried out on a sample of 184 students from two major Portuguese engineering schools. A model was found showing that beliefs, attitudes, and gender are predictors of students’ intentions towards sustainability, explaining 62.6% of its variance. Furthermore, the findings reveal that women have stronger beliefs and intentions towards sustainability than men and that students with higher emotional intelligence are more motivated to learn more about sustainability. In addition, emotional intelligence has a negative and significant moderating impact on the relationship between attitudes and students’ intentions towards sustainability, being stronger for lower levels of emotional intelligence and having a similar, yet non-significant, effect on the relationship between beliefs and students’ intentions towards sustainability. The results suggest that emotional intelligence should be considered a competence and a tool in engineering education in order to enhance students’ inclination towards sustainable development.
- Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic AlgorithmPublication . Morais, Hugo; Sousa, Tiago; Castro, Rui; Vale, ZitaThe introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).