Abstract
The European Project Semester (EPS), offered by the Instituto Superior de Engenharia do Porto (ISEP), is a capstone programme designed for undergraduate students in engineering, product design, and business. EPS@ISEP fosters project-based learning, promotes multicultural and interdisciplinary teamwork, and ethics- and sustainability-driven design. This study applies Natural Language Processing techniques, specifically text mining, to analyse project papers produced by EPS@ISEP teams. The proposed method aims to identify evidence of ethical concerns within EPS@ISEP projects. An innovative keyword mapping approach is introduced that first defines and refines a list of ethics-related keywords through prompt engineering. This enriched list of keywords is then used to systematically map the content of project papers. The findings indicate that the EPS@ISEP robotics project papers analysed demonstrate awareness of ethical considerations and actively incorporate them into design processes. The method presented is adaptable to various application areas, such as monitoring compliance with responsible innovation or sustainability policies. © 2025 Elsevier B.V., All rights reserved.

Abstract
Transformer models have significantly advanced the field of emotion recognition. However, there are still open challenges when exploring open-ended queries for Large Language Models (llms). Although current models offer good results, automatic emotion analysis in open texts presents significant challenges, such as contextual ambiguity, linguistic variability, and difficulty interpreting complex emotional expressions. These limitations make the direct application of generalist models difficult. Accordingly, this work compares the effectiveness of fine-tuning and prompt engineering in emotion detection in three distinct scenarios: (i) performance of fine-tuned pre-trained models and general-purpose llms using simple prompts; (ii) effectiveness of different emotion prompt designs with llms; and (iii) impact of emotion grouping techniques on these models. Experimental tests attain metrics above 70% with a fine-tuned pre-trained model for emotion recognition. Moreover, the findings highlight that llms require structured prompt engineering and emotion grouping to enhance their performance. These advancements improve sentiment analysis, human-computer interaction, and understanding of user behavior across various domains.

Abstract
The public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the Metropt data set from the metro operator of Porto, Portugal. The results are above 98 % for f-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high f-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.

Abstract
Pedestrian safety is a pressing subject in urban areas. The disorderly sharing of streets and roads between pedestrians and vehicles leads to potentially serious accidents for pedestrians. This student project aims to tackle the issue by placing an interactive gaming device at traffic lights. SMASHY by Stempe Safety offers pedestrians an amusing and active way to discourage jaywalking. The multipurpose solution features a smashing game with buttons on one side and a screen displaying useful information on the other side. While the traffic light remains red for pedestrians, the module buttons light up and the players can start smashing the buttons as fast as possible, until the light turns green and consequently, the game ends. Ultimately, the modules are connected to an app where, if desired by the player, scores can be tracked and difficulty can vary based on user performance. Multiple modules can be placed around the city and the app will track player scores by location. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
CoffeeMush is an innovative and sustainable project developed as part of the European Project Semester (EPS) at ISEP in 2024. This student project aims to tackle waste management environmental problems by turning coffee waste into mushrooms, a valuable food source. CoffeeMush consists of a smart device providing optimal conditions for mushroom cultivation, complemented by a user-friendly Android application for remote monitoring and control. The design was guided by ethical, sustainability, market and technical considerations. The paper describes the theoretical background of the project, the technical design, and the prototype development and testing. The results show the feasibility of CoffeeMush as a practical and environmentally friendly solution for urban mushroom cultivation, and its impact on sustainable food production and waste reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.