Abstract
This paper describes the project based learning experience of a multidisciplinary and multicultural team of students enrolled in the spring of 2023 on the European Project Semester at the Instituto Superior de Engenharia do Porto (EPS@ISEP). Animo is an original blimp based concept that aims to help farmers better manage their livestock. Its development was motivated by the difficulty to effectively monitor cattle herds over vast areas, especially in remote locations where locating animals is challenging. This environmentally friendly solution offers real-time livestock monitoring without thermal engines. Real-time monitoring is achieved through the blimp’s extensive animal data collection. Farmers may discover and handle quickly herd welfare issues by accessing information via a user-friendly App. With an emphasis on accessibility and environmental sustainability, Animo seeks to increase agricultural productivity and profitability. The user controls the blimp motion through the app to obtain a comprehensive farm view. Targeting Australia’s large cattle stations, it aims to enhance productivity while minimising the environmental impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The technological revolution experienced over the last two decades, together with changes in shopping behaviour, has led supermarkets to consider smart shopping trolleys. Recently, several companies have tested and implemented smart services and devices, such as smart shopping carts with scanners, automatic payment methods, or self-payment locations, to maximise supermarket profits by reducing staff and improving the customer experience. In the spring of 2023, a team of six students enrolled in the European Project Semester at Instituto Superior de Engenharia do Porto (ISEP) proposed FESmarket, an innovative smart shopping cart solution. The user-centred design focused on making the shopping interaction and experience more efficient, comfortable, and satisfactory. Form (balancing aesthetics with innovation), function (selecting functionalities based on the most disruptive technologies), market (fulfilling the identified needs), sustainability (minimising the use of resources), and ethics (respecting human values) are the pillars of the project. FESmarket proposes a smart shopping trolley equipped a built-in touch screen for real-time information on products and their location, cameras for product identification, an audio assistance system, a refrigeration chamber, and a mobile app interface for the customer. Finally, a proof-of-concept prototype was assembled and tested to validate the viability of the designed solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design programme in which students, organised in multidisciplinary and multicultural teams, develop a solution for a proposed problem, taking into account sustainability, ethical and market concerns. This paper describes a research project aimed at raising awareness and changing behaviour in relation to waste disposal, carried out by a team of EPS@ISEP students during spring 2023. BinIt, as the project is named, targets young adults who want to live in a cleaner city. Unlike other campaigns, it simplifies and stimulates proper waste disposal and recycling, tackling the root of the problem and creating a new social norm. BinIt includes a campaign, a web app and the Garbage Gladiator bin. The app consists of a city map where users can pin and check bin locations, and an educational platform with information on waste disposal and recycling issues. Gamification is incorporated through a ranking system. The Garbage Gladiator is a physical container for urban public spaces specially designed to encourage people to dispose of their waste correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.

Abstract
The paper presents the comparison of accuracy in the Speech Emotion Recognition task using the Hamming and Hanning windows for framing the speech and determining the spectrogram to be used as input of a convolutional neural network. The detection of between 4 and 10 emotional states was tested for both windows. The results show significant differences in accuracy between the two window types and provide valuable insights for the development of more efficient emotional state detection systems. The best accuracy between 4 and 10 emotions was 64.1% (4 emotions), 57.8% (5 emotions), 59.8% (6 emotions), 48.4% (7 emotions), 47.8% (8 emotions), 51.4% (9 emotions), and 45.9% (10 emotions). These accuracy is at the state-of-the art level.

Abstract
The increasing complexity of integrated energy systems with the electric power grid requires innovative control solutions for efficient management of smart buildings and distributed energy resources. Accurately predicting weather conditions and electricity demand is crucial to make such informed decisions. Machine learning has emerged as a powerful solution to enhance prediction accuracy by harnessing advanced algorithms, but often requires complex parameterizations and ongoing model updates. The Lawrence Berkeley National Laboratory's Autonomous Forecast Framework (AFF) was developed to greatly simplify this process, providing reliable and accurate forecasts with minimal user interaction, by automatically selecting the best model out of a library of candidate models. This work expands on the AFF by not only selecting the best model, but assembling a blend of multiple models into a hybrid forecast model. The validation within this work has shown that this combination of models outperformed the selected best model of the AFF 31%, while providing greater resilience to individual model's forecast error.