Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design program where students, organised in multidisciplinary and multicultural teams, create a solution for a proposed problem, bearing in mind ethical, sustainability and market concerns. The project proposals are usually aligned with the United Nations Sustainable Development Goals (SDG). New sustainable food production methods are essential to cope with the continuous population growth and aligned with SDG2 and SDG12. In this context, this paper describes the research and work done by a team of Erasmus students enrolled in EPS@ISEP during the spring of 2022. Since sustainable algae farming can be a suitable source of food, the team's goal was the design and develop a proof-of-concept prototype, named GREEN center dot flow, of a symbiotic aquaponic system to farm algae and fish. The smart GREEN center dot flow concept comprises a modular structure and an app for control and supervision. The proposed design was driven by state-of-the-art research, targeted to a specific market niche based on a market analysis, and considering sustainability and ethics concerns, all of which are described in this manuscript. A proof-of-concept prototype was built and tested to verify that it worked as intended.

Abstract

Abstract
Music therapy has emerged as a promising approach to support various mental health conditions, offering non-pharmacological therapies with evidence of improved well-being. Rapid advancements in artificial intelligence (AI) have recently opened new possibilities for ‘personalized’ musical interventions in mental health care. This article explores the application of AI in the context of mental health, focusing on the use of machine learning (ML), deep learning (DL), and generative music (GM) to personalize musical interventions. The methodology included a scoping review in the Scopus and PubMed databases, using keywords denoting emerging AI technologies, music-related contexts, and application domains within mental health and well-being. Identified research lines encompass the analysis and generation of emotional patterns in music using ML, DL, and GM techniques to create musical experiences adapted to user needs. The results highlight that these technologies effectively promote emotional and cognitive well-being, enabling personalized interventions that expand mental health therapies.

Abstract
Flexibility markets are emerging across Europe to improve the efficiency and reliability of distribution networks. This paper presents a methodology that integrates local flexibility markets into network maintenance scheduling, optimizing the process by contracting flexibility to avoid technical issues under the topology defined to operate the network during maintenance. A meta-heuristic approach, Evolutionary Particle Swarm Optimization (EPSO), is used to determine the optimal network topology.

Abstract

Abstract
Inherited retinal diseases (IRDs) are genetic disorders affecting photoreceptors and the retinal pigment epithelium, leading to progressive vision loss. Retinitis pigmentosa (RP), the most common IRD, manifests as night blindness, peripheral vision loss, and eventually central vision decline. RP is genetically diverse and can be categorized into non-syndromic and syndromic. Advanced imaging technologies such as fundus autofluorescence (FAF) and spectral-domain optical coherence tomography (SD-OCT) facilitate diagnosing and managing these conditions. The integration of artificial intelligence in analyzing retinal images has shown promise in identifying genes associated with RP. This study used a dataset from Portuguese public hospitals, comprising 2798 FAF images labeled for syndromic and non-syndromic RP across 66 genes. Three pre-trained models, Inception-v3, ResNet-50, and VGG-19, were used to classify these images, obtaining an accuracy of over 80% in the training data and 54%, 56%, and 54% in the test data for all models. Data preprocessing included class balancing and boosting to address variability in gene representation. Model performance was evaluated using some main metrics. The findings demonstrate the effectiveness of deep learning in automatically classifying retinal images for different RP-associated genes, marking a significant advancement in the diagnostic capabilities of artificial intelligence and advanced imaging techniques in IRD.