Abstract
Small living spaces require ingenious solutions that are functional, ergonomic and, above all, reconfigurable. This project for smart, ergonomic and adjustable furniture was embraced by a team of students from different countries, universities and study areas enrolled in the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). EPS is a design project where international students work in teams to create a solution to a real problem from scratch, analysing the state of the art, the market and the associated ethical and sustainability issues. As a project-based learning process, EPS aims to prepare engineering students to work together in multidisciplinary teams, develop personal skills and address the challenges of the contemporary world. The current project aims to design, simulate and test an ethically and sustainability-driven safe and transformable furniture. Amplea is the adjustable furniture solution developed by five EPS students in spring 2023. It transforms into a kitchen counter, dining table or standing desk. By transforming easily, Amplea’s design provides more comfort and saves space in small living spaces. This paper summarises the research, the design of the solution and the development and testing of the proof-of-concept prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Phonology is a linguistic discipline that is naturally computational. However, as many researchers are not familiar with the use of digital methods, most of the computation required is still performed by humans. This article presents a training experiment of master's students of the phonology seminar at the University of Porto, bringing the research process directly to the classroom. The experiment was designed to raise students' awareness of the potentialities of combining human and machine computation in phonology. The Centre for Digital Culture and Innovation (CODA) readily embraced this project to showcase the application of digital humanities as humanities in both research and training activities. During this experiment, students were trained to collect and process phonological data using various open-source and free web-based resources. By combining a strict protocol with some individual research freedom, the students were able to make valuable contributions towards Creolistic Studies, while enriching their individual skills. Finally, the interdisciplinary nature of the approach has demonstrated its potential within and beyond the humanities and social sciences fields (e.g., linguistics, archaeology, history, geography, ethnology, sociology, and genetics), by also introducing the students to basic concepts and practices of Open Science and FAIR principles, including Linked Open Data.

Abstract
Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.

Abstract
Background and aims: Healthy dietary patterns and exercise practices have been associated with improved metabolic and inflammatory profiles. However, studies regarding the combined effect of these interventions on plasma biomarkers and metabolome in older adults are sparser. The primary aim of this study was to investigate the impact of a combined Mediterranean Diet-based Sustainable Healthy Diet (SHD) and Multicomponent Training (MT) intervention on the plasma biomarkers and metabolome and how dietary intake and exercise could modulate these effects. Methods: SHD intervention included a weekly supply of Mediterranean Diet-based SHD food and four nutrition sessions involving a Mediterranean-Diet culinary workshop, and the exercise program included 50-min MT group sessions, held three times a week, lasting both 12 weeks. Plasma biomarkers were obtained through standard biochemical analysis. A proton (1H) 1 H) nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to study the metabolome in blood plasma. Repeated measures ANOVA were performed and adjusted for confounders. Results: SHD + MT intervention significantly decreased HDL-C and calcium. SHD + MT showed some changes in common with the SHD and MT group, namely a significant decrease in citrate levels (p = 0.009 for SHD + MT; p = 0.037 for SHDT) and an increase in pyruvate (p < 0.001 for MT and SHD + MT). The SHD + MT group also revealed specific changes in the levels of some amino acids (decrease in alanine, glutamine and lysine: p = 0.026; p < 0.001; p = 0.038, respectively). Increases in formate (p = 0.025) and unsaturated lipids (p = 0.011) are consistent with changes in energy and lipoprotein metabolism. Conclusion: Our data show that a combined lifestyle intervention program, including a Mediterranean Diet-based SHD and MT, could modulate biomarker and metabolome and there seems to be a metabolic path associated to these interventions in older adults. Due to its wide-ranging relevance, it is pertinent to assess to what extent combined SHD and MT can contribute to better clinical profiles. (c) 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
Wind power is a major source of green energy production. However, the energy generation of wind power is highly affected by uncertainty. Here, we consider the problem of designing the cable network that interconnects the turbines to the substation in wind farms, aiming to minimize both the infrastructure cost and the cost of the energy losses during the wind farm's lifetime. Nonetheless, the energy losses depend on wind direction and speed, which are rarely known with certainty in real situations. Hence, the design of the network should consider these losses as uncertain parameters. We assume that the exact probability distribution of these parameters is unknown but belongs to an ambiguity set and propose a distributionally robust two-stage mixed integer model. The model is solved using a decomposition algorithm. Three enhancements are proposed given the computational difficulty in solving real problem instances. Computational results are reported based on real data.

Abstract
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery's SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system's responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption.