Abstract
Intensive cattle farming as a means of protein production contributes with the direct emission of greenhouse gases and the indirect contamination of soil and water. The public awareness towards this issue is growing in western cultures, leading to the stagnation of meat consumption and to the willingness to adopt alternative sustainable sources of protein. A solution is to farm insects as they present a reduced environmental impact and constitute a well-known source of protein. However, for westerners, eating insects implies a cultural change as they are still seen as dirty and disgusting. In 2022, a team of five EPS@ISEP students chose to design a solution for this problem followed by the assembly and test of the corresponding proof-of-concept prototype. They decided to design a home farming kit to grow mealworms driven by ethical, sustainable and the market needs. Exploring the insect life-cycle, the kit provides protein for humans and animals, chitin for soil bacteria and frass for plants. It can also be used as an educational tool for children to learn about sustainability, social responsibility and insect life-cycles, helping to overtake the cultural barrier against insect eating from a young age. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Tourists nowadays tend to avoid tourist traps and are looking for engaging ways to explore cities in the limited time they have. Standard options to explore cities seldom offer a combination between efficiency and fun. Furthermore, a search for an exploration city app returns an unlimited supply of lookalike websites and apps, all claiming to be the best. This paper reports the development of QRioCity, an efficient and exciting way to explore cities, by the “Dragonics” student team. QRioCity offers users the option to sign up for a playful tour through the city of Porto using a public kiosk with an interactive touchscreen. There is no limit to the number of teams playing simultaneously nor there is need to provide personal data. The teams are led through the city using clues and are proposed assignments, like scanning QR codes, to earn points. At the end of the game, every team receives discount coupons for local shops or stores depending on their score, even when they play alone. This way QRioCity helps tourists enjoying the local city life while offering municipalities a chance to strengthen their local economy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

Abstract
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and F-measure).

Abstract
Involving people in urban planning offers many benefits, but current methods are failing to get a large number of citizens to participate. People have a high participation barrier when it comes to public participation in urban planning – as it requires a lot of time and initiative, only a small non-diverse group of citizens take part in governmental initiatives. In this paper, a product is developed to make it as easy as possibleforcitizenstogetinvolvedinconstructionprojectsintheircommunity at an early stage. As a solution, a public screen is proposed, which offers citizens the opportunity to receive information, view 3D models, vote and comment at the site of the construction project via smartphone – the solution was named Parcitypate. To explain the functions of the product, a prototype was created and tested. In addition, concepts for branding, marketing, ethics, and sustainability are presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
The MiFiRE (Microgravity Fine Regolith Experiment) experiment, which will be launched this year on a suborbital space flight, currently scheduled for August 2023, was designed with the aim of better understanding the initial stages of planetary formation. The fundamental and embryonic question is to contribute to the study of how the mineral and rock particles, which do not have enough mass for the gravitational force to be influential, can then aggregate through electrostatic forces. In order to recreate the environment of deep space, it is assumed that the composition of meteorites that collide with the Earth, are mainly of silicate mineralogical composition or rich in metallic alloys (eg Fe-Ni). Therefore, in the experiment some fine material, identical to the lunar regolith (JSC-1), is used, in other words, amphiboles, pyroxenes, olivines and volcanic glass, along with two larger elements, a basalt cube and a metalic (siderite) meteorite cube (Octahedrite from Campo del Cielo, Argentina). It is intended that the particles be subjected to the microgravity environment and thus contribute to a better understanding of the general behaviour and the processes of preference of aggregation between the various components. This, in turn, contributes the characterization of the progressive development of planetesimals. This experiment was selected amongst 5 competing proposals in a contest launched by Massachusetts Institute of Technology's national representation, MIT Portugal, in 2020. © 2023 International Multidisciplinary Scientific Geoconference. All rights reserved.