Abstract
To fulfil their mission, museums strive to ensure a balance between the inclusive fruition of collections and preservation. In this sense, as part of their non-formal educational functions, some try to develop multimedia resources to explore content, engage, and promote knowledge. This paper is based on a gamified mobile application developed for a museum in Portugal, targeting young people between 8 and 12 years old, focusing on the evaluation phase of the experience’s impact. To this end, the Method for Assessing eXperience (MAX) was adopted. MAX is presented, and introduced changes are shared, in order to simplify its application. Using MAX’s simplified version, four sessions were established with a target group of 44 young museum visitors. Results show an overall satisfaction towards the gamified mobile application. It is possible to understand that 68% of the 44 young people considered the application very useful and 57% expressed the desire to use it again in the near future. Regarding emotions during the experience, a mere 2% reported confusion, while an overwhelming 98% showcased positive reactions. This study concludes that the application may have contributed positively to raising awareness for the preservation of cultural heritage and this assessment has enabled the overall improvement of the prototype. It is believed that the provided fun involvement might help to develop further knowledge regarding museum practices, supporting and enhancing info-communicational flows. © 2025 Elsevier B.V., All rights reserved.

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.

Abstract
This paper introduces PRISMA, a hybrid multi-agent Deep Reinforcement Learning (DRL) framework for solving the Flexible Job-shop Scheduling Problem (FJSP). It uses Constraint Programming (CP) solutions to pretrain decentralized policies and to guide exploration during training. Although DRL can generate fast solutions for large combinatorial problems, it often fails to match the quality of optimization methods, motivating the integration with hybrid frameworks. The growing interest in embedding domain knowledge into learning algorithms has produced several hybrid formulations, yet their potential remains underexplored, particularly in multi-agent settings. PRISMA combines supervised and reinforcement learning within a multi-agent framework, where CP solutions are used to (i) learn expert decisions through imitation learning, and (ii) train an auxiliary network that guides DRL training via reward shaping. A shared graph network is adopted for transferring system-level knowledge into machine-level observations, enabling fast and consistent inference from enriched local embed-dings. To the best of our knowledge, PRISMA introduces the first expert-derived guidance mechanism for the FJSP and is among the earliest to apply imitation learning within a multi-agent formulation. By combining both modules, it strengthens the bridge between optimization and learning-based methods, where such dual integrations remain scarce. Experimental results show faster convergence and higher solution quality than state-ofthe-art DRL models. PRISMA achieves an average optimality gap of 6.74%, corresponding to a 50% relative improvement over the single-agent baseline, while reducing inference time. These findings reinforce the value of merging optimization accuracy with the flexibility of multi-agent DRL for efficient scheduling.

Abstract
Real-world datasets used in data-driven modelling are often affected by inconsistencies arising from discrepancies between recorded observations and actual system behaviour. Conventional approaches to data filtering and quality assessment rely primarily on statistical criteria and treat consistency as an intrinsic property of the dataset. However, such approaches may fail to identify observations that are statistically plausible yet incompatible with the structural assumptions underlying the model.This work introduces a model-dependent perspective on data consistency, in which the validity of observations is defined relative to the model used to interpret the data rather than to distributional properties alone. Within this framework, residuals are interpreted not merely as noise, but as indicators of incompatibility between observed data and model-defined behaviour.Importantly, inconsistencies may arise not only from anomalies in the target variable, but also from incorrect, incomplete, or misaligned representations of explanatory variables, even when observed outputs remain statistically valid. By formalising data consistency as a model-dependent property, this work challenges the conventional separation between data preprocessing and modelling, and reframes data filtering as part of the interpretation of model-data relations.The proposed framework provides a conceptual basis for integrating data consistency into data-driven modelling processes, with implications for data interpretation, representation, and validation in systems operating under imperfect real-world data conditions.

Abstract
This study applies phase plane analysis of medio-lateral, anteroposterior, and vertical directions for the coordination assessment of whole-body (WB) center of mass (COM) movement during the impulse phase of a standard maximum vertical jump (MVJ) with long, short, and no countermovement (CM). A video system and force platform were used, with the amplitudes of WB COM excursion obtained from image-based motion capture at each anatomical direction, and the 2D and 3D mean radial distance were compared under long, short, and no CM conditions. The estimate of the population mean length was used as a measure of distribution concentration, and the Rayleigh statistical test for circular data was applied with the sample distribution critical value. Watson's U2 goodness-of-fit test for the von Mises distribution was used with the mean direction and concentration factor. The applied metrics led to the detection of shared and specific features in the global and phase plane analysis of WB COM movement coordination in the medio-lateral, anteroposterior, and vertical directions during long, short, and no CM conditions in relation to MVJ performance assessed from ground reaction force (GRF) through the force platform. Thus, long, short, and no CM impulses share lower amplitudes of WB COM excursion in the medio-lateral direction and mean radial distance to its mean, whereas the anteroposterior and vertical excursion of WB COM, along with the 2D transversal and 3D spatial length of the WB COM path, present as potential predictors of MVJ performance, with distinct behavior in long CM compared to short and no CM. Additionally, the applied workflow on generalized phase plane analysis led to the detection, through complementary metrics, of the anatomical WB COM movement directions with higher coordination based on phase concentration tests at 5% significance, in line with MVJ performance under different CM conditions.

Abstract
Irrigation systems are a major source of energy consumption in agriculture, primarily due to water pumping. Increasing electricity price variability and renewable integration make energy-efficient irrigation operation challenging.This work proposes an energy-aware irrigation scheduling framework that combines predictive energy modelling with tariff-based optimisation. Rather than relying on explicit price forecasts, the approach uses relative cost representations derived from time-of-use tariffs, enabling robust scheduling under uncertain pricing conditions.Energy consumption is modelled as a function of aggregated system operation and embedded within a mixed-integer optimisation framework that determines irrigation schedules while satisfying agronomic, hydraulic, and operational constraints, including photovoltaic (PV) integration.The framework is evaluated using real-world data from a vineyard and an olive grove under varying demand, tariff, PV, and flexibility conditions. Results show substantial cost reductions without modifying irrigation demand. Benefits increase with tariff differentiation and system flexibility, while PV integration provides additional gains. In more flexible systems, scheduling may also reduce total energy consumption.Overall, irrigation scheduling emerges as an effective demand-side flexibility mechanism, improving both economic performance and energy efficiency.