Abstract
Objective: The study aimed to demonstrate the development of a mobile app prototype, BarrierBeGone, a system that identifies potential barriers for individuals with mobility disabilities and promotes accessibility using gamification strategies. The main goal is to raise awareness about mobility and accessibility difficulties, especially for wheelchair users, and to promote more responsible behaviours. Method: The User-Centred Design methodology was employed, going through three phases: requirements gathering, design and development, and evaluation. Additionally, interviews with five individuals with mobility disabilities helped define the initial system requirements. The development of the barrier identification system was followed by usability tests with nine representative users. Results: The results of the usability tests of the "BarrierBeGone" barrier identification system were extremely positive. Stakeholders recognized the utility and simplicity of the platform, considering it a motivating factor for future use. Conclusion: The results support the effectiveness of the proposed educational tool in increasing awareness about accessibility and social inclusion in smart cities. This study makes a significant contribution to the field of urban planning and inclusive design. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
The GRAVITY+ project consists of instrumental upgrades to the Very Large Telescope Interferometer (VLTI) for faint-science, high-contrast, milliarcsecond interferometric imaging. As an integral part of the GRAVITY+ Adaptive Optics (AO) architecture, the Wavefront Sensor (WFS) subsystem corrects image distortions caused by the turbulence of Earth's atmosphere. We present the opto-mechanical design of the WFS subsystem and the design strategies used to implement two payloads positioned diagonally opposite each other - Natural Guide Star (NGS) and Laser Guide Star (LGS) - within a single compact design structure. We discuss the implementation of relative motions of the two payloads covering their respective patrol fields and a nested motion within the LGS Payload covering the complete Sodium layer profile in the Earth's atmosphere.

Abstract
The literature on the economics of location regarding creative activities is relatively scarce. Estimations, based on 369 newly created firms operating in creative industries in Portugal, which incorporate spatial effects of neighbouring regions in the location choices, yield the following results: (i) the concentration of creative and knowledge-based activities play an important role in location decisions of new creative establishments; (ii) creative firms tend to favour a diversified industrial tissue and related variety, in order to enjoy from inter-sectorial synergies; (iii) high education at a regional level has a highly significant, positive effect on location decisions, while lower educational levels of human capital negatively affect those decisions; (iv) tolerant/open environments attract creative activities; (v) creative firms tend to favour municipalities where the stock of knowledge and conditions for innovative activity are higher; (vi) municipality's attributes are more important in terms of firms' location decisions than the characteristics of nearby regions.

Abstract
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be available. Moreover, global models may fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of time series datasets. The main contribution of this work is a novel method for generating univariate time series synthetic samples. Our approach stems from the insight that the observations concerning a particular time series of interest represent only a small fraction of all observations. In this context, we frame the problem of training a forecasting model as an imbalanced learning task. Oversampling strategies are popular approaches used to handle the imbalance problem in machine learning. We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Autonomous Surface Vehicles (ASVs) are bound to play a fundamental role in the maintenance of offshore wind farms. Robust navigation for inspection vehicles should take into account the operation of docking within a harbouring structure, which is a critical and still unexplored maneuver. This work proposes an end-to-end docking approach for ASVs, based on Reinforcement Learning (RL), which teaches an agent to tackle collision- free navigation towards a target pose that allows the berthing of the vessel. The developed research presents a methodology that introduces the concept of illegal actions to facilitate the vessel's exploration during the learning process. This method improves the adopted Actor-Critic (AC) framework by accelerating the agent's optimization by approximately 38.02%. A set of comprehensive experiments demonstrate the accuracy and robustness of the presented method in scenarios with simulated environmental constraints (Beaufort Scale and Douglas Sea Scale), and a diversity of docking structures. Validation with two different real ASVs in both controlled and real environments demonstrates the ability of this method to enable safe docking maneuvers without prior knowledge of the scenario.

Abstract
This study presents variability assessment of real time measurements from in-vivo internal joint loads with instrumented implant during post-operative (PO) recovery process from total hip arthroplasty on daily living gait activities. A total of 112 trials walking supported by crutches in both hands, contralateral and ipsilateral sides, walking on treadmill at constant velocities, accelerating, decelerating and free walking, were assessed from 9 different patients ranging 0.3 to 76-month PO. Variability was assessed based on standard deviation of the vertical joint load normalized to each subject body weight with this metric adequacy to monitor PO recover.