Abstract
This article introduces Friday, a Mobile Manipulator (MoMa) solution designed at iiLab - INESC TEC. Friday is versatile and applicable in various contexts, including warehouses, naval shipyards, aerospace industries, and production lines. The robot features an omnidirectional platform, multiple grippers, and sensors for localisation, safety, and object detection. Its modular hardware and software system enhances functionality across different industrial scenarios. The system provides a stable platform supporting scientific advancements and meeting modern industry demands, with results verified in the aerospace, automotive, naval, and logistics.

Abstract
With the European Union's requirement for reducing the amount of energy generated from non-renewable sources, there is a need for increased production of energy from renewable sources such as solar and wind power, among others. Due to the stochastic nature of natural resources that serve as these renewable energy sources, it necessitates adaptation by electrical energy systems. Predicting these resources is crucial for better planning and management of electrical energy systems. This paper aims to forecast wind speed using machine learning models, specifically comparing AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results show that the LSTM is able to reach a Root Mean Square Error (RMSE) and a Mean Absolute Error (MAE) of 3.145 and 2.245, respectively, while the ARIMA achieves a higher error of 3.460 and 3.031, respectively. The results allows to conclude that the LSTM model shows a more effective performance, with a lower error rate, due to its ability to recognize patterns over longer periods. © 2025 Elsevier B.V., All rights reserved.

Abstract
Stroke often leads to severe motor impairment, especially in the upper limbs, greatly reducing a patient's ability to perform daily tasks. Effective rehabilitation is essential to restore function and improve quality of life. Traditional therapies, while useful, may lack engagement, leading to low motivation and poor adherence. Gamification-using game-like elements in non-game contexts-offers a promising way to make rehabilitation more engaging. The authors explore a gamified rehabilitation system designed in Unity 3D using a Kinect V2 camera. The game includes key features such as adjustable difficulty, real-time and predominantly positive feedback, user friendliness, and data tracking for progress. The evaluations were conducted with 18 healthy participants, most of whom had prior virtual reality experience. About 77% found the application highly motivating. While the gameplay was well received, the visual design was noted as lacking engagement. Importantly, all users agreed that the game offers a broad range of difficulty levels, making it accessible to various users. The results suggest that the system has strong potential to improve rehabilitation outcomes and encourage long-term use through enhanced motivation and interactivity.

Abstract

Abstract
Documenting software architecture is essential to preserve architecture knowledge, even though it is frequently costly. Architecture pattern instances, including microservice pattern instances, provide important structural software information. Practitioners should document this information to prevent knowledge vaporization. However, architecture patterns may not be detectable by analyzing source code artifacts, requiring the analysis of other types of artifacts. Moreover, many existing pattern detection instance approaches are complex to extend. This article presents our ongoing PhD research, early experiments, and a prototype for a tool we call MicroPAD for automating the detection of microservice pattern instances. The prototype uses Large Language Models (LLMs) to analyze Infrastructure-as-Code (IaC) artifacts to aid detection, aiming to keep costs low and maximize the scope of detectable patterns. Early experiments ran the prototype thrice in 22 GitHub projects. We verified that 83% of the patterns that the prototype identified were in the project. The costs of detecting the pattern instances were minimal. These results indicate that the approach is likely viable and, by lowering the entry barrier to automating pattern instance detection, could help democratize developer access to this category of architecture knowledge. Finally, we present our overall research methodology, planned future work, and an overview of MicroPAD's potential industrial impact.

Abstract
Today, dependence on technology is increasing and, as a result, energy consumption has to keep up with this growth. To meet this demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth in dependence on renewable generation technologies, it is becoming increasingly common for studies to be carried out into consumption patterns in order to try to understand the needs of the population and thus make more rational and efficient use of energy. The aim of this article is to study, understand and explain the workings of some of the best forecasting methods available today for energy consumption patterns identification. Artificial neural networks, support vector machines, principal component analysis and even hierarchical clustering are some of the methods analyzed. © 2025 Elsevier B.V., All rights reserved.