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.

Abstract
This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making.