Abstract
Photovoltaic (PV) and battery energy storage system (BESS) capacities are among the fastest-growing renewable energy technologies worldwide. The optimal sizing of these technologies is crucial to achieving a cost-effective integration into existing energy systems and increase competitiveness. However, existing models often neglect cyclic (dynamic) BESS degradation and replacement costs, and assume a calendar aging (static) system lifetime, use low-resolution consumption and solar irradiation data, or determine the optimal size of only one component. This contribution proposes a cost optimization model for the size of a grid-connected PV-BESS system including cyclic battery degradation based on its intensity of use. The model considers the most relevant technical parameters of PV and BESS, including state of charge (SoC), round-trip efficiency, depth of discharge (DoD), and self-discharge rate, and the lifetime based on a maximum number of cycles. The energy flows of the system are based on the principle that PV generation initially covers consumption directly, surplus energy is used to charge the BESS, deficits are covered by discharging the BESS, and any remaining demand is drawn from the grid or surplus electricity is fed into the grid to generate revenue. The model is validated on the basis of a real-world use case, a single-family house in Vienna, Austria, with the hourly load profile and PV generation on site available. The results indicate that assumptions about calendarbased BESS degradation lead to shorter replacement periods and lower available BESS capacity compared to the cyclic degradation model, leading to higher costs for assumptions with calendarbased degradation. © 2025 Elsevier B.V., All rights reserved.

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.