Abstract
With the proliferation of ODR service providers, there is a critical necessity to establish mechanisms supporting their functioning, particularly while designing ODR processes. This article aims to examine the impact of process modelling using BPMN, and of its relevance in the integration of AI into ODR processes within the EU. BPMN allows a meticulous depiction of all the ODR process steps, stakeholders, and underlying data in structured formats that are readable and interpretable by both humans and AI, which enables its integration. The advantages include predictive analysis, identification of opportunities for continuous improvement, operational efficiency, cost and time reduction, and enhanced accessibility for self-represented litigants. Additionally, the transparency afforded by explicitly incorporating AI in BPMN notation fosters a clearer comprehension of processes, facilitating management and informed decision-making. Nevertheless, it remains imperative to address ethical concerns such as algorithmic bias, fairness, and privacy.

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