Abstract
Angle assessment is crucial in rehabilitation and significantly influences physiotherapists' decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5 degrees and 17 degrees, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easy-to-implement solution.

Abstract
Hybrid Energy Storage Systems (HESS) have attracted attention in recent years, promising to outperform single batteries in some applications. This can be in decreasing the total cost of ownership, extending the combined lifetime, having higher versatility in providing multiple services, and reducing the physical hosting location. The sizing of hybrid systems in such a way that proves to optimally replace a single battery is a challenging task. This is particularly true if such a tool is expected to be a practical one, applicable to different inputs and which can provide a range of optimal solutions for decision makers as a support. This article provides exactly that, presenting a technology -independent sizing model for Hybrid Energy Storage Systems. The model introduces a three-step algorithm: the first block employs a clustering of time series using Dynamic Time Warping (DTW), to analyze the most recurring pattern. The second block optimizes the battery dispatch using Linear Programming (LP). Lastly, the third block identifies an optimal hybridization area for battery size configuration (H indicator), and offers practical insights for commercial technology selection. The model is applied to a real dataset from an office building to verify the tool and provides viable and non-viable hybridization sizing examples. For validation, the tool was compared to a full optimization approach and results are consistent both for the single battery sizing, as well as for confirming the hybrid combination dimensioning. The optimal solution potential (H) in the example provided is 0.13 and the algorithm takes a total of 30s to run a full year of data. The model is a Pythonbased tool, which is openly accessible on GitHub, to support and encourage further developments and use.

Abstract
This paper explores the integration of virtual escape rooms as innovative educational tools in the realm of computer programming. Recognizing the need to engage and motivate learners in this complex domain, we investigate the use of virtual escape rooms in a typical educational setting where Learning Management Systems play a pivotal role. The paper starts by surveying existing escape rooms designed for teaching programming and related domains, considering factors such as interactivity, educational efficacy, and learner engagement. Additionally, it is emphasized the role of standards in creating interoperable learning environments, introducing IMS LTI for seamless integration with learning management systems and xAPI for tracking learner activities within escape rooms. By leveraging these standards and a Learning Record Store (LRS) as a central repository, an architectural framework is presented which enables personalized learning experiences and data-driven insights, catering to the diverse needs and preferences of the new generation of learners.

Abstract
Underwater scenarios pose additional challenges to perception systems, as the collected imagery from sensors often suffers from limitations that hinder its practical usability. One crucial domain that relies on accurate underwater visibility assessment is underwater pipeline inspection. Manual assessment is impractical and time-consuming, emphasizing the need for automated algorithms. In this study, we focus on developing learning-based approaches to evaluate visibility in underwater environments. We explore various neural network architectures and evaluate them on data collected within real subsea scenarios. Notably, the ResNet18 model outperforms others, achieving a testing accuracy of 93.5% in visibility evaluation. In terms of inference time, the fastest model is MobileNetV3 Small, estimating a prediction within 42.45 ms. These findings represent significant progress in enabling unmanned marine operations and contribute to the advancement of autonomous underwater surveillance systems.

Abstract
This paper presents a comprehensive assessment of the large-scale deployment of hydropower on the provision of frequency regulation services, when equipped with the extended flexibility solutions being developed and/or tested within the scope of the XFLEX HYDRO project. The current analysis is performed on the Iberian Peninsula (IP) power grid considering its interconnection to the Continental Europe (CE) system, since this power system zone is expected to have the most severe frequency transient behaviour in future scenarios with increased shares of variable renewable energies. For this purpose, prospective scenarios with increased shares of time variable renewable generation were identified and analysed. To assess the impacts of the hydropower flexibility solutions on frequency dynamics after a major active power loss, extensive time domain simulations were performed of the power system, including reliable reduced order dynamic models for the hydropower flexibility solutions under evaluation. This research assesses the effects of synchronous and synthetic inertia, and of the Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) services as specified in European grid codes. The main findings highlight the potential of hydropower inertia and of adopting a variable speed technology for enhancing frequency stability, while contribute to better understand the role of hydropower plants in future power systems.

Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.