Abstract
Purpose: Inherited retinal diseases (IRDs) are a group of degenerative disorders of the retina, that can be potentially associated with changes in the anterior segment, but their prevalence and impact are not known. Exploring these concomitant ophthalmologic changes with biomechanical assessment may help identify other non-retina causes of vision loss in these patients, such as corneal ectasia or susceptibility to glaucoma. This study aimed to measure and compare corneal biomechanics in patients with and without IRDs. Methods: A total of 77 patients (154 eyes) with IRD were recruited as the study group. The control group consisted of 77 healthy adults (154 eyes) with matched age and sphere equivalents. All participants underwent a comprehensive assessment including corneal tomography (Pentacam (R)) and biomechanical assessment (Corvis ST (R)). A total of 4 second-generation biomechanical parameters and 3 indexes were collected: Ambrosio Relational Thickness (ARTh), Deflection Amplitude Ratio Max (DARM), Integrated Radius (IR) and Stiffness Parameter at Applanation (SP-A1), the final deviation value D of the Belin/Ambrosio Enhanced Ectasia Display (BADResults: For IRD patients, there was a higher DARM (p < 0.001), lower ARTh (p < 0.001), higher CBI (p < 0.001), higher TBI (p<0.001), and higher BAD-D (p < 0.001) compared to the control group. Regarding discrimination of healthy subjects and IRD patients, ARTh was the most sensitive parameter. Conclusion: The results showed that IRD patients tend to have softer corneal behaviour, compared to eyes without pathology, which may predispose patients to corneal ectasia or glaucoma development. ARTh could be used to screen IRD patients if a non-retina cause of vision loss is suspected.

Abstract
This study introduces the development of a client-based software layer within the FGPE project, aimed at enhancing the usability of the FGPE programming learning environment through client-side processing. The primary goal is to enable the evaluation of programming exercises and the application of gamification rules directly on the client-side, thereby facilitating offline functionality. This approach is particularly beneficial in regions with unreliable internet connectivity, as it allows continuous student interaction and feedback without the need for a constant server connection. The implementation promises to reduce server load significantly by shifting the evaluation workload to the client-side. This not only improves response times but also alleviates the burden on server resources, enhancing overall system efficiency. Two main strategies are explored: 1) caching the gamification service interface on the client-side, and 2) implementing a complete client-side gamification service that synchronizes with the server when online. Each approach is evaluated in terms of its impact on user experience, system performance, and potential security concerns. The findings suggest that while client-side processing offers considerable benefits in terms of scalability and user engagement, it also introduces challenges such as increased system complexity and potential data synchronization issues. The study concludes with recommendations for balancing these factors to optimize the design and implementation of client-based systems for educational environments.

Abstract
Our contemporary society necessitates professionals equipped with 21st-century skills. Disciplines within Science, Technology, Engineering, Arts, and Mathematics (known as STEAM) have been particularly effective in fostering these skills. However, when considering students with disabilities, especially those with intellectual or developmental disabilities (IDD), this assertion often falls short. In this context, the RoboSTEAMSEN project emerges as an initiative designed to enhance educational processes by providing teachers of IDD students with the necessary resources to promote STEAM engagement. The project proposes the use of active learning methodologies and robotics to achieve this goal. The primary objective of the project is realized through several strategies: understanding the needs of students with disabilities and adapting the use of robotics and active learning methodologies accordingly; training teachers in the use of these resources; and creating a platform to exchange experiences, resources, lessons learned, tools, case scenarios, etc., while reaching other potential stakeholders such as caregivers and policymakers. The main outcomes of the project are teacher training programs and the development of associated competencies, tools to identify and classify resources for the students, and technological platforms to ensure the sustainability of the project once it concludes.

Abstract
The reliability of Free Space Optical (FSO) communications between a ground station and celestial objects is significantly hampered by the variability in atmospheric conditions. Enhancing the system's capabilities to recover the received signal can significantly increase the robustness and broaden the operational scope of this type of communication. One of the most promising avenues for improvement entails integrating Adaptive Optics systems with the latest Machine Learning techniques. We study different control laws based on a classical integrator, a LQG with a Kalman filter (with a second order autoregressive model) and a Reinforcement Learning approach : we evaluate the performance of the three control laws with the Strehl ratio.

Abstract
Self-adaptive Systems (SaS) are becoming increasingly important for adapting to dynamic environments and for optimizing performance on resource-constrained devices. A practical approach to achieving self-adaptability involves using a Pareto-Front (PF) to store the system's hyper-parameters and the outcomes of hyperparameter combinations. This paper proposes a novel method to approximate a PF, offering a configurable number of solutions that can be adapted to the device's limitations. We conducted extensive experiments across various scenarios, where all PF solutions were replaced, and real world scenarios were performed using actual measurements from a Human Activity Recognition (HAR) system. Our results show that our method consistently outperforms previous methods, mainly when the maximum number of PF solutions is in the order of hundreds. The effectiveness of our method is most apparent in real-case scenarios where it achieves, when executed in a Raspberry Pi 5, up to 87% energy consumption reduction and lower execution times than the second-best algorithm. Additionally, our method ensures a more evenly distributed solution across the PF, preventing the high concentration of solutions.

Abstract
A growing concern with current teaching approaches underscores the need for innovative paradigms and tools in computer programming education, aiming to address disparate user profiles, enhance engagement, and cultivate deeper understanding among learners This article proposes an innovative approach to teaching programming, where students are challenged to write statements for solutions automatically generated. With this approach, rather than simply solving exercises, students are encouraged to develop code analysis and problem formulation skills. For this purpose, a Web application was developed to materialize these ideas, using the OpenAI API to generate exercises and evaluate statements written by the students. The transformation of this application in H5P and its integration in a LMS gamified workflow is explored for wider and more effective adoption.