Abstract
Composites of poly(vinyl alcohol) (PVA) in the shape of braids, in combination with crystals of hydroxyapatite (HAp), were analyzed to perceive the influence of this bioceramic on both the quasi-static and viscoelastic behavior under tensile loading. Analyses involving energy-dispersive X-ray spectroscopy (EDS) and scanning electron microscopy (SEM) allowed us to conclude that the production of a homogeneous layer of HAp on the braiding surface and the calcium/phosphate atomic ratio were comparable to those of natural bone. The maximum degradation temperature established by thermogravimetric analysis (TGA) showed a modest decrease with the addition of HAp. By adding HAp to PVA braids, an increase in the glass transition temperature (Tg) is noticed, as demonstrated by dynamic mechanical analysis (DMA) and differential thermal analysis (DTA). The PVA/HAp composite braids' peaks were validated by Fourier transform infrared (FTIR) spectroscopy to be in good agreement with common PVA and HAp patterns. PVA/HAp braids, a solution often used in the textile industry, showed superior overall mechanical characteristics in monotonic tensile tests. Creep and relaxation testing showed that adding HAp to the eight and six-braided yarn architectures was beneficial. By exhibiting good mechanical performance and most likely increased biological qualities that accompany conventional care for bone applications in the fracture healing field, particularly multifragmentary ones, these arrangements can be applied as a fibrous fixation system.

Abstract
Robot Learning is one of the most important areas in Robotics and its relevance has only been increasing. The Robot Operating System (ROS) has been one of the most used architectures in Robotics but learning it is not a simple task. Additionally, ROS 1 is reaching its end-of-life and a lot of users are yet to make the transition to ROS 2. Reinforcement Learning (RL) and Robotics are rarely taught together, creating greater demand for tools to teach all these components. This paper aims to develop a learning kit that can be used to teach Robot Learning to students with different levels of expertise in Robotics. This kit works with the Flatland simulator using open-source free software, namely the OpenAI Gym and Stable-Baselines3 packages, and contains tutorials that introduce the user to the simulation environment as well as how to use RL to train the robot to perform different tasks. User tests were conducted to better understand how the kit performs, showing very positive feedback, with most participants agreeing that the kit provided a productive learning experience.

Abstract
The generation of short pulses in fiber lasers using saturable absorbers made of graphene oxide (GO), focusing on film thickness, was studied and optimized. The saturable absorber comprised a GO thin film deposited onto a single-mode fiber using the spray coating technique. Water-dispersed GO with a concentration of 4 mg/mL, characterized by a high proportion of monolayer flakes, was employed. This thin film was integrated into a cavity ring laser featuring an erbium-doped fiber amplifier (EDFA), resulting in a fiber laser emitting at a central emission wavelength of approximately 1564 nm and having a total cavity length of approximately 120 m. By controlling intracavity polarization, short-pulsed light was generated through mode-locking, Q switching, or a combination of both regimes. This work presents a comprehensive characterization of the cavity ring laser operating under the mode-locking regime. It encompasses an analysis of the spectral behavior, focusing on the evolution of the Kelly's sidebands with increasing pump power, as well as an assessment of its temporal stability. Moreover, the effects of the aging of the saturable absorber material were studied after a time period of 6 months after the fabrication. It was observed that the general characteristics of spectral signal of the laser were maintained, with long-term stability .

Abstract
This study compares two computer vision methods to detect yellow sticky traps using unmanned autonomous vehicles in olive tree cultivation. The traps aim to combat and monitor the density of the Bactrocera oleae, an important pest that damages olive fruit, leading to substantial economic losses annually. The evaluation encompassed two distinct methods: firstly, an algorithm employing conventional segmentation techniques like thresholding and contour localization, and secondly, a contemporary artificial intelligence approach utilizing YOLOv8, a state-of-the-art technology. A specific dataset was created to train and adjust the two algorithms. At the end of the study, both were able to locate the trap precisely. The segmentation algorithm demonstrated superior performance at proximal distances (50 cm), outperforming the outcomes achieved by YOLOv8. In contrast, YOLOv8 exhibited sustained precision, irrespective of the distance under examination. These findings affirm the versatility of both algorithms, highlighting their adaptability to various contexts based on distinct application demands. Consideration of trade-offs between accuracy and processing speed is essential in determining the most appropriate algorithm for a given application.

Abstract
This study focuses on investigating different CNN architectures and assessing their effectiveness in classifying Diabetic Retinopathy, a diabetes-associated disease that ranks among the primary causes of adult blindness. However, early detection can significantly prevent its debilitating consequences. While regular screening is advised for diabetic patients, limited access to specialized medical professionals can hinder its implementation. To address this challenge, deep learning techniques provide promising solutions, primarily through their application in the analysis of fundus retina images for diagnosis. Several CNN architectures, including MobileNetV2, VGG16, VGG19, InceptionV3, InceptionResNetV2, Xception, DenseNet121, ResNet50, ResNet50V2, and EfficientNet (ranging from EfficientNetB0 to EfficientNetB6), were implemented to assess and analyze their performance in classifying Diabetic Retinopathy. The dataset comprised 3662 Fundus retina images. Prior to training, the networks underwent pre-training using the ImageNet database, with a Gaussian filter applied to the images as a preprocessing step. As a result, the Efficient-Net stands out for achieving the best performance results with a good balance between model size and computational efficiency. By utilizing the EfficientNetB2 network, a model was trained with an accuracy of 85% and a screening capability of 98% for Diabetic Retinopathy. This model holds the potential to be implemented during the screening stages of Diabetic Retinopathy, aiding in the early identification of individuals at risk. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
The exponential growth in the digitisation of services implies the handling and storage of large volumes of data. Businesses and services see data sharing and crossing as an opportunity to improve and produce new business opportunities. The health sector is one area where this proves to be true, enabling better and more innovative treatments. Notwithstanding, this raises concerns regarding personal data being treated and processed. In this paper, we present a patient-centric platform for the secure sharing of health records by shifting the control over the data to the patient, therefore, providing a step further towards data sovereignty. Data sharing is performed only with the consent of the patient, allowing it to revoke access at any given time. Furthermore, we also provide a break-glass approach, resorting to Proxy Re-encryption (PRE) and the concept of a centralised trusted entity that possesses instant access to patients' medical records. Lastly, an analysis is made to assess the performance of the platform's key operations, and the impact that a PRE scheme has on those operations.