Abstract
Background: Virtual reality (VR) has the potential to be a powerful tool in promoting empathy towards inclusion, particularly for individuals with impairments such as mobility difficulties, vision deficits, or autism but also about pregnancy, which can create temporary difficulties. By immersing users in simulated environments that replicate the experiences of those with different abilities, VR can create a sense of understanding and empathy for those who face challenges in their daily lives. For example, VR experiences can simulate the experience of navigating space as someone with a mobility impairment, providing a new perspective and appreciation for the difficulties that others face. Similarly, VR experiences can simulate the experience of vision impairment, pregnancy, or autism, providing a window into the challenges faced by those with these conditions and fostering empathy and understanding. Material and Methods: During the development of this study, field experts were consulted to ensure the robustness of the methods employed. Then, questionnaires were specifically developed to explore disabilities and challenges related to inclusion and were administered to a large population. Additionally, guided interviews were conducted with individuals who possess specific impairments to gather first-hand insights. Results: The results obtained from the questionnaires and interviews provide a comprehensive overview of the inclusion challenges that necessitate attention and resolution. By drawing on the expertise of both experts and individuals with lived experiences, a holistic landscape of inclusion challenges has been established. Conclusions: The VR emerges as a powerful tool for promoting inclusion and fostering understanding among individuals. Its capacity to create immersive experiences that facilitate empathy has the potential to reshape society into a more compassionate and empathetic one. By leveraging the unique capabilities of VR, we can bridge the gap between different perspectives, fostering greater understanding, acceptance, and inclusivity.

Abstract
The assessment of differences between skeletal age and chronological age in childhood is often based on the comparison of the patient's left hand x-ray with a reference atlas, performed by a experienced professional. This procedure involves a manual image analysis, that can be subject to inter rater variability posing several problems for clinical applications. In this paper a new methodology for skeleton maturation estimation based on automatic hand X-ray assessment for pediatric applications on a low resource devices (e.g. mobile device) is proposed. The pipeline covers hand-area estimation and bone-area estimation to achieve maturation scores which are then indexed with references images, separately for male and female. The proposed approach is based on simple image processing functions always bearing in mind the application on a mobile context. The involved steps are thoroughly presented and all the used functions are explained. The performance of the system was then evaluated using the complete pipeline. The obtained results pointed to an average error rate of 15,38 +/- 3,31%, which is subject to improvements. In particular, contrast enhancement in some lower quality images still offers some challenges.

Abstract
The outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a watershed moment in human history, causing a profound shift in the global landscape that has affected every aspect of our lives [...]

Abstract
The technological market is increasingly evolving as evidenced by the innovative and streamlined manufacturing processes. Printed Circuit Boards (PCB) are widely employed in the electronics fabrication industry, resorting to the Gerber open standard format to transfer the manufacturing data. The Gerber format describes not only metadata related to the manufacturing process but also the PCB image. To be able to map the electronic circuit pattern to be printed, a parser to convert Gerber files into a bitmap image is required. The current literature as well as available Gerber viewers and libraries showed limitations mainly in the Gerber format support, focusing only on a subset of commands. In this work, the development of a recursive descent approach for parsing Gerber files is described, outlining its interpretation and the renderization of 2D bitmap images. All the defined commands in the specification based on Gerber X2 generation were successfully rendered, unlike the tested commercial parsers used in the experiments. Moreover, the obtained results were comparable to those parsers regarding the commands they can execute as well as the ground-truth, emphasizing the accuracy of the proposed approach. Its top-down and recursive architecture allows easy integration with other software regardless of the platform, highlighting its potential inclusion and integration in the production of electronic circuits.

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.