Laboratory tasks are a powerful pedagogical strategy for developing competences in science and engineering degrees, making students understand in a practical way the theoretical topics explained in the classroom. However, performing experiments in real conditions is usually expensive in terms of time, money and energy, as it requires expensive infrastructures that are generally difficult to maintain in good conditions. To overcome this problem, virtual reality has proven to be a powerful tool to achieve sustainability, making it easy to update laboratories without the need to acquire new equipment. Moreover, the ability to introduce practical knowledge into classrooms without leaving them, makes virtual laboratories capable of simulating typical operating environments as well as extreme situations in the operation of different devices. A typical subject in which students can benefit from the use of virtual laboratories is robotics. In this work we will develop an immersive virtual reality (VR) pedagogical simulator of industrial robotic arms for engineering students. With the proposed system, students will know the effects of their own designed trajectories on several different robotic arms and cell environments without having to buy all of them and being safe of damaging the cell components. The simulation will be checking for collisions of the elements in the scene and alert the student when they happen. This can be achieved with a robotic simulator, but the integration with immersive VR is intended to help students better understand robotics. Moreover, even having a real robotic arm available for students, with this proposed VR method, all the students have the opportunity to manage and learn his own version of the robotic cell, without waiting times generated by having less robotic arms than students in classroom.

The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare.

Objectives . This study investigates gender biases in AI perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants . The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods . This research employs an observational study design. The sample size includes 380 participants. The study utilizes a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings . The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI ( \(\text{p} \!\lt\! 0.005\) ), exposure awareness (p = 0.001), perceived ability to apply AI (p = 0.004), sensitivity towards AI use of private data (p = 0.004), positive impact on society (p = 0.002), support for AI development ( \(\text{p}\!\lt\!0.005\) ), and positive expectations towards AI ( \(\text{p}\!\lt\!0.005\) ). Statistical analysis, including nonparametric tests, was used to validate these observations. Conclusions . There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI.

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.