A simple experiment on the determination of the coefficient of restitution of different materials is taken as the basis of an extendable work that can be done by students in an autonomous way. On the whole, the work described in this paper would involve concepts of kinematics, materials science, air drag and buoyancy, and would help students to think of physics as a whole subject instead of a set of, more or less, isolated parts. The experiment can be done either in teaching laboratories or as an autonomous work by students at home. Students’ smartphones and cheap balls of different materials are the only experimental materials required to do the experiment. The proposed work also permits the students to analyse the limitations of a physical model used in the experiment by analysing the approximations considered in it, and then enhancing their critical thinking.

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).

The integration of artificial intelligence(AI) technologies into identity and access management (IAM) systems has greatly improved access control and management, offering more robust, adaptive, and intelligent solutions than traditional methods. AI-driven IAM systems enhance security, operational efficiency, and introduce new capabilities in industrial environments. In this narrative review, we present the state-of-the-art AI technologies in industrial IAM, focusing on methods such as biometric, comprising facial and voice recognition, and multifactor authentication for robust security. It addresses the challenges and solutions in implementing AI-based IAM systems in industrial settings, including security, privacy, evaluation, and continuous improvement. We present also the emerging trends and future directions, highlighting AI’s potential to transform industrial security measures. This review aims to guide researchers and practitioners in developing and implementing next-generation access control systems, proposing future research directions to address challenges and optimize AI applications in this domain.