Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.

Esta guía práctica enseña cómo configurar y administrar una base de datos en PostgreSQL utilizando la terminal de Ubuntu. Los pasos incluyen la creación de un nuevo usuario y base de datos, la asignación de permisos y la configuración del archivo pg_hba.conf para permitir la autenticación. Además, se explican comandos útiles como listar usuarios existentes. La actividad final implica crear una base de datos para historias clínicas, un usuario asociado y otorgarle los permisos necesarios. El informe de la solución debe incluir portada, introducción, objetivos, desarrollo con capturas de pantalla y conclusiones.

El propósito de la presente nota de clase es proporcionar a los estudiantes de Ingeniería, en los cursos de Algoritmia, Programación y afines, un documento de consulta y apoyo que amplíe, de forma clara y sencilla, la información sobre los conceptos básicos de datos requeridos para iniciar la implementación de algoritmos. Los temas que se trabajan en este documento son: tipo de datos, variables y constantes, operadores y funciones aritméticas, operadores lógicos y de caracter. A través de un lenguaje natural y proporcionando algunos ejemplos, este documento presenta conceptos, estructuras y aclaraciones sobre los conceptos fundamentales de datos y las operaciones que se pueden realizar con ellos, en el diseño y la construcción de algoritmos de computación. Se espera que, gracias a esta nota de clase, los estudiantes y lectores interesados puedan ampliar la información, aclarar dudas y asimilar los conceptos, de tal forma que esto ayude en la formación de las competencias propuestas para el curso de Algoritmia.

Rice feeds an estimated 3.5 billion people worldwide, making it central to global food security. Early detection of disease is essential to protect yields, and augmented reality (AR) offers unique potential through hands-free, firstperson data capture that can deliver real-time diagnostic support in the field. Yet AR-based disease recognition remains constrained by dataset scarcity, as infections are seasonal, environmentally dependent, and costly to annotate. To address this gap, we introduce a pipeline that uses DALL E 3 generative editing to synthesize diseased rice leaves from healthy AR captures. The resulting synthetic datasets were used to train a YOLOv11n model and benchmarked against real diseased AR images. The model achieved strong performance $(\text{mAP} {@} 0.50=0.78)$, confirming that AI-generated lesions preserved diagnostic traits relevant for robust detection. By providing a data-efficient pathway toward smarter crop health monitoring, this research supports Sustainable Development Goal 2 (Zero Hunger) and advances sustainable food security through technological innovation.

Mixed Reality (MR) serious games combine immersive technologies with game-based learning to support training, skill development, and decision-making across diverse disciplines in university education. Although previous research has demonstrated improvements in engagement, usability, and learning outcomes, less attention has been devoted to developing design recommendations and methodological approaches for studying MR serious games. A theory-derived Meta Quest 3-based MR serious game for diagnostic classification was developed by integrating learning theories, gameplay mechanics, and gamification features while functioning as both an educational intervention and a research instrument. The system was evaluated through two complementary studies involving undergraduate students: the first compared learning outcomes across instructional approaches, whereas the second examined learner performance and transfer using a mixed-method approach incorporating objective metrics, questionnaires, and interviews. The proposed system achieved learning outcomes comparable to expert-guided field training while significantly outperforming classroom instruction and self-directed study. Significant transfer to real-world diagnostic tasks was demonstrated, and complementary evidence was triangulated to derive preliminary design recommendations and a multi-dimensional methodological framework. These contributions provide an initial foundation for the systematic design and study of MR serious games in higher education.