Data repositories currently constitute essential programs within institutions. In fact, universities are the primary institutions that promote the creation, management and storage for the safekeeping of a variety of documents, data and/or projects. This work is carried out within the framework of institutional need and the application of knowledge by the students and professors in the software degree program at a university in Ecuador. This project centers on a web application that makes it possible to manage, upload and download resources. This initial version focuses on the resources related to academic projects, data sets and algorithms. The scalability characteristics have been carefully introduced to ensure a progressive evolution of other types of resources and functionalities, in accordance with educational needs. Several aspects of the web interfaces are based on the characteristics of other existing repositories, and a series of tests related to functionality have been conducted in order to move beyond the introductory stage of use, to begin with, by the university community. This project is carried out according to a conception based on contributions, in which the agents, in principle, are members of the institution. The intensive and collaborative use of this utility will contribute to the growth of a data culture within the institution. Furthermore, the collection and organization of data for the purposes of study and research by the different university degree programs makes it possible to analyze and contrast them from different perspectives.

La lectura comprensiva es esencial para el aprendizaje, pero se enfrenta a una resistencia entre los estudiantes jóvenes, quienes incluso recurren a herramientas de inteligencia artificial (IA) como sustitutos. Para mitigar esta problemática, en este trabajo se propone el uso de inteligencia artificial generativa desde una aplicación web diseñada a la medida. Partiendo de entrevistas estructuradas a profesionales de la educación de nivel básico, se definieron los requisitos para el diseño de la aplicación propuesta. Luego, se construyó la aplicación que incorpora un componente de lectura adaptativa respaldado por modelos de IA como GPT y DALL-E. Estos modelos se emplean para generar textos e imágenes personalizadas según las necesidades de cada estudiante. Además, se desarrollaron módulos para generar materiales didácticos y evaluaciones personalizadas. Por último, la evaluación de la aplicación se realizó mediante un estudio de usuarios que incluyó estudiantes y docentes, utilizando un cuestionario de usabilidad, y completando con entrevistas y análisis de desempeño en comprensión lectora. Los resultados muestran que la propuesta es prometedora para ser usada alternativamente a la lectura y actividades en papel, con una usabilidad aceptable, siendo efectiva desde el punto de vista académico.

Information on epidemic outbreaks is a key input for health surveillance, as it allows for the assessment of the spread and associated social perception. This study examines emotional and linguistic patterns in narratives disseminated by international organizations (WHO, UN, CDC) and digital platforms (Google News and Reddit) over a three-month period. The KDD process was applied in R Studio (selection, preprocessing, transformation, modeling, and evaluation), using Bing and NRC lexicons and a supervised Naive Bayes model to enhance the detection of emotional nuances. A total of 12,340 texts (3,100 from official sources, 4,240 from Google News, and 5,000 from Reddit) were analyzed using standardized queries in English: pandemic, confinement, epidemic, and HMPV. Official sources showed a greater presence of positive emotions linked to cooperation and security; Google News concentrated negative narratives with terms such as risk and dangerous; Reddit combined fear and sadness with appearances of hope. The analysis included t-tests and ANOVA with 95% confidence intervals. The work is exploratory and preliminary in nature and suggests that surveillance systems should integrate the monitoring of social networks and digital media, along with public policy measures to improve communication in health crisis situations.

The monitoring of physiological parameters in individuals undergoing frequent clinical follow-up continues to rely, in many cases, on invasive procedures or equipment that makes continuous monitoring outside the hospital setting difficult. In light of this situation, the present study evaluated a non-invasive system for measuring glucose, oxygen saturation, heart rate, and body temperature, supported by the Internet of Things and machine learning. The research was conducted in 2025 at a private clinic in the Buena Fe canton, Los Ríos province, Ecuador, using a quantitative, exploratory, descriptive, and quasi-experimental design, with a sample of 120 patients selected from a population of 174 users. Data collection was performed under controlled conditions, and each measurement obtained by the system was compared with values from commercial reference devices. For predictive processing, models such as linear regression, support vector machines, decision trees, and neural networks were analyzed and evaluated using performance metrics including R², MSE, RMSE, accuracy, F1-score, and AUC. The findings showed that the system performed well in estimating physiological signals, with better performance in the more complex machine learning-based models. The neural network achieved the highest performance indicators, with an accuracy of 95.42% and an F1-score of 98.13%, while the system exhibited low percentage errors in the clinical comparison for oxygen saturation. It is concluded that non- invasive physiological monitoring supported by IoT and machine learning constitutes a viable option for continuous clinical monitoring, with potential for use in telemedicine and in healthcare settings with limited resources.

Digital transformation continues to reshape higher education, with cloud computing emerging as a key enabler of enhanced accessibility, collaboration, and academic management. This study investigates the use of cloud computing in Ecuadorian universities by identifying its benefits, barriers, and opportunities through a survey of key stakeholders in the education system. A quantitative approach was employed using a structured questionnaire to collect data on participants’ knowledge levels, tools used, perceived advantages, challenges, and expectations. The main benefit identified was accessibility from any location (92%), followed by enhanced collaboration (73%) and the modernization of educational practices (43%). The primary challenges included lack of training (67%), limited connectivity (58%), associated costs (46%), and concerns about data security and privacy (34%). These findings underscore the need to strengthen technological infrastructure and provide targeted training to optimize the effective use of cloud computing. Regarding future perspectives, 71% of respondents advocated for greater integration into teaching and learning, while 64% suggested expanding its use across academic and administrative domains. Cloud computing represents a strategic asset for Ecuadorian higher education. However, its full adoption requires addressing infrastructure and capacity-building challenges through policies that promote collaboration, innovation, and the efficient management of institutional resources.