The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits these technologies bring, thus generating a significant improvement in health care in clinical settings and out of them. Likewise, countless ordinary users are being served from the advantages of the M-Health (Mobile Health) applications and E-Health (health care supported by ICT) to improve, help and assist their health. Applications that have had a major refuge for these users, so intuitive environment. The Internet of things is increasingly allowing to integrate devices capable of connecting to the Internet and provide information on the state of health of patients and provide information in real time to doctors who assist. It is clear that chronic diseases such as diabetes, heart and pressure among others, are remarkable in the world economic and social level problem. The aim of this article is to develop an architecture based on an ontology capable of monitoring the health and workout routine recommendations to patients with chronic diseases

La lengua de señas ecuatoriana (LSE) es el principal medio de comunicación empleado por la comunidad sorda o con alguna discapacidad auditiva para comunicarse en el Ecuador. Como toda lengua de señas natural, la LSE es un idioma con gramática y vocabulario propios. Esto implica que esta lengua debe ser aprendida por las personas interesadas para poder comunicarse. Sin embargo, no siempre existen los medios suficientes para ello, ya sea por no ser ampliamente conocida y usada, porque no hay suficientes personas o cursos para enseñar y aprender, o incluso por las insuficientes herramientas tecnológicas. Para dar una solución a este déficit en el conocimiento de la LSE se propone el empleo de software especializado que, haciendo uso de paradigmas de interacción actuales, contribuya a facilitar o apoyar el aprendizaje de esta lengua. Esto condujo al desarrollo de una aplicación siguiendo una metodología de desarrollo de software basada en prototipos. Una vez listo un prototipo funcional, se llevó a cabo una evaluación con personas. Los resultados muestran que la aplicación obtenida es dinámica y agradable para los usuarios interesados en aprender la LSE, por lo que podría ser empleada como un recurso de apoyo para la enseñanza-aprendizaje de esta lengua.

Technological advancement has created a growing gap between traditional education and the skills required today, posing challenges in student training from basic education. In this context, the Internet of Things (IoT) emerges as a key technology in various fields, yet its teaching remains limited at early educational levels, especially in institutions in developing countries. Considering this, this study aims to design a short introductory IoT course based on the STEAM approach to promote practical and interactive learning. The methodology included the design of educational materials, the selection of accessible hardware and software tools, and the creation of an interactive learning environment. The evaluation of the designed course was based on educational experience, academic performance, and the usability of the web-based guide. The results provide evidence of the proposal utility in the comprehension of IoT, highlighting the importance of continuously updating educational materials and incorporating feedback strategies to optimize its teaching. Thus, this proposal lays the foundation for future initiatives that integrate emerging technologies into basic education

Con este artículo se pretende obtener modelos basados en análisis probabilísticos de casos para la ayuda a la toma de decisiones en la educación y aprendizaje de los estudiantes de la UTEQ. Para la obtención del producto final, se ha distribuido el proceso de desarrollo en varias etapas. Objetivo Crear un modelo probabilístico para evaluar y diagnosticar a los estudiantes en función de un conjunto de características, las mismas que deberán ser aprendidas de forma automática mediante una generalización del modelo AutoClass permitiendo la existencia de variables ocultas, cada una de ellas afectando a un conjunto distinto de variables observables (respuestas de los alumnos a cuestiones planteadas por un sistema automático de aprendizaje). Materiales y Métodos. Nuestro estudio se llevará a cabo para para definir otra forma de aprendizaje estructural basado en la búsqueda de estructuras a través de modelos metaheurísticas evolutivas. Resultados Este modelo permitirá a las autoridades de la UTEQ determinar inconvenientes y contratiempos en el proceso de enseñanza aprendizaje. A la vez los resultados obtenidos permitirán una toma de decisiones inmediata para solucionar los problemas detectados y de esta manera cumplir con la misión institucional de formar profesionales con visión científica y humanista capaces de desarrollar investigaciones, crear tecnologías, mantener y difundir nuestros saberes y culturas ancestrales, para la construcción de soluciones a los problemas de la región y el país. Conclusiones se optimización de malla variable (VMO) metaheurístico al aprendizaje estructural de los clasificadores de redes bayesianas (BVMO).

Environmental education requires experiences that link learning and active participation in everyday spaces. This study proposes an educational intervention model supported by the Internet of Things, where playful interaction facilitates reflection on responsible practices. The proposal was developed based on a literature review, dialogue with specialists, and the integration of environmental sensors into an interactive touchscreen system. The experience was applied to 32 people, assessing interaction using the System Usability Scale. The results show a favorable perception, reflected in an average score of 75.3, along with observations aimed at visual and sensory adjustments. The experience demonstrates that the model allows environmental education to be brought into educational and community spaces, maintaining pedagogical consistency and offering possibilities for application in environments with similar conditions.

Ecuador has experienced one of the fastest escalations of lethal violence in Latin America, with annual intentional homicides rising from about 1000 in 2014–2019 to 9283 in 2025. This study evaluates how well such patterns can be forecast, and what governs that forecastability, using the complete official open dataset of 43,976 victim-level records covering January 2014–June 2026 (portal update 15 July 2026). Incidents are aggregated into four monthly pattern series—case volume, firearm share, mean victim age and geographic concentration in Guayas—and fifteen specifications—classical, automatically configured, and state-of-the-art neural and gradient-boosted baselines—are compared under a rolling-origin protocol with 31 evaluation origins and a 12-month horizon, with every configuration decision taken inside each training window. Accuracy is reported as the mean and standard deviation of MAE, RMSE, MAPE, MASE and $R^2$ across origins, interval quality as empirical coverage and the mean scaled interval score, and differences are tested with Diebold–Mariano statistics under Holm correction. No pairwise comparison among the 303 tested survives multiplicity control, and in all four series, the best and second-best specifications are statistically indistinguishable: algorithm choice is not what these data can resolve, and the result holds with current deep-learning architectures in the candidate set rather than in their absence. The test is conservative and its power is limited, so this is non-detectability at this sample size rather than evidence that the differences are zero. What does determine it are diagnosable properties of the series. Bounded, low-variability series are forecast with mean MASE between 0.58 and 1.13 across the full candidate set, and below 1.00 by twelve of the fifteen specifications on the firearm share, whereas unbounded count series under regime change exceed an MASE of 2.09 for every specification tested, including simple benchmarks. No series requires a seasonal difference—the OCSB test returns $D=0$ in all four and the automatic search never selects one—although weak seasonal autoregressive terms are retained at some origins, and the firearm share shows a small but significant monthly effect. A genuine external validation against the observed 18 months of 2025–2026, unseen by any model, reproduces the same asymmetry (firearm share, MAPE 2.3% with 100% interval coverage; volume, bias $-14.0$%). Splitting the evaluation by regime shows that the crisis made count series roughly twice as unpredictable and bounded series roughly twice as predictable: the escalation destabilised the level of the phenomenon while stabilising its composition.