Implementation of a Socio-Emotional Education Program at a Basic Level: An Approach from the Aftermath of the Covid-19 Pandemic

El siguiente trabajo aborda la situación socioemocional que enfrentaron algunos de los alumnos de una escuela primaria, en el que la diversidad de características familiares se hizo presente en el aprendizaje de los alumnos, lo que ocasionó una falta de autoconocimiento y autorregulación para actuar de manera asertiva en las diversas actividades escolares, así como las consecuencias de una pandemia llamada Covid-19, que vivenciaron los alumnos, como parte de la estrategia “Sana Distancia”, los alumnos cursaron primer grado de manera virtual, lo que no ayudó a un desenvolvimiento social y escolar de manera ordinaria, ingresando a segundo grado con características propias, como falta de seguridad en ellos mismos, miedos, ansiedades, aislamiento, impulsividad, entre otros, que afectaron de manera directa su proceso de aprendizaje.The following work addresses the socio-emotional situation faced by some of the students of a primary school, in which the diversity of family characteristics was present in the students' learning, which caused a lack of self-knowledge and self-regulation to act assertively. in the various school activities, as well as the consequences of a pandemic called Covid-19, which the students experienced, as part of the “Sana Distancia” strategy, the students attended first grade virtually, which did not help social development. and school in an ordinary way, entering second grade with their own characteristics, such as lack of self-confidence, fears, anxieties, isolation, impulsivity, among others, which directly affected their learning process

Prediction of biochemical oxygen demand in mexican surface waters using machine learning

The monitoring of surface water quality is insufficient in Mexico due to the limited water monitoring stations. The main monitoring parameter to evaluate surface water quality is the biochemical oxygen demand. This parameter estimates the biodegradable organic matter present in the water. Concentrations above 30 mg/l indicates a high level of contamination by domestic and industrial waste. Therefore, the aim of this work to provide a reference to the conventional process of determining biochemical oxygen demand using machine learning. The database used was collected by the National Water Commission (CONAGUA). Pearson’s correlation and Forward Selection techniques were applied to identify the parameters with the most important contribution to prediction of biochemical oxygen demand. Two groups were formed and used as input to four machine learning algorithms. Random forest algorithm obtained the best performance. Group 1 and 2 of parameters obtained a 0.76 and 0.75 coefficient of determination respectively. This allows choosing an adequate group of parameters that can be determined with the chemical analysis instruments available in the study area