Space-temporal models of water resources disturb factors for integrated Mayo river watershed management were evaluate. In some way, this is a continuation of past expertise and earlier studies. Base on these experiences, it was proposed to value the spatial behavioural of factors that disturbed or influence on a negative way, the water resources of Mayo river. It was used the GIS; ILWIS v 3.6, Arc View GIS v 3.3 and Arc GIS v 9.2 to model and analysis of factors. The model of settlements spatial arrangement obtained from the analysis in relation to the area is cluster form arrangement, it means from a general point of view, they are closer each other. According to the technical of measure of arrangement and except to the settlements that match order four are distribute in a regular way (observed frequency is higher than the expected). The others settlement are dispersing in a cluster way...

This study focuses on developing an environmental management strategy to mitigate the environmental and health risks of hazardous waste at Support Hospital II-1 in the province of Bagua. The objectives include performing an initial diagnosis of the management of this waste, assessing the associated risks, and characterizing the key factors to propose an effective strategy. The methodology included the characterization of hazardous waste generated during a week, using descriptive statistics to analyze daily volume and weight data. Hospital staff participated through questionnaires and checklists according to NTS No. 144-2018-DISA-DIGESA regulations. The results showed significant deficiencies in solid waste management, exposing staff and the environment to considerable risks. Among the most relevant findings, insufficient staff training and deficiencies in the infrastructure for the stora...

Predecir los resultados académicos de los estudiantes permite al docente buscar técnicas y estrategias en el tiempo indicado durante el proceso de enseñanza y aprendizaje con el fin de mejorar el logro de competencias en sus estudiantes. En esta investigación se implementó una red neuronal artificial (RNA) para predecir los resultados académicos del curso de física de los estudiantes del II ciclo de la carrera profesional de Ingeniería Civil de la universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua-Perú en función de datos históricos. La RNA se diseñó e implemento en el Software MATLAB, su arquitectura está formada por una capa de entrada, una capa oculta y una capa de salida, para el entrenamiento de la RNA se utilizó dos algoritmos que posee la Toolbox de MATLAB: el Scaled Conjugate Gradient logrando un porcentaje de predicción del 70% y el Levenberg-Mar...

Predicting the academic results of students allows the teacher to seek techniques and strategies at the indicated time during the teaching and learning process in order to improve the achievement of skills in their students. In this research, an artificial neural network (ANN) was implemented to predict the academic results of the physics course of the students of the II cycle of the Civil Engineering career of the National Intercultural University Fabiola Salazar Leguía de Bagua-Peru based on data historical. The RNA was designed and implemented in the MATLAB Software, its architecture is made up of an input layer, a hidden layer and an output layer, for the RNA training two algorithms that the MATLAB Toolbox has: the Scaled Conjugate Gradient achieving a prediction percentage of 70% and the Levenberg-Marquardt achieving a prediction percentage of 86%.

Predicting the academic results of students allows the teacher to seek techniques and strategies at the indicated time during the teaching and learning process in order to improve the achievement of skills in their students. In this research, an artificial neural network (ANN) was implemented to predict the academic results of the physics course of the students of the II cycle of the Civil Engineering career of the National Intercultural University Fabiola Salazar Leguía de Bagua-Peru based on data historical. The RNA was designed and implemented in the MATLAB Software, its architecture is made up of an input layer, a hidden layer and an output layer, for the RNA training two algorithms that the MATLAB Toolbox has: the Scaled Conjugate Gradient achieving a prediction percentage of 70% and the Levenberg-Marquardt achieving a prediction percentage of 86%.