Global climate change is contributing to significant water reduction, evident in Lake Titicaca, where water levels have declined markedly in recent years. This study employs ten advanced machine learning algorithms to improve the prediction accuracy of water level behavior in the Lake Titicaca basin. The models utilize year, month, day, precipitation, maximum temperature, and minimum temperature as input variables. Accurate predictions are crucial for informing multidisciplinary planning related to agricultural and livestock production and for developing contingency strategies against droughts and floods. Historical data were sourced from the SENAMHI ENAFER pier station in Puno. Comparative evaluation indicates that the Gradient Boosting Regressor algorithm achieves superior performance based on multiple metrics: MAE = 0.00035279, MSE = 0.00000247, RMSE = 0.0015725, Coefficient of Determination R² = 0.99999706, and Cross-Validation (average RMSE = 0.00199047). This research underscores the potential of machine learning algorithms in hydrology, offering valuable insights applicable to basin modeling worldwide.

El objetivo de la investigación fue aplicar el Project Management Body of Knowledge para optimizar la gestión de historias clínicas en establecimientos de salud de la Microred Metropolitano. Se tuvo como caso de estudio el sistema web eClinicalHistory utilizándose como marco para la gestión del proyecto el Project Management Body of Knowledge; que contiene datos básicos del paciente para la creación de su historia clínica. Se realizó en los 5 establecimientos de salud de la Microred Metropolitano entre junio y setiembre del 2018, siguió el diseño cuasi-experimental; a partir de las pruebas realizadas se encontró que |tc|=10.39109 > |tt|=2.10092204. Se concluye que la aplicación del Project Management Body of Knowledge en el desarrollo del sistema web optimiza la gestión de historias clínicas en establecimientos de salud de la Microred Metropolitano para un nivel de significancia de 0.05 o un nivel de confianza del 95%, esto con base a la información de los usuarios. Se determinó su efectividad optimizando los procesos técnico administrativo y asistencial reduciendo los tiempos de apertura de la historia clínica de 5'37" a 1'46"; la búsqueda de 2'41'' a 7'' en promedio; se mejora el acceso a las historias clínicas por personal autorizado, y para la investigación científica, se comprobó que el total de historias clínicas cuentan con datos necesarios para su atención, además del uso correcto del CIE10 en los diagnósticos, se redujo las pérdidas de historias clínicas de 6 a 0 al mes; la traspapelación de 11 a 1 al mes en promedio.Palabras clave: establecimiento de salud, gestión, historias clínicas, optimización, PMBOK, proceso técnico administrativo, proceso técnico asistencial.

The objective of the research is to obtain the economic benefits of drinking water projects using the parametric logit model, from an Invierte.pe perspective. The methodology applied was the parametric contingent valuation model using the Logit model to estimate the willingness to pay (WTP), information was collected through the application of surveys to project users on their willingness or unwillingness to pay an amount of money for improvement of the drinking water supply service. The result was a WTP of 4.27 soles and economic benefits of the project for the first year of 314,511.00 soles and for the twentieth year of 370,080.00 soles. In conclusion, it is possible to incorporate in Invierte.pe the parametric logit model in the estimation of economic benefits of drinking water projects.

Labor informality is a phenomenon that impacts the economic and social development of various regions, including the department of Puno, by limiting access to labor rights and perpetuating poverty and inequality. This study aims to determine the socioeconomic factors that influence labor informality in the department of Puno. To achieve this objective, a methodology with a quantitative approach of explanatory scope and a non-experimental cross-sectional design was used, analyzing data from the National Household Survey (ENAHO) of the National Institute of Statistics and Informatics (INEI), whose sample was made up by 1,771 individuals of working age from the department of Puno. The results revealed that factors such as sex (p = 0.0470), educational level (p = 0.0000), age (p = 0.0000), area of ​​residence (p = 0.0040), financial inclusion (p = 0.0000), the type of employer occupation (p = 0.0290), the type of contract (p = 0.0000), the monthly income (p = 0.0000), the size of the company (p = 0.0010) and the type of commercial economic activity (p = 0.0000) were determining factors of labor informality. In conclusion, it is established that socioeconomic factors significantly influenced labor informality in the department of Puno, which implies that public policies should focus on improving access to education and financial services to promote the formalization of employment and, therefore, improve the living conditions of the working population.

This research introduces a method to help improve quinoa farming in Puno Perú area by blending programming and machine learning methods to tackle issues faced in today’s global agricultural markets efficiently and effectively using information from a span of 26 years. Through our work and study details over these years we formulated a model that balances maximizing profits while handling market fluctuations effectively and efficiently Our results highlighted that market prices for quinoa are heavily impacted by market dynamics rather, than local production conditions (correlation coefficient; 0.147; significance level; p > 0.05).The understanding influenced the linear approach that determined the best prices for selling and break even points effectively surpass traditional techniques by utilizing machine learning models while ensemble approaches showed better performance outcomes surpass the average methods used in the industry. Moreover, reactionary models identified changes in market trends accurately while long short-term memory networks forecasted price fluctuation tendencies.