The study analyzed and validated the best machine learning model to predict dropout among students at the National University of Moquegua, Mariscal Nieto campus, between 2009 and 2019. It also identified the characteristics influencing dropout. The models used were logistic regression, decision trees, support vector machines, naive Bayes, the CRISP-DM methodology, and metrics such as the confusion matrix and K-fold cross-validation. We selected important features using two techniques, random forests and Featurewiz, employing Python and its libraries for the task. The research type is descriptive correlational, and the design is observational using secondary data sources. The sample was selected through stratified random probability sampling, resulting in a sample of 109 students who dropped out without completing their studies and 220 who did not, totalling 329 data points. Cross-validation indicated that decision trees achieved 76% accuracy, followed by logistic regression at 73%, support vector machines at 71%, and naive Bayes at 62%. The characteristics influencing dropout include general data such as cycle, age, and zonal address; economic aspects such as total income, family load, children in higher education, household components, student's housing, and head of the household; and housing aspects such as type of construction and number of bedrooms.

This study investigates the application of several machine learning models using PyCaret to forecast the monthly demand for electricity consumption; we analyze historical data of monthly consumption readings for the Cuajone Mining Unit of the company Minera Southern Peru Copper Corporation, recorded in the electricity yearbooks from the decentralized office of the Ministry of Energy and Mines in the Moquegua region between 2008 and 2018. We evaluated the performance of 27 machine learning models available in PyCaret for the forecast of monthly electricity consumption, selecting the three most effective models: Exponential Smoothing, AdaBoost with Conditional Deseasonalize and Detrending and ETS (Error-Trend-Seasonality). We evaluated the performance of these models using eight metrics: MASE, RMSSE, MAE, RMSE, MAPE, SMAPE, R2, and calculation time. Among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and an RMSE of 5922.63; among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and a RMSE of 5922.63, followed by AdaBoost with Conditional Deseasonalize and Detrending, while ETS also provided competitive results. Forecasts for 2018 were compared with actual data, confirming the high accuracy of these models. These findings provide a robust energy management and planning framework, highlighting the potential of machine learning methodologies to optimize electricity consumption forecasting.

Los aceites esenciales son una mezcla muy compleja de componentes y su composición puede variar en diferentes especies o variedades incluso dentro de la misma variedad. Origanum vulgare L. se encontró que el rendimiento es de 0,7% v / p. En este trabajo se determinó la composición química tentativa del aceite esencial y la evaluación de su actividad antioxidante en tres muestras de Moquegua. El aceite esencial se obtuvo de la extracción por destilación al vapor y se analizó mediante un cromatógrafo de gases acoplado a un espectrómetro de masas (GC-MS) para la determinación de los componentes. La actividad antioxidante se evaluó mediante la capacidad de eliminación de radicales (método DPPH). En promedio, los componentes principales de dos repeticiones de las tres muestras son: Iso Terpineol (31,23: 31,28; 27.94%), Timol (12,44; 12,48; 12,28%), gama-terpineno (6,56; 6,55; 7,644%), Antranilato de linalilo (6,49; 6,49; 6,72%), Cariofileno (4,13; 4,14; 4,33%) Terpineol (5,65; 5,65; 6.33%), Trans Terpineol (4,03; 4,02; 4,06%), Timol metileter (3,74; 3,75; 3,98% ), Gama Elemeno (3,15; 3,16; 3,16%), Careno (2,83; 2,82; 3,53%), p-Cimeno (2,48; 2,48; 2,43%), Sabineno (2,47; 2,45; 2,35%), Timol metileter (1,43; 1,43; 1,65%), Tran Ocimeno (1,21; 1,21; 1,13%) como los componentes más abundantes. El aceite vegetal mostró una actividad antioxidante IC50 en equivalentes de trolox para 10μL de aceite esencial: Orégano de Cuchumbaya (CU3750): 12,348 ± 0,066 μg, Orégano de Chojata (CH3475): 12,249 ± 0,035 μg, Orégano de Puquina (PU3041): 12,197 ± 0,07 μg. El análisis estadístico, ANOVA indica que existen diferencias significativas en las tres muestras, se realizó una prueba de múltiples rangos para ver las diferencias significativas entre cada una, mostró que hubo diferencias significativas entre CU3750 y CH3475, CU3750 y PU3041, en tanto, no hubo diferencias significativas entre el orégano CH3475 y PU3041 para la actividad antioxidante. Por tanto, los aceites mostraron una alta actividad antioxidante.