Es importante el estudio de la disponibilidad de energías renovables y en particular el eólico para su valorización. Por lo que este artículo analiza el potencial de la energía eólica de un sitio ubicado en el sur del Perú (Laraqueri), utilizando datos de viento de 2020 a una altura de 10 metros sobre el nivel del suelo. Se utilizaron dos métodos numéricos para estimar los parámetros de la función de distribución de Weibull y se calculó la densidad de potencia para cada mes. También se calculó el grado de error de la función de Weibull con los datos observados. Se concluye que, la ubicación propuesta es apropiada para la generación de energía eólica de baja potencia y la metodología propuesta se puede utilizar en otros lugares.

The solar energy that reaches a certain surface depends directly on the orientation of the surface and the position of the sun. The calculation of the Peak-Sun Hour is the main objective of this work, for this, the global solar radiation that affects a sloping surface is considered, and from this it is determined for periods, daily, monthly and annual. For validation, the global solar radiation data from NASA was used, obtaining a Pearson correlation of 0.84. An annual average of optimum tilt angle of -15.44° was also obtained, which is equivalent to the latitude of the study area. Likewise, an annual average value of 5.41 peak solar hours was obtained. The result of our work will help users and engineers to determine the optimal performance of solar collectors in the city of Juliaca and the Region.

This study examines the direct and indirect effects of mining activities on key socioeconomic indicators such as per capita income, the Human Development Index (HDI), and education in the Puno region of Peru, comparing short-term (2015–2019) and long-term (2003–2029) impacts. Using a random effects panel data model and incorporating spatial autocorrelation, the study analyzes data from 2003 to 2019 to assess the effects of mining on both mining and non-mining districts. The results show that in the short term, family income per capita in mining districts increased by PEN 65.03, while non-mining districts saw an indirect increase of PEN 80.59. In the long term, the direct impact on mining districts grew to PEN 239.44, with the indirect impact on non-mining districts reaching PEN 352.30. In education, mining districts experienced a 6.74 percentage point increase in secondary education for 18-year-olds in the short term, and non-mining districts had a 5.19 percentage point increase, with both showing positive impacts. Long-term effects showed a smaller increase, with mining districts at 12.27 percentage points and non-mining districts at 9.71 percentage points. Regarding the HDI, the direct impact in mining districts in the short term was an increase of 0.02 points, with a total impact of 0.03 points, while the indirect impact on non-mining districts was minimal. In the long term, the direct impact on mining districts grew to 0.09 points, with the total impact reaching 0.10 points, while non-mining districts showed an increase of 0.10 points as well. The study concludes that mining has significant short-term impacts, particularly on income and education, but the long-term effects are more pronounced, especially for income and the HDI, with substantial indirect benefits for non-mining districts, especially in terms of income. Educational improvements stabilize over time, and mining’s overall impact on the HDI increases as its economic and social effects deepen.

The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.

La Prueba de Turing pública completamente automatizada para distinguir computadoras de humanos (en inglés CAPTCHA: Completely Automated Public Turing Test to Tell Computers and Humans Apart), es una herramienta diseñada para verificar que los usuarios son humanos y no robots, y es ampliamente utilizada para prevenir accesos no autorizados. El propósito de este artículo fue desarrollar dos modelos para el reconocimiento de CAPTCHAs de texto: El reconocimiento óptico de caracteres (OCR) y las redes neuronales convolucionales (CNN), los cuales alcanzaron una precisión del 37% y 99% respectivamente. Para entrenar y evaluar los modelos se utilizaron 10 000 imágenes generadas con la librería Gregwar. Los nuevos modelos fueron efectivos para reconocer texto distorsionado o con ruido, y las CNN demostraron robustez en el reconocimiento de CAPTCHAs de texto. En consecuencia, si se utiliza la librería Gregwar se recomienda cambiarla por otras librerías que aún son difíciles de engañar, por ejemplo, los basados en imágenes.