Erick Flores-Chacón

Air pollution episodes in Metropolitan Lima pose persistent challenges for urban health protection and timely environmental decision-making. However, many machine learning approaches for air-quality prediction remain difficult to operationalize due to high latency, extensive hyperparameter tuning, and limited interpretability. This study addresses this gap by adopting an engineering-driven predictive knowledge modeling approach grounded in the Knowledge Discovery in Databases (KDD) framework to evaluate an efficient probabilistic classifier—Gaussian Naïve Bayes (GNB)—for predicting regulatory air-quality categories in Metropolitan Lima. A total of 768,185 hourly observations from SENAMHI monitoring stations covering the 2020–2025 period were analyzed, considering PM₁₀, PM_2.5, NO₂ concentrations, and the Air Quality Index (AQI). Data were preprocessed through validity checks, explicit outlier handling, and categorical encoding based on regulatory thresholds, while a time-based train–test split preserved temporal structure and prevented data leakage. The proposed model achieved strong predictive performance (global accuracy ≥ 0.925) and excellent probabilistic calibration (overall Brier Score ≈ 0.023; AQI Brier Score ≈ 0.010). These results demonstrate that GNB provides a robust, interpretable, and computationally efficient solution for operational air-quality management and early warning support, contributing to evidence-based urban environmental decision-making aligned with Sustainable Development Goal 13 (Climate Action).

The aim of this research was to determine the impact of the architecture of digital technologies in university teacher training as a catalyst for change and educational innovation. Methodologically, it is framed in a quantitative approach with a pre-experimental design and an explanatory level, with a sample of 269 teachers out of a total population of 450 at the university. The results show a significant impact of the architecture of digital technologies as a catalyst for change in university teacher training, with an increase of 22.88% from pre-test to post-test. The statistical significance of the results is supported by a P-value of 0.000, which means that it is less than the established significance level (α = 0.05), leading to the acceptance of hypothesis H1: The architecture of digital technologies as a catalyst for change in university teacher education has an impact. In conclusion, the architecture of digital technologies, consisting of the two academic pillar systems: the academic management system and the virtual learning system, contribute to the development of digital skills in key areas such as digital literacy, communication and collaboration, digital content creation and problem solving in virtual environments.