Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 942 attributes from the national survey of teachers from public and private educational institutions of regular basic education (ENDO-2018) carried out by the Ministry of Education of Peru. We used the ANOVA F-test filter and the Chi-Square filter as feature selection techniques. In the modeling phase, the logistic regression algorithms, Gradient Boosting, Random Forest, XGBoost and Decision Trees-CART were used. Among the algorithms evaluated, XGBoost and Random Forest stand out, obtaining similar results in 4 of the 8 metrics evaluated, these are: balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48 and negative predictive value of 0.94. However, in terms of the area under the ROC curve, XGBoost scores 0.83, while Random Forest scores 0.82. These algorithms also obtain the highest true-positive values (479 instances) and lowest false-negative values (168 instances) in the confusion matrix. Economic income, satisfaction with life, self-esteem, teaching activity, relationship with the director, perception of living conditions, family relationships; health problems related to depression and satisfaction with the relationship with colleagues turned out to be the most important predictors of job satisfaction in basic education teachers.

Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.

School principals encounter contemporary demands that impact their job satisfaction and leadership effectiveness. Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. The principal variables identified included satisfaction with salary, geographic location of the educational institution, relationships with students and teachers, workplace climate, student learning achievements, and job benefits. Economic factors proved important, such as gross and net income, and the minimum monthly amount required to meet household needs. Time-related aspects also exerted influence, including hours dedicated to training, time spent on administrative and/or teaching duties outside working hours, travel time to and from the Local Educational Management Unit (UGEL), duration of stays at the UGEL, and commuting time from principal residence to the educational institution. The Histogram-Based Gradient Boosting algorithm, optimized with Bayesian techniques and trained with data balanced through Random Oversampling, achieved a balanced accuracy of 0.63 on a test set with real-world class distribution. When using Generative Adversarial Networks to balance only the training set, better results were obtained in recall (0.74), precision (0.72), and F1 score (0.70). SHAP analysis revealed that economic factors primarily influenced dissatisfied principals, while interpersonal factors were more important for highly satisfied principals, suggesting a hierarchical pattern of needs. The findings could inform strategies to enhance principals' job satisfaction and strengthen data-driven educational policies.

La detección del estro es clave para optimizar la concepción en vacas y la eficiencia reproductiva del ganado. El método convencional requiere observación continua, demandando mano de obra y tiempo. Desarrollamos un sistema basado en IoT que automatiza el monitoreo del estro mediante un dispositivo multisensor montado en el cuello de la vaca. Este recopila datos y los transmite vía LoRaWAN a un Gateway, que los envía a The Things Stack y luego a TagoIO para visualización y almacenamiento. En pruebas de campo, tras sincronizar el estro de una vaca en la Amazonía peruana, se recolectaron datos y se analizó su variación. El sistema registró información fisiológica y de comportamiento, evidenciando que en 72 horas aumentaron el desplazamiento y la temperatura corporal, indicando el estro.

Objective: The objective of the scientific investigation was to determine the relationship between the preventive tax audit in the refunds linked to the general sales tax of exporting companies in the province of Tambopata, Peru. Method: The quantitative approach was considered, of a non-experimental type, with a descriptive and correlational design; considering a sample of 66 exporters from a universe of 80 to whom we applied two instruments, the first with 2 indicators and the second with 11, valid, with an alpha of 0.911 and 0.920 respectively. The statistical analysis was considered through the Pearson Chi square test and through a multiple linear regression. Results and conclusion: It were determined that the relationship between the preventive tax audit with the balance in favor of benefit, the perceptions not applied of the IGV and the excess or undue payments, is a high positive correlation. In view of the fact that, in the event of a 1% increase in the balance in favor of benefit matters, then the preventive tax audit will increase by 61.22%; if the non-applied IGV perceptions by the taxpayer are high, then the preventive tax audit will increase by 3.91%; and if the excess or improper payments increase by 1%, then preventive tax audit will increase by 15.47%. Finally, there is a high positive relationship between the preventive tax audit and the refunds linked to the IGV of the exporting companies of the province of Tambopata, since a Pearson correlation of 0.6957 was found. Implications of the investigation: The investigation shows an application of the theory to reality in the subject of the preventive tax audit in the returns linked to the general sales tax, the same that was carried out to the exporting companies of the province of Tambopata; all this in view of the fact that taxpayers and companies, when seeking to obtain greater economic profitability, avoid developing contribution actions with the State, since it is an obligation; That is why the supervisory and tax administration entities exercise the role of inspection, inspecting, investigating and controlling compliance with tax obligations. Originality/value: there are few investigations that analyze the relationship between the preventive tax audit with the refunds related to the general tax of the companies that are dedicated to the export of goods and services; which deserves to promote further investigations by types of exportable goods from Peru to the international market.

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R² = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R² = 0.397), VGG19 (MSE = 0.447, R² = 0.280), and ResNet50 (MSE = 0.474, R² = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems.