Seventy-five samples were collected from 15 beehives in the central highlands of Ecuador (Tungurahua–Chimborazo) to assess Africanization in managed bee populations using wing geometric morphometric and mitochondrial DNA analyses. The results indicated that when grouping the apiaries based on altitudinal floors into 2600–2800, 2801–3000, and 3001–3274 m above sea level, differences (p < 0.001) were observed. The morphotypes were similar in the first two floors, but the third indicated that altitude plays a crucial role in the differentiation of populations. When comparing with the pure subspecies, we found differences (p < 0.001); the nearest Mahalanobis distance was for Apis mellifera scutellata (D2 = 3.51), with 95.8% Africanization via father in the area. The maternal origin of all patterns belonged to lineage A (A. m. scutellata), with seven haplotypes. The most frequent haplotypes were A26 and A1; however, the A1q haplotype was not detected at the national level or in nearby countries. The identified haplotypes do not coincide with A4, which is predominant in South Africa and Brazil. The results indicate a double origin due to their presence in North Africa and the Iberian Peninsula. The formation of specific morphological groups within ecoregions is suggested.

The incorporation of emerging technologies in education has generated new alternatives to strengthen practical learning, especially in laboratory contexts, where limitations of time, resources, and safety often exist. In this regard, the present study aims to analyze the use of augmented reality in laboratory practices, considering its influence on the usability of technological tools and on student performance. The research was developed under a descriptive and analytical approach, through a systematic review of scientific literature and the analysis of documented educational experiences at different levels of education. The results show that the application of augmented reality facilitates the understanding of experimental procedures, improves student interaction with laboratory materials, and contributes to the reduction of errors during the execution of practices. Likewise, an increase in motivation and autonomous learning was identified when the tools present an intuitive design aligned with pedagogical objectives. The discussion of the findings allows these results to be interpreted as consistent with previous studies that highlight the value of interactive technologies in experiential learning. In conclusion, augmented reality constitutes an effective didactic strategy to optimize laboratory practices, provided that its implementation considers usability criteria and appropriate pedagogical integration.

Este estudio presenta el diseño de un producto turístico sostenible denominado "Experiencia SelvaViva: Ecoaventura en el Corazón del Yasuní". Su objetivo es proporcionar a los viajeros una experiencia única en la selva amazónica ecuatoriana, centrándose en la inmersión en la naturaleza, la interacción cultural y la conservación ambiental. Se llevan a cabo actividades como senderismo, avistamiento de vida silvestre, visitas a comunidades indígenas y talleres de artesanía. Se implementan prácticas de turismo responsable y se establecen alianzas con agencias de viajes, organizaciones ambientales y comunidades locales para garantizar la sostenibilidad del proyecto. Los hallazgos muestran un creciente interés en el turismo sostenible y una demanda de experiencias auténticas en entornos naturales. Se observa un aumento en la conciencia ambiental entre los viajeros, quienes buscan contribuir positivamente a la conservación del medio ambiente mientras disfrutan de sus vacaciones. Se destaca la importancia de la colaboración entre el sector turístico, las autoridades locales y las comunidades indígenas para asegurar la protección de los recursos naturales y culturales. Las implicaciones de este estudio son significativas en términos de desarrollo económico y conservación ambiental. El turismo sostenible puede proporcionar una fuente de ingresos alternativa para las comunidades locales, reduciendo la presión sobre los recursos naturales y promoviendo la conservación del ecosistema del Yasuní. Además, este enfoque contribuye a sensibilizar a los visitantes sobre la importancia de preservar la biodiversidad y la cultura indígena. En resumen, "Experiencia SelvaViva" representa un modelo innovador de turismo sostenible que busca equilibrar el desarrollo económico con la protección del medio ambiente y el patrimonio cultural.

The purpose of this research is to develop a mathematical linear programming model (LPM) in order to maximize the profits of tour operators in Puerto Francisco de Orellana, Ecuador. To this end, the model considers the quantity of tourist packages (P), services (S), and other sales (souvenirs) (O) as decision variables. Furthermore, the objective function represents net profits, taking into account costs, taxes, and annual fixed expenses, while the constraints are based on critical resources such as working time, vehicle usage, and operational capacity. Regarding data collection, a JSON format was employed to store specific information for each tour operator. Subsequently, a graphical interface was developed using PySide6 (Python) with the aim of facilitating data entry. Concerning modeling and optimization, the data from the JSON files was processed and adjusted to be used in a linear programming Solver through the PuLP library. Finally, to validate the model, it was applied to two real-world cases (Jaguar Travels and Amazon Travel), which allowed for the evaluation of its effectiveness. As a result, the model generated optimal solutions that maximized profits for both operators, adapting to their respective operational constraints. Additionally, the results were presented in structured reports in JSON format, which facilitated their interpretation and application. Similarly, it was confirmed that the model is scalable and adaptable to different operational contexts. In conclusion, the proposed LPM proved to be effective in optimizing the profits of tour operators under realistic conditions. On the other hand, the use of JSON along with a graphical interface significantly streamlined the data collection and processing process. However, it is worth mentioning that the model could be expanded by incorporating new variables and constraints to improve its accuracy in future research.

This study aims to bridge the gap between the high predictive accuracy of machine learning models and the transparency requirements of public policies for sustainable mobility by analyzing technological adoption within the national vehicle fleet of Ecuador. To this end, a methodological framework grounded in Design Science Research (DSR) and CRISP-DM was applied to a real administrative dataset of 482,754 vehicle registration records from the Ecuadorian Internal Revenue Service (SRI, 2025), comparing the performance and interpretability of two state-of-the-art gradient boosting algorithms, XGBoost and LightGBM, for multiclass classification of internal combustion engine (ICE), hybrid, and electric vehicles (EV) under severe class imbalance. The results demonstrate that both models achieve near-perfect predictive performance, with a consolidated Macro F1-score of 0.987, confirming their robustness and suitability for large-scale, policy-relevant administrative data even when electric vehicles represent only 1.3\% of the observed fleet. Beyond global performance metrics, the integration of explainable artificial intelligence through SHAP values reveals that fiscal appraisal value and engine capacity are the primary determinants of EV adoption, while territorial factors exhibit greater influence in the case of hybrid vehicles, highlighting qualitatively distinct adoption mechanisms across technologies. These findings show that advanced ``black-box'' models can be transformed into auditable and interpretable analytical tools capable of linking predictions to concrete economic, technical, and spatial variables relevant for decision-making. The study concludes that XAI-enabled gradient boosting provides a rigorous and transparent framework to support the design of targeted fiscal incentives, fleet electrification strategies, and evidence-based transport decarbonization policies in emerging economies, where data-driven governance is critical for accelerating the transition toward sustainable mobility.

El presente estudio tuvo como objetivo analizar la variabilidad de la temperatura superficial terrestre mediante herramientas de análisis geoespacial, con el fin de identificar patrones espaciales y temporales que evidencien los efectos del cambio climático en la región. La metodología se basa en la utilización del modelo climático BCC-CSM2-MR, bajo el escenario de bajas emisiones SSP1-2.6, considerando el periodo proyectado 2021–2040 y su comparación con el registro histórico 1901–2020. Para ello, se emplearon datos de temperatura mensual y se procesaron mediante técnicas de interpolación espacial, análisis de tendencia y reclasificación climática utilizando el sistema de Köppen-Geiger, lo que permitió delimitar zonas de variabilidad térmica y su evolución temporal. Los resultados muestran un incremento notable de la variabilidad térmica en el altiplano andino y en regiones de gran altitud de la cordillera de los Andes, con desplazamientos en los límites de las zonas climáticas y un aumento en la frecuencia de eventos cálidos. Se concluyó que la variabilidad térmica futura podría alterar significativamente los ecosistemas de montaña y los sistemas agrícolas dependientes del clima. Se recomendó fortalecer los programas de monitoreo climático y desarrollar estrategias de adaptación local basadas en información geoespacial, que permitan mitigar los impactos proyectados y apoyar la toma de decisiones en políticas de sostenibilidad ambiental y gestión del territorio.