Programa del congreso

This research developed a simulation model using FlexSim to represent the academic progression of Civil Engineering students at UNITEC Tegucigalpa, based on the 2015 curriculum. The primary goal was to evaluate the technical feasibility of simulating a curriculum structure through discrete-event modeling, allowing for the early identification of academic bottlenecks before implementing new programs or reforms. The study followed three main phases: data collection and refinement, model construction, and statistical validation. Students were segmented by academic performance, enabling a differentiated simulation approach according to academic profiles. The resulting model represented idealized academic paths, assigning approval or failure probabilities per subject. However, because FlexSim is primarily designed for industrial systems, significant customization was required. As a result, the model became highly complex, not scalable to other programs, and difficult to replicate across different contexts. Despite these limitations, the simulation helped identify critical courses, particularly in the mathematics track, and led to the creation of a methodological guide to support similar future studies. While the model itself is not universally transferable, the underlying logic, segmentation approach, and technical insights offer a solid foundation for developing more flexible and scalable academic simulations in platforms better suited to educational environments.

This study examines the role of student engagement and academic innovation as fundamental drivers of university entrepreneurship in Metropolitan Lima, Peru. Considering the social, economic, and educational challenges facing this region, universities are positioned as key spaces for promoting a comprehensive, transformative, and action-oriented education. Employing a quantitative, explanatory approach and a non-experimental, cross-sectional design, structural equation modeling (PLS-SEM) was applied to a sample of 385 undergraduate and graduate students involved in academic initiatives related to innovation and entrepreneurship. The findings reveal statistically significant associations between academic engagement and creativity (β = 0.61; p < 0.001), educational innovation and problem-solving (β = 0.57; p < 0.01), and entrepreneurial proactivity (β = 0.48; p < 0.05). Additionally, structural barriers were identified, including lack of methodological guidance (42%), academic overload (33%), and limited access to technological resources (23%). These limitations call for strategic actions such as integrating active methodologies into the curriculum, strengthening teacher training, and promoting inter-institutional partnerships. In conclusion, the synergy between student engagement and academic innovation enhances entrepreneurial competencies and positions universities as agents of social transformation and sustainable territorial development.

This Systematic Literature Review (SLR) examines the effectiveness of Large Language Models (LLMs) as educational tutoring tools. The study analyzes their impact on learning outcomes and academic performance when compared to conventional pedagogical methods. LLMs offer key empirical benefits such as real-time feedback, educational personalization, scalability in hybrid environments, and significant interdisciplinary potential, positioning them as essential tools for educational modernization. The primary objective was to systematically analyze the efficacy of LLMs as intelligent tutors, identifying differences in academic performance, ethical challenges, implementation strategies, and motivational factors. A systematic search was conducted in Scopus using the PICO-PRISMA framework. From a total of 973 initial records, 28 open-access studies published in English (between 2022 and 2025) were selected. The evidence gathered reveals significant improvements in academic performance, highlights critical ethical challenges, and presents structured pedagogical strategies with an 86.4% success rate. This analysis provides consolidated and valuable information for future developments and implementations of LLMs in the educational field.

This research seeks to analyze the relationship between social responsibility and the satisfaction of the residents of the Michiquillay Mining Project in Cajamarca - 2025. Under a quantitative research approach, non-experimental - cross-sectional design, descriptive type and with correlational level or scope, a sample of 76 people was selected to be surveyed from the Quinuamayo Alto Sector. To collect the data, a questionnaire validated by three experts in the field was used as an instrument and consisted of 32 closed questions, of which 7 questions referred to the respondent's general data and another 25 were distributed in 5 dimensions measured on a Likert scale. Cronbach's alpha obtained through the SPSSv27 software yielded a value of 0.877, determining excellent reliability for its application. After conducting both descriptive and inferential statistical analyses, it is concluded that the residents of the Quinuamayo Alto Sector are satisfied with the social responsibility plan implemented by the Michiquillay Mining Project, given that it meets their economic and social expectations. However, it should emphasize preventive and corrective environmental guidelines that minimize the effects generated by mining activities and promote responsible economic and social progress.

Keywords: Social responsibility, mining project, satisfaction, residents.

Abstract: The objective of this research was to conduct an analysis to determine whether financial management influences the credit risk of microfinance companies CMAC listed on the Lima Stock Exchange between 2016 and 2020, applying a simple linear regression model. To this end, a methodology was used that was guided by the following criteria: it was applied, non-experimental, with a quantitative approach of non-experimental design at the causal and longitudinal correlational level. The set of financial statements was evaluated to find a predictive model. The population and sample of the study included all municipal banks in the microfinance sector listed on the Lima Stock Exchange. The result shows an acceptable goodness of fit. The standard error of the estimate is low, indicating an optimal fit. The model is viable, as there is a significant relationship between the variables. The regression model obtained is: credit risk = 80.49 - 0.11 financial management. Based on the results, it can be concluded that the variables analyzed show that the regression model works because there is a causal relationship between the variables. Therefore, the greater the growth in financial management, the greater the effectiveness in credit risk management.

This study analyzes the factors that determine the price per pound of coffee in the Cup of Excellence® auctions across five Central American countries during the period 2016– 2024. A hedonic pricing model was applied to estimate the effect of variables such as variety, quality score, processing method, producing country, and year of participation on the final price. The estimated model explained 66.8% of the variability in the logarithmic price per pound. The results indicate that a higher cupping score significantly increases the price per pound by 35.80%. Geisha coffee showed the smallest decrease of 0.90% in price compared to the reference variety (Bourbon). Regarding processing methods, natural processing showed the smallest price reduction of 4.40%, while washed and honey processing reflected larger decreases. Guatemala stood out with a 41.20% price increase compared to El Salvador, while Nicaragua showed a reduction of 10.60%. The study also found an upward trend in prices over time, with an average annual increase of 1.11%. It is concluded that the perceived quality of coffee, measured by attributes such as cupping score and country of origin, significantly influences price formation in the specialty coffee market and contributes to narrowing an academic gap on the subject.

Resumen– El objetivo de la investigación es analizar cómo las estaciones de servicio enfrentan pérdidas conocidas como merma, ocasionadas principalmente por factores como la evaporación del combustible, deficiencias en el transporte y errores en la medición. Estas pérdidas afectan la rentabilidad del negocio, se utilizó un enfoque cuantitativo, no experimental y de corte transversal, con base en el análisis de los estados financieros de la estación, del periodo 2020–2024. Los resultados revelan una disminución de la merma por evaporación desde 1.87% en 2020 hasta 0.71% en 2024, destacando un caso atípico de -0.17% en 2023. La rotación de inventarios mejoró de 47.23 a 101.68 veces, y el tiempo promedio de rotación pasó de 7.62 a 3.54 días. En términos de rentabilidad, se observó un incremento del ROA de 1.1% (2020) a 4.3% (2024) y del ROE de 1.2% a 5.3%. El margen de utilidad neta creció de 1.04% a 1.13% y el margen de utilidad bruta, tras una caída en 2021 (19%), se recuperó de 31% (2020) a 24% (2024). Se concluyó que la aplicación de tecnologías de medición, control logístico y cumplimiento normativo tributario mejorando la rentabilidad.