Conference Agenda

The use of artificial intelligence (AI) tools is growing in many areas of human development, particularly in higher education. Among its benefits are personalized learning, adaptation of content and pace to students' individual needs, continuous feedback, improved monitoring of academic progress, and strengthened student autonomy and motivation. However, concerns are also arising among educators and other relevant stakeholders, particularly regarding plagiarism, academic integrity, data privacy, and inequitable access to these technologies. Considering the importance of education for the development of countries, this study reviews the literature on the application of artificial intelligence in higher education in Latin America and the Caribbean, specifically in engineering programs. Analyzing the opportunities and challenges arising from the use of AI is fundamental to guiding the formulation of strategies that promote the responsible, equitable, and contextualized adoption of these technologies, thereby strengthening educational and social development in the region.

Mature heavy oilfields in the Northern Peruvian Jungle have produced oil for over 40 years under waterdrive mechanism, with a wide range of ultimate recovery factor in between 10% to 60%; a reasonable estimation of recovery factor at an early stage of development and/or production is quite critical for sizing and scheduling development plans, as well as CAPEX investments.

This research article introduces a new approach that integrates empirical correlations, analytical methods and machine learning algorithms to estimate oil recovery factor in water-drive sandstone reservoirs at early development and production.

Preliminary studies showed that the most representative reservoir and fluid parameters, such as reservoir size, porosity, permeability, net thickness, residual oil saturation, API gravity, oil viscosity and initial pressure, which are typically measured during the exploration, appraisal and early development stages, can be correlated to expected recovery factors obtained from mature fields with long production history.

Literature empirical correlations will be initially tested with available information of oilfields of Marañón Basin to estimate correlation coefficient. Different regression ML learning algorithms will be compared using existing data to select the best one providing the most precise predictions. A new empirical correlation that significantly outperforms traditional industry equations will be adjusted with ML algorithms weights and biases.

The results of this comprehensive study will contribute to a better understanding of the water drive mechanism in the oilfields of the Northern Peruvian Jungle, as well as a more reliable Recovery Factor and EUR estimations at early development stages.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, underscoring the urgent need for non-invasive and cost-effective screening strategies. This study evaluates the feasibility of using the Cyranose 320 electronic nose to discriminate between CRC patients and healthy controls through volatile organic compound (VOC) analysis of sweat samples. A total of 65 sweat samples (31 CRC, 34 controls) were analyzed. The data processing pipeline included Relative Difference (RD) feature extraction, Quantile Transformer scaling, Orthogonal Signal Correction (OSC), and Principal Component Analysis (PCA), followed by supervised machine learning classification. PCA revealed strong class separability, with the first three principal components explaining 95.72% of the total variance (PC1: 91.28%). Supervised classification using nested cross-validation demonstrated robust performance across seven algorithms. Random Forest achieved the best results, with 95.4% accuracy, 93.5% sensitivity, 97.1% specificity, and an AUC of 0.967. Decision Tree showed comparable performance, while all evaluated models exceeded an AUC of 0.90. Confusion matrix analysis confirmed high true positive rates and minimal false positives, particularly for tree-based ensemble methods. These findings demonstrate that sweat-derived VOC profiling using the Cyranose 320, combined with advanced data preprocessing and multivariate analysis, provides strong discriminative capability for CRC detection. The results support the potential of sweat-based electronic nose systems as a non-invasive, scalable, and patient-friendly screening approach, warranting validation in larger independent cohorts.

The financial sustainability of public pension funds in emerging economies constitutes a critical challenge, exacerbated by macroeconomic volatility and structural asset-liability mismatches. This study evaluates the patrimonial solvency of Honduras' National Teachers' Pension Institute (INPREMA) by applying an integrated Economic-Financial Scenario Generator (ESG) and Asset-Liability Management (ALM) model. The methodology combined stochastic projections of macroeconomic variables with an ALM model to analyze the interaction between the investment portfolio and actuarial obligations under uncertainty. The results reveal a structural gap between the portfolio's expected return and the actuarial technical rate of 14.7%, increasing solvency vulnerability to adverse shocks. It is concluded that adopting an ESG-ALM framework provides a solid technical basis for reorienting investment policy, mitigating reputational risk, and strengthening patrimonial solvency in compliance with the Honduran regulatory framework. The study contributes an original quantitative assessment for public pension funds in contexts of high regulatory constraint and volatility.

Th Literature review is a core component of the scientific research process; however, the rapid growth of academic production has exceeded the capacity of traditional manual approaches. In response to this challenge, computational tools oriented toward automation and reproducibility have gained increasing relevance in bibliometric analysis. In this context, this paper introduces redalycR, an R package designed to automate bibliometric analysis through a reproducible architecture based exclusively on open data from the Redalyc database.
The paper describes the conceptual design and implementation of the package, as well as its integration into analytical workflows in R for the acquisition, cleaning, normalization, and structuring of bibliographic metadata. Through an illustrative application case, it is shown how redalycR enables the programmatic and reproducible generation of descriptive keyword analyses and keyword co-occurrence networks. The results demonstrate that the package facilitates the structural exploration of Spanish-language scientific corpora, complementing existing bibliometric tools and expanding analytical capabilities over open-access literature. Finally, the current limitations of the package are discussed, and a future development agenda aimed at its extension and strengthening is outlined.

countries is manifested in the limited generation of formal employment, forcing university graduates to seek alternative employment opportunities in highly competitive and globalized contexts. In this scenario, entrepreneurship emerges as a strategic option, especially when strengthened through higher education with interdisciplinary approaches that integrate creativity, innovation, and business management. However, gaps remain in our understanding of how this training influences students' entrepreneurial intentions.

The objective of this study is to analyze entrepreneurial intention among students in the Gastronomy Bachelor's program at the Central American Technological University (Honduras) and the Business Management Bachelor's program at the University of Guanajuato (Mexico), highlighting the role of interdisciplinarity as a link between creative and entrepreneurial training. The research was conducted using a quantitative approach, with a cross-sectional and comparative design. Data collection was carried out using a structured questionnaire, adapted from the Entrepreneurial Intention Questionnaire (EIQ) by Liñán and Chen, and adjusted to the sociocultural and educational characteristics of both countries.

The instrument assessed sociodemographic variables, attitudes toward entrepreneurship, subjective norms, perceived behavioral control, entrepreneurial intention, and perceptions of the interdisciplinary approach, using Likert-type scales. The results reveal distinct patterns between the two populations studied and suggest that the integration of interdisciplinary approaches is positively associated with higher levels of entrepreneurial intention, reaffirming the role of higher education in training entrepreneurs with economic and social impact.