Conference Agenda

The main objective of this study is the development, specification, and subsequent simulation of the algorithm for a Computerized Adaptive Test (CAT) based on an Item Bank (IB) that measures numerical reasoning (NR), verbal reasoning (VR), and spatial reasoning (SR). The IB consists of 247 questions, of which 82 assess NR, 97 assess VR, and 68 assess SR. These items were calibrated in a previous study using the Rasch model. In this study, the difficulty parameters of the items were equated, and the final IB for each type of reasoning was established. Subsequently, specification rules were developed for the test algorithm, and finally, the CAT algorithm was simulated under different stopping rules based on the standard error of estimation. We present a preliminary analysis of the algorithm's performance by simulating 1,000 response patterns. Overall, this study allows us to conclude that this IB enables the precise estimation of an ability level of 0, using a stopping criterion of a standard error of estimation ≤ 0.5. Additionally, concerning this error and in comparison, to paper-and-pencil administration, the test length could be reduced by approximately 26 to 28%. While further studies in both real and simulated conditions are needed, the present study provides evidence of the benefits of incorporating an innovative technological system in Argentina for assessing different cognitive abilities in educational settings.

Keywords-- Computerized Adaptive Testing, Cognitive Abilities, Simulation.

Musculoskeletal disorders (MSDs) represent a critical problem in underground mining due to biomechanical demands and adverse environmental conditions. Traditional observational methods, such as RULA, are subjective and do not allow for continuous monitoring. This article proposes a real-time machine vision framework for automated ergonomic assessment in harsh mining environments. The methodology comprises: (i) a video acquisition protocol adapted to dusty conditions, poor lighting, and PPE occlusions; (ii) frame preprocessing and filtering using MediaPipe Pose; (iii) construction of a dataset labeled with RULA risk levels (Low, Medium, High) based on joint angles; and (iv) a custom convolutional neural network (CNN) for postural classification. The model was trained and validated with data from real mining operations, employing cross-validation and metrics for accuracy, completeness, and F1 score. The results demonstrate the system's viability in providing objective, continuous, and real-time assessments, overcoming the limitations of traditional methods and offering a scalable tool for the proactive prevention of musculoskeletal disorders in underground mining

The COVID-19 pandemic constituted an exogenous shock of systemic magnitude that destabilized the bases of human capital accumulation in Latin America. This research addresses the urgent need to quantify educational erosion at the level of Secondary Education (Grades 10 to 12) in rural areas of Honduras, territories characterized by a structural digital divide. An administrative database was processed using a longitudinal panel design covering the period 2015-2023, an administrative database of the Ministry of Education (SEDUC) was processed with a non-probabilistic dynamic sample of more than 280,000 accumulated records. The methodology transcends descriptive statistics to implement machine learning algorithms. The findings are conclusive: while the multivariate linear regression showed severe insufficiencies in modeling the volatility of the crisis (R2 ≈ 0.60), the Decision Tree model with Gradient Boosting achieved exceptional predictive accuracy on final enrollment (R2 = 0.9825, MSE = 0.37). Additionally, the Chi-Cuadrado independence tests confirm that gender is a determining variable in post-pandemic dropout (p < 0.001), revealing that the economic crisis disproportionately affected female students.

This article presents the design and implementation of a escape room called “Arutam: The Call of the Spirit,” which is a virtual reality game developed with Unreal Engine 5 for implementation on the Meta Quest 3 platform. The main objective is to contribute to the preservation and dissemination of the Shuar people's worldview through an immersive experience based on symbolic narrative and interactive mechanics. The development followed an iterative approach, integrating narrative design, human-computer interaction, and programming using Blueprints and C++. Qualitative results suggest that virtual reality promotes immersion and cultural understanding, positioning the video game as a viable technological tool for the preservation of intangible cultural heritage.

Machine learning models are key tools in coastal environmental risk management, as they allow for the identification of critical areas, optimize waste management, and support the development of more effective and sustainable conservation policies globally. This research aimed to compare machine learning models for predicting the environmental risk associated with coastal waste globally, with the goal of identifying the most suitable model and guiding the formulation of coastal conservation policies. The research used a database of 165 countries with varying levels of environmental risk associated with coastal waste. The data were divided into a training sample (80%) and a validation sample (20%). The performance of five Machine Learning models —Random Forest, Gradient Boosting, XGBoost, LightGBM and CatBoost— was evaluated in predicting the probability of environmental risk associated with coastal waste at a global level, with the Random Forest model showing the best performance, with an accuracy of 0.5455, recall of 0.8000, F1-score of 0.6486, area under the ROC curve of 0.6852 and Gini index of 0.3704, demonstrating the greatest capacity for discrimination and predictive accuracy.

Abstract– The healthcare system in Peru has undergone several changes over the years. Both hospitals and clinics constantly face various challenges regarding their internal processes and meeting the growing demand for hospital care. Difficulties are evident in all areas of a healthcare center, from outpatient clinics to the emergency room. For example, annual surveys conducted over the last four years in the Obstetrics and Gynecology outpatient clinic of one of the country's leading hospitals show that the area does not achieve a level of total satisfaction, largely due to long queues, delays, and inefficient processes, especially on peak days. This, combined with factors such as poor signage, inadequate attention from non-medical staff, and redundant activities, increases waiting times and causes discomfort for pregnant women, who require specialized and timely care. All of this negatively impacts the patient's experience and the efficiency of the service. In response to this problem, the implementation of Lean tools such as Value Stream Mapping (VSM), 5S, and Single-Minute Exchange of Die (SMED) contributes to the improvement of internal processes, which in turn increases patient satisfaction by providing timely and high-quality car