Conference Agenda

This study analyzes how the visual and design elements of the cooperative's virtual store influence users' perception of trust, risk, and acceptance of e-commerce. To this end, a mixed methodology with a non-experimental and cross-sectional approach was used, combining eye-tracking techniques to assess visual attention with a structured questionnaire designed to measure perceived usefulness, ease of use, and risk, supplemented by multiple regression statistical analyses. The results indicate that perceived usefulness and ease of use are the most decisive factors for acceptance of the system, while conscious perceived risk did not show a significant effect. However, cognitive analysis identified critical areas within the interface, especially in the product features section, where visual processing overload was observed, hindering content comprehension and affecting the user experience.

This research analyzed the acceptance of e-commerce on the website of a Costa Rican SME through a mixed-method design that triangulated conscious survey data with unconscious neuromarketing metrics (Eye-Tracking, Face Reader) in a sample of 67 women. Multiple regression confirmed Perceived Utility and Usefulness as the only significant predictors of conscious acceptance. However, biometric analysis revealed a critical discrepancy: the "Refund Policy" captured 61% of the total visual time, demonstrating that risk mitigation is the dominant unconscious barrier. This finding, along with a 65% purchase abandonment rate due to usability failures, concludes that perceived risk—while consciously insignificant—generates a "Neutral" emotional experience and is the primary driver of abandonment at the unconscious behavioral level.

This study explored the relationship between risk perception, perceived usefulness, and ease of use in the context of hotel website usage, with the aim of understanding how these factors influence the acceptance of electronic commerce. A quantitative methodology was employed through the application of a structured questionnaire designed to assess different dimensions of perceived risk, perceived usefulness, and ease of use, as well as their relationship with users’ decision-making. Additionally, a descriptive eye-tracking analysis was conducted to explore patterns of visual attention during interaction with the interface and to understand how certain visual elements may influence the user experience. The questionnaire results show that perceived usefulness has a significant effect on the acceptance of electronic commerce, whereas variables associated with perceived risk and ease of use do not show a statistically significant relationship in the multivariate analysis. Meanwhile, the visual attention analysis suggests that users prioritize visual and informational elements of the interface, providing a complementary understanding of the digital experience without replacing the results obtained from the statistical analysis. These findings indicate that the acceptance of electronic commerce in the hotel context is primarily determined by perceived usefulness, while visual design plays a supporting role in communicating the functional value of the platform.

This work adapts a non-linear model with a ratio-dependent functional response to analyze the trophic interaction between the Peruvian anchoveta (Engraulis ringens) and the Peruvian pelican (Pelecanus thagus) in the Humboldt Current ecosystem. Beyond classifying local stability, the study introduces the estimation of the characteristic recovery time τ≈1/|λ_dom | based on the dominant eigenvalue, allowing for the quantification of the system's dynamic resilience.

Under baseline conditions, a stable node is obtained with approx λ_dom ≈-0.10, implying a recovery time of nearly 10 years. Under a severe El Niño scenario (r = 0.15, c = 2.0), the dominant eigenvalue approaches 0.02, extending the recovery time to approximately 50 years and reducing the Jacobian determinant by 89%. Although formal stability is maintained, a significant degradation of the dynamic margin is evident. This study allows for the detection of structural fragility before an observable collapse occurs, providing an additional quantitative criterion for ecological resilience assessment.

This study evaluates the stability and mobility of pedestrians during tsunami-induced flooding in Puerto Rico. The research was divided into two main phases: the development of an analytical stability model (Phase 1) and an experimental fatigue study (Phase 2). In Phase 1, pedestrians were represented as rectangular prism and cylindrical monoliths to determine the impact of water depth and velocity on their sliding and overturning stability. Drag, buoyancy, friction, and body weight forces were considered. Anthropometric data from Puerto Rican adults and 6-year-old Colombian children were used to evaluate the analytical model. The results demonstrated that instability could occur at low water levels and moderate velocities, that sliding is the predominant cause of instability, and that the footwear-to-ground friction coefficient is the critical parameter. It was also observed that children and women are the most vulnerable populations. In Phase 2, a gait study was conducted using two concentric pools simulating an endless evacuation walk, monitoring heart rate and oxygen consumption with a portable spirometer at different flood levels (ankle, knee, and hip). Contrary to the initial hypothesis, the greatest physiological effort (measured by oxygen consumption) occurred at intermediate levels (ankle and knee). At the hip level, buoyancy reduced muscle load but drastically decreased walking speed. These findings demonstrate that horizontal evacuation routes should be designed with pavements that provide a high coefficient of friction, and that extended evacuation under flooded conditions may be infeasible, highlighting the need to integrate vertical evacuation options in high-risk coastal areas.

Achieving precise navigation within the human circulatory system requires a robust understanding of the complex micro-scale forces at play. This paper presents a comprehensive theoretical and computational framework for the navigation of a composite magnetic micro-robot, composed of a ferromagnetic-polymer matrix, within a 3D vascular environment. The mathematical model integrates magnetic gradient actuation, non-linear hydrodynamic drag, apparent weight, and short-range forces, including electrostatic interactions and Hertzian contact mechanics. Crucially, to bridge the gap between biomechanical simulation and Artificial Intelligence, the physics-informed MATLAB environment was equipped with a high-frequency data extraction architecture. This system dynamically records teleoperated navigation as Markov Decision Process (MDP) tuples (S_t, A_t, R_t), using a dense reward function that heavily penalizes simulated tissue collisions. Results demonstrate not only the feasibility of navigating against dynamic hemodynamic conditions using multi-axial magnetic gradients, but also the successful generation of high-fidelity datasets. This provides a robust, pre-computed foundation for training autonomous medical interventions via Offline Reinforcement Learning, paving the way for safer and more accessible endovascular therapies.

The BioShield-AI project is a cutting-edge platform designed for the critical management of biosecurity and environmental traceability in real time. Its main goal is to offer a robust technological solution that ensures data integrity in biological risk scenarios, integrating advanced concepts of software engineering, cryptography, and sustainability. At the heart of the system lies an immutable Blockchain engine. BioShield-AI uses a chain-of-blocks structure through the SHA-256 algorithm, ensuring that any attempt to alter sensor data is immediately detected. This allows the integrity of the entire chain to be verified instantly, regardless of whether there are hundreds or thousands of records, guaranteeing an immediate response to incidents.The project features a Tactical Dashboard developed in Streamlit, which functions as a command center for researchers and managers. Through this panel, heat maps and scatter plots of simulated pathogens are displayed. The system incorporates a dynamic risk traffic light powered by artificial intelligence logic, which classifies threats into levels (Low, Moderate, High, and Critical), facilitating data-driven decision making. Under the principle of Green Engineering, the software includes a resource monitor that measures CPU, RAM consumption, and the carbon footprint of computational processes. Additionally, the project has been containerized using Docker, ensuring its adaptability and portability.

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Every year more than 75,000 women in Latin America are diagnosed with cervical cancer, and thousands die because the diagnosis arrives too late. The cause is not scientific but structural: healthcare systems in the region lack simple tools that allow identification of women at highest risk from the very first medical consultation. This work develops such an instrument through a data engineering pipeline that included winsorization without record loss, class weighting according to real prevalence, and stratified cross-validation, where each technical decision was justified by the clinical context. Three machine learning algorithms Logistic Regression, RF, and XGBoost competed under identical and reproducible conditions; XGBoost was selected for its greater robustness. That model was transformed into a scorecard consisting of 6 routine clinical questions and a maximum of 15 points, completable in less than two minutes without additional technology. The instrument operates under two adaptable scenarios: Scenario A, with 83% specificity to reduce unnecessary referrals where resources are scarce, and Scenario B, with 73% sensitivity and 53% fewer missed cases for mass screening campaigns. Applied at a regional scale, it could generate more than 31,000 additional diagnoses annually. The pipeline is open and replicable in any Latin American hospital using its own data.

This study presents a comprehensive review of the seismic hazard in the state of Colima, Mexico, using a Probabilistic Seismic Hazard Analysis (PSHA) approach. We propose a new seismogenic regionalisation based on an updated and declustered earthquake catalogue spanning the period from 1787 to 2019. The parameters for both the Truncated Gutenberg-Richter and the Characteristic Earthquake recurrence models are determined. The variation of seismic intensity across the study area was determined through the implementation of Next Generation Attenuation (NGA) models, which can account for geological, stratigraphic, and site-effect conditions. The analysis results are presented as uniform hazard spectra for rock site conditions in the cities of Manzanillo and Guadalajara and are compared directly with the spectra from the Manual de Diseño de Obras Civiles of the Comisión Federal de Electricidad (CFE, 2015), which is applicable as national guidelines for Seismic Design. Additionally, seismic hazard maps are generated, showing the spatial distribution of peak ground acceleration (PGA) and spectral accelerations for key structural periods, corresponding to return periods of 150 and 475 years. The results indicate that while the estimated seismic hazard for low return periods is consistent with that proposed by the CFE (2015), for the higher return period of 475 years, the obtained accelerations exceed the values recommended by these design’s guidelines by up to 20%. This discrepancy suggests that an update of the seismic hazard for the western region of Mexico is pertinent, with potential implications for the seismic safety of critical infrastructure.

Urban flooding in Panama affects thousands of people annually and generates millions in losses, exacerbated by rapid urbanization and climate change. Therefore, the purpose of this project is to promote the transition from traditional drainage systems to Sustainable Urban Drainage Systems (SUDS) through the design and construction of a pilot-scale prototype for flood risk reduction in urban environments. The implementation of this prototype aims to innovate in climate adaptation tools that support multiple Sustainable Development Goals (SDGs) of the 2030 Agenda. The methodology was developed in two stages: analysis of the hydraulic behavior of a traditional drainage model and design and digitization of the prototype. The results include a SUDS prototype represented in a bioretention area with four layers: three soil layers (gravel, silica sand, loam-sand mixture, and mulch) as a filtration medium, and an outer layer of vetiver (Chrysopogon zizanioides) vegetation. Additionally, an experimental rain simulator was assembled as a complementary tool to reproduce controlled rain events, evaluate hydrological performance, and validate the efficiency of the prototype under simulated conditions in future stages of the project.

This study proposes and analyzes Vitalia, a cooperative board game designed as an educational tool to sup-port emotional accompaniment and enhance understanding of treatment processes in pediatric oncology contexts. The project was developed under a Design-Based Research approach, integrating phases of conceptual design, physical prototyping, and exploratory validation through an evolutionary hybrid model. Preliminary validation was conducted in a non-clinical academic setting using a pretest–posttest design applied across four dimensions: treatment comprehension, motivation, emotional state, and perceived family support. Descriptive results revealed consistent improvements across all evaluated dimensions, particularly in conceptual clarity and perception of emotional accompaniment. The proposed model integrates principles of gamification, symbolic narrative, and cooperative dynamics, offering an accessible and culturally adaptable alternative to exclusively digital solutions. Furthermore, the study outlines a projection toward interactive digital development, in which analog validation precedes technological prototyping. Although the findings should be interpreted as preliminary evidence due to the exploratory nature of the study, the proposed hybrid model demonstrates potential as a complementary educational tool within Latin American contexts.

The rapid expansion of the Internet of Things (IoT) requires security solutions that address vulnerabilities while operating within resource-constrained environments. Our solution introduces a hybrid framework that combines Blockchain for decentralized, tamper-resistant communication with Artificial Intelligence (AI) for real-time anomaly detection. A local IoT testbed has been implemented using an RFID sensor, blockchain nodes, and edge AI modules to continuously monitor system behavior. This system has been evaluated for latency, throughput, power consumption, and detection accuracy. Results demonstrate that combining blockchain’s immutability with AI’s adaptability enhances IoT security and reliability in practical deployments.

This paper adapts Duffing’s nonlinear model to evaluate the vibration response of the slabs of Line 1 of the Lima Metro (Peru). The procedure consisted of reformulating the classical oscillator as a nonlinear autonomous system in state space and calibrating its dynamic parameters under operating conditions representative of the infrastructure.
Unlike traditional linear approaches, the formulation incorporates variable stiffness and loss of dissipative capacity, enabling the analysis of stability, parametric sensitivity, and global behavior in the phase plane. From the local stability study, two quantitative indicators of vibration performance were defined: the characteristic dissipation time (τ) and the dynamic margin (MD).
For the calibrated parameters, the system exhibits asymptotic stability with τ = 0.189 s and MD = 0.050, indicating rapid dissipation but with a relatively small dynamic margin. The sensitivity analysis showed that decreasing the damping increases the dissipation time by 32%, evidencing greater persistence of residual vibrations.
The proposed model links physical parameters with measurable metrics, serving as a quantitative pre-diagnostic tool in Structural Health Monitoring (SHM) systems to support railway maintenance decisions.

This project focuses on the design and development of an autonomous vertical takeoff and landing (VTOL) drone for delivering medical supplies to remote and hard-to-reach regions with limited access to healthcare infrastructure. The system utilizes a hybrid fixed-wing quadcopter configuration with tilting rotors, allowing for efficient VTOL operations combined with long-range, energy-efficient fixed-wing flights. After vertical takeoff, the drone transitions to forward flight by rotating its propellers from a vertical to horizontal orientation, then returns to VTOL mode for precision landing at the designated drop zone.

The design builds upon proven industry concepts while introducing a tandem tilting-motor arrangement that improves aerodynamic efficiency by reducing propeller wake interference. Advanced safety features, including obstacle avoidance, intelligent flight planning, and emergency response behaviors, are integrated to ensure reliable operation in dynamic real-world environments. Compared to existing delivery platforms, the system eliminates the need for launch infrastructure while maintaining high payload efficiency and operational flexibility.

Humanitarian response in highly vulnerable territories of the Colombian Caribbean faces structural constraints that limit the effectiveness of logistics operations, particularly in the department of La Guajira. This study provides a systemic characterization of the factors, risks, and actors influencing humanitarian logistics performance and proposes a system dynamics model to explain recurrent operational patterns under uncertainty. A mixed-method approach was applied, combining documentary and territorial analysis based on institutional reports and official statistics with system dynamics tools, including causal loop and stock–flow diagrams. The results reveal that inter-institutional coordination, information traceability, and last-mile governance are key determinants of logistics performance, while territorial dispersion, limited road accessibility, and climate seasonality increase response times and operational costs. A reinforcing feedback loop is identified: higher social vulnerability increases humanitarian demand, which extends response times and further deepens vulnerability. The proposed systemic model identifies leverage points such as minimum shared data standards, prepositioning strategies, seasonal routing, and the use of hubs and micro-hubs. These findings provide a technical basis for prioritizing interventions and for developing digital decision-support tools to improve humanitarian logistics management in highly constrained environments such as La Guajira.