Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

The research, entitled "Use of Artificial Intelligence in Social Research Projects," aims to create a strategy for the practical implementation of Artificial Intelligence in social research projects. A non-experimental design with a qualitative approach and proactive scope was used, taking a sample of 42% of the faculty members of the Faculty of Law, Social Sciences, and Education at the Technical University of Babahoyo, which has 106 faculty researchers. The study was conducted using a virtual survey, using a four-question questionnaire that allows for the analysis of the sample's varied characteristics. The questionnaire consists of ten multiple-choice questions to measure the variables, in addition to questions that provide a general characterization of the sample. The study concluded that there is a lack of awareness and limited use of digital tools in social research, especially in community engagement processes, as well as the factors that hinder their use and the need for training.

This study analyzes the impact of Ecuadorian educational policies on the pedagogical practice of Physical Education teachers, with the objective of establishing the nature and magnitude of the influence that governmental guidelines exert on pedagogical implementation in the classroom. The research identifies educational policies with direct influence on teaching practice through the analysis of current regulations, evaluates the level of knowledge that teachers possess regarding these policies, and proposes a methodological strategy to expand their understanding of the guidelines that affect their pedagogical work. The study is justified by the need to improve the quality of Physical Education teaching, ensuring that policies translate into informed and effective practices that contribute to the integral physical development of children, youth, and adolescents.

This study investigates the effect of fiber content and coupling agent concentration on the mechanical and thermal behavior of a polypropylene-based composite. The materials were fabricated via hot compression molding using pellets composed of polypropylene (PP) modified with maleic anhydride-grafted polypropylene (MAPP) and reinforced with short bamboo fibers derived from Guadua angustifolia Kunth (GAK). The fibers were previously extracted through the steam explosion technique. The research was carried out in two stages: first, the composite materials were produced; second, their mechanical and thermal properties were comprehensively characterized. The incorporation of GAK fibers and MAPP significantly altered the mechanical performance of the PP matrix, yielding stiffer composites with improved flexural strength and impact resistance. The optimal formulation, containing 50 wt% GAK fibers and 4 wt% MAPP, resulted in a 322% increase in elastic modulus (2.9 GPa) compared to neat polypropylene (0.7 GPa). Both variables, fiber content and compatibilizer concentration, were found to exert a substantial influence on the mechanical behavior of the resulting composites.

Electric vehicle (EV) charging management can be implemented through centralized or decentralized strategies. Strategic coordination between these approaches enhances system efficiency and balances energy loads, thereby supporting the widespread adoption of EVs and fostering a sustainable, emissions-free society. In this study, distribution network operators (DNOs), acting as centralized charging managers, are responsible for mitigating the lack of coordination among electric vehicle aggregators (EVAs), which represent decentralized managers. The primary objective of the centralized management in this research is to constrain each decentralized optimization model, characterized using Monte Carlo simulations. Three EV adoption scenarios--comprising 2,000, 2,500, and 3,750 vehicles--are evaluated by comparing decentralized charging management with an unregulated charging baseline in the IEEE 14-bus power system. Improvements are required only in the highest adoption scenario, where the proposed centralized coordination model is applied. The study models energy trading constraints for each EVA, assigning one aggregator per load-bearing bus in the system. Transmission-level results are analyzed and then synthesized for application in the IEEE 13-bus distribution power system. Findings demonstrate that coordinated centralized and decentralized charging management significantly improves operational conditions in both transmission and distribution networks without necessitating changes to travel behavior.

This article focuses on the implementation of a system to send and receive data using an Arduino MEGA and an Ethernet Shield, with an emphasis on communication with a microservices-based API. The relevance of this study lies in the growing demand for accessible technological solutions for automation and education, allowing the integration of low-cost systems with modern data management tools. The objective is to provide a detailed description of the components and configurations required to establish this communication, offering practical examples of the most common HTTP services: GET, POST, PUT, and DELETE. For the creation of the microservices, a MAMP server is used, and PHP is programmed using the Slim Framework. A comprehensive explanation is provided on how to implement each of these methods in Arduino projects, accompanied by code examples and practical demonstrations that facilitate understanding and application in various contexts. The results obtained demonstrate the viability of this technology in educational and automation projects, highlighting the effectiveness of combining Arduino with microservices for real-time data management. In conclusion, the combination of Arduino and microservices presents itself as an effective and adaptable solution for implementing technological projects in educational and automation contexts, offering a robust and efficient alternative for data handling.

The early detection of diabetic retinopathy remains a critical challenge in medical diagnostics, with deep learning techniques in artificial intelligence offering promising solutions for identifying pathological patterns in retinal images. This study evaluates and compares the performance of three convolutional neural network (CNN) architectures ResNet-18, ResNet-50, and a custom, non-pretrained CNN using a dataset of retinal images classified into five categories. The findings reveal significant differences in the models' ability to learn and generalize. The non-pretrained CNN consistently outperformed the pretrained ResNet-18 and ResNet-50 models, achieving an accuracy of 91% and demonstrating notable classification stability. In contrast, ResNet-18 suffered severe performance degradation, with accuracy dropping from 70% to 26%, while ResNet-50 required extensive tuning to improve its outcomes. The non-pretrained CNN excelled in handling class imbalances and capturing complex diagnostic patterns, emphasizing the potential of tailored architectures for medical imaging tasks. These results underscore the importance of designing domain-specific architectures, demonstrating that model complexity does not necessarily guarantee better performance. Particularly in scenarios with limited datasets, well-designed custom models can surpass pre-trained architectures in diagnostic imaging applications.

This study investigates the thermal behavior of three lithium-ion battery configurations under thermal runaway conditions, focusing on cooling systems based on air, water, and phase change materials (PCM). The analysis was conducted using sixteen cylindrical 18650 cells, each with a capacity of 2.15 Ah. The battery arrangements include Geometry 1, characterized by an irregular rhomboid shape, and Geometry 2, which adopts an irregular octagonal shape. Numerical simulations were carried out using Computational Fluid Dynamics (CFD) tools in ANSYS Fluent, employing a thermal abuse model rooted in a multidimensional, multiscale approach, and incorporating the empirical Newman-Tiedemann-Gauthier-Kim (NTGK) model. Transient simulations were performed under forced and natural convection scenarios to capture dynamic thermal behavior. The findings reveal that natural air cooling fails to prevent thermal runaway under the studied conditions. In contrast, water and PCM-based cooling systems effectively mitigate thermal runaway risks. Furthermore, forced convection with air and water significantly enhances thermal management and successfully prevents thermal runaway.

This study presents the design and implementation of an interactive, user-friendly electronic meter for energy consumption measurement. The proposed system serves as an educational tool for teaching electrical installations, offering a practical and hands-on learning experience. The primary objective is to develop an interactive meter tailored for residential use, capable of providing real-time feedback on energy consumption. Its deployment in educational institutions enhances the comprehension of technical concepts, while also proving beneficial in community outreach workshops focused on residential electrical systems. The system consists of a battery-powered setup featuring an ESP32 module for voltage and current data acquisition, an SPI-connected LCD screen for local data visualization, and a WiFi module for real-time data transmission to a cloud-based database. Designed to be reproducible, cost-effective, and open source, the system represents an accessible and versatile solution for energy monitoring applications. Validation tests were conducted over five months in both laboratory and residential environments. The results demonstrated high measurement accuracy, with error margins below 5% for voltage, current, energy consumption, and estimated costs. These findings confirm that the developed interactive energy meter is a reliable and effective tool for monitoring residential energy usage while fostering educational and community-based learning experiences.

Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI). Unlike traditional augmentation techniques, deep generative models can synthesize novel and diverse MRI images, enriching the training datasets. This study evaluates several generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Deep Convolutional GANs (DCGANs), Auxiliary Classifier GANs (ACGANs), InfoGANs, and Diffusion Models, using the Structural Similarity Index Measure (SSIM) and Cross-Correlation Coefficient (CC) to assess image quality and structural fidelity. While VAEs demonstrated limitations such as noticeable noise and blurriness, GAN-based models, particularly DCGANs and ACGANs, generated higher-quality and anatomically accurate images. Diffusion models achieved the highest image fidelity among all the methods evaluated, but required longer generation times. These findings underscore the trade-off between image quality and computational efficiency and highlight the potential of deep generative models to augment MRI datasets, thereby improving deep learning applications for HCM diagnosis.

This paper presents an architecture based on the MANET (Mobile Ad Hoc Network) paradigm as an emergency communication system between users of electric bicycles. The solution consists of 4 mobile nodes representing the users and a main fixed node, which emulates a bicycle docking station. This architecture allows multi-hop communication between the nodes, using the proactive routing protocols OLSR (Optimized Link State Routing) and BATMAN (Better Approach to Mobile Ad Hoc Networking). The study was divided into 3 main stages. First, an analysis of the wireless medium was performed to determine the maximum transmission distance and the maximum bitrate between 2 nodes. Subsequently, the throughput behavior was characterized in a multihop configuration consisting of 4 nodes in order to establish the network capacity in terms of bandwidth. Finally, a web application was implemented for the transmission of audio and text traffic. Regarding the evaluation of the proposal, two scenarios were designed to emulate the integration of a new cyclist to the network and the communication between two users in motion. The results reveal that OLSR provides a better system operation, with a throughput of 2.54 Mbps at 3 hops and a PRR (Packet Reception Rate) higher than 96%. In addition, it guarantees a delay within the ITU-T (International Telecommunication Union-Telecommunication) G.114 recommendation for bidirectional communication.