Varela Aldas, José
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Preferred name
Varela Aldas, José
Main Affiliation
Ambato
Email
josevarela@uti.edu.ec
ORCID
Scopus Author ID
57192546163

Research Output
Now showing 1 - 6 of 6
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Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial

2024 , Buele, Jorge , Avilés-Castillo, Fátima , Carolina Del-Valle-Soto , Varela Aldas, José , Guillermo Palacios-Navarro

Abstract Background The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant. Objective To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI. Methods Thirty-four older adults (men, women) with MCI were randomized to the experimental group (n = 17; 75.41 ± 5.76) or control (n = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions). Results Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (p < 0.001), with large effect sizes. There was no statistically significant improvement in any of the groups when evaluating their performance in ADLs (control, p = 0.28; experimental, p = 0.46) as expected. The completion rate in the experimental group was higher (82.35%) compared to the control group (70.59%). Likewise, participants in the experimental group reached a higher level of difficulty in the application and needed less time to complete the task at each level. Conclusions The application of a dual intervention, through motor training prior to a cognitive task based on Immersive VR was shown to be a beneficial non-pharmacological strategy to improve cognitive functions and reduce depression in patients with MCI. Similarly, the control group benefited from such dual intervention with statistically significant improvements. Trial registration ClinicalTrials.gov NCT06313931; https://clinicaltrials.gov/study/NCT06313931.

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An assessment of a ROS class using an educational mobile robot

2024 , Varela Aldas, José , Junta, Christian , Buele, Jorge , Guillermo Palacios-Navarro

The Robot Operating System (ROS) is a middleware that standardizes robot programming, both in simulation and with real equipment. Despite this open-source tool being available for several years, there's still a need to enhance its utilization in robotics education across all educational levels. In this study, a ROS class is assessed among university students using a commercial educational robot. The primary objective is to measure academic emotions in learning and student performance to determine the impact of the class using the open-access tool from a GitHub repository (https://github.com/joseVarelaAldas/ROS-Crowbot). This tool is based on the rosserial package, compatible with the ESP32 board. For class, CrowBot robots connected to the local wireless network via WiFi are used. TThe participants in this study were eight students from an electronics degree program at a higher education institution, who had no prior experience with ROS and received practical training using the educational mobile robot. For data collection on class performance, three parameters are assessed: execution time, functionality, and motivation, and to measure academic emotions, a validated self-report instrument is used. The results show an overall performance of 82.1%, and in the self-report on academic emotions, a high score in enjoyment (95%) and the lowest score in boredom (24.1 %) were obtained. In conclusion, the repository provides an interesting, practical, and accessible tool for an introduction into the world of robotics using ROS.

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Educational Quality in Teaching Magnetism Using Dedicated Mobile Devices

2024 , Junta, Christian , Varela Aldas, José , Washington Collay , David Rivas-Lalaleo , Guillermo Palacios-Navarro

New technologies offer unprecedented opportunities to enhance classroom education, especially in practical subjects like physics, where experiments can be controlled via mobile devices. This facilitates both theoretical and practical learning. This study assesses the impact of using dedicated mobile devices in educational settings compared to traditional teaching methods. The application allows students to view educational information on an M5Stack Core2 device that also includes an electromagnet. We use the Student Evaluation of Educational Quality (SEEQ) to gather feedback and evaluate various aspects of the learning experience. Results indicate significant improvements in learning, enthusiasm, organization, group interaction, individual relationships, and breadth of device usage. Evaluations show that device-assisted classes outperform traditional classes in most categories, highlighting the benefits of integrating technology into education. The findings emphasize the importance of adapting teaching methods to enhance the overall quality of education and student engagement using dedicated mobile devices.

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Influence of Higher Education on IoT Acceptance through Hands-On Learning

2025 , Varela Aldas, José , Christian Junta , Elias Choque , Guillermo Palacios-Navarro

The Internet of Things (IoT) applications are pervasive across various sectors; however, there remains some resistance to its adoption. Education 4.0 promotes the full integration of new technologies, both as tools for learning and instruments for professional development. This work studies the influence of higher education on the willingness towards IoT adoption after hands-on learning experiences. The primary objective is to determine whether a correlation exists between IoT adoption and the education of university students from three distinct professional degrees. The methodology employed involves a practical class where students engage in developing applications for manual data collection. These applications are designed to send data to the Internet, which is then visualized through a web interface. Tailored to each respective degree, three similar applications are developed. For this research, M5 Stack Core2 kits are utilized, along with UIFLOW programming language and the ThingSpeak platform, operating under the MQTT protocol. Following the training, students complete a Technology Acceptance Model (TAM) survey for IoT. The analysis of the influence of higher education on IoT acceptance employs ANOVA to identify differences between group means. The results reveal statistically significant differences in IoT acceptance between students in Industrial and Architecture degrees.

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Optimization of Electric Transformer Operation Through Load Estimation Based on the K-Means Algorithm

2025 , Pedro Torres-Bermeo , Varela Aldas, José , Kevin López-Eugenio , Nancy Velasco , Guillermo Palacios-Navarro

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD.

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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models

2025 , Pedro Torres-Bermeo , Kevin López-Eugenio , Carolina Del-Valle-Soto , Guillermo Palacios-Navarro , Varela Aldas, José

The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.

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