Avilés-Castillo, Fátima
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Avilés-Castillo, Fátima
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Ambato
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faviles@indoamerica.edu.ec
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Scopus Author ID
57845057800

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Ethical and Social Challenges in Virtual Reality: A Bibliometric Analysis

2024 , Buele, Jorge , Homero J. Velastegui , Avilés-Castillo, Fátima , Hugo Paz-León , Guillermo Palacios-Navarro

Virtual reality has revolutionized human interaction with digital environments, creating immersive simulations that have significantly impacted sectors such as education, urban planning, and mental health. Along with its transformative potential, VR presents ethical and societal challenges. This study analyzes trends in research addressing these challenges, focusing on articles published through 2023 in the Web of Science database. Visual and statistical analyses reveal a sustained increase in publications since 2013. Patterns in authorship and collaboration networks emphasize the global and interdisciplinary nature of VR research. Dominant themes include the metaverse and therapeutic applications, as well as concerns about data privacy, virtual identity representation, and psychological effects such as cybersickness. Research highlights temporary symptoms such as nausea and eye strain associated with immersive systems, demonstrating the need for ergonomic design and age-specific usage guidelines. Furthermore, active video games using VR have shown promise in promoting physical activity, offering innovative solutions to combat sedentary lifestyles. Looking ahead, efforts must prioritize equitable access, user safety, and robust ethical frameworks to ensure the responsible and sustainable integration of VR technologies.

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Robotics in higher education and its impact on digital learning

2025 , Zamora Lascano, Patrick , Lozada, Alan , Buele, Jorge , Avilés-Castillo, Fátima

In recent years, robotics has transformed various industrial processes but has also influenced teaching methodologies. Although there are literature reviews on its application in professional training, many are outdated or lack a current focus on its impact in higher education. Addressing this gap, the present mini review examines the impact, challenges, and opportunities of this technology in the university setting. To this end, a search was conducted in the PubMed, Scopus, IEEE Xplore, APA PsycNet, and Web of Science databases, selecting 11 studies that addressed diverse applications of robotics, including educational robotics, robotic telepresence, human-robot interaction, and artificial intelligence applications. Their effects on teaching, the factors influencing their adoption, and the strategies used to optimize their implementation were analyzed. The findings show that educational robotics enhances student motivation and engagement, with prediction models reaching an accuracy of 98.78% in assessing academic engagement. Additionally, robotic telepresence emerges as an effective alternative for hybrid education, and social robots and AI-based tutors demonstrated their potential to personalize learning. However, methodological and structural challenges persist, such as the need to develop more accurate evaluation metrics and ensure accessibility and educational equity. Future research should focus on improving these aspects, enabling more efficient integration to enhance teaching processes. This study was registered in the Open Science Framework under the code: 10.17605/OSF.IO/KHDTU.

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Power Flow Optimization in Electrical Networks using Gekko

2025 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima , Riba-Romeva, Carles

Power flow optimization in the electrical grid is critical to improve the stability and performance of power systems. The main challenge lies in finding an optimal distribution of power generation that meets the constraints imposed by the grid, such as voltage limits and power system stability conditions. The objective of this research was to evaluate the performance of Gekko in power flow optimization in electrical grids. To do so, a comparison was made with SciPy, a widely used benchmark framework in numerical optimization in order to assess their relative efficiency in problems with complex constraints. The comparison is based on metrics such as solution accuracy, convergence speed, and number of objective function evaluations. The results showed that both methods achieved the same objective value: SciPy (19.7) and Gekko (19.7). However, SciPy was slightly faster (0.01496 seconds vs. 0.0191 seconds), but required 60 objective function evaluations. In contrast, Gekko demonstrated greater computational efficiency, reducing the number of evaluations required for convergence. While SciPy is more efficient on small problems with explicit constraints, Gekko offers greater flexibility on problems with more complex constraints, making it more suitable for larger power systems.

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Towards Smart Agriculture: An Overview of Big Data in the Agricultural Industry

2025 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima

Agriculture is currently undergoing progressive diversification and expansion, as demonstrated by the wide range of research topics and methodologies being employed. The growing need for technology to enhance agricultural processes is increasingly evident and prominently highlighted in recent studies. To assess the influence of Big Data on agriculture and its potential to advance Smart Agriculture, this bibliometric study was conducted. The research consolidates bibliometric data from the Scopus database, using the Bibliometrix package to identify trends in this field. The findings show a growing annual publication rate, indicating increasing interest in the integration of data analysis methodologies within agriculture. The collaborative nature of the research, combined with a high citation rate per document and diversity of key terms, underscores the importance of this field and its potential contribution to achieving Smart Agriculture. The convergence of Big Data, the Internet of Things, and agriculture is particularly noteworthy, as these technologies are improving decision-making and efficiency in the agricultural sector. Despite certain limitations, this study highlights the transformative potential of these advancements and suggests areas for future research, thus laying the groundwork for a more sustainable, productive, and intelligent agricultural future.

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Evolution, Collaborations, and Impacts of Big Data Research in Ecuador: Bibliometric Analysis

2024 , Avilés-Castillo, Fátima , Ayala-Chauvin, Manuel Ignacio , Buele J.

Big Data has been gaining significant attention globally due to its potential to drive innovation, guide decision-making, and stimulate economic growth. As part of this global trend, Ecuador has also witnessed a surge in Big Data-related research over the past decade. This study comprehensively analyzes Big Data research evolution, collaborations, and impacts in Ecuador from 2012 to 2023. By examining the patterns of publication, researcher demographics, primary languages, significant publishers, most cited research papers, patterns of author collaboration, and prevalent keywords, we strive to construct a detailed portrayal of the Big Data research landscape in the country. Our investigation reveals a noticeable increase in Big Data research activity post-2015, particularly within major cities like Quito and Guayaquil. Notably, the study also underscores the predominance of English in research publications, with leading publishers such as IEEE and Springer playing significant roles. The diverse themes of the most cited articles illustrate the wide-ranging applications of Big Data research within the country. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Analysis of Natural Lighting in Traditional Homes: Pilahuin Case Study

2024 , Bustán-Gaona, Darío , Yomara Jiménez-Sanchez , Avilés-Castillo, Fátima

Traditional homes in the Ecuadorian Andean region showcase their culture and tradition in their design. However, they may not be effectively harnessing natural light, making it necessary to analyze this condition. Natural light is essential for creating comfortable and habitable environments in homes. This research aims to understand the influence of natural lighting in traditional homes. On-site measurements and digital simulations were conducted to evaluate the luminous flux in specific areas of these homes. Parameters unique to this type of construction, such as the materials used, orientation, date, and time of day, were considered to ensure the accuracy of the simulations. Using Velux software, it was possible to simulate the sun's trajectory throughout the day and year to comprehend and enhance the luminous performance of these spaces. Measurements and simulations revealed that the levels of illumination in traditional Andean homes fall below the standards established in the Ecuadorian Construction Regulations. This is partly due to the design of small windows on the main facade and the materials used in floors and walls. This demonstrates that integrating natural light into architectural design is crucial for improving the quality of life for occupants and promoting sustainability.

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Performance and Real-World Variability of Predictive Maintenance Models for Vehicle Fleets

2024 , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , Ayala-Chauvin, Manuel Ignacio , Elena Blanco-Romero

This study presents a comprehensive evaluation of predictive maintenance models for vehicle fleets, detailing a sequence of systematic steps to ensure model performance and address real-world variability. The process begins with database creation and data preprocessing, where relevant maintenance records are filtered, and datetime columns are converted to facilitate time-based calculations. Grouping and aggregation techniques are then applied to count occurrences of specific maintenance activities and identify common failure types. For model training, we define a neural network architecture comprising dense and dropout layers to mitigate overfitting, compile the model with suitable loss functions and optimizers, and train it using the prepared data. The trained model, along with the scaler and encoder, is saved for future use. To augment the dataset, synthetic data is generated using the Faker library and random distributions, with added noise to mimic real-world variability. Preprocessing steps are reapplied to this synthetic data to ensure consistency. By implementing this neural network, we achieved a sensitivity of 0.93 and an ROC of 0.71. Following these detailed steps, we develop a robust predictive maintenance model that effectively identifies failures and non-failures, ultimately enhancing the reliability and efficiency of vehicle fleet management.

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Adaptive Learning Platforms and Their Influence on Higher Education: A Scoping Review

2025 , Núñez Hernández, Corina , Avilés-Castillo, Fátima , Buele, Jorge

Over the past decade, adaptive learning platforms have transformed higher education by adjusting content and activities based on students’ abilities and progress. This approach personalizes learning and optimizes academic performance, particularly in complex disciplines such as engineering and science. However, challenges remain related to their technological implementation and acceptance by both students and educators. This scoping review analyzes the impact of adaptive learning platforms in higher education, focusing on their influence on academic performance, student motivation, practical implementation challenges, and future implications for research. An exploratory literature review was conducted using the PRISMA-ScR method, identifying recent studies published between 2019 and 2024 in databases such as ERIC, Scopus, IEEE Xplore, Web of Science, and APA PsycNet. Quantitative and qualitative research studies were selected, evaluating the impact of these platforms in university settings and the outcomes they produce. The results indicate that adaptive learning platforms significantly enhance academic performance by providing personalized experiences and immediate feedback. They also increase student motivation and engagement, particularly in online learning environments. However, challenges were identified, including the need for adequate technological infrastructure and specialized training for educators. Adaptive learning platforms hold transformative potential in higher education but require overcoming technological and pedagogical barriers. Further qualitative research is recommended to better understand user experiences and maximize their effectiveness across diverse educational contexts.

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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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Optimizing Natural Language Processing: A Comparative Analysis of GPT-3.5, GPT-4, and GPT-4o

2024 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima

In the last decade, the advancement of artificial intelligence has transformed multiple sectors, with natural language processing standing out as one of the most dynamic and promising areas. This study focused on comparing the GPT-3.5, GPT-4 and GPT-4o language models, evaluating their efficiency and performance in Natural Language Processing tasks such as text generation, machine translation and sentiment analysis. Using a controlled experimental design, the response speed and quality of the outputs generated by each model were measured. The results showed that GPT-4o significantly outperforms GPT-4 in terms of speed, completing tasks 25% faster in text generation and 20% faster in translation. In sentiment analysis, GPT-4o was 30% faster than GPT-4. Additionally, analysis of response quality, assessed using human reviews, showed that while GPT-3.5 delivers fast and consistent responses, GPT-4 and GPT-4o produce higher quality and more de-tailed content. The findings suggest that GPT-4o is ideal for applications that require speed and consistency, while GPT-4, although slower, might be preferred in contexts where text accuracy and quality are important. This study highlights the need to balance efficiency and quality in the selection of language models and suggests implementing additional automatic evaluations in future research to complement the current findings.

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