Ayala-Chauvin, Manuel Ignacio
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Ayala-Chauvin, Manuel Ignacio
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Ambato
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mayala@uti.edu.ec
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57215428914

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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms

2022 , Espinosa Pinos, Carlos Alberto , Ayala-Chauvin, Manuel Ignacio , Buele, Jorge

Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Data Analysis for Performance Improvement of University Students Using IoT

2024 , Ayala-Chauvin, Manuel Ignacio , Lara-Álvarez P. , Castro R.

Using Internet of Things (IoT) devices and data analysis techniques can potentially transform how universities approach improving student achievement. In this sense, the project is based on implementing a remotely operated pneumatic bank applying IoT for university education. With this technology, it is possible to obtain information about the factors that impact student achievement and design targeted interventions to help students improve their performance. The control system with low-cost technology was developed with Raspberry Pi, AnyDesk, and Canvas LMS for the remote connection. The experiment was carried out with two groups of 7 people, and it was identified that there are correlations of 0.87 and 0.62 between the performance of the students and the time they dedicate to studying and the hours they spend on the platform; this suggests a positive correlation between these variables. Therefore, as students spend more time studying and spending more hours on the platform, they are more likely to achieve better academic results. On the other hand, the study time of the group of students who used the bank remotely increased by 32% compared to those who used the bank in person; therefore, we can infer that with the implementation of the IoT, the use of the system is encouraged. Finally, based on the insights gained from the analysis, targeted interventions can be designed to help students improve their academic performance. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Anthropization and growth of the electricity grid as variables for the analysis of urban infrastructure

2020 , Ayala-Chauvin, Manuel Ignacio , Huaraca D. , Varela Aldas, José , Ordóñez A. , Riba G.

City growth goes together with the development of infrastructure, and the power network is one of the most relevant towards economic development. The study of urban infrastructure through the analysis of anthropization coupled with power network growth can produce a tool that supports sustainable infrastructure planning, both economic and environmental. The case study focuses on Ambato, Ecuador, in the period from 1950 to 2019, and assesses quantitatively the changes in the city layout and the evolution of its power network. The data are adjusted to a sigmoid-type objective function through a non-linear least squares problem, that is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Anthropization data show how the urban area grew during the study period: 37% (1950-1960), 53% (1960-1970), 80% (1970-1980), 35% (1980-1990), 39% (1990-2000), 38% (2000-2010), and 11% (2010-2019), mostly at the expense of agricultural land. The forecast for new power network users by 2050 yields a result of 203,630 total users with a population density of 4850 people/km2. The conclusion is that this type of analysis can help city planners and decision makers further understand city and infrastructure growth dynamics and produce policies that bolster sustainable city growth. © 2020 by the authors.

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Lifecycle assessment of electricity generation transition in ecuador

2021 , Ayala-Chauvin, Manuel Ignacio , Samaniego-Ojeda C. , Riba G. , Maldonado-Correa J.

Ecuador’s energy mix has greatly reduced its dependency on fossil fuels the last 15 years, down to a marginal role (5%) in electricity generation in 2017. The development plan for the Ecuadorian power network aims to keep adding hydropower to meet the increasing demand. A prospective lifecycle assessment (LCA) of the future power network (2012–2050) can determine the feasibility of the development plan and its environmental sustainability in the long run. For a quantitative analysis of the energy transition over the entire lifecycle, the simulation software® Global Emission Model of Integrated System (GEMIS) is used. The results show that the current development path of the Ecuadorian energy system reduces the emissions of CO2 per kWh generated by 65% due to the large share of renewable energies, mainly hydropower, which costs 1% of Gross Domestic Product. The obtained LCA footprints are similar to the literature benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

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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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Big Data as a Tool for Analyzing Academic Performance in Education

2024 , Ayala-Chauvin, Manuel Ignacio , Chucuri-Real B. , ESCUDERO VILLA, PEDRO FERNANDO , Buele, Jorge

Educational processes are constantly evolving and need upgrading according to the needs of the students. Every day an immense amount of data is generated that could be used to understand children’s behavior. This research proposes using three machine learning algorithms to evaluate academic performance. After debugging and organizing the information, the respective analysis is carried out. Data from eight academic cycles (2014–2021) of an elementary school are used to train the models. The algorithms used were Random Trees, Logistic Regression, and Support Vector Machines, with an accuracy of 93.48%, 96.86%, and 97.1%, respectively. This last algorithm was used to predict the grades of a new group of students, highlighting that most students will have acceptable grades and none with a grade lower than 7/10. Thus, it can be corroborated that the daily stored data of an elementary school is sufficient to predict the academic performance of its students using computational algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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System for Monitoring and Controlling Industrial Lighting with Amazon Alexa

2021 , Ayala-Chauvin, Manuel Ignacio , Saá F. , Villarroel-Córdova F. , de la Fuente-Morato A.

Intelligent devices, used along with sensors, are becoming more commonplace in industrial contexts. One such device, Amazon Echo (which runs Amazon Alexa), can be used to interact with other industrial systems via voice commands. Taking advantage of this, a skill to control the illumination system of a company has been developed, while also being able to measure power consumption in real time. Besides Echo, the system employs easily obtainable electronic components such as NodeMCU4 and Sonoff Pow, while running open-source software like IDE Arduino and Amazon Developer. Besides the voice commands, the system can be controlled via a cell phone touch app and a manual system. Tests show the skill successfully controls the illumination system and provides accurate power consumption data in real time. This skill can also be replicated in other industrial contexts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Vortex Optimization of a Low-Head Gravity Hydroelectric Power Plant

2022 , Ayala-Chauvin, Manuel Ignacio , Rojas-Asuero H. , Riba-Sanmartí G. , Ramón-Campoverde J.

Gravitational Water Vortex Power Plant (GWVPP) is a Small-Scale Hydropower System which converts energy in a moving fluid to rotational energy. The main advantage of this technology is the low head hydraulic requirements. The aim of this work is to optimize the hydraulic geometry of the vortex, to achieve this, two prototypes (A and B) were designed and built to validate the proposed design process. The prototype A has a flat-bottom chamber and prototype B has a conical chamber outlet; both induce spiraling fluid streamlines. Prototypes were studied numerically and experimentally. The numerical study was developed in ANSYS CFX R19.0 software and the experimental phase was carried out in the fluid’s laboratory of the Technical University of Loja in Ecuador. The results show that the conical chamber improves strong free-surface vortex formation and increases water velocity in the center of the vortex flow. Finally, the proposed design method was validated and allows to reproduce the hydraulic structures of the gravity water vortex power plant. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Prevention of Failures in the Footwear Production Process by Applying Machine Learning

2022 , Tierra-Arévalo M. , Ayala-Chauvin, Manuel Ignacio , Nacevilla C. , de la Fuente-Morato A.

At present, the handcrafted footwear sector is affected by the high competitiveness due to the increasing automation of companies. In this sense, in order to improve its competitiveness, a system was proposed to predict the failures of a production system and to carry out preventive maintenance actions. Samples were taken from 25 productions and 7 activities were established: cutting, stitching, pre fabrication, final preparation, gluing, assembly and finishing. The company produces batches of 90 pairs per day, with a standard time of 274.53 min and a promised productivity of 1.8. A support vector machine model was developed to predict the possible failures of the process taking as a reference the standard time of each stage. Finally, the results allow predicting the faults to optimise the production process by applying Support Vector Machine (SVM). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Enhancing Sustainability through Accessible Health Platforms: A Scoping Review

2023 , Ramírez-Saltos D. , Acosta-Vargas P. , Acosta-Vargas G. , Santórum M. , Carrion-Toro M. , Ayala-Chauvin, Manuel Ignacio , Ortiz-Prado E. , Maldonado-Garcés V. , González-Rodríguez M.

The digital transformation of healthcare platforms has ushered in a new era of accessibility, making health information and services widely available. This comprehensive scoping review delves into the accessibility landscape of health platforms by analyzing 29 carefully selected research articles. These studies employ automated tools and manual evaluations to evaluate platform accessibility comprehensively. This study revealed that (52%) of these articles are based on automated methods, while 34% combine automated and manual approaches. Most studies show compliance with the latest versions of the Web Content Accessibility Guidelines (WCAG), with a significant focus (70%) on compliance with level A. This study reveals recurring issues within the perceivable operable, understandable, and robust categories, underscoring the pressing need for strict the accessibility testing of health platforms. This study demonstrates substantial agreement between raters, reinforced by a Cohen’s kappa coefficient of 0.613, indicating their reliability in classifying the articles. Future efforts should persist in refining accessibility standards, advocating for compliance with the WCAG, exploring innovative methods to assess the accessibility of healthcare platforms, and conducting user-centered research. This review highlights the paramount importance of ensuring equitable access to health information and services for people, regardless of their abilities or conditions, which resonates significantly with the issue of sustainability in healthcare and its socioeconomic and environmental implications. © 2023 by the authors.

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