Centro de Investigación de Ciencias Humanas y de la Educación

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Universidad Indoamérica

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Temperature control system with hysteresis for drying oven using IoT
(2023)
Bucay-Valdiviezo J.
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Veronica-Ocana Parra S.
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Saa F.
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Varela Aldas, José
The fourth industrial revolution (Industry 4.0) spread to the whole world, improving all kinds of industrial processes. Thus, temperature control and monitoring of ovens through the Internet of Things (IoT) is part of this technology, especially in the automotive industry. In this work, the temperature control with hysteresis of a drying oven is carried out incorporating the IoT, with the objective of maintaining the temperature within a certain range in the drying process inside an oven. For this, a thermocouple, heat lamps, couplers and an electronic board are used. The temperature data from the drying oven is sent to the internet via WiFi using a router. The oven temperature data is recorded in the cloud for its respective subsequent analysis using the ThingSpeak services. For the results, the temperature is configured in the range of 50 ° C to 70 ° C that is monitored through a mobile application developed in App Inventor. The tests carried out demonstrate the proper functioning of the system and the high usability of the application. © 2023 IEEE.
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Voltammetric Electronic Tongues Applied to Classify Sucrose Samples Through Multivariate Analysis
(2021)
Fuentes, E.M.
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Varela Aldas, José
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Verdú S.
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Meló R.G.
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Alcañiz M.
The aim of the present study was to classify samples of sugar with different concentrations through a Voltammetric Electronic tongues (VET), with a generic pulse sequence consisted of 22 pulses ranging from –1000 mV to + 1000 mV with a duration of 20 ms/pulse over different samples such as 1.25mM, 2.5mM, 5mM and 10mM, of sucrose concentration, these were measured 4 times each concentration and the test was developed 4 times, giving a total number of 506.880 data supervised learning algorithm using support vector machine was employed, choosing a linear function as a classifying element. In the training, 75% of the data was used to determine the coefficients of the classification function, and the remaining (25%) was used to evaluate the performance of the proposal. The results showed a concordance of more than 80% in the separation of sample, allowing to conclude as acceptable the performance of the classifier and the data acquired through the voltammetric tongue. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Determining of university desertion: survival analysis
(2022)
Núñez-Naranjo, Aracelly Fernanda
University desertion is the subject of analysis by several authors over the years. The different causes for which a student abandons his studies and the ways to reduce it, entail the interest of the study. The research was positive with a quantitative approach. The data collection was carried out through a survey, and an observation sheet to the student’s files, we proceeded with the analysis of the Kaplan Meier survival curve, on a base made up of 1078 undergraduate students enrolled in the first semester. in 2014 with follow-up to 2019. It is concluded that the existing university dropout factors are personal and social. The incident variables are age, gender, marital status, region, school, family, venue, modality, homologation, and career. There are no significant differences in ethnicity, school, age and quintile, averages. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms
(2022)
Espinosa Pinos, Carlos Alberto
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Ayala-Chauvin, Manuel Ignacio
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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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Experimental Studies on TiO2 NT with Metal Dopants through Co-Precipitation, Sol–Gel, Hydrothermal Scheme and Corresponding Computational Molecular Evaluations
(2023)
Estévez Ruiz E.P.
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Lago J.L.
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Thirumuruganandham, Saravana Prakash
In the last decade, TiO2 nanotubes have attracted the attention of the scientific community and industry due to their exceptional photocatalytic properties, opening a wide range of additional applications in the fields of renewable energy, sensors, supercapacitors, and the pharmaceutical industry. However, their use is limited because their band gap is tied to the visible light spectrum. Therefore, it is essential to dope them with metals to extend their physicochemical advantages. In this review, we provide a brief overview of the preparation of metal-doped TiO2 nanotubes. We address hydrothermal and alteration methods that have been used to study the effects of different metal dopants on the structural, morphological, and optoelectrical properties of anatase and rutile nanotubes. The progress of DFT studies on the metal doping of TiO2 nanoparticles is discussed. In addition, the traditional models and their confirmation of the results of the experiment with TiO2 nanotubes are reviewed, as well as the use of TNT in various applications and the future prospects for its development in other fields. We focus on the comprehensive analysis and practical significance of the development of TiO2 hybrid materials and the need for a better understanding of the structural–chemical properties of anatase TiO2 nanotubes with metal doping for ion storage devices such as batteries. © 2023 by the authors.
Scopus© Citations 7 24
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Data Analysis for Performance Improvement of University Students Using IoT
(2024)
Ayala-Chauvin, Manuel Ignacio
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Lara-Álvarez P.
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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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Power Flow Optimization in Electrical Networks using Gekko
(2025)
Ayala-Chauvin, Manuel Ignacio
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Avilés-Castillo, Fátima
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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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From Virtual Worlds to Real-World Equity: A Scoping Review of the Metaverse as Computer-Assisted Learning for STEM Competencies
(2026)
Franklin Parrales-Bravo
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Roberto Tolozano-Benites
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Janio Jadán-Guerrero
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Leonel Vasquez-Cevallos
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Víctor Gómez-Rodríguez
This scoping review critically synthesizes 34 studies (2015–2026) examining the metaverse’s role in fostering six core STEM competencies, moving beyond descriptive reporting to interrogate whether these technologies constitute genuine pedagogical transformation, whose learners are served or excluded, and how isolated interventions connect into lifelong learning pathways. Following PRISMA-ScR guidelines, our analysis reveals that while technology literacy and collaboration appear in 91.2% of our selected studies, mathematical application is addressed in fewer than half (44.1%), raising unanswered questions about whether this pattern reflects an equitable distribution of mathematical learning opportunities across diverse learner populations—a question the current evidence base cannot answer but one that warrants urgent investigation. The evidence demonstrates substantial immediate learning gains through embodied presence and risk-free experimentation, yet a deeper reading suggests this often represents technological optimization of traditional goals rather than epistemological transformation. More troublingly, the concentration of inclusivity evidence on select populations—while rendering students with physical disabilities, Indigenous learners, and refugee students entirely invisible—reveals an equity paradox where immersive technologies may inadvertently amplify existing disparities. The absence of any longitudinal data linking short-term engagement to sustained STEM participation leaves the field’s claim to transformative impact unsubstantiated. This review argues for moving beyond fragmented interventions toward designing coherent, equitable learning pathways that fulfill the metaverse’s potential for all learners. © 2026 by the authors.
3
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Performance and Real-World Variability of Predictive Maintenance Models for Vehicle Fleets
(2024)
Avilés-Castillo, Fátima
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Dayanara Yánez-Arcos
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Ayala-Chauvin, Manuel Ignacio
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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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Evaluation of Accessibility on the PAR Platform from the Perspective of Physicians
(2024)
Patricia Acosta-Vargas
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Gloria Acosta-Vargas
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Marco Santórum
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Mayra Carrión-Toro
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Ayala-Chauvin, Manuel Ignacio
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