Ayala-Chauvin, Manuel Ignacio

Preferred name

Main Affiliation

Centro de Investigación de Ciencias Humanas y de la Educación

Ambato

mayala@uti.edu.ec

ORCID

0000-0002-3911-377X

Scopus Author ID

57215428914

Now showing 1 - 10 of 43

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Road Drainage Infrastructure Diagnostics and Deficiency Indexing in ENSO-Vulnerable Andean Corridors: A STEM–PjBL Field Assessment
(MDPI AG, 2026-05-15)
Benavides-Muñoz, Holger Manuel
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Ayala-Chauvin, Manuel Ignacio
;
Benavides-Ortega, Leirys María
Road drainage infrastructure in ENSO-vulnerable Andean regions faces compounding threats from climatic variability, geometric inadequacy, and systemic maintenance neglect. This study presents a STEM-integrated Project-Based Learning (PjBL) diagnostic framework applied to 42 road segments along corridors connecting Loja, Ecuador, selected through a purposive-stratified spatial-coverage protocol. Using ArcGIS Survey123, standardised field data were collected on structure presence, geometry, failure modes, and condition across four structure types: crown gutters, road gutters, hydraulic chutes, and culverts. The Composite Drainage Deficiency Index (DDI, 0–100) was derived from five equally weighted binary indicators and validated through Monte Carlo Dirichlet weight-perturbation analysis and jackknife leave-one-out resampling, confirming rank-order invariance to admissible alternative weightings. The results reveal severe systemic deficiencies, including crown gutters absent at 88.1% (95% CI: 75.0–94.8) and road gutters at 81.0% (95% CI: 66.7–90.0) of sites. Every segment exhibited at least one drainage failure (100%; 95% CI: 91.6–100). The DDI identified 73.8% of segments in the High or Critical band (DDI ≥ 60; mean = 60.2 ± 20.4). Hierarchical clustering isolated one geometric outlier whose exclusion altered the aggregate metrics by <1.2%. These findings establish a georeferenced baseline for maintenance prioritisation and validate the methodological reproducibility of academically integrated field protocols for infrastructure diagnostics. © 2026 by the authors.
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IoT Monitoring to Control a Bicycle Parking Lot
(2022)
Ruales, María Belén
;
Lara-Alvarez P.
;
Riba C.
;
Ayala-Chauvin, Manuel Ignacio
In recent years, the development of new technologies has improved the management of resources and services at the urban level. In this sense, several cities worldwide have developed intelligent infrastructures such as Smart Cities in which, through data collection and management, they aim to achieve social, environmental and economic improvements. Innovative bike racks are a promising solution to traffic-related problems in major cities around the world; however, there is a lack of low-cost solutions for controlling and monitoring bike racks and thus boosting the mobility of cyclists. This paper presents a system to monitor and control a bicycle parking lot. In order to achieve this goal, software and hardware specifications were defined and characterised by the control system. The conceptual design and detail of the prototype and the materialisation proceeded, where technology with ESP8266 microcontrollers and Raspberry Pi+Ethernet/WiFi microprocessors was used in the MQTT communication protocol to implement its architecture. The system implements in the bicycle parking lot of the Universidad Tecnológica Indoamérica. The series of data collected allowed for determining the frequency of use. With this, a database creates where the information on the frequency of use of bicycles is stored. Finally, through a mobile application, the availability of parking spaces can be consulted, and bikes in the parking lot can monitor. © 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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U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity
(2024)
Dayanara Yánez-Arcos
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Ayala-Chauvin, Manuel Ignacio
;
Elena Blanco-Romero
Breast cancer remains a critical global health issue, necessitating continuous research and innovative approaches for diagnosis, treatment, and prevention. This study evaluates the effectiveness of U -Net models in enhancing diagnostic precision and efficiency using real hospital samples. We aim to improve key diagnostic metrics such as accuracy, sensitivity, and specificity through the application of U-Net models. Our image classification model, tailored for 256 × 256 × 3 input images, excels in detecting and categorizing tumor cells. The architecture begins with initial convolutional layers featuring 64 filters, progresses to layers with 128 filters, and includes a Dropout layer to prevent overfitting. The deep network for object detection utilizes both region proposal and regression/classification approaches, achieving 92.27% confidence and 100% accuracy. Additionally, our deep learning algorithms accurately segment nuclei in histopathological images, employing a clustering strategy that delivers 88.81% confidence and 100% accuracy. Visual results demonstrate precise tumor cell localization and prediction confidence. Performance metrics from ten experimental runs indicate average confidence levels between 74.19% and 92.31%, with 90.0% accuracy and specificity in benign analysis. The model's ability to classify non-carcinomas versus carcinomas achieved an AUC of 0.78, illustrating its effective differentiation between classes.
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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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Augmented Reality Application with Multimedia Content to Support Primary Education
(2023)
Buele, Jorge
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Espinoza J.
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Ruales, María Belén
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Camino-Morejón V.M.
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Ayala-Chauvin, Manuel Ignacio
Education is continually reinventing itself to meet the growing needs of learners. In the context of an emergency, confinement and mobilization difficulties have necessitated the adoption of online education. This new offer brings technological means to conventional processes, but its main disadvantage is the limited access in some places. This has motivated the present study, which proposes the development of a mobile application using augmented reality (AR) to complement primary education. This application aims to disseminate multimedia content without needing a stable internet connection. A high-performance computer is required for the design and a mid-range smartphone for its execution. Four scripts are generated in c# programming language using Visual Studio. The environment and the three-dimensional objects are developed using Unity software and the packages ARFoundation, Unity MARS, and Vuforia. Two groups of 13 children each participated in the experimental testing. The experimental group used the application for two weeks to complement the virtual classes in the subjects of language, natural sciences, and mathematics. The analysis with SPSS software shows a statistically significant increase in the average grades compared to the control group. This research shows that the use of technology can contribute to improving the current teaching-learning processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities
(2024)
Ayala-Chauvin, Manuel Ignacio
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Avilés-Castillo, Fátima
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Dayanara Yánez-Arcos
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Buele, Jorge
In industrial robotics, predictive maintenance is important to improve efficiency and reduce costs, addressing early detection and diagnosis of failures. The use of Big Data allows us to identify patterns and trends that at first glance are complex. This review examines research on the application of big data in predictive maintenance of industrial robots, which use advanced techniques such as cloud-based architectures, filtering algorithms, and machine learning. The review methodology included an analysis of the big data techniques used, the challenges identified, and the opportunities presented. The results show significant improvements in the accuracy of predictions and fault diagnoses. Key anomaly drivers were identified that improved production performance and enabled accurate fault identification and reduced downtime in industrial robots. Despite the benefits, challenges remain in data security and communications latency, underscoring the need to develop innovative algorithms and techniques to balance computing load and minimize delays. The continuous evolution of these techniques promises to improve the failure management capacity in industrial robotics, thus optimizing the operability and efficiency of robotic systems.
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Sustainable Development in Higher Education Curricula for Software Engineering Chairs
(2023)
León Toro, Jenny Marcela
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Buele, Jorge
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Camino-Morejón V.M.
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Ayala-Chauvin, Manuel Ignacio
Nowadays, society demands that high quality teaching practices must be part of the curriculum in higher education institutions. The interdisciplinarity view of the contents taught has made the technical aspects of engineering merge with social, cultural, and economic nuances. In this sense, the new generations of students show their interest in learning and carrying out activities that contribute to sustainability, for this reason, the inclusion of ecological themes in the subjects of computer science and software career is required. A bibliographical analysis was carried out that allowed recognition of main concepts and methodologies applied to the subject. As a result of them, an adjustment of chairs is presented allowing integrating conventional teaching with the new trends of green technology. Reforms were implemented from introductory courses to theoretical knowledge of green software, to the development of web applications with the same approach. In the same way, it involves the management of computer projects, modeling, monitoring, and optimization of resources, and green evaluation. According to the socio-educational model, the articulation between technology and sustainability will allow managing software projects that provide real solutions to problems in context. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Radar system for the reconstruction of 3D Objects: A preliminary study
(2020)
Barberán, J.
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Moreta, D.
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Ayala-Chauvin, Manuel Ignacio
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Obregón, J.
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Domínguez, R.
Objects recognition and their reconstruction is a process that involves a significant economic investment. This manuscript presents the basis for the design of a system that detects objects, extracts its main characteristics and digitally reconstructs them in three dimensions considering a reduced economic investment. The popular technological tool Kinect on its version 2.0 and MATLAB software have been linked to develop an efficient algorithm. Next, the process to obtain this prototype is briefly described, as well as the results from the corresponding experimental tests. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.
Scopus© Citations 1 24
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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.
Scopus© Citations 1 16

Ayala-Chauvin, Manuel Ignacio

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