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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Web-based pulmonary telehabilitation: a systematic review

2024 , Ayala-Chauvin, Manuel Ignacio , Chicaiza Claudio, Fernando , Patricia Acosta-Vargas , Jadán Guerrero, Janio , Verónica Maldonado-Garcés , Esteban Ortiz-Prado , Gloria Acosta-Vargas , Mayra Carrión-Toro , Marco Santórum , Mario Gonzalez-Rodriguez , Camila Madera , Wilmer Esparza

Web-based pulmonary telerehabilitation (WBPTR) can serve as a valuable tool when access to conventional care is limited. This review assesses a series of studies that explore pulmonary telerehabilitation programmes delivered via web-based platforms. The studies involved participants with moderate to severe chronic obstructive pulmonary disease (COPD). Of the 3190 participants, 1697 engaged in WBPTR platforms, while the remaining 1493 comprised the control groups. Sixteen studies were included in the meta-analysis. Web-based pulmonary telerehabilitation led to an increase in daily step count (MD 446.66, 95% CI 96.47 to 796.86), though this did not meet the minimum clinically important difference. Additionally, WBPTR did not yield significant improvements in the six-minute walking test (MD 5.01, 95% CI − 5.19 to 15.21), health-related quality of life as measured by the St. George’s Respiratory Questionnaire (MD − 0.15, 95% CI − 2.24 to 1.95), or the Chronic Respiratory Disease Questionnaire (MD 0.17, 95% CI − 0.13 to 0.46). Moreover, there was no significant improvement in dyspnoea-related health status, as assessed by the Chronic Respiratory Disease Questionnaire (MD − 0.01, 95% CI − 0.29 to 0.27) or the modified Medical Research Council Dyspnoea Scale (MD − 0.14, 95% CI − 0.43 to 0.14). Based on these findings, this review concludes that WBPTR does not offer substantial advantages over traditional care. While slight improvements in exercise performance were observed, no meaningful enhancements were noted in dyspnoea or quality of life metrics. Overall, WBPTR remains a complementary and accessible option for managing and monitoring COPD patients. However, further research and innovation are required to improve its efficacy and adapt it to various clinical environments.

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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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Sustainable Development in Higher Education Curricula for Software Engineering Chairs

2023 , León Toro, Jenny Marcela , Buele, Jorge , Camino-Morejón V.M. , 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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Static reactive power compensator design, based on three-phase voltage converter

2021 , Ayala-Chauvin, Manuel Ignacio , Kavrakov B.S. , Buele, Jorge , Varela Aldas, José

At present, electrical network stability is of the utmost importance because of the increase in electric demand and the integration of distributed generation deriving from renewable energy. In this paper, we proposed a static reactive power compensator model with common direct current voltage sources. Converter parameters were calculated and designed to fulfill specifications. In order to ascertain the device response for different operating modes as reactive power consumer and generator, we developed the model’s power and control circuits in Matlab Simulink. Simulations were performed for different conditions, and as a result, the current and voltage waveforms and the circular power chart were obtained. This paper has theoretically proven it is possible to achieve the consumption or generation of purely active or reactive power by implementing a static reactive power compensator with common DC voltage sources. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity

2024 , Dayanara Yánez-Arcos , 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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U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity

2024 , Dayanara Yánez-Arcos , 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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Control System Test Platform for a DC Motor

2022 , Saá-Tapia F. , Mayorga-Miranda L. , Ayala-Chauvin, Manuel Ignacio , Domènech-Mestres C.

Currently, control systems are used to improve the behavior of actuators that are part of an equipment or process. However, to enhance their performance, it is necessary to perform tests to evaluate the responses of its operation depending on the type of controller. In this sense, a test platform was developed to compare and optimize the speed control of a DC motor with three types of controllers: Predictive Model Control (MPC), Proportional Integral Derivative (PID) and Fuzzy Logic. Data acquisition was performed using the Arduino MEGA board and LabVIEW software. The mathematical model of the three controllers was developed, taking into account the electrical and physical properties of the DC motor. Through MATLAB IDENT, the state space (SS) and transfer function F(S) equations were generated for the MPC and PID controller, respectively; on the other hand, input/output ranges for the Fuzzy Logic controller were input/output ranges defined by assigning belonging functions and linguistic variables. Experimental tests were carried out with these models under no-load and load. Tests performed in vacuum show that performance index with the motor at 100 rpm results in a PID of 0.2245, a Fuzzy Logic of 0.3212 and an MPC of 0.3576. On the other hand, with load at 100 rpm, a PID of 0.2343, a Fuzzy Logic of 0.3871 and an MPC of 0.3104 were obtained. It was determined that the Fuzzy Logic controller presents a higher over impulse; the PID and MPC have a faster stabilization time and with negligible over impulses. Finally, the MPC controller presents a better performance index analysis according to the Integral Square Error criterion (ISE). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Evaluation of Accessibility on the PAR Platform from the Perspective of Physicians

2024 , Patricia Acosta-Vargas , Gloria Acosta-Vargas , Marco Santórum , Mayra Carrión Toro , Ayala-Chauvin, Manuel Ignacio , Verónica Maldonado-Garcés , Mario Gonzalez-Rodriguez

Web accessibility is fundamental to inclusive healthcare. Digital health platforms must be accessible to all healthcare professionals, including those with disabilities. This study evaluates the accessibility of the PAR platform from the perspective of physicians, highlighting critical aspects of digital inclusion in healthcare. 11 web pages of the platform were analyzed using the WAVE tool and evaluation manual by accessibility experts and doctors specialized in physiotherapy. Although the platform meets WCAG 2.2 accessibility standards at level \mathbf{A}, several barriers are identified, such as a lack of image descriptions, contrast, and navigation issues. The platform achieved a 98.9% compliance rate, with only 15 issues identified. The ARIA category presented significant challenges, with a compliance rate of 14.5%. The need to improve the automatic description of images and optimize navigation for users with disabilities is highlighted. The Perceptible and Understandable principles showed a compliance rate of 66.9% and 16.1%, respectively, pointing to opportunities for improvement in information presentation and clarity. These findings inform future improvements focused on addressing identified barriers and promoting an inclusive experience on the PAR platform for healthcare professionals.

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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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Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities

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