Guevara Maldonado, César Byron

Preferred name

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Quito

cesarguevara@uti.edu.ec

ORCID

0000-0003-1571-5829

Scopus Author ID

56452706300

Now showing 1 - 10 of 47

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Effect of Burnout Syndrome on work performance in administrative personnel
(2024)
Verónica Adriana Freire Palacios
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Sridam David Arévalo Lara
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María Belén Espíndola Lara
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Andrea Ramírez Casco
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David Miguel Larrea Luzuriaga
Burnout syndrome can negatively affect workers' performance. Objective: To determine the prevalence of Burnout Syndrome and its impact on the Administrative Performance of the Human Talent at the Chimborazo Sports Federation. This study is quantitative, descriptive, and cross-sectional, involving 21 administrative workers. The Maslach Burnout Inventory Questionnaire was used to measure burnout, and a Job Performance Questionnaire was applied. Descriptive and correlational analyses were conducted. Results showed that 10 % had high levels of burnout, 14 % medium, and 76 % low. The most affected dimensions were personal accomplishment and depersonalization. Job performance was mostly regular (90 %). A significant correlation was found between burnout and job performance (r=0,689, p=0,001). Burnout explained 41,7 % of the variability in performance. Conclusions: There is an inverse relationship between burnout syndrome and job performance in this group of workers. Preventive measures are recommended.
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Development of a Convolutional Neural Network for Detection of Ovarian Cancer Based on Computed Tomography Images
(2024)
Gabriela Narvaez-Chunillo
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Ronny Ordoñez-Sanchez
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Lizbeth Ortiz-Vinueza
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Diego Almeida-Galárraga
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Fernando Villalba-Meneses
Ovarian cancer is one of the most frequent gynecologic malignancies in women, but it is often detected in late stage, leaving patients with little time to follow a successful therapy. Specialists have opted to use computer-aided diagnosis (CAD) for the detection of ovarian cancer through the analysis of computed tomography (CT) images, in which the professional examines the size, shape and different characteristics that enable a precise diagnosis in the ovary. This present project purposes a Convolutional Neural Network (CNN) which consist on four convolutional layers; including two pooling layer and two fully-connected layer. The cancerous ovaries images is selected from the Cancer Imaging Achive dataset for training and validation of the model. Moreover, the training of the CNN contain filters to ensure that all of the images are the same dimensions and pixel size. The testing results from the training of the images showed that the proposed model obtained a range of accuracy that goes from 90.0% to the best of the cases 98.85%. The variables obtained like the data of the pressure and loss of the training were compared with those of the validation, allowing for the determination of a successful CNN training.
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Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm
(2020)
Guevara Maldonado, César Byron
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Penas M.S.
In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate - the key information to prioritize medical care - was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized. © 2013 IEEE.
Scopus© Citations 11 20
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BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment
(2024)
Villalba-Meneses F.
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Guevara Maldonado, César Byron
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Velásquez-López P.A.
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Arias-Serrano I.
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Guerrero-Ligña S.A.
Low back pain (LBP) is a common issue that negatively affects a person’s quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion–extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement’s test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist’s evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist’s evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management. © 2024 by the authors.
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Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model
(2023)
Sánchez-Real E.
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Otuna-Hernández D.,
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Fajardo-Cabrera A.
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Davies-Alcívar R.
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Madrid-Pérez M.
Keratoconus is an eye disease that distorts the shape of the cornea. This study aimed to analyze the effect of an increase in intraocular pressure applied to eyes with different severity of keratoconus disease using patient-specific models. Finite element models of the normal eye, eye with keratoconus, and eye with keratoglobus were constructed. The loading conditions considered the intraocular pressure increment as well as their physiological intraocular pressure. The analysis was performed with distinct materials for normal and keratoconic eyes. The finite element analysis revealed differences in the three models in terms of their deformation and maximum principal stress, and differences were observed in corneal curvature and thickness. These findings could enhance research in the biomechanical area, leading to more successful treatment options and a more individualized approach in the field of practical ophthalmology. Further investigation with larger sample sizes and more precise data on eye material would allow us to evaluate whether these disparities could inform the diagnosis of keratoconus. © 2023 by the authors.
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Health Impact of Gnathostomiasis and its Integral Approach to Parasitic Infection: A Systematic Review
(2024)
Gisnella María Cedeño Cajas
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José Andrés Zaporta Ramos
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Andrea Stefannia Flores Villacrés
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Guevara Maldonado, César Byron
The present study focuses on gnathostomiasis, a parasitic disease caused by the nematode gnathostoma that affects both humans and other animals, with a prevalence of 0,14 %. The aim of the study is to analyze the main research related to gnathostomiasis, its diagnosis and treatment. To achieve this objective, a systematic review of clinical cases, observational and retrospective studies of the disease was carried out, following the PRISMA methodology. The literature search, conducted between 2018 and 2022 in the Web of Science, Scopus, PubMed, Redalyc and Dialnet databases, resulted in the identification of five articles relevant and pertinent to the topic. The study findings indicate that gnathostomiasis, on the rise in Latin America and Asia, is transmitted mainly through the consumption of raw fish infected with Gnathostoma larvae. Although preventive measures and treatments, such as albendazole, are available, their efficacy is limited, and it is difficult to implement changes in dietary habits. Therefore, more research is needed to better understand the disease, develop more effective diagnostics and treatments, and raise awareness among physicians of its increasing global prevalence.
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Editorial Design Based on User Experience Design
(2020)
Borja Galeas, Carlos
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Guevara Maldonado, César Byron
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Castillo Salazar, David Ricardo
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Arias Flores, Hugo Patricio
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Fierro-Saltos, W.
This research deals with editorial design based on user experience design. The traditional editorial design has had to adapt to the new digital media composition, where multimedia audiovisual elements unthinkable a few years ago need to be integrated. The participation of the reader, as an external observer who only receives information through texts and images, now has new scenarios in which he can actively participate and decide what will come to his hands. In this study, a work methodology based on UxD User Experience Design is presented, in which will generate the editorial design of an educational book on environmental issues, which includes augmented reality for children from 6 to 8 years of age. The aim of this study is to know if an editorial product with augmented reality and developed from the user experience design can improve meaningful learning in a playful and active way. For its development, a composition model based on the Fibonacci sequence and the golden ratio will be used. Additionally, its graphic composition will be guided by the Massimo Vignelli canon and will be complemented by the reticular model of Beth Tondreau. The augmented reality markers position will also be based on the composition model previously mentioned, which will allow keeping the attention of the reader in the printed document and in the augmented reality animations. The user experience design will be applied with teachers, parents and students from 4 schools in Quito and Ambato. Once the production is completed, the impact on teaching-learning process will be evaluated with a control and a test group, and the methodology with which they will work in the classroom with the educational material developed will be defined. At the end of the study, copies of the book will be delivered to the participating schools of this research for its implementation. © Springer Nature Switzerland AG 2020.
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Analysis and design of an internal Top-Down network applying international standards [Análisis y diseño de una red interna Top-Down aplicando estándares internacionales]
(2023)
Almeida A.
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Suarez B.
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Guevara Maldonado, César Byron
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Coronel D.
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Hidalgo J.
Telecommunications networks have become something essential within public or private companies, since they contribute to technological development. A network infrastructure with adequate cabling structured together with rules and standards enables the integration of multiple technologies and services. Currently, most institutions in Latin American countries do not apply norms, standards or good practices in their design, due to lack of knowledge or to save resources, without understanding that this generates an unreliable and unstable network infrastructure. Consequently, this study focuses on the design, architecture and administration of the network of a public institution in the city of Tulcán-Ecuador. The main objective of this proposal was the design of a network infrastructure that facilitates the administration of the network at a logical and physical level, taking into account the requirements and facilities of the institution. This network proposal applied the Top-Down Network Design By Cisco methodology to design a centralized, stable, flexible, and secure network. In addition, different international network design and management standards and regulations (ANSI/TIA/EIA/ISO) were used to generate a high-quality network. © 2023 ITMA.
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Emotion classification using EEG headset signals and Random Forests [Clasificación de emociones utilizando señales de auriculares EEG y Random Forests]
(2023)
Vasquez R.
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Carrion-Jumbo J.
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Riofrio-Luzcando D.
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Guevara Maldonado, César Byron
Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect, recognize and understand emotions using computational systems to improve communication between people and machines, which would facilitate the ability of computers to understand the communication between humans. This study proposes the creation of a model that allows the classification of people's emotions based on their EEG signals, for which the brain-computer interface EMOTIV EPOC was used. This allowed the collection of electroencephalographic information from 50 people, all of whom were shown audiovisual resources that helped to provoke the desired mood. The information obtained was stored in a database for the generation of the model and the corresponding classification analysis. Random Forest model was created for emotion prediction (happiness, sadness and relaxation), based on the signals of any person. The results obtained were 97.21% accurate for happiness, 76% for relaxation and 76% for sadness. Finally, the model was used to generate a real-time emotion prediction algorithm; it captures the person's EEG signals, executes the generated algorithm and displays the result on the screen with the help of images representative of each emotion. © 2023 ITMA.
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Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance
(2023)
Pacheco-Mendoza S.
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Guevara Maldonado, César Byron
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Mayorga-Albán A.
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Fernández-Escobar J.
This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values < 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode. © 2023 by the authors.
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Guevara Maldonado, César Byron

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