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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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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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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The triple helix model linked to knowledge transfer and economic progress from universities [El modelo de la triple hélice vinculado a la transferencia de conocimiento y progreso económico desde las universidades]
(2023)
Bonilla-Jurado, Diego
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Guevara Maldonado, César Byron
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Montero I.K.S.
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Pazmiño S.J.I.
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Zuta M.E.C.
The strategic actions that are being developed from the Ecuadorian universities, are heading towards the linking of innovative factors under an interrelated scheme known as Triple Helix, whose intention is framed in connecting entrepreneurship, using knowledge and society as a platform, generating a model sustainable development between university-state-business. The objective of this research is to show the relationship between university-company-state with entrepreneurial research through the triple helix functional model, with a view to innovative potentializing that serves as a boost to socioeconomic progress. The research approach is qualitative at a descriptive level, using a hermeneutical review focused on entrepreneurship studies, business and university alliances, government plans and the triple helix theory. The results indicate that scientific research based on the triple helix method should be strengthened, the main obstacles being lack of communication, business disinterest and distorted state policies. The Ecuadorian universities UEM, UTB and UEB must make concerted efforts so that the investigations are directed towards the true social needs of each area. The conclusions indicate that the links of the triple helix model lead to socioeconomic strengthening through the development of research and scientific projects, without neglecting technological advances. © Este es un artículo en acceso abierto.
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Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness
(2024)
Eugenia Ulzurrun
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Ana Grande-Pérez
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Daniel del Hoyo
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Guevara Maldonado, César Byron
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Carmen Gil
Introduction: SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes. Materials and methods: Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations. Results: Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure. Discussion: Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.
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Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data
(2024)
Guevara Maldonado, César Byron
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Dennys Coronel
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Byron Salazar
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Jorge Salazar
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Arias Flores, Hugo Patricio
Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.
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Detection and Classification of Facial Features Through the Use of Convolutional Neural Networks (CNN) in Alzheimer Patients
(2020)
Castillo Salazar, David Ricardo
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Varela Aldas, José
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Borja M.
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Guevara Maldonado, César Byron
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Arias Flores, Hugo Patricio
In recent years, the widespread use of artificial neural networks in the field of image processing has been of vital relevance to research. The main objective of this research work is to present an effective and efficient method for the detection of eyes, nose and lips in images that include faces of Alzheimer’s patients. The methods to be used are based on the extraction of deep features from a well-designed convolutional neural network (CNN). The result focuses on the processing and detection of facial features of people with and without Alzheimer’s disease. © Springer Nature Switzerland AG 2020.
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Using Artificial Intelligence and X-ray Images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment
(2025)
Bryan Juárez-Gonzalez
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Fernando Villalba-Meneses
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Jonathan Cruz-Varela
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Andrés Tirado-Espín
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Paulina Vizcaino-Imacaña
In January 2022, Ecuador experienced a peak in COVID-19 cases, with 890,541 confirmed cases and 35,658 deaths. During 2019-2020, influenza and pneumonia also ranked among the top causes of death. Traditional chest X-rays and chest CT scans are commonly used for diagnosing COVID-19, but studies by Wong et al. indicate that chest X-rays are less sensitive compared to CT scans unless artificial intelligence is utilized. Tahir et al. highlighted that AI models such as U-net and neural networks achieve high diagnostic accuracy, with U-Net++ and ResNet18 models showing sensitivities above 99% and perfect specificity using a large dataset of 33,920 chest X-ray images. The rapid detection of symptoms could have helped in prioritizing critical care, potentially reducing deaths. In the current study, it is demonstrated that combining AI with chest X-rays can achieve a binary accuracy above 98% for COVID-19 detection using transfer learning with Xception, VGG16, and VGG19 neural networks. Using 27,052 chest X-ray images, the VGG19 model achieved an excellent F1-score of 98.53% for COVID-19 and normal class classification. The VGG19 model also performed well in multiclass classification with an F1-score above 89%. The study concludes that AI-enhanced diagnostic tools, such as VGG19, are valuable for hospitals in diagnosing COVID-19 and future improvements might include larger datasets and enhanced segmentation techniques.
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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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