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Sección/Capítulo de Libro Materias > Educación Universidad Internacional Iberoamericana México > Docencia > Libros Abierto Español El presente trabajo esboza algunos de los que considero desafíos en la enseñanza universitaria actual en cualquiera de sus procesos formativos, así como reflexiones acerca de las respuestas que emergen en las condiciones de desarrollo de los contextos educativos universitarios. Abordo, de este modo, particularidades que valoro como fundamentales, destacando el papel del docente y de las herramientas que ofrecen las nuevas tecnologías de la información y las co-municaciones, como condiciones que median las dinámicas de las relaciones que se establecen en estos procesos complejos y en construcción. Apunto los que considero como principales retos de los actores que intervienen en este proceso, de manera particular, y de las universidades actuales, en sentido general, enfatizan-do en la necesidad de sus (re)invenciones como manera de responder a las condiciones de estos tiempos metadata García Rodríguez, Deysi Emilia y Cordovés Santiesteban, Alexander Armando mail SIN ESPECIFICAR, alexander.cordoves@unini.edu.mx (2021) Desafíos actuales en la educación universitaria. Procesos que se (re)inventan. In: Estudios socioculturales y de educación: puentes investigativos para (re)pensar desafíos actuales. Karywa, Sao Leopoldo, pp. 47-60. ISBN 978-65-86795-10-3
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Libro Materias > Ingeniería Universidad Internacional Iberoamericana México > Docencia > Libros Cerrado Español SIN ESPECIFICAR metadata Orozco González, Nelly y Salinero Martín, Juan José mail nelly.orozco@unini.edu.mx, SIN ESPECIFICAR (2021) Estadística II. Fundación Universitaria Iberoamericana. ISBN 9788411020121
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Libro Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Docencia > Libros Cerrado Español Las ideas del neoliberalismo tomaron fuerza a partir de la década de los 70 s debido a la crisis de producción fordista ya la ineficiencia de las políticas keynesianas para contrarrestarla. Estas ideas delinearon las transformaciones económicas de la mayoría de los países latinoamericanos. El neoliberalismo no representa una explicación sistémica de la formación socioeconómica, se trata de una configuración de configuraciones. Por un lado, es una concepción del mundo basada en la teoría neoclásica y la hipótesis de la elección racional; por el otro, es un conjunto de políticas orientadas al funcionamiento del libre mercado en contraposición de las políticas keynesianas de la postguerra metadata Rojo Gutiérrez, Marco Antonio; López Núñez, Henry Robert; Espinosa Gómez, Ángela Aurora; Bonilla Jurado, Diego Mauricio y García Ramírez, Roberto Fernando mail marco.rojo@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2018) Consideraciones teóricas en el análisis del mercado laboral. Centro de Investigación y Desarrollo Profesional, Babahoyo. ISBN 978-9942-8703-7-7
Sección/Capítulo de Libro Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Docencia > Libros Cerrado Español SIN ESPECIFICAR metadata Rojo Gutiérrez, Marco Antonio mail marco.rojo@unini.edu.mx (2016) Eficiencia de los sistemas nacionales de innovación de los países de la OCDE. Eficiencia relativa del sistema mexicano. In: Innovación en América Latina. Universidad Autónoma Metropolitana ; Biblioteca Nueva, Ciudad de México, D.F.. ISBN 978-607-28-0794-5
Libro Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Docencia > Libros Cerrado Español El trabajo que aquí se presenta es un ejercicio econométrico para estudiar la segmentación del mercado laboral. Esta investigación se presenta como un estudio de caso aplicativo al mercado laboral urbano de la Ciudad de México (CDMX) a principios de este siglo. Esta investigación resulta de interés no sólo por las herramientas econométricas que emplea, sino por los hallazgos que presenta sobre el comportamiento del mercado laboral. Se ofrece un análisis de la sectorización de la clase trabajadora dividida de forma dual en un sector formal y otro sector informal. La explicación teórica que se ofrece sobre la existencia de un sector informal en la economía parte de la revisión del estado del arte, en particular, de las aportaciones que realiza la teoría neoclásica. A la par del marco teórico, se presenta una retrospección de la economía mexicana con el objetivo de eslabonar una explicación al surgimiento del creciente sector informal. Se plantean los factores que han ocasionado este incremento sustancial y las distintas problemáticas que esto trae consigo. Dos preguntas sirven de eje en esta investigación. La primera ¿cuál es la probabilidad de que un trabajador ocupado se encuentre en el sector formal?; y la segunda ¿qué rinde más entre los trabajadores ocupados de la CDMX, encontrarse en el sector formal o en el sector informal del mercado laboral? La respuesta de ambas preguntas se da una vez que se explica el modelo Logit y la función semi-logarítmica de ingresos. Se presentan las variables analizadas, una descripción de las mismas y finalmente su distribución para cada uno de los dos sectores. Los datos utilizados en este estudio se tomaron de la Encuesta Nacional de Empleo Urbano (ENEU) para el tercer trimestre del año 2002. Al final del estudio se presentan las conclusiones, un anexo gráfico y las fuentes bibliográficas de las cuales se hizo uso. metadata Rojo Gutiérrez, Marco Antonio; Ramírez Mukul, Álvaro Efraín; Guerrero Luzuriaga, Aura del Cisne; Bonilla Jurado, Diego Mauricio Bonilla Jurado y Cavero Álvarez, Omar Damián mail marco.rojo@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2018) Ejercicio econométrico para el estudio de la segmentación del mercado laboral. Universidad Tecnológica Empresarial de Guayaquil, Guayaquil. ISBN 978-9942-757-26-5
Libro Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Docencia > Libros Cerrado Español SIN ESPECIFICAR metadata Rojo Gutiérrez, Marco Antonio y Bonilla Jurado, Diego Mauricio Bonilla Jurado mail marco.rojo@unini.edu.mx, SIN ESPECIFICAR (2017) Sistema Estatal de Innovación: una propuesta para el Estado de Campeche. Centro de Investigación y Desarrollo Profesional, Babahoyo. ISBN 978-9942-8689-1-6
<a class="ep_document_link" href="/10290/1/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.
Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,
Alemany Iturriaga
<a class="ep_document_link" href="/12747/1/sensors-24-03754%20%281%29.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Hafeez Ur Rehman Siddiqui mail , Ambreen Akmal mail , Muhammad Iqbal mail , Adil Ali Saleem mail , Muhammad Amjad Raza mail , Kainat Zafar mail , Aqsa Zaib mail , Sandra Dudley mail , Jon Arambarri mail jon.arambarri@uneatlantico.es, Ángel Gabriel Kuc Castilla mail , Furqan Rustam mail ,
Siddiqui
<a href="/12749/1/fnut-11-1083759.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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In the last decades, the world population and demand for any kind of product have grown exponentially. The rhythm of production to satisfy the request of the population has become unsustainable and the concept of the linear economy, introduced after the Industrial Revolution, has been replaced by a new economic approach, the circular economy. In this new economic model, the concept of “the end of life” is substituted by the concept of restoration, providing a new life to many industrial wastes. Leaves are a by-product of several agricultural cultivations. In recent years, the scientific interest regarding leaf biochemical composition grew, recording that plant leaves may be considered an alternative source of bioactive substances. Plant leaves’ main bioactive compounds are similar to those in fruits, i.e., phenolic acids and esters, flavonols, anthocyanins, and procyanidins. Bioactive compounds can positively influence human health; in fact, it is no coincidence that the leaves were used by our ancestors as a natural remedy for various pathological conditions. Therefore, leaves can be exploited to manufacture many products in food (e.g., being incorporated in food formulations as natural antioxidants, or used to create edible coatings or films for food packaging), cosmetic and pharmaceutical industries (e.g., promising ingredients in anti-aging cosmetics such as oils, serums, dermatological creams, bath gels, and other products). This review focuses on the leaves’ main bioactive compounds and their beneficial health effects, indicating their applications until today to enhance them as a harvesting by-product and highlight their possible reuse for new potential healthy products.
Lucia Regolo mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Yasmany Armas Diaz mail , Bruno Mezzetti mail , Maria Elexpuru Zabaleta mail maria.elexpuru@uneatlantico.es, Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Luca Mazzoni mail ,
Regolo
<a href="/12750/1/s41598-024-63831-0.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Efficient deep learning-based approach for malaria detection using red blood cell smears
Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.
Muhammad Mujahid mail , Furqan Rustam mail , Rahman Shafique mail , Elizabeth Caro Montero mail elizabeth.caro@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,
Mujahid
<a class="ep_document_link" href="/12751/1/s12874-024-02249-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.
Tumpa Rani Shaha mail , Momotaz Begum mail , Jia Uddin mail , Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Imran Ashraf mail , Md. Abdus Samad mail ,
Shaha