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Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The provision of Wireless Fidelity (Wi-Fi) service in an indoor environment is a crucial task and the decay in signal strength issues arises especially in indoor environments. The Line-of-Sight (LOS) is a path for signal propagation that commonly impedes innumerable indoor objects damage signals and also causes signal fading. In addition, the Signal decay (signal penetration), signal reflection, and long transmission distance between transceivers are the key concerns. The signals lose their power due to the existence of obstacles (path of signals) and hence destroy received signal strength (RSS) between different communicating nodes and ultimately cause loss of the packet. Thus, to solve this issue, herein we propose an advanced model to maximize the LOS in communicating nodes using a modern indoor environment. Our proposal comprised various components for instance signal enhancers, repeaters, reflectors,. these components are connected. The signal attenuation and calculation model comprises of power algorithm and hence it can quickly and efficiently find the walls and corridors as obstacles in an indoor environment. We compared our proposed model with state of the art model using Received Signal Strength (RSS) and Packet Delivery Ratio (PDR) (different scenario) and found that our proposed model is efficient. Our proposed model achieved high network throughput as compared to the state-of-the-art models. metadata Khan, Muhammad Nasir; Waqas, Muhammad; Abbas, Qamar; Qureshi, Ahsan; Amin, Farhan; de la Torre Díez, Isabel; Uc Ríos, Carlos Eduardo y Fabian Gongora, Henry mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, henry.gongora@uneatlantico.es (2024) Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment. PLOS ONE, 19 (7). e0305039. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users’ mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user’s similarity as compared to the existing similarity approach. metadata Memon, Ambreen; Kilby, Jeff; Breñosa, Jose; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2022) Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix. Sensors, 22 (24). p. 9898. ISSN 1424-8220

Artículo Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Innovation plays a pivotal role in the progress and goodwill of an organization, and its ability to thrive. Consequently, the impact analysis of innovation on the performance of an organization holds great importance. This paper presents a two-stage analytical framework to examine the impact of business innovation on a firm’s performance, especially firms from the manufacturing sector. The prime objective is to identify the factors that have an impact on firm-level innovation, and to examine the impact of firm-level innovation on business performance. The framework and its analysis are based on the latest World Bank enterprise survey, with a sample size of 696 manufacturing firms. The first stage of the proposed framework establishes the analytical results through Bivariate Probit, which indicates that research and development (R&D) has a significantly positive impact on the product, process, marketing, and organizational innovations. It thus highlights the important role of the allocation of lump-sum amounts for R&D activities. The statistical analysis shows that innovation does not depend on the size of the firms. Moreover, the older firms are found to be wiser at conducting R&D than newer firms that are reluctant to take risks. The second stage of the proposed framework separately analyzes the impacts of the product and organizational innovation, and the process and marketing innovation on the firm performance, and finds them to be statistically significant and insignificant, respectively. metadata Aslam, Mahrukh; Shafi, Imran; Ahmad, Jamil; Álvarez, Roberto Marcelo; Miró Vera, Yini Airet; Soriano Flores, Emmanuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, emmanuel.soriano@uneatlantico.es, SIN ESPECIFICAR (2022) An Analytical Framework for Innovation Determinants and Their Impact on Business Performance. Sustainability, 15 (1). p. 458. ISSN 2071-1050

Artículo Materias > Ingeniería
Materias > Comunicación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%. metadata R, Sudheesh; Mujahid, Muhammad; Rustam, Furqan; Shafique, Rahman; Chunduri, Venkata; Gracia Villar, Mónica; Brito Ballester, Julién; Diez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach. Information, 14 (9). p. 474. ISSN 2078-2489

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Industries need solutions that can automatically monitor oil leakage from deployed underwater pipelines and to rapidly report any damage. The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. Moreover, the problem of ores and mineral deposits on the seafloor comes into play. The abovementioned challenges necessitate for the deployment of underwater wireless sensor networks (UWSNs). Anchor-based localization techniques are segregated into range-free and range-based processes. Range-based schemes depend on various techniques like angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI). In this article, the localization of these leakages is performed by using range-based metrics for calculating the distance among anchor nodes (ANs) and target nodes (TNs). This estimated distance is further optimized to minimize the estimation error. A multilateralism procedure is used to estimate the optimal position of each TN. The results exhibit that the proposed algorithm shows a high performance when compared to previous works, in terms of minimum energy consumption, lower packet loss, rapid location estimation, and lowest localization error. The benefit of using the proposed methodology greatly impacts on identifying the leakage area in mobility-assisted UWSN, where rapid reporting helps to lower the loss of resources. metadata Goyal, Nitin; Nain, Mamta; Singh, Aman; Abualsaud, Khalid; Alsubhi, Khalid; Ortega-Mansilla, Arturo y Zorba, Nizar mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR (2022) An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring. IEEE Canadian Journal of Electrical and Computer Engineering, 45 (4). pp. 466-474. ISSN 2694-1783

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected. metadata Mir, Tahira Sarwar; Liaqat, Hannan Bin; Kiren, Tayybah; Sana, Muhammad Usman; Álvarez, Roberto Marcelo; Miró Vera, Yini Airet; Pascual Barrera, Alina Eugenia y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, alina.pascual@unini.edu.mx, SIN ESPECIFICAR (2022) Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing. Sensors, 22 (22). p. 8778. ISSN 1424-8220

Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Los programas educativos cada vez más se inclinan a la potenciación de valores que favorezcan el desarrollo integral de los educandos, para ello se implementan diversas fórmulas que pretenden desde lo metodológico ajustarse a las exigencias sociales, educativas y curriculares. En este acercamiento a la formación del Bachiller Ecuatoriano, se analizan sus principios legales, lineamientos curriculares y estándares de calidad educativa enfocado al cumplimiento del perfil de salida del bachillerato, así como la percepción de estos por parte de estudiantes y docentes de la Unidad Educativa del Milenio Manuel J. Calle de la ciudad de Cuenca, a partir de aquí se propone una estrategia de mejora con el uso del Método de Aprendizaje Basado en Proyectos (ABP), aplicada en una muestra de 92 estudiantes del 2do año del Bachillerato General Unificado (BGU), quienes cursaron el Programa de Participación Estudiantil (PPE), específicamente el PPE (2017-2018), cuyos resultados evidencian que el Método ABP empleado en el PPE caso de estudio contribuye significativamente a elevar la calidad del Perfil de Salida del Bachiller (PSB) por medio del desarrollo de habilidades para la vida. El Método de Aprendizaje Basado en Proyectos ABP es una alternativa adecuada para elevar el proceso formativo del país, a la vez facilita la convivencia armónica en el marco escolar para quienes la utilizan directa e indirectamente. metadata Jara Quito, Daysi Margoth; Martínez Sierra, Ricel y Orúe Sierra, Amalia Beatriz mail SIN ESPECIFICAR, ricel.martinez@unini.org, SIN ESPECIFICAR (2023) Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos. MLS Educational Research (MLSER), 7 (1).

Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés, Español Los programas educativos cada vez más se inclinan a la potenciación de valores que favorezcan el desarrollo integral de los educandos, para ello se implementan diversas fórmulas que pretenden desde lo metodológico ajustarse a las exigencias sociales, educativas y curriculares. En este acercamiento a la formación del Bachiller Ecuatoriano, se analizan sus principios legales, lineamientos curriculares y estándares de calidad educativa enfocado al cumplimiento del perfil de salida del bachillerato, así como la percepción de estos por parte de estudiantes y docentes de la Unidad Educativa del Milenio Manuel J. Calle de la ciudad de Cuenca, a partir de aquí se propone una estrategia de mejora con el uso del Método de Aprendizaje Basado en Proyectos (ABP), aplicada en una muestra de 92 estudiantes del 2do año del Bachillerato General Unificado (BGU), quienes cursaron el Programa de Participación Estudiantil (PPE), específicamente el PPE (2017-2018), cuyos resultados evidencian que el Método ABP empleado en el PPE caso de estudio contribuye significativamente a elevar la calidad del Perfil de Salida del Bachiller (PSB) por medio del desarrollo de habilidades para la vida. El Método de Aprendizaje Basado en Proyectos ABP es una alternativa adecuada para elevar el proceso formativo del país, a la vez facilita la convivencia armónica en el marco escolar para quienes la utilizan directa e indirectamente. metadata Orúe López, Amalia Beatriz; Martínez Sierra, Ricel y Jara Quito, Daysi Margoth mail SIN ESPECIFICAR, ricel.martinez@unini.org, daysi.jara@doctorado.unini.edu.mx (2023) Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos. MLS Educational Research, 7 (1). ISSN 2603-5820

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Se analizó la preferencia en el uso de la Biblioteca presencial y sus bases de datos virtuales con información fidedigna, que la Universidad posee frente al uso del Internet con sus datos fiables y también con información dudosa. Esto se llevó a cabo por los estudiantes de tercer y cuarto año de la Universidad de Puerto Rico Recinto Universitario de Mayagüez (UPRRUM). El propósito es ayudar al estudiante a utilizar el medio más correcto de conseguir información fidedigna en sus trabajos y tareas universitarias. No sólo se evaluará lo accesible que sea uno u otro, sino también se buscarán las maneras de llegar directamente a los estudiantes y que éstos desarrollen destrezas para usar los diferentes instrumentos de navegación asignados a la búsqueda de información. La investigación se centra en el método cuantitativo. De acuerdo al análisis de los objetivos de la investigación se concluye que al cuantificar con qué frecuencia los estudiantes acceden a la biblioteca física para la búsqueda de información se observó que aunque los estudiantes conocen los servicios que se ofrecen en ella no patrocinan el uso de ésta, para buscar información y realizar sus trabajos asignados. Dentro del renglón de análisis concluyente se observa que aunque los estudiantes conocen las facilidades de la biblioteca virtual, ésta demostró gran dificultad en los estudiantes para navegar por la base de datos de la misma, no siendo las que frecuentemente utilicen para realizar sus tareas universitarias. metadata Soto Paz, Pedro mail Kamelotstudios@hotmail.com (2019) Análisis de preferencias en la búsqueda de información; utilización de internet y/o biblioteca física. Caso de estudio en la universidad de Puerto Rico. MLS Educational Research, 3 (2). pp. 47-64. ISSN 26035820

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented. metadata Salinari, Alessia; Machì, Michele; Armas Diaz, Yasmany; Cianciosi, Danila; Qi, Zexiu; Yang, Bei; Ferreiro Cotorruelo, Maria Soledad; Gracia Villar, Santos; Dzul López, Luis Alonso; Battino, Maurizio y Giampieri, Francesca mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases, 11 (3). p. 97. ISSN 2079-9721

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. metadata Siddiqui, Hafeez-Ur-Rehman; Raza, Ali; Saleem, Adil Ali; Rustam, Furqan; Díez, Isabel de la Torre; Gavilanes Aray, Daniel; Lipari, Vivian; Ashraf, Imran y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features. Diagnostics, 13 (6). p. 1096. ISSN 2075-4418

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation. metadata Shafi, Imran; Khan, Harris; Farooq, Muhammad Siddique; Diez, Isabel de la Torre; Miró Vera, Yini Airet; Castanedo Galán, Juan y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, yini.miro@uneatlantico.es, juan.castanedo@uneatlantico.es, SIN ESPECIFICAR (2023) An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation. Energies, 16 (10). p. 4171. ISSN 1996-1073

Artículo Materias > Psicología Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés, Español La trata humana, es un fenómeno que crece en las entrañas de muchos países del mundo. Puerto Rico, no es la excepción a esta situación. En la investigación que se presenta en este artículo, se analizan las repercusiones que ha tenido la ausencia de protocolos de trata humana para menores de edad, en la lucha contra este fenómeno en Puerto Rico. Para lograr ese propósito, se realizó una investigación de enfoque mixto y diseño exploratorio. Respecto a las hipótesis del estudio, a través de estas, se intentó probar que las efectos negativos que acarrea la ausencia de protocolos, en la lucha contra este fenómeno, disminuirían con la presencia de protocolos de trata humana para menores en Puerto Rico. También, se auscultó si la identificación e inclusión de los factores correctos, en un protocolo para menores, podría mejorar las estrategias de detección de casos. Se utilizó la percepción y conocimiento de expertos que ofrecen servicios a la población de menores, en algunas agencias públicas de Puerto Rico y en algunas organizaciones no gubernamentales (ONG). La muestra seleccionada, fue no aleatoria y por disponibilidad. La técnica utilizada para obtener la información, fue la entrevista por medio de un cuestionario. El cuestionario, se redactó utilizando una escala Likert, además, se realizaron preguntas abiertas. Se cumplió con los objetivos principales de crear un prototipo de plan de prevención juvenil de trata huma e identificar los factores que debe incluir un protocolo de prevención y protección de la trata para menores en Puerto Rico. metadata I Alvarez, Nydia mail SIN ESPECIFICAR (2020) Ausencia de protocolos de prevención de trata humana para menores de edad en Puerto Rico. MLS Psychology Research, 3 (1). pp. 65-78. ISSN 26055295

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Project-based organizations need to procure different commodities, and the failure/success of a project depends heavily on procurement management. Companies must refine and develop methods to simplify and optimize the procurement process in a highly competitive environment. This paper presents a methodology to help managers of project-based organizations analyze procurement processes to determine the optimal framework for simultaneously addressing multiple objectives. These goals include minimizing the time between the generation and required approval for a purchase, identifying unnamed activities, and allocating the budget efficiently. In this paper, we apply process mining algorithms to a dataset consisting of event logs on Oracle Financials-based enterprise resource planning (ERP) procurement processes in ERP systems and demonstrate interesting results leading to project procurement intelligence (PPI). The provided log data is the real-life data consisting of 180,462 events referring to seven activities within 43,101 cases. The logged procurement processes are filtered and analyzed using the open-source process mining frameworks PrOM and Disco. As a result of the process mining activities, a simulation of the discovered process model derived from the event log of the entire procurement process is presented, and the most frequent potential behaviors are identified. This analysis and extraction of frequent processes from corporate event logs help organizations understand, adapt, and redesign procurement operations and, most importantly, make them more efficient and of higher quality. This study shows that after the successful formulation of guiding principles, data refinement, and process structure optimization, the case study results are considered significant by the organization’s management. metadata Butt, Naveed Anwer; Mahmood, Zafar; Sana, Muhammad Usman; Díez, Isabel de la Torre; Castanedo Galán, Juan; Brie, Santiago y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juan.castanedo@uneatlantico.es, santiago.brie@uneatlantico.es, SIN ESPECIFICAR (2023) Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach. Applied Sciences, 13 (7). p. 4145. ISSN 2076-3417

Artículo Materias > Ciencias Sociales Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español El objetivo del artículo fue analizar las bondades de la Bioeconomía frente a la enfermedad por coronavirus (COVID-19), erigiéndose como el nuevo paradigma que ayude a superar la recesión y facilite el tránsito a una nueva normalidad, como parte de la política pública boliviana a corto y largo plazo. Un análisis cualitativo permitió describir la situación que Bolivia está atravesando en el contexto de la Pandemia. Con enfoque cuantitativo y alcance descriptivo, se simuló, aplicando un modelo econométrico, la política productiva, a través de la inversión pública orientada a la producción de transables de base bioeconómica. Se confirmó que la Bioeconomía es una vía oportuna para reactivar la economía boliviana post pandemia. metadata Quiroga Canaviri, Jorge León mail SIN ESPECIFICAR (2021) Bioeconomía: retos de la política pública para enfrentar la enfermedad por coronavirus (COVID-19) en Bolivia. Panel - Revista de Administración, 3 (1). pp. 11-33. ISSN 2788-6557

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers’ trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system. metadata Alam, Shadab; Farooq, Muhammad Shoaib; Ansari, Zain Khalid; Alvi, Atif; Rustam, Furqan; Díez, Isabel De La Torre; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2024) Blockchain based transparent and reliable framework for wheat crop supply chain. PLOS ONE, 19 (1). e0295036. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes. metadata Iqbal, Faiza; Altaf, Ayesha; Waris, Zeest; Gavilanes Aray, Daniel; López Flores, Miguel Ángel; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, miguelangel.lopez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction. Sensors, 23 (11). p. 5263. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes metadata Chaganti, Rajasekhar; Rustam, Furqan; Daghriri, Talal; Díez, Isabel de la Torre; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model. Sensors, 22 (19). p. 7692. ISSN 1424-8220

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Alpha-linolenic acid (ALA) is a long-chain polyunsaturated essential fatty acid of the Ω3 series found mainly in vegetables, especially in the fatty part of oilseeds, dried fruit, berries, and legumes. It is very popular for its preventive use in several diseases: It seems to reduce the risk of the onset or decrease some phenomena related to inflammation, oxidative stress, and conditions of dysregulation of the immune response. Recent studies have confirmed these unhealthy situations also in patients with severe coronavirus disease 2019 (COVID-19). Different findings (in vitro, in vivo, and clinical ones), summarized and analyzed in this review, have showed an important role of ALA in other various non-COVID physiological and pathological situations against “cytokines storm,” chemokines secretion, oxidative stress, and dysregulation of immune cells that are also involved in the infection of the 2019 novel coronavirus. According to the effects of ALA against all the aforementioned situations (also present in patients with a severe clinical picture of severe acute respiratory syndrome-(CoV-2) infection), there may be the biologic plausibility of a prophylactic effect of this compound against COVID-19 symptoms and fatality. metadata Cianciosi, Danila; Diaz, Yasmany Armas; Gaddi, Antonio Vittorino; Capello, Fabio; Savo, Maria Teresa; Pali-Casanova, Ramón; Martínez Espinosa, Julio César; Pascual Barrera, Alina Eugenia; Navarro‐Hortal, Maria‐Dolores; Tian, Lingmin; Bai, Weibin; Giampieri, Francesca y Battino, Maurizio mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ramon.pali@unini.edu.mx, ulio.martinez@unini.edu.mx, alina.pascual@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, SIN ESPECIFICAR (2023) Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection? Food Frontiers. ISSN 2643-8429

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. metadata López-Izquierdo, Raúl; del Pozo Vegas, Carlos; Sanz-García, Ancor; Mayo Íscar, Agustín; Castro Villamor, Miguel A.; Silva Alvarado, Eduardo René; Gracia Villar, Santos; Dzul López, Luis Alonso; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Soriano, Joan B. y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digital Medicine, 7 (1). ISSN 2398-6352

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background The negative effects of COVID-19 infections during pregnancy have been amply described, however, the persistent sequels of this infection have not been explored so far. Objective The aim of this study was to describe persisting symptoms after COVID-19 infection in pregnant and non-pregnant women in Ecuador. Methods A cross-sectional analysis based on an online, self-reporting questionnaire was conducted in Ecuador from April to July 2022. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 457 surveys were included in this study. We compared risk factor variables and long-term persisting symptoms of pregnant and non-pregnant women in Ecuador. Results Overall, 247 (54.1 %) responders claimed to have long-term symptoms after SARS-CoV-2 infection. Most of these symptoms were reported by non-pregnant women (94.0 %). The most common Long-COVID symptoms in pregnant women were fatigue (10.6 %), hair loss (9.6 %), and difficulty concentrating (6.2 %). We found that pregnant women who smoked had a higher risk of suffering fatigue. Conclusions The most frequent Long-COVID symptoms in pregnant women were fatigue, hair loss, and difficulty concentrating. Apparently, the patterns of presentation of long-term sequelae of SARS-CoV-2 infection in pregnant women do not differ significantly from reports available from studies in the general population. metadata Vásconez-González, Jorge; Fernandez-Naranjo, Raul; Izquierdo Condoy, Juan Sebastian; Delgado-Moreira, Karen; Cordovez, Simone; Tello-De-la-Torre, Andrea; Paz, Clara; Castillo, Diana; Izquierdo-Condoy, Nathaly; Carrington, Sarah J. y Ortiz-Prado, Esteban mail SIN ESPECIFICAR (2023) Comparative analysis of long-term self-reported COVID-19 symptoms among pregnant women. Journal of Infection and Public Health, 16 (3). pp. 430-440. ISSN 18760341

Artículo Materias > Biomedicina
Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Aim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses. metadata Enriquez de Salamanca Gambara, Rodrigo; Sanz-García, Ancor; del Pozo Vegas, Carlos; López-Izquierdo, Raúl; Sánchez Soberón, Irene; Delgado Benito, Juan F.; Martínez Díaz, Raquel; Mazas Pérez-Oleaga, Cristina; Martínez López, Nohora Milena; Dominguez Azpíroz, Irma y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, raquel.martinez@uneatlantico.es, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, SIN ESPECIFICAR (2024) A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study. Diagnostics, 14 (12). p. 1292. ISSN 2075-4418

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues. metadata Shafi, Imran; Fatima, Anum; Afzal, Hammad; Díez, Isabel de la Torre; Lipari, Vivian; Breñosa, Jose y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2023) A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics, 13 (13). p. 2196. ISSN 2075-4418

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions metadata Farooq, Muhammad Shoaib; Suhail, Maryam; Qureshi, Junaid Nasir; Rustam, Furqan; de la Torre Díez, Isabel; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics. Sensors, 22 (21). p. 8582. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models metadata Hafeez, Rabab; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Fatima, Tayyaba; Martínez Espinosa, Julio César; Dzul López, Luis Alonso; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ulio.martinez@unini.edu.mx, luis.dzul@uneatlantico.es, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Contextual Urdu Lemmatization Using Recurrent Neural Network Models. Mathematics, 11 (2). p. 435. ISSN 2227-7390

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés This paper presents a current- and voltage-driven protection scheme for transmission lines based on a hybrid mix of Stockwell transform (ST) and Hilbert transform (HT). Use of both current and voltage waveforms to detect and categorize faults, improves the reliability of this protection scheme and avoids false tripping. Current and voltage waveforms captured during a period of fault are analyzed using ST to compute a median intermediate fault index (MIFI), a maximum value intermediate fault index (MVFI), and a summation intermediate fault index (SIFI). Current and voltage signals are analyzed via applying HT to compute a Hilbert fault index (HFI). The proposed hybrid current and voltage fault index (HCVFI) is obtained from the MIFI, MVFI, SIFI, and HFI. A threshold magnitude for this hybrid current and voltage fault index (HCVFITH) is set to 500 to identify the faulty phase. The HCVFIT is selected after testing the method for various conditions of different fault locations, different fault impedances, different fault occurrence angles, and reverse flows of power. Fault classification is performed using the number of faulty phases and an index for ground detection (IGD). The ground involved in a fault is detected by comparison of peak IGD magnitude with a threshold for ground detection (THGD). THGD is considered equal to 1000 in this study. The study is carried out using a two-terminal transmission line modeled in MATLAB software. The performance of the proposed technique is better compared to a discrete wavelet transform (DWT)-based technique, a time–frequency approach, and an alienation method. Our algorithm effectively detected an AG fault, observed on a practical transmission line. metadata Tang, Ligang; Mahela, Om Prakash; Khan, Baseem y Miró Vera, Yini Airet mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, yini.miro@uneatlantico.es (2023) Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques. Sustainability, 15 (7). p. 5715. ISSN 2071-1050

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models. metadata Rehman, Madiha; Anwer, Humaira; Garay, Helena; Alemany Iturriaga, Josep; Díez, Isabel De la Torre; Siddiqui, Hafeez ur Rehman y Ullah, Saleem mail SIN ESPECIFICAR, SIN ESPECIFICAR, helena.garay@uneatlantico.es, josep.alemany@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning. Sensors, 24 (21). p. 6965. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches metadata Fatima, Anum; Shafi, Imran; Afzal, Hammad; Mahmood, Khawar; Díez, Isabel de la Torre; Lipari, Vivian; Brito Ballester, Julién y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR (2023) Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection. Healthcare, 11 (3). p. 347. ISSN 2227-9032

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively. metadata Shafi, Imran; Mazhar, Muhammad Fawad; Fatima, Anum; Álvarez, Roberto Marcelo; Miró Vera, Yini Airet; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2023) Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance. Drones, 7 (1). p. 31. ISSN 2504-446X

Artículo Materias > Ingeniería
Materias > Psicología Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances. metadata Rizwan, Muhammad; Mushtaq, Muhammad Faheem; Rafiq, Maryam; Mehmood, Arif; Diez, Isabel de la Torre; Gracia Villar, Mónica; Garay, Helena y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, helena.garay@uneatlantico.es, SIN ESPECIFICAR (2024) Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble. Computers, Materials & Continua, 78 (2). pp. 2047-2066. ISSN 1546-2226

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Este trabalho apresenta o desafio e perspectivas da educação durante a Pandemia. O uso de tecnologias no ambiente escolar é essencial na contemporaneidade, devido ao cenário atual com a Pandemia de Coronavírus. Tratam-se de métodos praticamente indispensáveis para o dia a dia do ser humano nos dias atuais. As tecnologias adentraram o âmbito escolar, objetivando uma melhor qualidade no ensino, além de mais praticidade para o docente e para os alunos. Diante do exposto, esta pesquisa visa apresentar a defasagem na alfabetização. Para tal, foram realizadas pesquisas bibliográficas de cunho qualitativo e caráter descritivo, realizadas em artigos científicos, livros e acervos online. metadata Alves Guimarães, Ueudison; Aparecida dos Santos, Leidiane y Rodrigues Dantas de Brito, Junea Graciele mail SIN ESPECIFICAR (2022) Desafios e perspectivas de educação: una visão dos professores durante a pandemia. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (8). e381745. ISSN 2675-6218

Artículo Materias > Ciencias Sociales
Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español La concepción sobre Competencia Comunicativa Intercultural (CCI) como mediación entre culturas no permite observar con detenimiento la dinámica cultural como una narrativa que los individuos y las comunidades van transformando constantemente. Para observar y hacer estos cambios palpables en la formación de identidades en los estudiantes de la clase de español como lengua extranjera (ELE), se presenta un ejemplo de instrucción explícita en reflexión crítica sobre formación de identidades, educación emocional, etnografía participante, y literacidad crítica con el fin de examinar nociones claves como identidad, memoria histórica, estereotipos, y (pos)conflicto armado en Colombia. El contexto es una clase de nivel C1 según el Marco Común Europeo de Referencia (MCER) en una universidad privada de Bogotá, Colombia. metadata González Montejo, Félix mail SIN ESPECIFICAR (2019) Desarrollo de la competencia comunicativa intercultural: formación de identidades en un contexto colombiano. marcoELE. Revista de Didáctica Español Lengua Extranjera (28). ISSN 1885-2211

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Español Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans metadata Shafi, Imran; Farooq, Muhammad Siddique; De La Torre Díez, Isabel; Breñosa, Jose; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2022) Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics. Healthcare, 10 (11). p. 2174. ISSN 2227-9032

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate. metadata Islam, Md. Milon; Shafi, Imran; Din, Sadia; Farooq, Siddique; Díez, Isabel de la Torre; Breñosa, Jose; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2024) Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing. PLOS ONE, 19 (3). e0298582. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Retinitis pigmentosa (RP) is a group of genetic retinal disorders characterized by progressive vision loss, culminating in blindness. Identifying pigment signs (PS) linked with RP is crucial for monitoring and possibly slowing the disease’s degenerative course. However, the segmentation and detection of PS are challenging due to the difficulty of distinguishing between PS and blood vessels and the variability in size, shape, and color of PS. Recently, advances in deep learning techniques have shown impressive results in medical image analysis, especially in ophthalmology. This study presents an approach for classifying pigment marks in color fundus images of RP using a modified squeeze-and-excitation ResNet (SE-ResNet) architecture. This variant synergizes the efficiency of residual skip connections with the robust attention mechanism of the SE block to amplify feature representation. The SE-ResNet model was fine-tuned to determine the optimal layer configuration that balances performance metrics and computational costs. We trained the proposed model on the RIPS dataset, which comprises images from patients diagnosed at various RP stages. Experimental results confirm the efficacy of the proposed model in classifying different types of pigment signs associated with RP. The model yielded performance metrics, such as accuracy, sensitivity, specificity, and f-measure of 99.16%, 97.70%, 96.93%, 90.47%, 99.37%, 97.80%, 97.44%, and 90.60% on the testing set, based on GT1 & GT2 respectively. Given its performance, this model is an excellent candidate for integration into computer-aided diagnostic systems for RP, aiming to enhance patient care and vision-related healthcare services. metadata Rashid, Rubina; Aslam, Waqar; Mehmood, Arif; Ramírez-Vargas, Debora L.; Diez, Isabel De La Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images. IEEE Access, 12. pp. 28297-28309. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Requirements specifications written in natural language enable us to understand a program’s intended functionality, which we can then translate into operational software. At varying stages of requirement specification, multiple ambiguities emerge. Ambiguities may appear at several levels including the syntactic, semantic, domain, lexical, and pragmatic levels. The primary objective of this study is to identify requirements’ pragmatic ambiguity. Pragmatic ambiguity occurs when the same set of circumstances can be interpreted in multiple ways. It requires consideration of the context statement of the requirements. Prior research has developed methods for obtaining concepts based on individual nodes, so there is room for improvement in the requirements interpretation procedure. This research aims to develop a more effective model for identifying pragmatic ambiguity in requirement definition. To better interpret requirements, we introduced the Concept Maximum Matching (CMM) technique, which extracts concepts based on edges. The CMM technique significantly improves precision because it permits a more accurate interpretation of requirements based on the relative weight of their edges. Obtaining an F-measure score of 0.754 as opposed to 0.563 in existing models, the evaluation results demonstrate that CMM is a substantial improvement over the previous method. metadata Aslam, Khadija; Iqbal, Faiza; Altaf, Ayesha; Hussain, Naveed; Gracia Villar, Mónica; Soriano Flores, Emmanuel; Diez, Isabel De La Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Alimentación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español En la presente investigación, se evaluó la presencia de E. coli, hongos y levaduras en la harina proveniente de las cascarillas de las variedades de cacao Nacional Arriba y el cacao CCN51 en Ecuador, para su uso en los procesos de elaboración de galletas, panes y pasteles. Se tomaron cinco muestras de 2000 g cada una, de 5 lotes diferentes para cada variedad de cacao en dos industrias cacaoteras en la provincia del Guayas. Se realizó la molienda con un molino marca Oster, se homogenizaron las muestras y se empacaron 500 g de las cáscaras molidas de cada variedad en fundas de polietileno de baja densidad, para poder realizar los análisis microbiológicos. Las muestras fueron enviadas a un laboratorio certificado para realizar los análisis correspondientes. Debido a que no existe alguna norma de calidad para la harina proveniente de cascarillas de cacao en Ecuador, se aplicaron las normas INEN 616 e INEN 621, las cuales definen los requisitos que debe cumplir la harina de trigo y los chocolates respetivamente para que sea apta para el consumo humano, comprobando que la harina proveniente de las cascarillas de ambas variedades cumple con los criterios establecidos en estas normas. metadata El Salous, Ahmed y Pascual Barrera, Alina Eugenia mail SIN ESPECIFICAR, alina.pascual@unini.edu.mx (2018) Determinación de e. Coli, hongos y levaduras en la harina proveniente de las cascarillas de dos variedades de cacao en Ecuador. Revista Universidad y Sociedad, 10 (1). pp. 164-167.

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide. metadata Alam, Md Nuho Ul; Hasnine, Ibrahim; Bahadur, Erfanul Hoque; Masum, Abdul Kadar Muhammad; Briones Urbano, Mercedes; Masías Vergara, Manuel; Uddin, Jia; Ashraf, Imran y Samad, Md. Abdus mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, mercedes.briones@uneatlantico.es, manuel.masias@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network. Journal of Big Data, 11 (1). ISSN 2196-1115

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Objective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions. metadata Khalid, Madiha; Raza, Ali; Akhtar, Adnan; Rustam, Furqan; Brito Ballester, Julién; Rodríguez Velasco, Carmen Lilí; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, julien.brito@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data. DIGITAL HEALTH, 10. ISSN 2055-2076

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Las metodologías para el diseño y gestión de proyectos son cada vez más necesarias y aplicadas con mayor frecuencia en el sector público en Latinoamérica. Continuamente hay actualizaciones y nuevos enfoques en la gestión de proyectos de inversión, por lo que el estudio en las metodologías es relevante a nivel investigativo. El diseño de instrumentos de investigación confiables que sirvan para promover el uso de estas metodologías es importante para asegurar la calidad en el proceso. Por lo que el objetivo de este estudio es diseñar y validar un instrumento que permita recolectar y gestionar sistemáticamente información de proyectos para obtener las variables que permitan definir la metodología apropiada para cada organización, en este estudio se ha tomado como referencia en el sector público la Subsecretaría de Recursos Pesqueros (SRP) en Ecuador. El instrumento, toma como referencia la Norma International Organization for Standardization (ISO) 10006, la Guía de Fundamentos de Gestión de Proyectos, por su nombre en inglés Project Management Body of Knowledge (PMBOK), las Metodologías de Diseño de Proyectos de la Universidad Politécnica de Cataluña (MDP-UPC) y de la Secretaría Nacional de Planificación y Desarrollo (SENPLADES) del Ecuador. Como resultado, se desarrolló una encuesta, a cuyo instrumento se realizó la validación interna y externa en función de parámetros de confiabilidad, contenido y constructo. Se realizó análisis factorial para determinar variables utilizando sistema estadístico SPSS. Finalmente, se cuenta con la validación del instrumento diseñado asegurando que es confiable y cumple con los parámetros necesarios para obtener variables que definan la metodología para elaboración de proyectos en el sector público de Ecuador. metadata Bazurto Roldán, José Antonio; Álvarez, Roberto Marcelo; Miró Vera, Yini Airet y Brie, Santiago mail jose.bazurto@unini.org, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, santiago.brie@uneatlantico.es (2023) Diseño y validación de un instrumento de investigación para proponer metodología de gestión de proyectos. Revista de Iniciación Científica, 9 (1). ISSN 2412-0464

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. metadata Ramzan, Mahrukh; Shoaib, Muhammad; Altaf, Ayesha; Arshad, Shazia; Iqbal, Faiza; Kuc Castilla, Ángel Gabriel y Ashraf, Imran mail SIN ESPECIFICAR (2023) Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm. Sensors, 23 (20). p. 8642. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. metadata Farooq, Hamza; Altaf, Ayesha; Iqbal, Faiza; Castanedo Galán, Juan; Gavilanes Aray, Daniel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juan.castanedo@uneatlantico.es, daniel.gavilanes@uneatlantico.es, SIN ESPECIFICAR (2023) DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents. Sensors, 23 (12). p. 5388. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences. metadata Kaur, Ramneet; Gupta, Deepali; Madhukar, Mani; Singh, Aman; Abdelhaq, Maha; Alsaqour, Raed; Breñosa, Jose y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2022) E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation. Electronics, 11 (20). p. 3354. ISSN 2079-9292

Artículo Materias > Alimentación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés University students frequently develop unhealthy eating habits. However, it is unknown if students enrolled in academic programs related to nutrition and culinary arts have healthier eating habits. We evaluated the relationship of eating habits and nutritional status of students in academic programs with knowledge on nutrition, as well as cooking methods and techniques. A descriptive cross-sectional study was conducted in spring of 2019, while we completed a survey measuring eating habits and knowledge on nutrition, as well as cooking methods and techniques. Anthropometric measurements were collected for nutritional status estimation. The non-probabilistic convenience sample comprised 93 students pursuing degrees at Universidad Ana G. Mendez, Puerto Rico. Inadequate body mass index (BMI) was observed in 59% of the students. Eating habits, knowledge on nutrition, and knowledge on cooking methods and techniques were inadequate in 86%, 68%, and 41% of the population, respectively. Eating habits were associated with knowledge on nutrition and academic program, but not with knowledge on cooking methods and techniques. Most students reported having inadequate eating habits and BMI. Nutrition and dietetics students had the best knowledge on nutrition compared to culinary management students, a majority of whom had inadequate knowledge. We can conclude that there are other factors inherent to students’ life that may have a stronger influence on eating habits metadata Rivera Medina, Christian; Briones Urbano, Mercedes; de Jesús Espinosa, Aixa y Toledo López, Ángel mail SIN ESPECIFICAR, mercedes.briones@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2020) Eating Habits Associated with Nutrition-Related Knowledge among University Students Enrolled in Academic Programs Related to Nutrition and Culinary Arts in Puerto Rico. Nutrients, 12 (5). p. 1408. ISSN 2072-6643

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español El objetivo de este estudio fue determinar eclécticamente cuáles son aquellos métodos y estrategias que mejorarían el proceso de enseñanza-aprendizaje de la lengua española. El estudio fue del tipo cuantitativo, cuyo diseño corresponde a estudios críticos sobre métodos de enseñanza y aprendizaje de idiomas y las estrategias específicas de cada uno de ellos. La muestra estuvo conformada por 80 estudiantes de una escuela secundaria de Massachusetts, EEUU. La técnica de recolección de datos fue el Inventario Estratégico para el Aprendizaje de Idiomas y las herramientas empleadas fueron el cuestionario y examen de Pre y Post-Prueba de nivel gramatical del español básico. Según los resultados obtenidos los mejores métodos para el aprendizaje de la lengua española se basan en que el alumno esté en mayor contacto con ella mediante actividades auditivas, visuales, conversacionales, lectivas y de escritura. Se concluye que no hay un método que se pueda denominar como el mejor. Se demostró que, a mayor exposición a la lengua extranjera, mejor es el resultado en el dominio y uso de la misma. metadata Altamar Manga, René mail SIN ESPECIFICAR (2021) Eclecticismo hacia los métodos de enseñanza-aprendizaje del español como segunda lengua en alumnos de Secundaria. TecnoHumanismo, 1 (2). pp. 1-20.

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O objetivo foi discutir sobre a educação em contexto: experiências e práticas docentes. O conhecimento empírico vem da história de vida pessoal de cada professor e é um conhecimento gerado pelos professores em sua prática diária. A partir dessa questão, buscamos possíveis fontes de conhecimento empírico e sua possível relação com a formação docente, a prática docente e sua atuação nas escolas, buscando encontrar caminhos para qualificar a prática docente. O método de revisão de literatura permite a inclusão de pesquisas experimentais e não experimentais, a combinação da obtenção de dados empíricos e teóricos, pode levar à definição de conceitos, identificação de lacunas no campo da pesquisa, revisão teórica e análise de métodos de pesquisa sobre um determinado tema. O desenvolvimento desse método requer recursos, conhecimentos e habilidades metadata Rodrigues Dantas de Brito, Junea Graciele y Alves Guimarães, Ueudison mail SIN ESPECIFICAR (2022) Educação em contexto: experiências e práticas. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (4). e341396. ISSN 2675-6218

Artículo Materias > Educación física y el deporte Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Introducción: Las caídas se consideran como uno de los síndromes geriátricos más importantes por su alta incidencia en los adultos mayores de 65 años. Las caídas pueden generar diversas e importantes consecuencias físicas y/o psicológicas, deterioro funcional, dependencia e incluso la muerte. Objetivo: Determinar la efectividad del entrenamiento propioceptivo para prevenir el riesgo de caídas en el adulto mayor de 65 años residente en un hogar de reposo en el km 1 vía a Dapa, Valle del Cauca. Metodología: Se realizó una investigación cuasiexperimental de corte transversal, con muestra no probabilística constituida por 12 mujeres y 3 hombres adultos mayores de 65 años residentes en un hogar de reposo, participando de manera voluntaria en un entrenamiento propioceptivo de 6 semanas, dos veces a la semana durante los meses de marzo y abril de 2021. La factibilidad de la propuesta de ejercicios propioceptivos se validó a partir de la técnica de investigación grupo nominal. Los resultados incluyeron las pruebas Short Physical Performance Battery (SPPB) y Timed up and go (TUG) evaluadas pre y post intervención. Resultados: Hubo diferencias significativas en el nivel de funcionalidad pre- post intervención, (p<0,05), las dos variables (nivel de riesgo de caída y nivel de funcionalidad) se correlacionan en sentido inverso (p<0,05). Conclusiones: El entrenamiento propioceptivo es efectivo para mejorar el equilibrio estático/dinámico, la velocidad de la marcha y fuerza de extremidades inferiores en los adultos mayores de 65 años que residen en un hogar de reposo. metadata Vélez Alape, Natalia; Hernández Cruz, Leonardo de Jesús y Velarde-Sotres, Álvaro mail SIN ESPECIFICAR, leonardo.hernandez@unib.org, alvaro.velarde@uneatlantico.es (2022) Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores. MLS Sport Research, 2 (2).

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients. metadata Shafi, Imran; Din, Sadia; Khan, Asim; Díez, Isabel De La Torre; Pali-Casanova, Ramón; Tutusaus, Kilian y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ramon.pali@unini.edu.mx, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR (2022) An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14 (21). p. 5457. ISSN 2072-6694

Artículo Materias > Biomedicina
Materias > Ingeniería
Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections. metadata Sumbul, X.; Sultana, Arshiya; Heyat, Md Belal Bin; Rahman, Khaleequr; Akhtar, Faijan; Parveen, Saba; Briones Urbano, Mercedes; Lipari, Vivian; De la Torre Díez, Isabel; Khan, Azmat Ali y Malik, Abdul mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, mercedes.briones@uneatlantico.es, vivian.lipari@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning. Frontiers in Chemistry, 12. ISSN 2296-2646

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Objective: This study aims to determine the efficacy of the Acacia arabica (Lam.) Willd. and Cinnamomum camphora (L.) J. Presl. vaginal suppository in addressing heavy menstrual bleeding (HMB) and their impact on participants' health-related quality of life (HRQoL) analyzed using machine learning algorithms. Method: A total of 62 participants were enrolled in a double-dummy, single-center study. They were randomly assigned to either the suppository group (SG), receiving a formulation prepared with Acacia arabica gum (Gond Babul) and camphor from Cinnamomum camphora (Kafoor) through two vaginal suppositories (each weighing 3,500 mg) for 7 days at bedtime along with oral placebo capsules, or the tranexamic group (TG), receiving oral tranexamic acid (500 mg) twice a day for 5 days and two placebo vaginal suppositories during menstruation at bedtime for three consecutive menstrual cycles. The primary outcome was the pictorial blood loss assessment chart (PBLAC) for HMB, and secondary outcomes included hemoglobin level and SF-36 HRQoL questionnaire scores. Additionally, machine learning algorithms such as k-nearest neighbor (KNN), AdaBoost (AB), naive Bayes (NB), and random forest (RF) classifiers were employed for analysis. Results: In the SG and TG, the mean PBLAC score decreased from 635.322 ± 504.23 to 67.70 ± 22.37 and 512.93 ± 283.57 to 97.96 ± 39.25, respectively, at post-intervention (TF3), demonstrating a statistically significant difference (p < 0.001). A higher percentage of participants in the SG achieved normal menstrual blood loss compared to the TG (93.5% vs 74.2%). The SG showed a considerable improvement in total SF-36 scores (73.56%) compared to the TG (65.65%), with a statistically significant difference (p < 0.001). Additionally, no serious adverse events were reported in either group. Notably, machine learning algorithms, particularly AB and KNN, demonstrated the highest accuracy within cross-validation models for both primary and secondary outcomes. Conclusion: The A. arabica and C. camphora vaginal suppository is effective, cost-effective, and safe in controlling HMB. This botanical vaginal suppository provides a novel and innovative alternative to traditional interventions, demonstrating promise as an effective management approach for HMB. metadata Fazmiya, Mohamed Joonus Aynul; Sultana, Arshiya; Heyat, Md Belal Bin; Parveen, Saba; Rahman, Khaleequr; Akhtar, Faijan; Khan, Azmat Ali; Alanazi, Amer M.; Ahmed, Zaheer; Díez, Isabel de la Torre; Brito Ballester, Julién y Saripalli, Tirumala Santhosh Kumar mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, julien.brito@uneatlantico.es, SIN ESPECIFICAR (2024) Efficacy of a vaginal suppository formulation prepared with Acacia arabica (Lam.) Willd. gum and Cinnamomum camphora (L.) J. Presl. in heavy menstrual bleeding analyzed using a machine learning technique. Frontiers in Pharmacology, 15. ISSN 1663-9812

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Mujahid, Muhammad; Rustam, Furqan; Shafique, Rahman; Caro Montero, Elizabeth; Silva Alvarado, Eduardo René; de la Torre Diez, Isabel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Efficient deep learning-based approach for malaria detection using red blood cell smears. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués A física relaciona-se com as necessidades humanas básicas, saúde, moradia, alimentação, transporte e muito mais. No entanto, a física tem demonstrado ter uma das maiores taxas de reprovação nas escolas há algum tempo. Muitos alunos veem isso como: muito difícil, abstrato e irrelevante para a vida cotidiana. No entanto, alguns pesquisadores atribuem essa percepção aos métodos tradicionais de ensino utilizados nas escolas, que dão mais ênfase à memorização de fórmulas, fatos, teorias, símbolos e modelos ao invés de proporcionar aos alunos a contextualização do conteúdo ao invés de se preocupar em explorar o contexto em que leis e teorias são apresentados, resultando na dogmatização do conhecimento científico. Portanto, o objetivo deste estudo foi compreender o processo de desenvolvimento desde o início da eletricidade até sua aplicação prática em escala comercial. Para tanto, foram realizadas revisões bibliográficas de literaturas científicas. O processo da geração à distribuição de energia elétrica, enfatizando o contexto histórico e social, promove o debate, a investigação e vincula o conhecimento físico à vida cotidiana, promovendo a compreensão do que se estuda metadata Alves Guimarães, Ueudison; Rodrigues Dantas de Brito, Junea Graciele y Olímpio dos Santos, José mail SIN ESPECIFICAR (2022) Eletricidade estática: o processo da geração à distribuição de energia elétrica, enfatizando o contexto histórico e social. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (9). e391942. ISSN 2675-6218

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%. metadata Qazi, Awais Salman; Farooq, Muhammad Shoaib; Rustam, Furqan; Gracia Villar, Mónica; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) Emotion Detection Using Facial Expression Involving Occlusions and Tilt. Applied Sciences, 12 (22). p. 11797. ISSN 2076-3417

Artículo Materias > Educación
Materias > Comunicación
Materias > Psicología Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Communication professionals are experiencing a growing level of exposure to traumatic events as a result of their involvement in the coverage of various tragedies, including accidents, climatic disasters, rights violations, and acts of terrorism. However, it is worth noting that journalism and communication university courses often lack comprehensive instruction on effectively managing emotional challenges, anxiety, trauma, self-care, and the prevention of vicarious trauma. The objective of this study is to assess the inclusion of emotional management within the curricula of Journalism and Communication programmes offered by two universities in Catalonia, namely the University of Barcelona and the Autonomous University of Barcelona. In order to accomplish this objective, a series of semi-structured interviews were carried out with a total of twelve (12) professors who specialise in the fields of Journalism and Communication. Additionally, a thorough analysis was conducted on a set of 97 study plan guides. The results indicate that none of the participants in the interviews possess knowledge regarding any existing training programmes focused on emotional management. Furthermore, they unanimously agree on the importance of implementing such courses. The study plans did not include any subjects that were specifically dedicated to the topic of emotional management. This study presents a set of strategies aimed at creating a cross-disciplinary teaching-learning model that offers a comprehensive educational experience for students. This entails integrating precise subject matter on the previously mentioned topics, fostering critical contemplation and discourse regarding emotions within the educational setting, and advocating for ethical and sound professional behaviours. metadata Escudero, Carolina; Prola, Thomas; Fraga, Leticia y Soriano Flores, Emmanuel mail SIN ESPECIFICAR, thomas.prola@uneatlantico.es, leticia.fraga@uneatlantico.es, emmanuel.soriano@uneatlantico.es (2023) Emotional Management in Journalism and Communication Studies. Social Space, 23 (2). pp. 507-534.

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions. metadata Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Gracia Villar, Santos; Dzul Lopez, Luis; Diez, Isabel de la Torre; Rustam, Furqan y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics, 13 (18). p. 2881. ISSN 2075-4418

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results. metadata Rustam, Furqan; Al-Shamayleh, Ahmad Sami; Shafique, Rahman; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Gonzalez, J. Pablo Miramontes y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations. Prompt diagnosis and appropriate treatment are crucial in managing thyroid disorders and improving patients’ quality of life. Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. An open-access thyroid disease dataset based on 3,772 male and female patient observations is used for this research experiment. This study uses the nominal continuous synthetic minority oversampling technique (SMOTE-NC) for data balancing and a fine-tuned light gradient booster machine (LGBM) technique to diagnose thyroid illness and handle class imbalance problems. The proposed SNL (SMOTE-NC-LGBM) approach outperformed the state-of-the-art approach with high-accuracy performance scores of 0.96. We have also applied advanced machine learning and deep learning methods for comparison to evaluate performance. Hyperparameter optimizations are also conducted to enhance thyroid diagnosis performance. In addition, we have applied the explainable Artificial Intelligence (XAI) mechanism based on Shapley Additive exPlanations (SHAP) to enhance the transparency and interpretability of the proposed method by analyzing the decision-making processes. The proposed research revolutionizes the diagnosis of thyroid disorders efficiently and helps specialties overcome thyroid disorders early. metadata Raza, Ali; Eid, Fatma; Caro Montero, Elisabeth; Delgado Noya, Irene y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, elizabeth.caro@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR (2024) Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models. BMC Medical Informatics and Decision Making, 24 (1). ISSN 1472-6947

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality. metadata Siddiqui, Hafeez Ur Rehman; Younas, Faizan; Rustam, Furqan; Soriano Flores, Emmanuel; Brito Ballester, Julién; Diez, Isabel de la Torre; Dudley, Sandra y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, emmanuel.soriano@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors, 23 (15). p. 6839. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an "Ensemble Partition Sampling" method within the "one-vs-all" (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. The findings suggest that EPS is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets. metadata Jabir, Brahim; Díez, Isabel De la Torre; Bautista Thompson, Ernesto; Ramírez-Vargas, Debora L. y Kuc Castilla, Ángel Gabriel mail SIN ESPECIFICAR (2023) Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español La presencia de nuevos avances tecnológicos en el ámbito educativo, exigen una intensa intervención y adaptación hacia el uso de metodologías relacionadas con las tecnologías de la información y las comunicaciones (TICs) que contribuyan al mejoramiento del rendimiento académico de los estudiantes. El objetivo de este artículo fue comprobar si existió un impacto favorable en el rendimiento escolar con el uso de la metodología de Aula Invertida en comparación con el uso de la metodología tradicional en los estudiantes de los niveles noveno y décimo grado en los cursos de español, inglés y matemáticas. Por otro lado, también se intentó dar una breve descripción del concepto de Aula invertida con la revisión de algunas aportaciones de investigadores como aporte hacia el estudiante y al profesorado. Este estudio es de corte cuantitativo y con un diseño cuasi-experimental. La muestra fue de 116 estudiantes y se dividió en grupo control (grupos de 65 estudiantes) y grupo experimental (grupos de 51 estudiantes) de cada asignatura con un total de 116 estudiantes. Como variable independiente, los instrumentos aplicados fueron sobre el rendimiento académico de cada unidad (pruebas pre-post), cuyos resultados fueron analizados utilizando la prueba estadística t de Student para comprobar su fiabilidad. Para el análisis estadístico se utilizó la aplicación IBM SPSS Versión 22. Con respecto a los resultados obtenidos, no se observó una diferencia estadísticamente significativa entre las pruebas pre-post entre los grupos experimental y grupos control motivos que se darán a conocer dentro de la discusión y conclusión del texto. Nunca he visto esto en un resumen, lo normal es que las pongan aquí. metadata Schmeisser, Carolina y Medina-Talavera, José mail cschmeisser@boscotech.edu, jose.medina@unini.org (2018) Estudio comparativo entre metodología de aula invertida y metodología tradicional en clases de español, inglés y matemáticas. MLS Educational Research, 2 (2). pp. 159-176. ISSN 26035820

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español El estudio que se presenta estuvo dirigido a la exploración de la percepción de estudiantes de Bachillerato en Enfermería sobre la utilización de la Evaluación Clínica Objetiva Estructurada (ECOE / OSCE) en cursos de enfermería en una universidad en Puerto Rico. La ECOE, es una metodología educativa internacionalmente reconocida por su validez y fiabilidad para evaluar las competencias clínicas en los profesionales de ciencias de la salud de manera formativa y (o) sumativa. El paradigma de la investigación es cuantitativo no experimental descriptivo transversal. Los datos fueron recopilados, mediante la aplicación de un cuestionario semiestructurado con preguntas abiertas y cerradas utilizando la escala Likert. Para el análisis estadístico se utilizaron medidas de tendencia central y dispersión, frecuencias, porcentajes y el coeficiente de correlación de Pearson. Preguntas abiertas relacionadas con fortalezas, debilidades y recomendaciones relacionadas a la ECOE mencionadas por los estudiantes, fueron examinadas mediante el análisis de contenido. Los resultados de la distribución porcentual y absoluta de los estudiantes por ítems del cuestionario utilizado para este estudio, revelaron que los participantes percibían la ECOE como una herramienta de ayuda en la evaluación de destrezas clínicas en enfermería. Los resultados comprobaron, además, la efectividad de la ECOE para medir el logro de las competencias profesionales en cursos de enfermería. Mediante las propias expresiones de los estudiantes en cuanto a las fortalezas y debilidades de la ECOE, pudieron identificarse áreas a mejorar al utilizar la misma en cursos de enfermería. metadata Rivera Vélez, Reina del Carmen mail rerivera@suagm.edu (2018) Estudio sobre la percepción de los estudiantes de Bachillerato en Enfermería al utilizar la evaluación clínica objetiva estructurada (ECOE) en simulación clínica en cursos de Enfermería en Puerto Rico. MLS Educational Research, 2 (2). pp. 211-225. ISSN 26035820

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects. metadata García Villena, Eduardo; Pascual Barrera, Alina Eugenia; Álvarez, Roberto Marcelo; Dzul López, Luis Alonso; Tutusaus, Kilian; Vidal Mazón, Juan Luis; Miró Vera, Yini Airet; Brie, Santiago y López Flores, Miguel A. mail eduardo.garcia@uneatlantico.es, alina.pascual@unini.edu.mx, roberto.alvarez@uneatlantico.es, luis.dzul@uneatlantico.es, kilian.tutusaus@uneatlantico.es, juanluis.vidal@uneatlantico.es, yini.miro@uneatlantico.es, santiago.brie@uneatlantico.es, miguelangel.lopez@uneatlantico.es (2022) Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean. Applied Sciences, 12 (21). p. 11188. ISSN 2076-3417

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background: The rapid spread of the SARS-CoV-2 virus, especially during the first year of the COVID-19 pandemic, has caused an unprecedented health crisis worldwide. Fear of getting infected, and the high mortality rates in some places, prompted the general population to engage in self-medication practices. Methods: We conducted a cross-sectional analysis of Ecuador's prescription and self-medication consumption trends during the first two years COVID-19 pandemic. Data came from an integrated countrywide database of the physician prescribing trends, the use of over-the-counter medicines, (OTC) and the medicine-related spending levels through the COVID-19 pandemic in Ecuador. We compared the absolute difference in monthly and yearly demand and calculated excessive expenditure from previous years. Findings: We found that in Ecuador, the pre-pandemic (2017-2019) yearly expenditure among these ATC groups was, on average, $150’646,206 while during 2020 and 2021, the same groups represented $228.327.210, a significant 52% increase. Of this amount, 13% were OTC Medicines, and 87% required a formal prescription. The most remarkable growth in drug sales came from ivermectin with 2,057%, followed by hydroxychloroquine with 171%. Interpretation: Our study shows that people consumed large quantities of medicines during the first two years of the pandemic in Ecuador, including drugs with no proven benefit to treat or reduce the risk of progression due to COVID-19. We suggest that the lack of local prescription guidelines and prescription control, as well as generalized fear and misinformation led doctors and patients to prescribe and consume vast amounts of unnecessary medicines. metadata Ortiz-Prado, Esteban; Izquierdo Condoy, Juan Sebastian; Mora, Carla; Vásconez-González, Jorge y Fernández, Raúl mail SIN ESPECIFICAR (2022) Excessive Sales of Pharmaceutical Drugs in a Low and Middle-Income Country During the COVID-19 Pandemic: The Case of Ecuador. SSRN Electronic Journal. ISSN 1556-5068 (En Evaluación)

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Vaccination coverage in Ecuador has decreased since 2013, falling short of the World Health Organization’s vaccination goal. There are several causes for this deficiency in coverage, one of these are lost vaccination opportunities, which are caused when a patient without contraindications postpones, or for other reasons fails to receive a recommended immunization. The objective of this study was to determine the state of knowledge regarding vaccination contraindications among the Metropolitan District of Quito health personnel to assess missed vaccination opportunities. Through this cross-sectional descriptive study, health personnel were surveyed online and asked 18 clinical scenarios which were created to evaluate their knowledge of the true contraindications of vaccination, and measure missed opportunities. A total of 273 surveys were collected; 74% belonged to the public health system, and the rest represented by private practitioners. Of those surveyed, 98.2% of health personnel had improperly denied vaccination at least once. We specifically found vaccinations were incorrectly denied more frequently in cases where the hypothetical patient presented mild or moderate fever cases. The use of corticosteroids, autoimmune diseases, and egg allergy were also incorrectly denied (89%, 71.4%, 72.9%, and 58.6%, respectively). Among the health personnel surveyed, there is an apparent lack of knowledge of the true contraindications of vaccination and differences in knowledge about contraindications according to personnel in charge of administering immunization to children. Our preliminary results suggest that lack of education related to side effects could be biasing medical professionals’ decisions, causing them to unnecessarily delay or deny vaccinations, which likely contributes to explaining low overall vaccination coverage in Quito, the capital city of Ecuador. metadata Andrade-Guerrero, Felipe; Tapia, Adriana; Andrade, Vinicio; Vásconez-González, Jorge; Andrade-Guerrero, José; Noroña-Calvachi, Carlos; Izquierdo Condoy, Juan Sebastian; Yeager, Justin y Ortiz-Prado, Esteban mail SIN ESPECIFICAR (2022) False Contraindications for Vaccinations Result in Sub-Optimal Vaccination Coverage in Quito, Ecuador: A Cross-Sectional Study. Vaccines, 11 (1). p. 60. ISSN 2076-393X

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Shaha, Tumpa Rani; Begum, Momotaz; Uddin, Jia; Yélamos Torres, Vanessa; Alemany Iturriaga, Josep; Ashraf, Imran y Samad, Md. Abdus mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vanessa.yelamos@funiber.org, josep.alemany@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms. BMC Medical Research Methodology, 24 (1). ISSN 1471-2288

Artículo Materias > Biomedicina
Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Cardiovascular diseases (CVDs) are one of the main causes of mortality and morbidity worldwide. A healthy diet rich in plant-derived compounds such as (poly)phenols appears to have a key role in improving cardiovascular health. Flavan-3-ols represent a subclass of (poly)phenols of great interest for their possible health benefits. In this review, we summarized the results of clinical studies on vascular outcomes of flavan-3-ol supplementation and we focused on the role of the microbiota in CVD. Clinical trials included in this review showed that supplementation with flavan-3-ols mostly derived from cocoa products significantly reduces blood pressure and improves endothelial function. Studies on catechins from green tea demonstrated better results when involving healthy individuals. From a mechanistic point of view, emerging evidence suggests that microbial metabolites may play a role in the observed effects. Their function extends beyond the previous belief of ROS scavenging activity and encompasses a direct impact on gene expression and protein function. Although flavan-3-ols appear to have effects on cardiovascular health, further studies are needed to clarify and confirm these potential benefits and the rising evidence of the potential involvement of the microbiota. metadata Godos, Justyna; Romano, Giovanni Luca; Laudani, Samuele; Gozzo, Lucia; Guerrera, Ida; Dominguez Azpíroz, Irma; Martínez Díaz, Raquel; Quiles, José L.; Battino, Maurizio; Drago, Filippo; Giampieri, Francesca; Galvano, Fabio y Grosso, Giuseppe mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, jose.quiles@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action. Nutrients, 16 (15). p. 2471. ISSN 2072-6643

Artículo Materias > Ingeniería
Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The purpose of this research article was to contrast the benefits of the optimal probability threshold adjustment technique with other imbalanced data processing techniques, in its application to the prediction of post-graduate students’ late dropout from distance learning courses in two universities in the Ibero-American space. In this context, the optimization of the Logistic Regression, Random Forest, and Neural Network classifiers, together with different techniques, attributes, and algorithms (Hyperparameters, SMOTE, SMOTE_SVM, and ADASYN) resulted in a set of metrics for decision-making, prioritizing the reduction of false negatives. The best model was the Neural Network model in combination with SMOTE_SVM, obtaining a recall index of 0.75 and an f1-Score of 0.60. Likewise, the robustness of the Random Forest classifier for imbalanced data was demonstrated by achieving, with an optimal threshold of 0.427, very similar metrics to those obtained by the consensus of the three best models found. This demonstrates that, for Random Forest, the optimal prediction probability threshold is an excellent alternative to resampling techniques with different optimal thresholds. Finally, it is hoped that this research paper will contribute to boost the application of this simple but powerful technique, which is highly underrated with respect to data resampling techniques for imbalanced data. metadata Rodríguez Velasco, Carmen Lilí; García Villena, Eduardo; Brito Ballester, Julién; Durántez Prados, Frigdiano Álvaro; Silva Alvarado, Eduardo René y Crespo Álvarez, Jorge mail carmen.rodriguez@uneatlantico.es, eduardo.garcia@uneatlantico.es, julien.brito@uneatlantico.es, durantez@uneatlantico.es, eduardo.silva@funiber.org, jorge.crespo@uneatlantico.es (2023) Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data. International Journal of Emerging Technologies in Learning (iJET), 18 (04). pp. 120-155. ISSN 1863-0383

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Este artículo da cuenta del rastreo bibliográfico de numerosos estudios sobre práctica pedagógica, formación docente y perfil profesional, con miras al mejoramiento continuo de procesos formativos y de resignificación curricular que posibiliten la formación de docentes competentes en su disciplina y en el de ser modelos de las infancias. El método implementado es de corte cualitativo mediante la técnica de revisión documental; en este, se logra destacar como resultado el análisis de diversas fuentes que permiten dilucidar cómo la formación integral de los licenciados en educación infantil debe orientarse hacia la construcción de competencias generales y específicas desde lo actitudinal, procedimental y conceptual, especialmente a la formación en habilidades blandas, tales como la escucha empática enmarcada en el rol del docente no solo en un conjunto de conocimientos a impartir, sino como un modelo humano a seguir. metadata González de Cortés, Nora Luz y Restrepo Areiza, Ana María mail SIN ESPECIFICAR (2019) Formación de maestros basada en competencias: una esfera de oportunidades para cualificar su praxis pedagógica. Educación y Territorio, 9 (16). pp. 60-80. ISSN 2256-3989

Artículo Materias > Ciencias Sociales
Materias > Ingeniería Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Esta es una investigación que aborda la actividad de la formulación de proyectos de gestión organizacional (PGO) en micro y pequeñas empresas (Mypes), con un ejercicio investigativo de enfoque cuantitativo, correlacional y transversal, dentro de un estudio de caso, para probar la hipótesis que: A mayor cantidad de elementos teóricos utilice un microempresario en la formulación de sus proyectos, mayor desarrollo de los factores determinantes de supervivencia empresarial logra, proceso que permite determinar la existencia o no de un modelo adoptado a las particularidades operativas de este tipo empresarial, utilizando para ello un instrumento Ad hoc validado por juicio de expertos, en dos etapas (diseño y calibración), con CVC de 0.98, Fleiss de Kappa para constructo y objetividad de 0.95, con coeficiente de estabilidad temporal de Pearson de 0.95 y alfa de Cronbach de 0.81. El resultado indica que en el caso de la población objeto del estudio, no existe un modelo adaptado y usado mayoritariamente por las Mypes, además con un Rho de Spearman de 0.617 se evidencia una correlación fuerte entre las variables estudiadas, lo que permite aportar el concepto de incorporar los Rasgos Distintivos Operacionales (RDO) de las Mypes al desarrollo de teorías gerenciales adaptadas a sus realidades operativas para la formulación de PGO. metadata Muñoz Bonilla, Hugo Alejandro y Soriano Flores, Emmanuel mail SIN ESPECIFICAR, emmanuel.soriano@uneatlantico.es (2023) Formulación de proyectos en Mypes: evidencia empírica de la ausencia de un modelo práctico. Project Design and Management, 5 (1).

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés, Portugués Objective: The general objective of this article is to study the implementation of free geotechnologies, based on free software, in basic sanitation management. Theoretical benchmark: The research brings perspectives aiming to demonstrate that the implementation of geotechnologies, based on free and free software, in the management of basic sanitation can reduce the costs of implementing information technology, as well as assist the process of combating water losses and waste. Method: This study consisted of exploratory research with application in a case study at the Bahia Water and Sanitation Company. To assess the hypotheses raised by the survey, data from the company's information systems were used, as well as the answers from the online questionnaire applied to professionals in the area of geotechnologies. Results and conclusion: The results pointed out that the free geotechnologies, based on free and free software, implemented in the basic sanitation company allowed a better management of basic sanitation, being important for the specific process of combating water losses. Research Implications: The research contributes with the literature review and practical application of free and free geotechnologies applied in the management of basic sanitation, which allow the economicity and scalability of technological projects of this nature. Originality/value: The results obtained in the present study are unprecedented, innovative and relevant for the scientific community, in the context of the use of free and free geotechnologies, in the management of basic sanitation and its process of combating water losses. metadata Guimarães Aragão, Helder; Pereira, Vilmar Alves y Florencio da Silva, Rodrigo mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx, SIN ESPECIFICAR (2022) Free Geotechnologies Applied to Basic Sanitation Management: a Case Study at the Empresa Baiana de Águas e Saneamento. RGSA –Revista de Gestão Social e Ambiental, 16 (2). pp. 1-16. ISSN 1981-982X

Artículo Materias > Biomedicina
Materias > Educación física y el deporte
Materias > Alimentación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español El presente estudio tiene como objetivo determinar las relaciones entre los valores de funcionalidad motriz, estado nutricional e índices antropométricos de salud en adolescentes chilenos de 12 a 15 años. Estudio de corte transversal con una muestra no probabilística y por conveniencia, con una muestra final de 384 escolares (13,04 ± 0,85 años). Todos los participantes asistieron a dos sesiones de evaluación, donde se les realizó un registro de la historia clínica y una examinación física médica. En la segunda sesión, se realizaron evaluaciones antropométricas y las pruebas consideradas en la batería Functional Movement Screen (FMS). Los resultados muestran un 46,62% de los adolescentes posee sobrepeso y/u obesidad. El score total de FMS fue de 14,29±2,85 y se encontraron diferencias significativas en el IMC (índice de masa corporal) p=0,000 y en el peso p=0,002 según dependencia administrativa. Existe una relación entre FMS y PC (Perímetro de cintura), IMC e ICE (índice cintura estatura) (r=-0,31**p<0,003, r=-0,14**p<0,004 y r=0,38**p<0,003 respectivamente). También se encontró que aquellos escolares que presentan riesgo cardio metabólico también ostentarían un mayor riesgo relacionado con una baja calidad de la funcionalidad motriz. Se concluye que los niveles elevados de parámetros antropométricos de riesgo cardiovascular en especial el exceso de peso y el elevado perímetro de cintura se relacionan con una deficiente funcionalidad motriz. Y por otra parte se generan problemáticas cardiovasculares en esta etapa de la vida sin mayor distinción de sexo y dependencia administraba de los colegios, lo cual hace ver que la mal nutrición y la falta de actividad física impacta de manera transversal a la sociedad. metadata Rodríguez Canales, Carolina; Hinojosa Torres, Claudio; Merellano-Navarro, Eugenio; Barraza-Gómez, Fernando y Hecht-Chau, Gernot mail carolina.rodriguez@unini.org, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Funcionalidad motriz, estado nutricional e índices antropométricos de riesgo cardiometabólico en adolescentes chilenos de 12 a 15 años. Retos: nuevas tendencias en educación f\'\isica, deporte y recreación (45). pp. 400-409.

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Este artigo apresenta as relações estabelecidas entre os profissionais de educação e o gestor no século XXI, além de promover reflexões com finalidade de reavaliar sua prática numa perspectiva democrática para uma melhor educação. A pesquisa trata-se de revisão bibliográfica com caráter exploratório e metodologia qualitativa. Ao investigar as competências e funções desenvolvidas pelo gestor, foram destacados os preceitos pedagógicos que a instituição deve seguir, que são: analisar, avaliar e acompanhar os planos do ensino; sugerir recursos e livros; acompanhar as metodologias dos professores, analisando aspectos que possam vir a atrapalhar as atividades da escola; além de organizar reuniões de professores para concedê-los assistência metodológica e pedagógica; e estimular e sugerir atividades que possam tornar a experiência da educação eficiente para todos. Este trabalho visa demonstrar que a escola é o ambiente onde acontecem a aprendizagem e o desenvolvimento humano. Por isso, uma das funções do Gestor escolar é gerar novas formas de participação e incentivar o trabalho em grupo com membros da comunidade escolar, permeando assim, um ambiente que preze por um modelo de excelência e instigue uma convivência mútua entre todos os inseridos no processo. metadata Alves Guimarães, Ueudison; Moraes da Cruz Gomez, Eliane y Rodrigues Moniz, Sibele Selvina de Oliveira mail SIN ESPECIFICAR (2022) Gestão da diversidade no campo educacional: o papel da gestão escolar nesse processo. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (8). e381808. ISSN 2675-6218

Artículo Materias > Educación física y el deporte
Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Hoje a sociedade passa por uma década que favorece a juventude ao sedentarismo e consequentemente ao desenvolvimento de diversas doenças como pressão alta, colesterol, diabetes, entre outras relacionadas não apenas a má alimentação como o mau hábito de vida e a não prática atividades físicas. A realização de atividades físicas na escola é tida como crucial para a qualidade de vida e inclusão das crianças. Pensando nisso, o presente trabalho tem como proposta a reflexão de alguns problemas enfrentados pelos professores com relação a prática de atividades físicas na disciplina de Educação Física e a promoção do desenvolvimento da saúde física e mental que ela proporcionaria. Portanto, este estudo se trata de uma pesquisa bibliográfica de cunho qualitativo e caráter descritivo com objetivo de apresentar a importância da Educação Física escolar na formação do indivíduo. Conclui-se pela necessidade e importância de a Educação Física estreitar as relações entre teoria e prática e inovar pedagogicamente, a fim de seguir contribuindo para a formação integral do alunado e para a apropriação crítica da cultura corporal de movimento. metadata Alves Guimarães, Ueudison; Olímpio dos Santos, José y Rodrigues Moniz, Sibele Selvina de Oliveira mail SIN ESPECIFICAR (2022) História da Educação Física no ensino infantil. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (10). e3101943. ISSN 2675-6218

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background Radiology is a useful tool for diagnosis and intervention in medical practice, and all the components within the teaching-learning process of this subject during undergraduate studies influence successful knowledge application. Objective This study aimed to describe the level of knowledge in radiology of students in the last two years of medical school and curricular characteristics of their courses in seven Latin American countries. Methods A multicenter cross-sectional study was carried out on medical students of 7 Latin American countries (Bolivia, Brazil, Colombia, Ecuador, Mexico, Paraguay, and Peru) in their final two years of medical school, using an online questionnaire validated by experts and adapted for each country that assessed knowledge and curricular characteristics in radiology subject. Scores were assigned according to the number of correct answers for the knowledge test. The T-test, and regression analysis with one-way ANOVA were used to search for relationships between the level of knowledge and other variables. Results A total of 1514 medical students participated in this study. All countries had similar participation (n > 200); most participants were women 57.8%. The country with the highest knowledge score was Brazil. Male, sixth year (internship) and from public universities students had higher knowledge score (n < 0.05). Participants, who considered radiology more important, and who reported higher compliance with teaching staff with the proposed syllabus, and programmed classes, obtained better scores (n < 0.05). Conclusions Latin American medical students included in this study have a regular overall level of knowledge of Radiology, apparently influenced by curricular differences such as class and academic program compliance. Efforts to better understand and improve academic training are indispensable. Limitations The study was subject to selection bias determined by non-probability convenience sampling. The questionnaire assessed only theoretical knowledge and the evaluation system was designed by the investigators. metadata Izquierdo Condoy, Juan Sebastian; Simbaña-Rivera, Katherine; Nati-Castillo, Humberto Alejandro; Cassa Macedo, Arthur; Cardozo Espínola, Claudia Diana; Vidal Barazorda, Gabriela M.; Palazuelos-Guzmán, Ideli; Trejo García, Brayan; Carrington, Sarah J. y Ortiz-Prado, Esteban mail SIN ESPECIFICAR (2023) How much do Latin American medical students know about radiology? Latin-American multicenter cross-sectional study. Medical Education Online, 28 (1). ISSN 1087-2981

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Much of nutrition research has been conventionally based on the use of simplistic in vitro systems or animal models, which have been extensively employed in an effort to better understand the relationships between diet and complex diseases as well as to evaluate food safety. Although these models have undeniably contributed to increase our mechanistic understanding of basic biological processes, they do not adequately model complex human physiopathological phenomena, creating concerns about the translatability to humans. During the last decade, extraordinary advancement in stem cell culturing, three-dimensional cell cultures, sequencing technologies, and computer science has occurred, which has originated a wealth of novel human-based and more physiologically relevant tools. These tools, also known as “new approach methodologies,” which comprise patient-derived organoids, organs-on-chip, multi-omics approach, along with computational models and analysis, represent innovative and exciting tools to forward nutrition research from a human-biology-oriented perspective. After considering some shortcomings of conventional in vitro and vivo approaches, here we describe the main novel available and emerging tools that are appropriate for designing a more human-relevant nutrition research. Our aim is to encourage discussion on the opportunity to explore innovative paths in nutrition research and to promote a paradigm-change toward a more human biology-focused approach to better understand human nutritional pathophysiology, to evaluate novel food products, and to develop more effective targeted preventive or therapeutic strategies while helping in reducing the number and replacing animals employed in nutrition research. metadata Cassotta, Manuela; Cianciosi, Danila; Elexpuru Zabaleta, Maria; Elío Pascual, Iñaki; Sumalla Cano, Sandra; Giampieri, Francesca y Battino, Maurizio mail manucassotta@gmail.com, SIN ESPECIFICAR, maria.elexpuru@uneatlantico.es, inaki.elio@uneatlantico.es, sandra.sumalla@uneatlantico.es, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es (2024) Human‐based new approach methodologies to accelerate advances in nutrition research. Food Frontiers. pp. 1-32. ISSN 2643-8429

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India’s largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers metadata Yadav, Arvind; Ali Albahar, Marwan; Chithaluru, Premkumar; Singh, Aman; Alammari, Abdullah; Kumar, Gogulamudi Vijay y Miró Vera, Yini Airet mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, yini.miro@uneatlantico.es (2023) Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization. Water, 15 (3). p. 522. ISSN 2073-4441

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks metadata Faheem, Zaid Bin; Ishaq, Abid; Rustam, Furqan; de la Torre Díez, Isabel; Gavilanes, Daniel; Masías Vergara, Manuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, SIN ESPECIFICAR (2023) Image Watermarking Using Least Significant Bit and Canny Edge Detection. Sensors, 23 (3). p. 1210. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés This research paper aims to examine the impact of innovative HRM practices, including employee participation, performance appraisal, reward and compensation, recruitment and selection, and redeployment–retraining on firm performance. For this purpose, four different models are utilized to examine the impact of innovative HRM department practices on the performance of small and medium enterprises (SMEs) in a country. The dependent variable, firm performance, is proxified by different variables such as labor productivity, product innovation, process innovation, and marketing innovation. For empirical analysis, primary data are collected using a questionnaire. Estimation is conducted using ordinary least squares (OLS) and logit regression techniques. The estimated results indicate that most innovative HRM practices have a statistically significant impact on firm performance in terms of labor productivity, product, process, and marketing innovations. These results imply that SMEs in a country may observe the benefits of devoting greater attention to innovative HRM practices to achieve their future growth potential. metadata Aslam, Mahvish; Shafi, Imran; Ahmed, Jamil; Garat de Marin, Mirtha Silvana; Soriano Flores, Emmanuel; Rojo Gutiérrez, Marco Antonio y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, SIN ESPECIFICAR (2023) Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance. Sustainability, 15 (7). p. 6273. ISSN 2071-1050

Artículo Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Financial management is a critical aspect of firms, and entails the strategic planning, direction, and control of financial endeavors. Risk assessment, fraud detection, wealth management, online transactions, customized bond scheme, customer retention, virtual assistant and so on, are a few of the critical areas where Industry 4.0 technologies intervention are highly required for managing firms' finance. It has been identified from the previous studies that they are limited studies that have addressed the significance and application of integrating of Industry 4.0 technologies such as Internet of Things (IoT), cloud computing, big data, robotic process automation (RPA), artificial intelligence (AI), Blockchain, Digital twin, and Metaverse. With the motivation from the above aspects, this study aims to discuss the role of these technologies in the area of financial management of a firm. Based up on the analysis, it has been concluded that these technologies assist to credit risk management based on real-time data; financial data analytics of risk assessment, digital finance, digital auditing, fraud detection, and AI- and IoT- based virtual assistants. This study recommended that digital technologies be deeply integrated into the financial sector to improve service quality and accessibility, as well as the creation of innovative rules that allow for healthy competition among market participants. metadata Bisht, Deepa; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Singh, Aman; Caro Montero, Elisabeth; Priyadarshi, Neeraj y Twala, Bhekisipho mail SIN ESPECIFICAR (2022) Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective. Electronics, 11 (19). p. 3252. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O presente trabalho descreve a implantação de geotecnologias, utilizando software livre e gratuito, com a finalidade de disseminar as informações geoespaciais da Empresa Baiana de Águas e Saneamento (EMBASA). A EMBASA é a concessionária responsável pelo saneamento básico do Estado da Bahia. Este artigo descreve, além do uso e da implantação, uma análise dos custos economizados pela companhia ao longo dos anos em função da adoção do software livre e gratuito. metadata Guimarães Aragão, Helder y Pereira, Vilmar Alves mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx (2023) Implantação de geotecnolgias livres e gratuitas em uma empresa de saneamento básico. REVISTA FOCO, 16 (3). e1235. ISSN 1981-223X

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O presente trabalho descreve a implantação de geotecnologias, utilizando software livre e gratuito, com a finalidade de disseminar as informações geoespaciais da Empresa Baiana de Águas e Saneamento (EMBASA). A EMBASA é a concessionária responsável pelo saneamento básico do Estado da Bahia. Este artigo descreve, além do uso e da implantação, uma análise dos custos economizados pela companhia ao longo dos anos em função da adoção do software livre e gratuito. metadata Guimarães Aragão, Helder y Pereira Alves, Vilmar mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx (2022) Implantação de geotecnolgias livres e gratuitas em uma empresa de saneamento básico / Implantation of free geotechnologies in a basic sanitation company. Brazilian Journal of Development, 8 (4). pp. 26616-26622. ISSN 2525-8761

Artículo Materias > Ciencias Sociales
Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Energy is a crucial element for human needs today. Traditional systems of energy generation have represented a problem in terms of their costs, their impact on the environment, and their impact on community life. Therefore, the search for clean and renewable energy sources that meet the needs of contemporary society becomes increasingly essential in the search for alternatives related to energy sources. The photovoltaic energy generation system explores the solar irradiation, making it possible to generate and store energy. This system finds good conditions for implementation in Brazil in terms of climatic characteristics, but investments and public policies that encourage and favor this process are still needed. This study aimed to identify how the deployment of photovoltaic mini-generation power plant in a federal university, the Federal University of Paraná (UFPR), can contribute to the university community in relation to cost reduction and environmental preservation. The methodology used was descriptive-exploratory, qualitative, through which an open questionnaire and a semi-structured interview were carried out, guided by the theme. After analyzing the data, the conclusion was that the system can bring benefits in the long term and that most of the interviewees consider Brazil's great potential in expanding the exploration of other sources of energy, besides hydroelectric, which, besides being costly, brings fewer advantages related to the environmental and social contexts. metadata Miura, Augusto Takashi; Pereira, Vilmar Alves y Florencio da Silva, Rodrigo mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx, SIN ESPECIFICAR (2022) Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil. Latin American Journal of Development, 4 (4). pp. 1514-1532. ISSN 2674-9297

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of α=1.5 and γ=2.5 with the varying number of nodes. metadata Rani, Shalli; Babbar, Himanshi; Shah, Syed Hassan Ahmed y Singh, Aman mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es (2022) Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities. Scientific Reports, 12 (1). ISSN 2045-2322

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés There is growing evidence that Alzheimer’s disease (AD) can be prevented by reducing risk factors involved in its pathophysiology. Food-derived bioactive molecules can help in the prevention and reduction of the progression of AD. Honey, a good source of antioxidants and bioactive molecules, has been tied to many health benefits, including those from neurological origin. Monofloral avocado honey (AH) has recently been characterized but its biomedical properties are still unknown. The aim of this study is to further its characterization, focusing on the phenolic profile. Moreover, its antioxidant capacity was assayed both in vitro and in vivo. Finally, a deep analysis on the pathophysiological features of AD such as oxidative stress, amyloid-β aggregation, and protein-tau-induced neurotoxicity were evaluated by using the experimental model C. elegans. AH exerted a high antioxidant capacity in vitro and in vivo. No toxicity was found in C. elegans at the dosages used. AH prevented ROS accumulation under AAPH-induced oxidative stress. Additionally, AH exerted a great anti-amyloidogenic capacity, which is relevant from the point of view of AD prevention. AH exacerbated the locomotive impairment in a C. elegans model of tauopathy, although the real contribution of AH remains unclear. The mechanisms under the observed effects might be attributed to an upregulation of daf-16 as well as to a strong ROS scavenging activity. These results increase the interest to study the biomedical applications of AH; however, more research is needed to deepen the mechanisms under the observed effects metadata Romero-Márquez, Jose M.; Navarro-Hortal, María D.; Orantes, Francisco J.; Esteban-Muñoz, Adelaida; Mazas Pérez-Oleaga, Cristina; Battino, Maurizio; Sánchez-González, Cristina; Rivas-García, Lorenzo; Giampieri, Francesca; Quiles, José L. y Forbes-Hernandez, Tamara Y. mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, tamara.forbes@unini.edu.mx (2023) In Vivo Anti-Alzheimer and Antioxidant Properties of Avocado (Persea americana Mill.) Honey from Southern Spain. Antioxidants, 12 (2). p. 404. ISSN 2076-3921

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Analisar reflexões sobre a indisciplina escolar é o propósito desta pesquisa, assim o presente artigo tem como objetivo analisar a necessidade do psicopedagogo na escola. Portanto, ainda que a indisciplina escolar seja uma expressão particular, via regra, traduz o que ocorre no âmbito coletivo. Para realizar a referida análise foram feitos estudos bibliográficos e leituras. Por meio destas pesquisas, foi possível perceber a importância e a necessidade da atuação de um psicopedagogo na escola, pois ele pode auxiliar de forma mais efetiva toda a comunidade escolar e, principalmente, aos professores, apontando formas, ações e estratégias de como trabalhar com os alunos com problemas de aprendizagem ou de comportamento. metadata Alves Guimarães, Ueudison; Teixeira Zimmermann, Jussara Aparecida y Dantas Tanaka, Gislaine Araujo mail SIN ESPECIFICAR (2022) Indisciplina escolar e a necessidade de um psicopedagogo. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (7). e371738. ISSN 2675-6218

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Angola, as with many countries on the African continent, has great inequalities or asymmetries between its provinces. At the economic, financial, and technological level, there is a great disparity between them, where it is observed that the province of Luanda is the largest financial business center to the detriment of others, such as Moxico, Zaire, and Cabinda. In the latter, despite the advantages of high oil production, from a regional point of view, they remain almost stagnant in time, in a social dysfunction where the population lives on extractivism and artisanal fishing. This article analyzes the most important events in contemporary regional history, the Portuguese occupation that was the Portuguese colonial rule over Angola (1890–1930) and the civil war that was a struggle between Angolans for control of the country (1975–2002), in the consolidation of the asymmetries between provinces. For this work, a theoretical-reflective study was conducted based on the reading of books, articles, and previous investigations on the phenomenon studied. Considering the interpretation and analysis of the theoretical content obtained through the bibliographic research conducted, this theoretical construction approaches the qualitative approach. We conclude that the deep inequalities between regions and within them, between the provinces studied, originated historically in the form of exploitation of the regions and from the consequences of the war. The asymmetries, observed through the variables studied show that the provinces historically explored and considered object regions present a lower growth compared to those that were considered subject regions in which the applied geopolitical strategy, as they are centers of primary production flows, was different. We also observe that, due to the conflicts of the civil war in the less developed regions, the inequalities have deepened, contributing seriously to a higher level of poverty and a lower development of the provinces where these conflicts took place. metadata Catoto Capitango, João Adolfo; Garat de Marin, Mirtha Silvana; Soriano Flores, Emmanuel; Rojo Gutiérrez, Marco Antonio; Gracia Villar, Mónica y Durántez Prados, Frigdiano Álvaro mail SIN ESPECIFICAR, silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, monica.gracia@uneatlantico.es, durantez@uneatlantico.es (2022) Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War. Social Sciences, 11 (8). p. 334. ISSN 2076-0760

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Alemany Iturriaga, Josep; Velarde-Sotres, Álvaro; Jorge, Javier y Giglio, Kamil mail josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria. Cogent Education, 11 (1). ISSN 2331-186X

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The purpose of this article was to evaluate the level of satisfaction of a sample of graduates in relation to different online postgraduate programs in the environmental area, as part of the process of continuous improvement in which the educational institution was immersed for the renewal of its accreditation before the corresponding official bodies. Based on the bibliographic review of a series of models and tools, a Likert scale measurement instrument was developed. This instrument, once applied and validated, showed a good level of reliability, with more than three quarters of the participants having a positive evaluation of satisfaction. Likewise, to facilitate the relational study, and after confirming the suitability of performing a factor analysis, four variable grouping factors were determined, which explained a good part of the variability of the instrument’s items. As a result of the analysis, it was found that there were significant values of low satisfaction in graduates from the Eurasian area, mainly in terms of organizational issues and academic expectations. On the other hand, it was observed that the methodological aspects of the “Auditing” and “Biodiversity” programs showed higher levels of dissatisfaction than the rest, with no statistically significant relationships between gender, entry profile or age groups. The methodology followed and the rigor in determining the validity and reliability of the instrument, as well as the subsequent analysis of the results, endorsed by the review of the documented information, suggest that the instrument can be applied to other multidisciplinary programs for decision making with guarantees in the educational field metadata García Villena, Eduardo; Pueyo Villa, Silvia; Delgado Noya, Irene; Tutusaus, Kilian; Ruiz Salces, Roberto y Pascual Barrera, Alina Eugenia mail eduardo.garcia@uneatlantico.es, silvia.pueyo@uneatlantico.es, irene.delgado@uneatlantico.es, kilian.tutusaus@uneatlantico.es, roberto.ruiz@uneatlantico.es, alina.pascual@unini.edu.mx (2021) Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field. Sustainability, 13 (9). p. 5112. ISSN 2071-1050

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Regulatory dispersion and a utilitarian use of sustainability deepen the gap within the teaching–learning process and limit the introduction of sustainable criteria in organizations through projects. The objective of this research consisted in developing a sustainable and holistic educational proposal for an online postgraduate program belonging to the Universidad Europea del Atlántico (UNEATLANTICO) within the field of projects. The proposal was based on the instrumentalization of a model comprised of national and international bibliographic references, resulting in a sustainability guide with significant improvements in relation to the reference standard par excellence: ISO 26000:2010. This guide formed the basis of a sustainability management plan, which was key in the project methodology and during the development of sustainable objectives and descriptors for each of the subjects. Lastly, the entities, attributes, and cardinal relationships were established for the development of a physical model used to facilitate the management of all this information within a SQL database. The rigor when determining the educational program, as well as the subsequent analysis of results as supported by the literature review, presupposes the application of this methodology toward other multidisciplinary programs contributing to the adoption of good sustainability practices within the educational field metadata Gracia Villar, Mónica; Álvarez, Roberto Marcelo; Brie, Santiago; Miró Vera, Yini Airet y García Villena, Eduardo mail monica.gracia@uneatlantico.es, roberto.alvarez@uneatlantico.es, santiago.brie@uneatlantico.es, yini.miro@uneatlantico.es, eduardo.garcia@uneatlantico.es (2023) Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area. Education Sciences, 13 (1). p. 97. ISSN 2227-7102

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved. metadata Kanwal, Tabassum; Rehman, Saif Ur; Ali, Tariq; Mahmood, Khalid; Gracia Villar, Santos; Dzul Lopez, Luis y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, SIN ESPECIFICAR (2023) An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field. Agriculture, 13 (8). p. 1600. ISSN 2077-0472

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés This paper presents the design, development, and testing of an IoT-enabled smart stick for visually impaired people to navigate the outside environment with the ability to detect and warn about obstacles. The proposed design employs ultrasonic sensors for obstacle detection, a water sensor for sensing the puddles and wet surfaces in the user’s path, and a high-definition video camera integrated with object recognition. Furthermore, the user is signaled about various hindrances and objects using voice feedback through earphones after accurately detecting and identifying objects. The proposed smart stick has two modes; one uses ultrasonic sensors for detection and feedback through vibration motors to inform about the direction of the obstacle, and the second mode is the detection and recognition of obstacles and providing voice feedback. The proposed system allows for switching between the two modes depending on the environment and personal preference. Moreover, the latitude/longitude values of the user are captured and uploaded to the IoT platform for effective tracking via global positioning system (GPS)/global system for mobile communication (GSM) modules, which enable the live location of the user/stick to be monitored on the IoT dashboard. A panic button is also provided for emergency assistance by generating a request signal in the form of an SMS containing a Google maps link generated with latitude and longitude coordinates and sent through an IoT-enabled environment. The smart stick has been designed to be lightweight, waterproof, size adjustable, and has long battery life. The overall design ensures energy efficiency, portability, stability, ease of access, and robust features. metadata Farooq, Muhammad Siddique; Shafi, Imran; Khan, Harris; Díez, Isabel De La Torre; Breñosa, Jose; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2022) IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition. Sensors, 22 (22). p. 8914. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés An Internet of Things (IoT) network is prone to many ways of threatening individuals. IoT sensors are lightweight, lack complicated security protocols, and face threats to privacy and confidentiality. Hackers can attack the IoT network and access personal information and confidential data for blackmailing, and negatively manipulate data. This study aims to propose an IoT threat protection system (IoTTPS) to protect the IoT network from threats using an ensemble model RKSVM, comprising a random forest (RF), K nearest neighbor (KNN), and support vector machine (SVM) model. The software-defined networks (SDN)-based IoT network datasets such as KDD cup 99, NSL-KDD, and CICIDS are used for threat detection based on machine learning. The experimental phase is conducted by using a decision tree (DT), logistic regression (LR), Naive Bayes (NB), RF, SVM, gradient boosting machine (GBM), KNN, and the proposed ensemble RKSVM model. Furthermore, performance is optimized by adding a grid search hyperparameter optimization technique with K-Fold cross-validation. As well as the NSL-KDD dataset, two other datasets, KDD and CIC-IDS 2017, are used to validate the performance. Classification accuracies of 99.7%, 99.3%, 99.7%, and 97.8% are obtained for DoS, Probe, U2R, and R2L attacks using the proposed ensemble RKSVM model using grid search and cross-fold validation. Experimental results demonstrate the superior performance of the proposed model for IoT threat detection. metadata Akram, Urooj; Sharif, Wareesa; Shahroz, Mobeen; Mushtaq, Muhammad Faheem; Gavilanes Aray, Daniel; Bautista Thompson, Ernesto; Diez, Isabel de la Torre; Djuraev, Sirojiddin y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System. Sensors, 23 (14). p. 6379. ISSN 1424-8220

Artículo Materias > Biomedicina
Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Isoflavones are a group of (poly)phenols, also defined as phytoestrogens, with chemical structures comparable with estrogen, that exert weak estrogenic effects. These phytochemical compounds have been targeted for their proven antioxidant and protective effects. Recognizing the increasing prevalence of cardiovascular diseases (CVD), there is a growing interest in understanding the potential cardiovascular benefits associated with these phytochemical compounds. Gut microbiota may play a key role in mediating the effects of isoflavones on vascular and endothelial functions, as it is directly implicated in isoflavones metabolism. The findings from randomized clinical trials indicate that isoflavone supplementation may exert putative effects on vascular biomarkers among healthy individuals, but not among patients affected by cardiometabolic disorders. These results might be explained by the enzymatic transformation to which isoflavones are subjected by the gut microbiota, suggesting that a diverse composition of the microbiota may determine the diverse bioavailability of these compounds. Specifically, the conversion of isoflavones in equol—a microbiota-derived metabolite—seems to differ between individuals. Further studies are needed to clarify the intricate molecular mechanisms behind these contrasting results. metadata Laudani, Samuele; Godos, Justyna; Romano, Giovanni Luca; Gozzo, Lucia; Di Domenico, Federica Martina; Dominguez Azpíroz, Irma; Martínez Díaz, Raquel; Giampieri, Francesca; Quiles, José L.; Battino, Maurizio; Drago, Filippo; Galvano, Fabio y Grosso, Giuseppe mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved? Pharmaceuticals, 17 (2). p. 236. ISSN 1424-8247

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español La formación pedagógica del docente debe partir de las competencias que el profesor debe desarrollar para aplicar a las TIC de manera educativa a lo largo de su vida docente. Este estudio, que es parte del Proyecto Europeo Lovedistance – Learning Optmization and Academic Inclusion Via Equitative Distance Teaching and Learning 609949-EPP-1-2019-1-PTEPPKA2-CBHE-JP, tiene como objetivo medir los niveles y dimensiones de las competencias determinadas por la UNESCO (2016) en cuanto a la acción docente frente a la utilización de las TIC en escenarios educativos. Se destacan las posibilidades de llevar a cabo una planificación basada en las TIC; el diseño o la capacidad de organización y construcción de escenarios de aprendizaje con las TIC; y la evaluación o la posibilidad de medida de la efectividad de las TIC para la educación a lo largo de la vida, en los espacios educativos que se desarrollan como docentes (Coll, 2008). El presente estudio se basa en el diseño de encuesta y utiliza como instrumento el cuestionario, aplicando una complementariedad metodológica con unos resultados que indican a través de un análisis descriptivo que la formación del profesorado, la coordinación y cooperación docente, y la profundización en el manejo de las tecnologías, son factores de gran importancia y favorecedores del uso de las TIC en la comunidad educativa. metadata Sartor-Harada, Andresa mail andresa.sartor@uneatlantico.es (2021) La comunidad docente y las competencias digitales: la formación a lo largo de la vida. Revista Conhecimento Online, 1. p. 177. ISSN 2176-8501

Artículo Materias > Ciencias Sociales Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español En respuesta a cuán y cómo de bien se vive, la presente investigación tuvo como objetivo generar un índice de desarrollo considerando los fundamentos filosóficos, culturales, costumbristas y lingüísticos del paradigma prevalente del Vivir Bien. El andamiaje estadístico - matemático desarrollado se ajustó al modelo boliviano basado en los 25 principios propuestos por Choquehuanca. Partiendo de los fundamentos de la tetraléctica y la tetrasociología, se formaron pautas para desarrollar un propio modelo ontológico que ayude a definir las dimensiones y las variables que mejor describan este paradigma. Como resultado se propuso un índice conceptualmente robusto – Índice del Vivir Bien (IVB) – que supera los índices de pobreza, desarrollo humano y otras métricas, configurado en un sistema en el que la Bioeconomía juega un rol preponderante para llegar a la pentaléctica. Se resalta la importancia de habilitar una conexión con la economía, lo social y medioambiental, que permita consolidar una base ética, moral e ideológica que promueva el Vivir Bien como fundamento transversal, tanto interna como internacionalmente. metadata Quiroga Canaviri, Jorge León y Zaiduni Salazar, Marcelo Eduardo mail SIN ESPECIFICAR (2022) La métrica del vivir bien: Construcción de un índice de reconciliación y equilibrio. Revista Enfoques, 6 (21). pp. 79-105. ISSN 2616-8219

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead. metadata Alsukayti, Ibrahim S. y Singh, Aman mail SIN ESPECIFICAR, aman.singh@uneatlantico.es (2022) A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks. IEEE Access, 10. pp. 111115-111133. ISSN 2169-3536

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background: Several reports from around the world have reported that some patients who have recovered from COVID-19 have experienced a range of persistent or new clinical symptoms after a SARS-CoV-2 infection. These symptoms can last from weeks to months, impacting everyday functioning to a significant number of patients. Methods: A cross-sectional analysis based on an online, self-reporting questionnaire was conducted in Ecuador from April to July 2022. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 2103 surveys were included in this study. We compared socio-demographic variables and long-term persisting symptoms at low (<2500 m) and high altitude (>2500 m). Results: Overall, 1100 (52.3%) responders claimed to have Long-COVID symptoms after SARS-CoV-2 infection. Most of these were reported by women (64.0%); the most affected group was young adults between 21 to 40 years (68.5%), and most long-haulers were mestizos (91.6%). We found that high altitude residents were more likely to report persisting symptoms (71.7%) versus those living at lower altitudes (29.3%). The most common symptoms were fatigue or tiredness (8.4%), hair loss (5.1%) and difficulty concentrating (5.0%). The highest proportion of symptoms was observed in the group that received less than 2 doses. Conclusions: This is the first study describing post-COVID symptoms’ persistence in low and high-altitude residents. Our findings demonstrate that women, especially those aging between 21–40, are more likely to describe Long-COVID. We also found that living at a high altitude was associated with higher reports of mood changes, tachycardia, decreased libido, insomnia, and palpitations compared to lowlanders. Finally, we found a greater risk to report Long-COVID symptoms among women, those with previous comorbidities and those who had a severer acute SARS-CoV-2 infection. metadata Izquierdo Condoy, Juan Sebastian; Fernandez-Naranjo, Raul; Vasconez-González, Eduardo; Cordovez, Simone; Tello-De-la-Torre, Andrea; Paz, Clara; Delgado-Moreira, Karen; Carrington, Sarah; Viscor, Ginés y Ortiz-Prado, Esteban mail SIN ESPECIFICAR (2022) Long COVID at Different Altitudes: A Countrywide Epidemiological Analysis. International Journal of Environmental Research and Public Health, 19 (22). p. 14673. ISSN 1660-4601

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The prevalence of sleep disorders, characterized by issues with quality, timing, and sleep duration is increasing globally. Among modifiable risk factors, diet quality has been suggested to influence sleep features. The Mediterranean diet is considered a landmark dietary pattern in terms of quality and effects on human health. However, dietary habits characterized by this cultural heritage should also be considered in the context of overall lifestyle behaviors, including sleep habits. This study aimed to systematically revise the literature relating to adherence to the Mediterranean diet and sleep features in observational studies. The systematic review comprised 23 reports describing the relation between adherence to the Mediterranean diet and different sleep features, including sleep quality, sleep duration, daytime sleepiness, and insomnia symptoms. The majority of the included studies were conducted in the Mediterranean basin and reported a significant association between a higher adherence to the Mediterranean diet and a lower likelihood of having poor sleep quality, inadequate sleep duration, excessive daytime sleepiness or symptoms of insomnia. Interestingly, additional studies conducted outside the Mediterranean basin showed a relationship between the adoption of a Mediterranean-type diet and sleep quality, suggesting that biological mechanisms sustaining such an association may exist. In conclusion, current evidence suggests a relationship between adhering to the Mediterranean diet and overall sleep quality and different sleep parameters. The plausible bidirectional association should be further investigated to understand whether the promotion of a healthy diet could be used as a tool to improve sleep quality. metadata Godos, Justyna; Ferri, Raffaele; Lanza, Giuseppe; Caraci, Filippo; Rojas Vistorte, Angel Olider; Yélamos Torres, Vanessa; Grosso, Giuseppe y Castellano, Sabrina mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, angel.rojas@uneatlantico.es, vanessa.yelamos@funiber.org, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence. Nutrients, 16 (2). p. 282. ISSN 2072-6643

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español La cuenca del Río Grande de Loíza de Puerto Rico es la más grande de la isla, compuesta por 15 subcuencas. Estos ríos están contaminados con patógenos relacionados a las Comunidades Sin Alcantarillado Sanitario denominadas ComSAS, que utilizan pozos sépticos defectuosos o descargas directas a los cuerpos de agua. La Agencia Federal de Protección Ambiental la describe como la de mayor prioridad de atención. La investigación plantea el objetivo de elaborar un modelo geoespacial para incorporarse al proceso de cumplimiento de la ley de agua limpia. Incluye el diseño de una metodología ad hoc, que selecciona los factores ambientales y establece los parámetros para priorizar las áreas por nivel y tipo de riesgo. El análisis multicriterio incorporó las capas de información geográficas que incluyen estructuras/km2, la cercanía a los ríos, la clasificación de uso de terrenos, y la presencia de suelos hídricos con grupos hidrológicos tipo D. El resultado generó la capa de información geográfica que identifica el 27 % del área de estudio como alto y muy alto riesgo. Las agencias estatales y federales pueden podrán incorporar esta herramienta de innovación en el proceso de toma de decisiones para evaluar de forma rápida las comunidades de alto riesgo metadata Fernández Valencia, María de Lourdes y Rivera Rivas, María del Carmen mail SIN ESPECIFICAR, maricarmen.rivera@unib.org (2022) Modelo geoespacial para priorizar los factores de riesgo ambiental de las comunidades sin alcantarillado sanitario en la cuenca del Río Grande de Loíza en Puerto Rico. Revista Umbral, 1 (18). pp. 183-209. ISSN 2 1 5 1 - 8 3 8 6

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Background and Aims The 2022-mpox outbreak has spread worldwide in a short time. Integrated knowledge of the epidemiology, clinical characteristics, and transmission of mpox are limited. This systematic review of peer-reviewed articles and gray literature was conducted to shed light on the epidemiology, clinical features, and transmission of 2022-mpox outbreak. Methods We identified 45 peer-reviewed manuscripts for data analysis. The standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement and Cochrane Collaboration were followed for conducting the study. Results The case number of mpox has increased about 100 times worldwide. About 99% of the cases in 2022 outbreak was from non-endemic regions. Men (70%–98% cases) were mostly infected with homosexual and bisexual behavior (30%–60%). The ages of the infected people ranged between 30 and 40 years. The presence of HIV and sexually transmitted infections among 30%–60% of cases were reported. Human-to-human transmission via direct contact and different body fluids were involved in the majority of the cases (90%–100%). Lesions in genitals, perianal, and anogenital areas were more prevalent. Unusually, pharyngitis (15%–40%) and proctitis (20%–40%) were more common during 2022 outbreak than pre-2022 outbreaks. Brincidofovir is approved for the treatment of smallpox by FDA (USA). Two vaccines, including JYNNEOSTM and ACAM2000®, are approved and used for pre- and post-prophylaxis in cases. About 100% of the cases in non-endemic regions were associated with isolates of IIb clade with a divergence of 0.0018–0.0035. Isolates from B.1 lineage were the most predominant followed by B.1.2 and B.1.10. Conclusion This study will add integrated knowledge of the epidemiology, clinical features, and transmission of mpox. metadata Sharif, Nadim; Sharif, Nazmul; Alzahrani, Khalid J.; Halawani, Ibrahim F.; Alzahrani, Fuad M.; Díez, Isabel De la Torre; Lipari, Vivian; López Flores, Miguel Ángel; Parvez, Anowar K. y Dey, Shuvra K. mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, miguelangel.lopez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review. Health Science Reports, 6 (10). ISSN 2398-8835

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O presente artigo reflete sobre a realidade das mulheres em tempos de pandemia. A problemática que envolve o presente trabalho parte da pergunta: como tem sido a experiência das mulheres na pandemia do coronavírus (Covid-19), devido ao acúmulo dos cuidados como tarefa feminina e o grito uterino que vem dessa situação? Para responder a essa questão, buscamos referências da teologia feminista, que parte do princípio da experiência das mulheres para a análise da realidade e a reflexão teológica e que coloca a vida mesma em sua amplitude como critério hermenêutico. A metodologia utilizada é bibliográfica, a partir de artigos de revistas, entrevistas e livros. Além do mais, somos três mulheres, profissionais, afetadas também pelo home office que se mistura com o trabalho da casa e a necessidade de uma nova organização. O processo de ensino aprendizagem da pandemia tem sido cruel e tem afetado, especialmente, a vida das mulheres. A casa, que deveria ser um lugar seguro, apresenta-se para muitas como um lugar de perigo constante. Muitos trabalhos de cuidado remunerados ou não são realizados pelas mulheres. Historicamente o cuidado tem sido delegado às mulheres, sendo, por um lado, exaltado como parte do ser/fazer feminino (mãe e dona da casa) e, por outro lado, é um trabalho não remunerado ou mal remunerado (enfermeiras, assistentes sociais). Apresenta-se o artigo em três partes: a experiência das mulheres, a necessidade de reinventar a economia do cuidado e o grito uterino que ecoa com justa indignação. Evidencia-se que a pandemia visibilizou questões preexistentes: o aumento do cuidado sob os ombros das mulheres seja em casa ou nas diferentes profissões em que as mulheres estão na linha de frente, a violência contra as mulheres. A pandemia acentua a desigualdade social, racial e de gênero da sociedade brasileira, sendo que as mais atingidas são mulheres pobres, negras, pardas, idosas e com deficiência. O grito que nasce do feminismo clama por uma reinvenção do mundo que habitamos metadata Ulrich, Claudete Beise; Núñez de la Paz, Nivia Ivette y Ströher, Marga Janete mail SIN ESPECIFICAR (2020) Mulheres em tempos de pandemia: a cotidianidade, a economia do cuidado e o grito uterino! Estudos Teológicos, 60 (2). p. 554. ISSN 0101-3130

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Mobility and low energy consumption are considered the main requirements for wireless body area sensor networks (WBASN) used in healthcare monitoring systems (HMS). In HMS, battery-powered sensor nodes with limited energy are used to obtain vital statistics about the body. Hence, energy-efficient schemes are desired to maintain long-term and steady connectivity of the sensor nodes. A sheer amount of energy is consumed in activities such as idle listening, excessive transmission and reception of control messages, packet collisions and retransmission of packets, and poor path selection, that may lead to more energy consumption. A combination of adaptive scheduling with an energy-efficient protocol can help select an appropriate path at a suitable time to minimize the control overhead, energy consumption, packet collision, and excessive idle listening. This paper proposes a region-based energy-efficient multipath routing (REMR) approach that divides the entire sensor network into clusters with preferably multiple candidates to represent each cluster. The cluster representatives (CRs) route packets through various clusters. For routing, the energy requirement of each route is considered, and the path with minimum energy requirements is selected. Similarly, end-to-end delay, higher throughput, and packet-delivery ratio are considered for packet routing. metadata Akbar, Shuja; Mehdi, Muhammad Mohsin; Jamal, M. Hasan; Raza, Imran; Hussain, Syed Asad; Breñosa, Jose; Martínez Espinosa, Julio César; Pascual Barrera, Alina Eugenia y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, alina.pascual@unini.edu.mx, SIN ESPECIFICAR (2022) Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring. Healthcare, 10 (11). p. 2297. ISSN 2227-9032

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés This is an effort to analyze the reaction of stock prices of Indian public and private banks listed in NSE and BSE to the announcement of seven best case news events. Several recent studies have analyzed the correlation between stock prices and news announcements; however, there is no evidence on how private and public sector Indian bank stocks react to important news events independently. We examine these features by concentrating on a sample of banking and government news events. We classify these news events to create a group of negative and a group of positive tone of announcements (sentiments). The statistical results show that the negative banking news announcements had a one-month impact on private banks, with statistically significant negative mean CARs. However, with highly statistically substantial negative mean CARs, the influence of the negative banking news announcements on public banks was observed for two months after the news was published. Furthermore, the influence of the positive banking news on private banks persisted a month after the news was published. Positive banking news events had an influence on public banks for five days after they were published. The study concludes that public bank stocks react more to negative news announcements than positive news announcements in the same manner as the sentimental polarity of the news announcements as compared to private bank stocks. First, we retrieved the news articles published in prominent online financial news portals between 2017 and 2020, and the seven major news events were extracted and classified using multi-class text classification. The Random Forest classifier produced a significant accuracy of 94% with pre-trained embeddings of DistilBERT, a neural network model, which outperformed the traditional feature representation technique, TF-IDF. The training data for the classifier were balanced using the SMOTE sampling technique metadata Dogra, Varun; Alharithi, Fahd S.; Álvarez, Roberto Marcelo; Singh, Aman y Qahtani, Abdulrahman M. mail SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, aman.singh@uneatlantico.es, SIN ESPECIFICAR (2022) NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange. Systems, 10 (6). p. 233. ISSN 2079-8954

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés New approaches to software testing are required due to the rising complexity of today’s software applications and the rapid growth of software engineering practices. Among these methods, one that has shown promise is the introduction of Natural Language Processing (NLP) tools to software testing practices. NLP has witnessed a rise in popularity within all IT fields, especially in software engineering, where its use has improved the way we extract information from textual data. The goal of this systematic literature review (SLR) is to provide an in-depth analysis of the present body of the literature on the expanding subject of NLP-based software testing. Through a repeatable process, that takes into account the quality of the research, we examined 24 papers extracted from Web of Science and Scopus databases to extract insights about the usage of NLP techniques in the field of software testing. Requirements analysis and test case generation popped up as the most hot topics in the field. We also explored NLP techniques, software testing types, machine/deep learning algorithms, and NLP tools and frameworks used in the studied body of literature. This study also stressed some recurrent open challenges that need further work in future research such as the generalization of the NLP algorithm across domains and languages and the ambiguity in the natural language requirements. Software testing professionals and researchers can get important insights from the findings of this SLR, which will help them comprehend the advantages and challenges of using NLP in software testing. metadata Boukhlif, Mohamed; Hanine, Mohamed; Kharmoum, Nassim; Ruigómez Noriega, Atenea; García Obeso, David y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, atenea.ruigomez@uneatlantico.es, david.garcia@uneatlantico.es, SIN ESPECIFICAR (2024) Natural Language Processing-Based Software Testing: A Systematic Literature Review. IEEE Access, 12. pp. 79383-79400. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model. metadata Chakraborty, Gouri Shankar; Batra, Salil; Singh, Aman; Muhammad, Ghulam; Yélamos Torres, Vanessa y Mahajan, Makul mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, vanessa.yelamos@funiber.org, SIN ESPECIFICAR (2023) A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics, 13 (10). p. 1806. ISSN 2075-4418

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory. metadata Pal, Rishi; Adhikari, Deepak; Heyat, Md Belal Bin; Guragai, Bishal; Lipari, Vivian; Brito Ballester, Julién; De la Torre Díez, Isabel; Abbas, Zia y Lai, Dakun mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students’ Cardiac Signal and MSY. Bioengineering, 9 (12). p. 793. ISSN 2306-5354

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization. metadata Alam, Shadab; Aslam, Muhammad Shehzad; Altaf, Ayesha; Iqbal, Faiza; Nigar, Natasha; Castanedo Galán, Juan; Gavilanes Aray, Daniel; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juan.castanedo@uneatlantico.es, daniel.gavilanes@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Novel model to authenticate role-based medical users for blockchain-based IoMT devices. PLOS ONE, 19 (7). e0304774. ISSN 1932-6203

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Objective To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients. Methods Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis. Results From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66–88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001). Conclusion Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients. metadata Martín-Rodríguez, Francisco; Sanz-García, Ancor; Ortega, Guillermo J.; Delgado-Benito, Juan F.; Garcia Villena, Eduardo; Mazas Pérez-Oleaga, Cristina; López-Izquierdo, Raúl y Castro Villamor, Miguel A. mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.garcia@uneatlantico.es, cristina.mazas@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19. Annals of Medicine, 54 (1). pp. 646-654. ISSN 0785-3890

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score ( RS ) and normalized discounted cumulative gain ( nDCG ) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products. metadata Hussain, Naveed; Mirza, Hamid Turab; Iqbal, Faiza; Altaf, Ayesha; Shoukat, Ahtsham; Gracia Villar, Mónica; Soriano Flores, Emmanuel; Rojo Gutiérrez, Marco Antonio y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, SIN ESPECIFICAR (2023) PRUS: Product Recommender System Based on User Specifications and Customers Reviews. IEEE Access, 11. pp. 81289-81297. ISSN 2169-3536

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Este estudio se enfocó en conocer las percepciones de los usuarios de esta tecnología y su uso en los cursos de español para hacer los ajustes necesarios luego de tres años de su introducción. Arlington Public Schools se dio a la tarea de atemperar las necesidades con la utilización de tabletas en todos sus cursos académicos en todos los niveles escolares. Interacción educativa entre estudiantes y maestros se ha visto alterada por la adaptación e integración de la tecnología en las últimas décadas. Al ir avanzando los adelantos tecnológicos, todas las partes de las comunidades educativas se han atemperado en cuestión de su funcionamiento y como han comenzado a crear una nueva educación y se han enfocado en su proceso de implementación como herramienta o instrumento de enseñanza. Conocer el sentir del ente educativo en la utilización de tabletas en sus currículos y cómo influye en las formas de enseñar en los cursos de español. Los miedos, retos, necesidades e inseguridades de los usuarios serán considerados al realizar los cambios hacia un currículo integrador con la tecnología y el español. metadata Sánchez Jiménez, José Antonio mail SIN ESPECIFICAR (2019) Percepciones de estudiantes y maestros de español: realidades ante las tabletas. Revista Innova Educación, 1 (3). pp. 266-280. ISSN 2664-1496

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity. metadata de Santos Castro, Pedro Ángel; del Pozo Vegas, Carlos; Pinilla Arribas, Leyre Teresa; Zalama Sánchez, Daniel; Sanz-García, Ancor; Vásquez del Águila, Tony Giancarlo; González Izquierdo, Pablo; de Santos Sánchez, Sara; Mazas Pérez-Oleaga, Cristina; Dominguez Azpíroz, Irma; Elío Pascual, Iñaki y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, irma.dominguez@unini.edu.mx, inaki.elio@uneatlantico.es, SIN ESPECIFICAR (2024) Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Nas últimas décadas temos vivenciado a dificuldade em efetivar o uso da linguagem inclusiva ao tempo em que se evidencia a facilidade de implantação de uma neolíngua que provém do generismo queer. São tempos sombrios para a luta feminista, que sofre não só a difamação e deturpação da sua agenda e cânon, senão também a usurpação de conceitos e espaços. No ano 2021, que marca o centenário do pedagogo brasileiro Paulo Freire, estende-se sobre nós uma outra onda ameaçadora de irracionalismo, como aquela advertida por ele no século passado. O neoliberalismo mata, assim como mata o machismo; e as mulheres, a vida das mulheres, não vão além de uma objetificação para ambos. Nosso objetivo, com esse artigo, é dialogar desde e com a proposta pedagógica freiriana tomando como contraponto os conceitos ‘linguagem inclusiva’ e ‘neolíngua’ no intuito de denunciar e, principalmente, de enfatizar a necessidade permanente de uma educação emancipadora e libertadora para transformar os sistemas de opressão e morte. Uma teologia da vida não pode ficar em silêncio perante a ignomínia contemporânea metadata Ulrich, Claudete Beise; Ströher, Marga Janete y Núñez de la Paz, Nivia Ivette mail SIN ESPECIFICAR (2021) Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer. Revista Caminhos - Revista de Ciências da Religião, ESPECIAL,, 19 (4). pp. 228-247. ISSN 1678-3034

Artículo Materias > Educación física y el deporte
Materias > Comunicación
Materias > Psicología Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español En este artículo, presento la importancia de aplicar la perspectiva de la sociología de la comunicación, en estudios sobre la influencia de las noticias de la prensa deportiva en el estado de ansiedad de las personas atletas de alto rendimiento en el Comité Olímpico de Puerto Rico (COPUR). Con este propósito, realicé una revisión de las investigaciones llevadas a cabo en Puerto Rico durante dos décadas (2000-2020), puntualizando en aquéllas realizadas con instrumentos objetivos para medir ansiedad en personas atletas de alto rendimiento y la influencia de la prensa deportiva en el estado emocional de éstas en eventos de competencia. Encontré un total de ocho investigaciones relacionadas con la revisión de literatura. Cinco de estas atienden la psicología deportiva y las manifestaciones emocionales de las personas atletas de alto rendimiento, mientras que tres trabajos le dan atención a la influencia de los medios en el estado emocional de las personas deportistas. Este estudio reconoce la necesidad de continuar realizando investigaciones en Puerto Rico dirigidas a evaluar la relación de la prensa deportiva, el estado de ansiedad de las personas atletas y su ejecución deportiva. metadata Acosta González, Zacha mail SIN ESPECIFICAR (2022) Perspectiva de la sociología de la comunicación al análisis de la influencia de las noticias de prensa deportiva y la contribución a la ansiedad en atletas de alto rendimiento de Puerto Rico. Revista Puertorriqueña de Psicología, 33 (2). pp. 218-231.

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Este artículo tiene como objetivo presentar los conceptos y prácticas que orientan la Educación del Campo en América Latina tomando como referencia el contexto de Brasil y Nicaragua y la defensa de una Educación del Campo con un horizonte popular. Resulta de la experiencia dialógica en Formación Docente en la perspectiva de la Educación Popular del Campo entre Brasil y Nicaragua. A partir de un enfoque hermenéutico en un primer momento, además de reforzar las visiones latinoamericanas de la Educación del Campo, el estudio pretende reflexionar sobre la identidad y pertenencia de la Educación del Campo que hacemos. En un segundo, presentar posibilidades de surgimiento de Pedagogías que emergen de los saberes del campo. Como resultado se reconoce el potencial del pensamiento popular progresista desde la perspectiva de Paulo Freire, entre otros, que orientan prácticas y saberes de la Educación del Campo. El estudio demuestra el esfuerzo de los dos países para lograr este propósito. metadata Pereira, Vilmar Alves y Cerda, Cristian Danilo mail vilmar.alves@unini.edu.mx, SIN ESPECIFICAR (2022) Perspectivas de la educación popular del campo en América Latina: un diálogo Brasil-Nicaragua. Revista Brasileira de Educação do Campo. pp. 1-20. ISSN 25254863

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment. metadata Ali, Mudasir; Shahroz, Mobeen; Akram, Urooj; Mushtaq, Muhammad Faheem; Carvajal-Altamiranda, Stefanía; Aparicio Obregón, Silvia; Díez, Isabel De La Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, stefania.carvajal@uneatlantico.es, silvia.aparicio@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model. IEEE Access, 12. pp. 34691-34707. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés β-Thalassemia is one of the dangerous causes of the high mortality rate in the Mediterranean countries. Substantial resources are required to save a β-Thalassemia carriers’ life and early detection of thalassemia patients can help appropriate treatment to increase the carrier’s life expectancy. Being a genetic disease, it can not be prevented however the analysis of several indicators in parents’ blood can be used to detect disorders causing Thalassemia. Laboratory tests for Thalassemia are time-consuming and expensive like high-performance liquid chromatography, Complete Blood Count (CBC) with peripheral smear, genetic test, etc. Red blood indices from CBC can be used with machine learning models for the same task. Despite the available approaches for Thalassemia carriers from CBC data, gaps exist between the desired and achieved accuracy. Moreover, the data imbalance problem is studied well which makes the models less generalizable. This study proposes a highly accurate approach for β-Thalassemia detection using red blood indices from CBC augmented by supervised machine learning. In view of the fact that all the features do not carry predictive information regarding the target variable, this study employs a unified framework of two features selection techniques including Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD). The data imbalance between β-Thalassemia carrier and non-carriers is handled by Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN). Extensive experiments are performed using many state-of-the-art machine learning models and deep learning models. Experimental results indicate the superiority of the proposed approach over existing approaches with an accuracy score of 0.96. metadata Rustam, Furqan; Ashraf, Imran; Jabbar, Shehbaz; Tutusaus, Kilian; Mazas Pérez-Oleaga, Cristina; Pascual Barrera, Alina Eugenia y de la Torre Diez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, kilian.tutusaus@uneatlantico.es, cristina.mazas@uneatlantico.es, alina.pascual@unini.edu.mx, SIN ESPECIFICAR (2022) Prediction β-Thalassemia carriers using complete blood count features. Scientific Reports, 12 (1). ISSN 2045-2322

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Objective The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age. Design Prospective, multicentre, observational study. Setting Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered. Participants Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS. Primary and secondary outcome measures The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions. Results A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality. Conclusions This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation. metadata del Pozo Vegas, Carlos; Zalama-Sánchez, Daniel; Sanz-Garcia, Ancor; López-Izquierdo, Raúl; Sáez-Belloso, Silvia; Mazas Pérez-Oleaga, Cristina; Dominguez Azpíroz, Irma; Elío Pascual, Iñaki y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, irma.dominguez@unini.edu.mx, inaki.elio@uneatlantico.es, SIN ESPECIFICAR (2023) Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study. BMJ Open, 13 (11). e078815. ISSN 2044-6055

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Background: Nowadays, there is no gold standard score for prehospital sepsis and sepsis-related mortality identification. The aim of the present study was to analyze the performance of qSOFA, NEWS2 and mSOFA as sepsis predictors in patients with infection-suspected in prehospital care. The second objective is to study the predictive ability of the aforementioned scores in septic-shock and in-hospital mortality. Methods: Prospective, ambulance-based, and multicenter cohort study, developed by the emergency medical services, among patients (n = 535) with suspected infection transferred by ambulance with high-priority to the emergency department (ED). The study enrolled 40 ambulances and 4 ED in Spain between 1 January 2020, and 30 September 2021. All the variables used in the scores, in addition to socio-demographic data, standard vital signs, prehospital analytical parameters (glucose, lactate, and creatinine) were collected. For the evaluation of the scores, the discriminative power, calibration curve and decision curve analysis (DCA) were used. Results: The mSOFA outperformed the other two scores for mortality, presenting the following AUCs: 0.877 (95%CI 0.841–0.913), 0.761 (95%CI 0.706–0.816), 0.731 (95%CI 0.674–0.788), for mSOFA, NEWS, and qSOFA, respectively. No differences were found for sepsis nor septic shock, but mSOFA’s AUCs was higher than the one of the other two scores. The calibration curve and DCA presented similar results. Conclusion: The use of mSOFA could provide and extra insight regarding the short-term mortality and sepsis diagnostic, backing its recommendation in the prehospital scenario. metadata Melero-Guijarro, Laura; Sanz-García, Ancor; Martín-Rodríguez, Francisco; Lipari, Vivian; Mazas Pérez-Oleaga, Cristina; Carvajal-Altamiranda, Stefanía; Martínez López, Nohora Milena; Dominguez Azpíroz, Irma; Castro Villamor, Miguel A.; Sánchez Soberón, Irene y López-Izquierdo, Raúl mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, cristina.mazas@uneatlantico.es, stefania.carvajal@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study. Frontiers in Medicine, 10. ISSN 2296-858X

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh. metadata Sharif, Nadim; Sharif, Nazmul; Khan, Afsana; Dominguez Azpíroz, Irma; Martínez Díaz, Raquel; Díez, Isabel De la Torre; Parvez, Anowar Khasru y Dey, Shuvra Kanti mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis. Frontiers in Immunology, 14. ISSN 1664-3224

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Introduction: Co-prevalence of long-COVID-19, cardiovascular diseases and diabetes is one of the major health challenges of the pandemic worldwide. Studies on long-COVID-19 and associated health outcomes are absent in Bangladesh. The main aim of this study was to determine the prevalence and impact of long-COVID-19 on preexisting diabetes and cardiovascular diseases (CVD) on health outcomes among patients in Bangladesh. Methods: We collected data from 3,250 participants in Bangladesh, retrospectively. Multivariable logistic regression model was used to determine the odds ratio between independent and dependent variables. Kaplan-Meier survival curve was used to determine the cumulative survival. Results: COVID-19 was detected among 73.4% (2,385 of 3,250) participants. Acute long-COVID-19 was detected among 28.4% (678 of 2,385) and chronic long-COVID-19 among 71.6% (1,707 of 2,385) patients. CVD and diabetes were found among 32%, and 24% patients, respectively. Mortality rate was 18% (585 of 3,250) among the participants. Co-prevalence of CVD, diabetes and COVID-19 was involved in majority of fatality (95%). Fever (97%), dry cough (87%) and loss of taste and smell (85%) were the most prevalent symptoms. Patients with co-prevalence of CVD, diabetes and COVID-19 had higher risk of fatality (OR: 3.65, 95% CI, 2.79–4.24). Co-prevalence of CVD, diabetes and chronic long-COVID-19 were detected among 11.9% patients. Discussion: Risk of hospitalization and fatality reduced significantly among the vaccinated. This is one of the early studies on long-COVID-19 in Bangladesh. metadata Sharif, Nadim; Sharif, Nazmul; Khan, Afsana; Halawani, Ibrahim F.; Alzahrani, Fuad M.; Alzahrani, Khalid J.; Díez, Isabel De la Torre; Ramírez-Vargas, Debora L.; Kuc Castilla, Ángel Gabriel; Parvez, Anowar Khasru y Dey, Shuvra Kanti mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh. Frontiers in Public Health, 11. ISSN 2296-2565

Artículo Materias > Ciencias Sociales Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Organizaciones de economía solidarias, son iniciativas empresariales sin ánimo de lucro en las que sus campos de acción están encaminadas para una proyección a las comunidades de bajos ingresos, población vulnerables, desplazada, primera infancia, grupos etnias como indígenas, negritudes, afro-descendientes, entre otras. Toda asociación no gubernamental necesita un buen manejo administrativo que le permita realizar sus funciones sociales con eficiencia, pues es importante resaltar que de una buena gestión depende el éxito de las funciones de las entidades. En ese sentido, se constituirá en un aporte investigativo social, puesto que en primera instancia se busca un estado actual organizaciones en sus áreas administrativa, operacional, financiera, comercial, talento humano y todo esto articulado con la alta dirección, para la consecución de nuevos usuarios y sostenimiento. Es por eso que buscamos fortalecer la capacidad empresarial de este sector, esto nos hace pensar que debemos trabajar en la situación actual con el fin de equilibrar su comportamiento organizacional, esto nos llevará a la obtención de una estructura para el beneficio de estas organizaciones en la adopción de sistemas de calidad, basado en Norma ISO 9001, gestión ambiental, ISO 14001, gestión seguridad y salud en el trabajo OHSAS 18001, para la mejora continua. metadata Carmona Campo, Carlos Javier mail SIN ESPECIFICAR (2015) Procesos de gestión en las organizaciones solidarias. Revista ADGNOSIS, 4 (4). ISSN 2344-7516

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués Este trabalho apresenta como tema a Psicomotricidade e o Ensino da Aprendizagem na Educação Infantil, abordando a necessidade da inserção da Psicomotricidade Relacional, porém é através da interação que a criança consegue resolver seus problemas e aprende a lidar com as frustrações. Na Educação Infantil, a Psicomotricidade Relacional pode orientar as capacidades de apropriação e o conhecimento cognitivo, afetivo, emocional e ético para a formação de indivíduos independentes e seguros, proporcionando seu progresso escolar, o professor deve propiciar situações prazerosas, porém muitas vezes, a criança é privada de brincar, tanto em casa quanto no ambiente escolar. Envolvendo brincadeiras e aprendizagem se reforça a Psicomotricidade Relacional como uma maneira preventiva no desenvolvimento integral da criança, onde o corpo está articulado com a motricidade, possibilitando as experiências psicomotoras, elevando a autoestima e motivando a buscar novos conhecimentos. metadata Alves Guimarães, Ueudison; Rodrigues Dantas de Brito, Junea Graciele y Alves de Barros, Vania mail SIN ESPECIFICAR (2022) Psicomotricidade relacional e o ensino aprendizagem na educação infantil. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (9). e391944. ISSN 2675-6218

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. metadata Rustam, Furqan; Ishaq, Abid; Hashmi, Muhammad Shadab Alam; Siddiqui, Hafeez Ur Rehman; Dzul Lopez, Luis; Castanedo Galán, Juan y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, luis.dzul@unini.edu.mx, juan.castanedo@uneatlantico.es, SIN ESPECIFICAR (2023) Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data. Sensors, 23 (16). p. 7018. ISSN 1424-8220

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Español Esta investigación tuvo por objetivo valorar la utilización de un Instrumento para la evaluación de Entornos Virtuales de Aprendizaje (EVA), específicamente el DELES (Distance Education Learning Environments Survey) para el Proyecto Europeo de Educación Inclusiva denominado LOVEDISTANCE (Learning Optimization and Academic Inclusion Via Equitative Distance Teaching and Learning). El supuesto inicial es que el instrumento puede ser útil, pero está desactualizado y no necesariamente enfocado a los objetivos del proyecto LOVEDISTANCE, en particular al de Educación Inclusiva. El ejercicio académico se llevó a cabo en la Universidad de Levinsky, en Tel Aviv, Israel, y el análisis de la información se hizo con un enfoque cuanti-cualitativo, donde se utilizó, en una primera parte, la medida del consenso entre expertos para medir la fiabilidad estadística de las respuestas de los expertos, y después se realizó un análisis de la varianza (ANOVA) para determinar si existían diferencias significativas entre las medias de los grupos; posteriormente, se hizo un análisis cualitativo pormenorizado de las observaciones a partir de tres ejes de análisis: consideraciones del ejercicio investigativo, perfil de los investigadores y análisis de cada escala del instrumento. Algunas de las conclusiones más relevantes fueron que el instrumento es, en su mayoría, útil para los propósitos del proyecto LOVEDISTANCE, pero precisa mejoras en lo referido a las siguientes escalas: relevancia del aprendizaje para el alumno, apoyo por parte del instructor y la medición en la autonomía del estudiante. metadata Garat de Marin, Mirtha Silvana; Rodríguez Velasco, Carmen Lilí; Prola, Thomas y Soriano Flores, Emmanuel mail silvana.marin@uneatlantico.es, carmen.rodriguez@uneatlantico.es, thomas.prola@uneatlantico.es, emmanuel.soriano@uneatlantico.es (2024) Readaptación de un instrumento para la evaluación de entornos virtuales de aprendizaje en el proyecto europeo de educación inclusiva denominado LOVEDISTANCE. MLS Educational Research, 8 (1). ISSN 2603-5820

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%. metadata Aziz, Romila; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Kuc Castilla, Ángel Gabriel; Uc-Rios, Carlos; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Real Word Spelling Error Detection and Correction for Urdu Language. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Educación física y el deporte Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Español Objetivo: El objetivo de este estudio fue describir la funcionalidad motriz de bomberos por medio de la batería Functional Movement Screen (FMS), e identificar su relación con factores antropométricos de riesgo cardiovascular (RC). Método: Participaron un total de 139 bomberos, de los cuales 122 corresponden a hombres y 17 a mujeres pertenecientes a 16 compañías de bomberos de la comuna de Valparaíso, Chile. Se aplicó la batería de evaluación FMS y se tomaron medidas de perímetro cintura (PC), índice de masa corporal (IMC) y el índice cintura-estatura (ICE). Resultados: Para las mujeres se reportaron correlaciones bajas entre el FMS y las variables antropométricas de RC, mientras que para los hombres estas correlaciones fueron significativas y negativas entre FMS y las variables PC, ICE e IMC (r = -,37, p < ,001; r = -,34, p < ,001; –-,40, p < ,002), respectivamente. El 64,02% de los participantes en ICE se clasificó en un alto RC (≥ ,50) y en el IMC un 73% en las categorías de sobrepeso y obesidad. De acuerdo con los resultados de la evaluación FMS, el 45,33% de los participantes presentan una baja funcionalidad motriz. Conclusión: La baja funcionalidad motriz y los elevados valores en parámetros antropométricos de RC son factores que debería preocupar a comunidades como las compuestas por bomberos, quienes desarrollan labores de alta exigencia física y mental, en condiciones extremas que muchas veces pueden poner en peligro la salud de los propios voluntarios metadata Barraza-Gómez, Fernando; Rodríguez Canales, Carolina; Hecht-Chau, Gernot; Alvear-Ordenes, Ildefonso y Enríquez-Valenzuela, Matías mail SIN ESPECIFICAR, carolina.rodriguez@unini.org, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Relación entre funcionalidad motriz y factores antropométricos de riesgo cardio metabólico en bomberos de la región de Valparaíso, Chile. Retos, 44. pp. 1148-1154.

Artículo Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés The objective of this article is to do bibliographic research lined up with the worldwide COVID-19 pandemic in which the main focus will be inclusion, Pygmalion effect as well as teaching and learning remotely. The basis is to gather different perspectives to build knowledge through exhaustive reading of books, doctoral dissertations, masters programs dissertations, current news, reliable sources of information, scientific papers, and own experience amongst others. The analysis of this bibliographic research will be useful for future research on topics related. Emotional and affective relations are important and have impact when facing English learning difficulties and when working to have inclusion. In the context of Covid-19, the learning and teaching conditions got complex when migrating to virtual classes. Remote teaching is not a solution to face learning and teaching difficulties. In this way, affection is the best tool for inclusive learning in virtual environments. It is said that one of the biggest barriers is teacher’s formation whose career is based on traditional pedagogy not easily adaptable to digital environments. This situation creates a deep feeling of frustration in teachers, which can be reflected in the low academic performance of English students. Several reflections will be made to encourage the actors involved in the teaching-learning process and in the educational system to ask themselves: Which are the challenges of remote teaching and learning during the Covid-19 pandemic being inclusive through the Pygmalion effect? metadata Rizo Peñafort, Andrea María y Pereira, Vilmar Alves mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx (2022) Remote English Teaching during Covid-19 Pandemic: Challenges and Lessons in Higher Education Teaching in Colombia. Research, Society and Development, 11 (2). e44111226000. ISSN 2525-3409

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor’s algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The National Institute of Standards and Technology (NIST) has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two post-quantum cryptography (PQC) algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing. metadata Farooq, Sana; Altaf, Ayesha; Iqbal, Faiza; Bautista Thompson, Ernesto; Ramírez-Vargas, Debora L.; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms. Sensors, 23 (12). p. 5379. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection. metadata Ashiq, Waqar; Kanwal, Samra; Rafique, Adnan; Waqas, Muhammad; Khurshaid, Tahir; Caro Montero, Elizabeth; Bustamante Alonso, Alicia y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, elizabeth.caro@uneatlantico.es, alicia.bustamante@uneatlantico.es, SIN ESPECIFICAR (2024) Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Mutations allow viruses to continuously evolve by changing their genetic code to adapt to the hosts they infect. It is an adaptive and evolutionary mechanism that helps viruses acquire characteristics favoring their survival and propagation. The COVID-19 pandemic declared by the WHO in March 2020 is caused by the SARS-CoV-2 virus. The non-stop adaptive mutations of this virus and the emergence of several variants over time with characteristics favoring their spread constitute one of the biggest obstacles that researchers face in controlling this pandemic. Understanding the mutation mechanism allows for the adoption of anticipatory measures and the proposal of strategies to control its propagation. In this study, we focus on the mutations of this virus, and we propose the SARSMutOnto ontology to model SARS-CoV-2 mutations reported by Pango researchers. A detailed description is given for each mutation. The genes where the mutations occur and the genomic structure of this virus are also included. The sub-lineages and the recombinant sub-lineages resulting from these mutations are additionally represented while maintaining their hierarchy. We developed a Python-based tool to automatically generate this ontology from various published Pango source files. At the end of this paper, we provide some examples of SPARQL queries that can be used to exploit this ontology. SARSMutOnto might become a ‘wet bench’ machine learning tool for predicting likely future mutations based on previous mutations. metadata Bakkas, Jamal; Hanine, Mohamed; Chekry, Abderrahman; Gounane, Said; de la Torre Díez, Isabel; Lipari, Vivian; Martínez López, Nohora Milena y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, nohora.martinez@uneatlantico.es, SIN ESPECIFICAR (2023) SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations. Viruses, 15 (2). p. 505. ISSN 1999-4915

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the last two decades, there is an increasingly broad line of studies that warn about the emotional health of journalists and the challenges that it poses for communication professionals to be able to separate work issues from their personal lives. The coverage of COVID-19 exposed many journalists to situations of frustration, discomfort and stress for various reasons: long working hours, not having the appropriate technological material, added to an environment of uncertainty caused by the pandemic. This study aims to examine the possible scope of technostress –in some cases associated to digital divide– in journalists and analyze if they are aware of the uses of care of the self as a way to deal with stressful situations. For this, our research is based on documentary analysis, a survey answered by (50) fifty Argentinean journalists, and twelve (12) in-depth interviews to experienced journalists. Our findings suggest that constant exposure to computers and smartphones during the lockdown together with difficulties to connect to Internet or to have the adequate materials and the lack of coping strategies –as the care of the self– confirms the presence of technostress. Another result that emerges from this research, it should be addressed in future studies, is that some journalists’ reactions about care of the self could respond to the third person effect theory to maintain high self-esteem and not demonstrate vulnerability. metadata Escudero, Carolina; Prola, Thomas; Soriano Flores, Emmanuel y Silva Alvarado, Eduardo René mail SIN ESPECIFICAR, thomas.prola@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.silva@funiber.org (2023) The Scope of Technostress and Care of The Self on Journalists During the Pandemic. Przestrzeń Społeczna (Social Space), 23 (4). pp. 20-43. ISSN 20841558

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The rapid generation of data from various sources by the public sector, private corporations, business associations, and local communities is referred to as big data. This large and complex dataset is often regarded as the ‘new oil’ by public administrations (PAs), and data-driven approaches are employed to transform it into valuable insights that can improve governance, transparency, digital services, and public engagement. The government’s big-data ecosystem (GBDE) is a result of this initiative. Effective data management is the first step towards large-scale data analysis, which yields insights that benefit your work and your customers. However, managing big data throughout its life cycle is a daunting challenge for public agencies. Despite its widespread use, big data management is still a significant obstacle. To address this issue, this study proposes a hybrid approach to secure the data management life cycle for GBDE. Specifically, we use a combination of the ECC algorithm with AES 128 BITS encryption to ensure that the data remain confidential and secure. We identified and analyzed various data life cycle models through a systematic literature review to create a data management life cycle for data-driven governments. This approach enhances the security and privacy of data management and addresses the challenges faced by public agencies. metadata Zahid, Reeba; Altaf, Ayesha; Ahmad, Tauqir; Iqbal, Faiza; Miró Vera, Yini Airet; López Flores, Miguel Ángel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, yini.miro@uneatlantico.es, miguelangel.lopez@uneatlantico.es, SIN ESPECIFICAR (2023) Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective. Systems, 11 (8). p. 380. ISSN 2079-8954

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system’s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance. metadata Hussain, Shahzad; Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Alemany Iturriaga, Josep; Velarde-Sotres, Álvaro; Díez, Isabel De la Torre y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models. Sensors, 24 (19). p. 6325. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses. metadata Ali, Tariq; Rehman, Saif Ur; Ali, Shamshair; Mahmood, Khalid; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Khurshaid, Tahir y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations. metadata Rashid, Chaudhary Hamza; Shafi, Imran; Ahmad, Jamil; Bautista Thompson, Ernesto; Masías Vergara, Manuel; Diez, Isabel De La Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, manuel.masias@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Software Cost and Effort Estimation: Current Approaches and Future Trends. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error. metadata Shafi, Imran; Sohail, Amir; Ahmad, Jamil; Martínez Espinosa, Julio César; Dzul Lopez, Luis Alonso; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, luis.dzul@unini.edu.mx, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety. Applied Sciences, 13 (9). p. 5475. ISSN 2076-3417

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10. metadata Usmani, Salman Sadullah; Tuhin, Izaz Ahmmed; Mia, Md. Rajib; Islam, Md. Monirul; Mahmud, Imran; Uc Ríos, Carlos Eduardo; Fabian Gongora, Henry; Ashraf, Imran y Samad, Md. Abdus mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, henry.gongora@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides. PLOS ONE, 19 (11). e0313835. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation metadata Yadav, Arvind; Chithaluru, Premkumar; Singh, Aman; Albahar, Marwan Ali; Jurcut, Anca; Álvarez, Roberto Marcelo; Mojjada, Ramesh Kumar y Joshi, Devendra mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models. Mathematics, 10 (22). p. 4263. ISSN 2227-7390

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués A segurança do trabalho é uma área de estudo de muita importância, visto que toda a economia mundial depende das atividades laborais e, com isso, torna-se importante proteger as pessoas envolvidas nesse processo. É um consenso comum de que se deve preservar a vida dos trabalhadores, entretanto há uma objeção no que diz respeito aos custos que essa proteção pode gerar. Em um mercado competitivo, onde as empresas precisam reduzir custos, não se pode considerar de forma utópica de que devemos proteger os trabalhadores a qualquer custo, pois assim a empresa não consegue controlar suas finanças e, consequentemente, não consegue se posicionar de forma competitiva no mercado. Na construção civil não é diferente. Por se tratar de uma das áreas da economia que mais emprega pessoas e uma das que apresenta maiores riscos à vida dos trabalhadores, acaba sendo também uma das áreas que tem maior índice de acidentes. Nesse cenário, a segurança do trabalho se encarrega de atuar para minimizar todos esses riscos e danos, sendo necessário que se realize estudos como este que se encarregue de maximizar a segurança oferecida aos trabalhadores com o menor custo possível, apresentando-se como uma boa alternativa para ambos os lados. Este trabalho se trata de um estudo de casos cujo objetivo é analisar situações de riscos em construção de edificações e classificar as proteções mais comum de forma a obter o melhor custo/benefício. O intuito é verificar se há sistemas mais eficientes que outros, considerando investimentos similares, sendo possível priorizar essas alternativas, permitindo ao gestor da empresa adotar as melhores medidas, de forma sustentável e economicamente viável. Os resultados apontam que os sistemas de retenção contra queda em altura, como linha de vida e ancoragem, são os que apresentam melhor relação custo-benefício. metadata Ferreira, Rafael Vaz; Pereira, Vilmar Alves y Florencio da Silva, Rodrigo mail SIN ESPECIFICAR, vilmar.alves@unini.edu.mx, SIN ESPECIFICAR (2022) Sustentabilidade em sistemas de segurança do trabalho na construção civil. Brazilian Journal of Development. pp. 56951-56969. ISSN 2525-8761

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés COVID-19 made its debut as a pandemic in 2020; since then, more than 607 million cases and at least 6.5 million deaths have been reported worldwide. While the burden of disease has been described, the long-term effects or chronic sequelae are still being clarified. The aim of this study was to present an overview of the information available on the sequelae of COVID-19 in people who have suffered from the infection. A systematic review was carried out in which cohort studies, case series, and clinical case reports were included, and the PubMed, Scielo, SCOPUS, and Web of Science databases were extracted. Information was published from 2020 to 1 June 2022, and we included 26 manuscripts: 9 for pulmonary, 6 for cardiac, 2 for renal, 8 for neurological and psychiatric, and 6 for cutaneous sequelae. Studies showed that the most common sequelae were those linked to the lungs, followed by skin, cutaneous, and psychiatric alterations. Women reported a higher incidence of the sequelae, as well as those with comorbidities and more severe COVID-19 history. The COVID-19 pandemic has not only caused death and disease since its appearance, but it has also sickened millions of people around the globe who potentially suffer from serious illnesses that will continue to add to the list of health problems, and further burden healthcare systems around the world. metadata Vásconez-González, Jorge; Izquierdo Condoy, Juan Sebastian; Fernandez-Naranjo, Raul y Ortiz-Prado, Esteban mail SIN ESPECIFICAR (2022) A Systematic Review and Quality Evaluation of Studies on Long-Term Sequelae of COVID-19. Healthcare, 10 (12). p. 2364. ISSN 2227-9032

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques. metadata Khan, Saad Mazhar; Shafi, Imran; Butt, Wasi Haider; Diez, Isabel de la Torre; López Flores, Miguel Ángel; Castanedo Galán, Juan y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, miguelangel.lopez@uneatlantico.es, juan.castanedo@uneatlantico.es, SIN ESPECIFICAR (2023) A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions. Land, 12 (8). p. 1514. ISSN 2073-445X

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Currently, sustainability is a vital aspect for every nation and organization to accomplish Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG) metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53% of respondents in the BlackRock survey are concerned about the availability of low ESG data, which is critical for determining the organization’s sustainability level. This obstacle can be overcome by implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction, transparency, authentication, and structured data. Based on the review of previous studies, it was determined that only a few studies discussed the implementation of Industry 4.0 technologies for ESG data and evaluation. The objective of the study is to discuss the significance of ESG data and report, which is used for the evaluation of the sustainability of an organization. In this regard, the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG data and suggest that these digital technologies have a crucial role in framing an accurate ESG report. After analysis a few vital conclusions are drawn such as ESG investment has benefited from AI capabilities, which previously relied on self-disclosed, annualized company information that was susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations that can be implemented for future work metadata Saxena, Archana; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Twala, Bhekisipho; Singh, Aman; Caro Montero, Elisabeth y Priyadarshi, Neeraj mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, SIN ESPECIFICAR (2022) Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape. Sustainability, 15 (1). p. 309. ISSN 2071-1050

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach. metadata Shafi, Imran; Sajad, Muhammad; Fatima, Anum; Gavilanes Aray, Daniel; Lipari, Vivian; Diez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19. Sensors, 23 (15). p. 6837. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study. metadata Mehmood, Aneela; Farooq, Muhammad Shoaib; Naseem, Ansar; Rustam, Furqan; Gracia Villar, Mónica; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) Threatening URDU Language Detection from Tweets Using Machine Learning. Applied Sciences, 12 (20). p. 10342. ISSN 2076-3417

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet. metadata Godos, Justyna; Scazzina, Francesca; Paternò Castello, Corrado; Giampieri, Francesca; Quiles, José L.; Briones Urbano, Mercedes; Battino, Maurizio; Galvano, Fabio; Iacoviello, Licia; de Gaetano, Giovanni; Bonaccio, Marialaura y Grosso, Giuseppe mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, mercedes.briones@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet. Journal of Translational Medicine, 22 (1). ISSN 1479-5876

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML. metadata Akrami, Nouhaila El; Hanine, Mohamed; Flores, Emmanuel Soriano; Aray, Daniel Gavilanes y Ashraf, Imran mail SIN ESPECIFICAR (2023) Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis. IEEE Access, 11. pp. 78879-78903. ISSN 2169-3536

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The primary objectives of this research article were twofold. Firstly, to categorise a total of 294 individuals who aspired to three distinct competency profiles associated with the supervision of international car sales (SPV). Secondly, to prioritise the criteria used for measurement and assess the level of satisfaction attained following the provision of targeted online training for each respective position. Segmentation was performed using the K-Means algorithm on a Likert scale importance questionnaire. Satisfaction indicators were derived by applying fuzzy set methods to the results of a satisfaction questionnaire, also using a Likert scale. The measurement criteria did not show any clear negative perceptions. The overall satisfaction index was 0.7, which was supported by classic statistics and placed in a high category. Additionally, a variable analysis revealed that candidates from the Euro-Asian region exhibited significantly low levels of satisfaction. However, no significant associations were observed between satisfaction levels and gender, income profile, completed training action, or age groups. The researchers rigorously employed a methodology that included assessing the validity and reliability of the instrument. A review of relevant literature also supported the analysis of the results. These findings suggest that the method could be applied to other multidisciplinary programmes to make informed decisions in the field of training. metadata Brito Ballester, Julién; Gracia Villar, Mónica; Soriano Flores, Emmanuel y García Villena, Eduardo mail julien.brito@uneatlantico.es, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.garcia@uneatlantico.es (2023) Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive. International Journal of Operations and Quantitative Management, 29 (2). pp. 223-251.

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer’s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer’s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each. metadata Shahzadi, Samra; Butt, Naveed Anwer; Sana, Muhammad Usman; Elío Pascual, Iñaki; Briones Urbano, Mercedes; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, inaki.elio@uneatlantico.es, mercedes.briones@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches. Diagnostics, 13 (18). p. 2871. ISSN 2075-4418

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés At this time, efforts are being made on a worldwide scale to accomplish sustainable development objectives. It has, thus, now become essential to investigate the part of technology in the accomplishment of these Sustainable Development Goals (SDGs), as this will enable us to circumvent any potential conflicts that may arise. The importance of wastewater management in the accomplishment of these goals has been highlighted in the study. The research focuses on the role of fourth industrial revolution in meeting the Sustainable Goals for 2030. Given that water is the most important resource on the planet and since 11 of the 17 Sustainable Goals are directly related to having access to clean water, effective water management is the most fundamental need for achieving these goals. The age of Industry 4.0 has ushered in a variety of new solutions in many industrial sectors, including manufacturing, water, energy, healthcare, and electronics. This paper examines the present creative solutions in water treatment from an Industry-4.0 viewpoint, focusing on big data, the Internet of Things, artificial intelligence, and several other technologies. The study has correlated the various concepts of Industry 4.0 along with water and wastewater management and also discusses the prior work carried out in this field with help of different technologies. In addition to proposing a way for explaining the operation of I4.0 in water treatment through a systematic diagram, the paper makes suggestions for further research as well. metadata Pandey, Shivam; Twala, Bhekisipho; Singh, Rajesh; Gehlot, Anita; Singh, Aman; Caro Montero, Elisabeth y Priyadarshi, Neeraj mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, SIN ESPECIFICAR (2022) Wastewater Treatment with Technical Intervention Inclination towards Smart Cities. Sustainability, 14 (18). p. 11563. ISSN 2071-1050

Artículo Materias > Biomedicina
Materias > Ciencias Sociales Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background Despite worldwide progress in terms of clean water supply, sanitation, and hygiene knowledge, some middle and most of low-income countries are still experiencing many diseases transmitted using unsafe water and the lack of sanitation. Methods To understand the impact of all waterborne diseases (WBD) registered in Ecuador. We performed a population-based analysis of all cases and deaths due to WBD in Ecuador based on the national public databases of hospital discharges as a proxy of incidence, in-hospital mortality, and countrywide general mortality rates from 2011 to 2020. Results In Ecuador, mestizos (mixed European and Indigenous American ancestry) had the greatest morbidity rate (141/100,000), followed by indigenous (63/100,000) and self-determined white patients (21/100,000). However, in terms of mortality, indigenous population have the greatest risk and rates, having a 790% additional mortality rate (2.6/100,000) than the reference group (self-determined white populations) at 0.29/100,000. The burden of disease analysis demonstrated that indigenous had the highest burden of disease caused by WBD with 964 YLL per every 100,000 people while mestizos have 360 YYL per 100,000 and self-determined white Ecuadorians have 109 YYL per 100,000. Conclusions In Ecuador, waterborne diseases (WBD) are still a major public health problem. We found that indigenous population had higher probability of getting sick and die due to WBD than the rest of the ethnic groups in Ecuador. We also found that younger children and the elderly are more likely to be admitted to the hospital due to a WBD. These epidemiological trends are probably associated with the lower life expectancy found among Indigenous than among the rest of the ethnic groups, who die at least, 39 years earlier than the self-determined white populations, 28 years earlier than Afro-Ecuadorians and 12 years earlier than the mestizos. metadata Ortiz-Prado, Esteban; Simbaña-Rivera, Katherine; Cevallos, Gabriel; Gómez-Barreno, Lenin; Cevallos, Domenica; Lister, Alex; Fernandez-Naranjo, Raul; Ríos-Touma, Blanca; Vásconez-González, Jorge y Izquierdo Condoy, Juan Sebastian mail SIN ESPECIFICAR (2022) Waterborne diseases and ethnic-related disparities: A 10 years nationwide mortality and burden of disease analysis from Ecuador. Frontiers in Public Health, 10. ISSN 2296-2565

Artículo Materias > Biomedicina
Materias > Ciencias Sociales Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés Background Despite worldwide progress in terms of clean water supply, sanitation, and hygiene knowledge, some middle and most of low-income countries are still experiencing many diseases transmitted using unsafe water and the lack of sanitation. Methods To understand the impact of all waterborne diseases (WBD) registered in Ecuador, we performed an analysis of all cases and deaths related to WBD to compute incidence and mortality rates. Results We found that in Ecuador, mestizo people had the greatest morbidity rate (141/100,000) patient followed by indigenous (63/100,000) and self-determined white patients (21/100,000). However, in terms of mortality, indigenous population have a 790% increase in mortality rate (2.6 /100,000) when compared to self-determined white populations (0.29/100,000) or 176% more when compared to mestizos (0.94/100,000). This trend remains the same among children and the elderly who have higher mortality rates when compared to other ethnic groups. Conclusions In Ecuador, water borne diseases (WBD) are still a major public health problem. We found that younger children and elderly are more likely to be get sick and die due to water borne diseases. In terms of morbidity, mestizos reported the highest rate, while in terms of mortality, indigenous populations are the most affected, having the highest mortality among different ethnic groups. We hypostatize that reduced health care access is linked to fewer reporting incidence rates among indigenous populations but higher mortality rates. metadata Ortiz-Prado, Esteban; Simbaña-Rivera, Katherine; Cevallos-Sierra, Gabriel; Cevallos, Domenica; Lister, Alex; Fernandez-Naranjo, Raul; Ríos-Touma, Blanca; Vasconez, Jorge; Izquierdo Condoy, Juan Sebastian y Gomez-Barreno, Lenin mail SIN ESPECIFICAR (2022) Waterborne diseases as an indicator of health disparities: A nationwide study of WaSH related morbidity and mortality in Ecuador from 2011-2020. Research square. (Inédito)

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity. metadata Rustam, Furqan; Aslam, Naila; De La Torre Díez, Isabel; Khan, Yaser Daanial; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. Healthcare, 10 (11). p. 2230. ISSN 2227-9032

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O objetivo deste trabalho é mostrar a importância da análise PDCA como ferramenta para preparar os alunos de ensino público para o futuro, devido a facilidade que a ferramenta tem para a aplicação do método com metodologias que podem potencializar a aprendizagem dos alunos. Auxiliando na execução de um planejamento estratégico de acordo com os objetivos e metas estabelecidas, ou seja, um direcionamento adequado para o futuro da instituição de ensino. A metodologia utilizada foi de pesquisa bibliográfica em artigos e sites acadêmicos com autores encontrados no Google Acadêmico e SciELLO. Ao realizar a pesquisa, foi estudado material que aborda sobre a utilização do PDCA nas organizações e como o uso da aprendizagem baseada em projetos se mostra eficiente e pode trazer benefícios para a escola. Com isso, é possível considerar que essa ferramenta deve ser aplicada nas escolas, aumentando a autonomia dos alunos tendo em vista trazer benefícios. metadata Alves Guimarães, Ueudison; Rodrigues Moniz, Sibele Selvina de Oliveira y Olímpio dos Santos, José mail SIN ESPECIFICAR (2022) A análise PDCA como ferramenta de suporte a instituição escolar. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (11). e3112265. ISSN 2675-6218

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Abierto Inglés Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies. metadata Khan, Hikmat Ullah; Anam, Rimsha; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Diez, Isabel de la Torre; Silva Alvarado, Eduardo René; Soriano Flores, Emmanuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, emmanuel.soriano@uneatlantico.es, SIN ESPECIFICAR (2024) A deep learning approach for Named Entity Recognition in Urdu language. PLOS ONE, 19 (3). e0300725. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM’s quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM’s state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset. metadata Farooq, Omer; Shahid, Maida; Arshad, Shazia; Altaf, Ayesha; Iqbal, Faiza; Vera, Yini Airet Miro; Flores, Miguel Angel Lopez y Ashraf, Imran mail SIN ESPECIFICAR (2024) An enhanced approach for predicting air pollution using quantum support vector machine. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués O estudo aqui edificado apresenta como seu principal desígnio avaliar o modo como uma importante ferramenta de gestão pode contribuir, mostrando a eficiência em resultados na aprendizagem por meio de uma gestão escolar que busca ser democrática e como isso influencia no dia a dia de uma unidade escolar. Para tanto, falar-se-á aqui acerca da contribuição da ferramenta Swot na Gestão Escolar, revelando-se como ela funciona e como poderá beneficiar nesta área tão importante. Referindo-se à metodologia aproveitada para a edificação deste breve estudo, cita-se a escolha pela pesquisa bibliográfica, por meio da qual tornou-se possível colher material que contribuirá com a futura abordagem teórica que será feita, tendo em vista pensamentos e conjecturas de estudiosos famosos como Lima (2013), Nóvoa (2002) e outros. Por meio de tal análise acerca do material colhido e estudado durante a efetivação da pesquisa, concluiu-se ser clara a incoerência vivenciada entre a realidade escolar, o que a escola quer, o que a escola faz, e o dia a dia da gestão escolar, a qual precisa tomar decisões que, certamente, acabarão impactando, positiva ou negativamente, tanto no desenvolvimento quanto na formação de seus educandos. Conclui-se, pois, a importância de se trabalhar com uma ferramenta como Swot, especialmente quando se fala do trabalho encarado pela gestão escolar. metadata Alves Guimarães, Ueudison; Rodrigues Dantas de Brito, Junea Graciele; Rodrigues Moniz, Sibele Selvina de Oliveira y Picinini Lengler, Loreni mail SIN ESPECIFICAR (2022) A ferramenta SWOT na gestão escolar. RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3 (11). e3112271. ISSN 2675-6218

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category. metadata Raza, Ali; Younas, Faizan; Siddiqui, Hafeez Ur Rehman; Rustam, Furqan; Gracia Villar, Mónica; Silva Alvarado, Eduardo René y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, eduardo.silva@funiber.org, SIN ESPECIFICAR (2024) An improved deep convolutional neural network-based YouTube video classification using textual features. Heliyon, 10 (16). e35812. ISSN 24058440

Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés, Español, Portugués Este estudo tem por objetivo apresentar reflexões a partir da experiência de estudantes em nível de doutorado sobre a modalidade da Internacionalização em Casa no contexto da Pós-graduação, através da Educação a Distância. Pesquisa de horizonte qualitativo com abordagem da Hermenêutica Filosófica, num primeiro momento, apresenta demarcações conceituais sobre a necessidade da internacionalização, suas formas e desafios no contexto da região da América Latina e Caribe. Num segundo, apresenta resultados de uma experiência com estudantes que vivenciam esta modalidade no Chile, Colômbia e Brasil. Os resultados expressam as motivações, avaliações, aprendizagens e desafios em cursar um doutorado nessa modalidade. A internacionalização em casa na Pós-graduação propicia a emergência de uma nova relação entre uma instituição internacional diretamente com o estudante. Para os estudantes, a satisfação está na realização de um curso que em outros moldes não seria possível sem perder os vínculos pessoais e profissionais. O maior desafio passa pela disciplina e gestão de espaços e tempos de estudo. metadata Pereira, Vilmar Alves mail vilmar.alves@unini.edu.mx (2022) A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância. Revista Ibero-Americana de Estudos em Educação. pp. 2476-2493. ISSN 2446-8606

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Objective This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. Results The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Conclusion Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance. metadata Alvi, Sohaib Bin Khalid; Nayyer, Muhammad Ziad; Jamal, Muhammad Hasan; Raza, Imran; de la Torre Diez, Isabel; Rodríguez Velasco, Carmen Lilí; Breñosa, Jose y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carmen.rodriguez@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2023) A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation. DIGITAL HEALTH, 9. ISSN 2055-2076

Artículo Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Portugués A família constitui o núcleo fundamental de uma sociedade, constituída por um grupo sanguíneo onde se transmitem valores morais e culturais e a Educação formal e informal, são pilares fundamentais para que ela guie-se. Partindo sempre do princípio de que, quem educa uma mulher, está educar uma sociedade. A desestruturação da família, têm causado vários transtornos nos lares e vários investigadores apontam o surgimento da violência doméstica como principal causa e depois, surge a delinquência nos filhos. Desta forma, ao longo do presente estudo, vários autores defenderam como principais causa do fenómeno violência domestica, o consumo excessivo de bebidas alcoólicas, a perda de valores religiosos, a infidelidade conjugal, o baixo nível de escolaridade entre os parceiros, a pobreza, acusações de feitiçaria, o desemprego etc. O presente trabalho de campo e de natureza exploratória com abordagem quanti-qualitativa, enquanto produto de uma investigação bibliográfica e empírica, tem como objectivo reflectir sobre o processo de enfrentamento à violência contra as mulheres no Município da Cela, Província do Cuanza Sul. Para o desenvolvimento do mesmo, entrevistou-se um grupo focal de 14 mulheres, 6 homens com uma amostra de 20 participantes, entre eles, autoridades tradicionais, religiosas, membros da sociedade civil e da Direcção Municipal da Família e Promoção da Mulher, membros ligados aos Serviços de Investigação Criminal do Município da Cela, A colecta de dados ocorreu de Outubro á Novembro de 2021 metadata Graça da Costa, Mario; Bailão Pio Carlos, Edna Martinha y Santos e Campos, María Aparecida mail SIN ESPECIFICAR, SIN ESPECIFICAR, maria.santos@unini.edu.mx (2022) A violência doméstica no município da Cela, província do Cuanza-Sul –Angola: um fenómeno que tem preocupado o governo e a sociedade. Revista Ibero-Americana de Humanidades, Ciências e Educação, 8 (4). pp. 30-62. ISSN 2675-3375

Tesis

Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Tesis Doctorales Abierto Español La presente investigación identifica la relación entre la ansiedad y agresividad que presentan los estudiantes del séptimo, octavo y noveno grado del instituto adventista dentro y fuera de sus salones de clases y su entorno familiar. A partir de un estudio mixto, se ha podido medir los factores que influyen en las familias en cuanto a los niveles de estrés emocional, que posteriormente se comprobaran de manera más específica por los instrumentos aplicados por la investigadora. Para este estudio se dispone de material empírico, el cual fue recolectado durante nuestra estadía en el centro educativo el área administrativa, documentación facilitada por el centro y la obtenido por la investigadora a través del análisis de los datos recopilados por medio de la observación, entrevistas y cuestionarios que identificaron comportamientos en diferentes niveles de ansiedad y agresividad en las relaciones entre padres e hijos, padres y docentes. Se destaca la importancia de las intervenciones docentes en cuanto a los apoyos para el cumplimiento de los reglamentos educativos y como estímulo positivo a las conductas del alumnado participante. Los resultados de la investigación muestran las causas que aportan o dan origen al nivel de ansiedad o agresividad que los estudiantes manifiestan durante su estadía en el centro educativo. Los resultados serán compartidos con el departamento de orientación y psicología del centro educativo que es objeto de estudio, sirviendo también como de apoyo para futuras intervenciones en el centro educativo. Concluimos que el ambiente del hogar está asociado de manera directa e impactante en las conductas que presentan los estudiantes que fueron objeto de estudio y que la relación que los padres tienen con sus hijos influye en los comportamientos que estos muestran dentro del centro educativo y fuera de este. metadata Cruz Galeas, Fany Guadalupe mail fgcg_2012@yahoo.com (2024) Análisis de la relación entre el entorno familiar, la ansiedad y agresividad de los estudiantes de 7º., 8º. y 9º. grado del centro de educación no gubernamental adventista de San Pedro Sula Honduras. Doctoral thesis, Universidad Internacional Iberoamericana Puerto Rico.

Revista

Revista Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Español La revista Environmental Sciences and Practices (ESAP) nace como una publicación semestral con el objetivo de invitar a la reflexión y el debate para entender correctamente cual es la función, aporte y responsabilidad medioambiental no solo del mundo académico sino además en el espacio profesional. Comenzando por entender que el área de ESAP, es un espacio interdisciplinario, bajo un concepto innovador, colaborativo e integral hacia todas las áreas que convergen en una temática de interés común: el medio ambiente. Los artículos incluidos en esta revista se publican en español, portugués e inglés, atendiendo de esta manera a un espacio internacional y multicultural que permita una gestión del conocimiento actual, propia y necesaria del área medioambiental. A partir de esta página, podrá acceder a los índices de todas las ediciones de la revista Environmental Sciences and Practices, los resúmenes del artículo y los textos completos. Asimismo, en la sección "Acerca de" encontrará toda la información sobre nuestra revista, su equipo editorial, sistema de publicación y envíos en línea. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2022) Environmental Sciences and Practices. [Revista]

Revista Materias > Comunicación Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Inglés El objetivo principal de Revista MLS Communication Journal es difundir obras inéditas relacionadas con los grandes retos y desafíos de la comunicación en sus diferentes ámbitos: el periodismo, la publicidad, la comunicación audiovisual, la comunicación interactiva o la comunicación en las organizaciones, entre otros. La revista tiene interés en la difusión de trabajos académicos y científicos que identifiquen, describan y divulguen hallazgos inéditos y de interés en estos campos desde la revisión teórica, la innovación metodológica, la experimentación y la apuesta por la innovación. Los estudios publicados en MLS Communication Journal se centran en reflexionar sobre los grandes hitos, las principales interrogantes y las tendencias más destacadas del escenario comunicativo, adoptando una perspectiva de estudio teórico-práctica. La revista tiene un marcado carácter iberoamericano e internacional, por lo que puede ser utilizada para su publicación en cualquier país de origen, siempre que éstos cumplan con las diferentes fases de la investigación con rigor metodológico. Constituye, por lo tanto, un medio de difusión del conocimiento derivado de diferentes entornos socioculturales. MLS Communication Journal pública trabajos en el idioma castellano, portugués e inglés, y se edita totalmente en el último idioma, manteniendo también una edición en el idioma original del manuscrito. Su estructura organizativa se compone principalmente de investigadores, ya que una revista científica, basada en principios, debe tener sus raíces en la comunidad investigadora que tiene la producción intelectual y las contribuciones relevantes en el tema dentro de sus respectivas instituciones. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2021) MLS Communication Journal. [Revista]

Revista Materias > Alimentación Universidad Europea del Atlántico > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas Abierto Inglés La revista MLS Health and Nutrition Research nace como una publicación semestral con el objetivo de publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución científica al progreso de cualquier ámbito de la salud y nutrición como objetivo principal. Los artículos incluidos en esta revista se publican en español, portugués e inglés. La vocación internacional de esta revista promueve la difusión del conocimiento en sus diferentes áreas. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2022) MLS Health and Nutrition Research. [Revista]

Revista Materias > Educación Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Inglés Antigua Revista internacional de apoyo a la inclusión, logopedia, sociedad y multiculturalidad La revista MLS Inclusion and Society Journal es la continuación de la Revista internacional de apoyo a la inclusión, logopedia, sociedad y multiculturalidad (RIAI), revista heredera de la revista RIALAIM con mayor antigüedad, pero de la cual se independizó para tomar las directrices de las revistas actuales con indicadores de impacto. La revista MLS Inclusion and Society Journal cuenta actualmente con artículos de investigación y teóricos, tanto internacionales como nacionales, que están arbitrados por pares ciegos externos a la revista, en un proceso riguroso de selección. Los ejes temáticos son: educación inclusiva, logopedia, sociedad y multiculturalidad. La MLS Inclusion and Society Journal tiene una periodicidad de dos números al año (junio y diciembre) metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2022) MLS Inclusion and Society Journal. [Revista]

Revista Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Español MLS - Law and International politics (MLSLIP) es una publicación de periodicidad semestral con el objetivo de ser un canal que contribuya a la discusión, el intercambio de conocimiento y el debate entre académicos, responsables de política pública, empresarios, tecnólogos, científicos y los distintos actores interesados en temas de Derecho y Política, que deriven en el crecimiento del conocimiento científico de esas ciencias, producto de trabajo vinculado entre sectores público, privado y académico. MLS - Law and International politics (MLSLIP) se enfoca también en colaboraciones que engloben avances en materia de ciencia jurídica y política, con un impacto social y que contribuyan a la solución de problemas nacionales e internacionales. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2022) MLS Law and International Politics. [Revista]

Revista Materias > Educación Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Español La revista MLS Pedagogy, Culture and Innovation (MLSPCI) nace como una publicación interdisciplinar en la que tienen cabida todo tipo de trabajos procedentes del ámbito académico, social o cultural en los que prime el carácter innovador de las aportaciones. Abarca un gran número de temáticas actuales como pueden ser la tecnología educativa, interculturalidad e inclusión, desarrollo curricular, formación docente, tutoría, organización de centros, entre otras. La revista está abierta a recibir estudios y experiencias sobre las mismas de ámbito europeo e iberoamericano preferentemente. Los artículos se publican en español, portugués e inglés. A partir de esta misma página, podrá acceder a los índices de todas las ediciones de la revista, los resúmenes del artículo y los textos completos. Asimismo, en la sección "Sobre la revista" encontrará toda la información sobre nuestra revista, su equipo editorial, sistema de publicación y envíos en línea. metadata Multi-Lingual Scientific Journals, (MLS) mail mls@devnull.funiber.org (2024) MLS Pedagogy, Culture and Innovation. [Revista]

Revista Materias > Psicología Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Inglés MLS Psychology Research es una revista científica que tiene como finalidad publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución al progreso de cualquier ámbito de la psicología científica como objetivo principal. MLSPR acogerá a artículo que analicen la conducta y procesos mentales tanto de individuos como de grupos, y que abarque aspectos de la experiencia humana. MLSPR atenderá a diferentes enfoques dentro de la psicología: Psicología clínica, Psicoterapea, Psicología educativa, Psicología del desarrollo, Neuropsicología, Psicología social, etc. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2018) MLS Psychology Research. [Revista]

Revista Materias > Educación física y el deporte Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Inglés MLS Sport Research es una revista científica que tiene como objetivo publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución al progreso en el ámbito de las Ciencias de la Actividad Física y del Deporte. Los estudios publicados deben cumplir con las diferentes fases de la investigación con rigor metodológico. MLS Sport Research atenderá a diferentes ámbitos dentro de la actividad física y el deporte: salud, educación física, prevención y readaptación de lesiones, socorrismo, nuevas tecnologías, fisiología, nutrición, psicología, dirección y gestión, entrenamiento y rendimiento deportivo. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2021) MLS Sport Research. [Revista]

Revista Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas Abierto Español La revista Project Design and Management nace como una publicación semestral con el objetivo de invitar a la reflexión y el debate para entender correctamente cual es la función, aporte y responsabilidad del área Project, Design y Management (PDM) en la actualidad, no solo del mundo académico sino además en el espacio profesional. Comenzando por entender que el área de PDM, es un espacio interdisciplinario, bajo un concepto innovador, colaborativo e integral hacia todas las áreas que participan, no solo en la administración de los recursos necesarios para un proyecto sino además, en el diseño o desarrollo del mismo. Los artículos incluidos en esta revista se publican en español, portugués e inglés, atendiendo de esta manera a un espacio internacional y multicultural que permita una gestión del conocimiento actual, propia y necesaria del área PDM. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2019) Project Design and Management. [Revista]

Este listado fue generado el Fri Dec 20 23:58:43 2024 UTC.

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Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria

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.

Producción Científica

Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,

Alemany Iturriaga

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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

Producción Científica

Tariq Ali mail , Saif Ur Rehman mail , Shamshair Ali mail , Khalid Mahmood mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Tahir Khurshaid mail , Imran Ashraf mail ,

Ali

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Carotenoids Intake and Cardiovascular Prevention: A Systematic Review

Background: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain.

Producción Científica

Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Thomas Prola mail thomas.prola@uneatlantico.es, Raquel Martínez Díaz mail raquel.martinez@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,

Sumalla Cano

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StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10.

Producción Científica

Salman Sadullah Usmani mail , Izaz Ahmmed Tuhin mail , Md. Rajib Mia mail , Md. Monirul Islam mail , Imran Mahmud mail , Carlos Eduardo Uc Ríos mail carlos.uc@unini.edu.mx, Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Imran Ashraf mail , Md. Abdus Samad mail ,

Usmani

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Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization

With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection.

Producción Científica

Waqar Ashiq mail , Samra Kanwal mail , Adnan Rafique mail , Muhammad Waqas mail , Tahir Khurshaid mail , Elizabeth Caro Montero mail elizabeth.caro@uneatlantico.es, Alicia Bustamante Alonso mail alicia.bustamante@uneatlantico.es, Imran Ashraf mail ,

Ashiq

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