Repositorio Universidad Europea del Atlántico
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Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning
Get PDFBreast cancer is prevalent in women and the second leading cause of death. Conventional breast cancer detection methods require several laboratory tests and medical experts. Automated breast cancer detection is thus very important for timely treatment. This study explores the influence of various feature selection technique to increase the performance of machine learning methods for breast cancer detection. Experimental results shows that use of appropriate features tend to show highly accurate predictio
Crononutrición: efecto de la hora de la ingesta en el metabolismo de los nutrientes.
No full textLas alteraciones metabólicas suponen hoy en día una de las afecciones más padecidas en todo el mundo. Es por ello, que la indagación en el estudio sobre la influencia de la hora de la ingesta en el metabolismo de un nutriente, es de gran importancia para el desarrollo y aplicación de nuevos tratamientos en lo que a estas enfermedades respecta. Mediante esta revisión bibliográfica, a través de la búsqueda bibliográfica profunda en diferentes bases de datos, se han obtenido diversos archivos, documentos, artículos y estudios que han servido para el análisis, desarrollo y ejecución del vigente artículo. La molécula de la glucosa presenta niveles más acentuados en la tarde versus la mañana, debido a la disminución de la actividad de la insulina con el avance del día. La mayoría de los lípidos presentan sus niveles más altos en la mañana, a excepción de los triglicéridos mostrándolos en la tarde. En cuanto a las proteínas se necesita más estudio para su conocimiento en este aspecto. Se requiere de más investigación para poder obtener una conclusión más exacta. Aun así, se puede concluir en que la hora de la ingesta es un factor que afecta en la ritmicidad de los procesos metabólicos, interfiriendo y alterando la actividad y respuesta de los nutrientes
The Empirical Study of the Impact of Firm-and Country-level Factors on Debt Financing Decisions of ICT Firms
Get PDFThe capital structure has been extensively analysed in the empirical literature. Despite of the great contribution of the technological industry to the global economy, little research has been conducted regarding corporate finance of ICT firms. Moreover, the previous literature barely considers the effect of macroeconomic variables on financial decisions, focusing much more on internal determinants, such as cash flow, firm’s size or growth opportunities. The objective of this work is to reduce this gap by disentangling the reasons behind the financial decisions of technological firms. The sample included 1,510 public ICT firms from 23 countries over the period 2004 – 2019 (17,342 observations). The variables used in this study are obtained from S&P Capital IQ, World Development Indicators, Main Science and Technology Indicators from OECD, and FMI dataset. The two-step system generalized method of moments (GMM) was used as methodology. Consistent with the extant literature, more profitable and liquid ICT firms and those with an increased non-debt tax shields are less leveraged. However, the companies which present higher risk, measured as volatility of EBIT, increase their use of debt financing. Contrary to the findings of many other studies, the analysis of a firm’s size and tangible assets shows non-conclusive results. Regarding macroeconomic determinants, only economic growth and foreign direct investment inflows were found to generate a positive effect on financial decisions of ICT firms. The findings of this work can be used to design and develop policies, measures, and facilitate mechanisms for optimal management of the financing decisions of ICT firms
Eficacia de la terapia cognitivo conductual sobre el trastorno por atracón: una revisión sistemática
No full textEl Trastorno por Atracón afecta en torno al 2-5% de la población general. Se caracteriza por la presencia de atracones definidos como la ingesta compulsiva de alimento en un corto período de tiempo, en ausencia de comportamientos compensatorios posteriores. El propósito de este estudio fue analizar e integrar la información referida a la eficacia de la Terapia Cognitivo Conductual en sus distintos formatos de aplicación, sobre pacientes diagnosticados con Trastorno por Atracón. Se realizó la búsqueda en las bases de datos PubMed y Scopus con los términos “Binge Eating Disorder” AND “Cognitive Behavioral Therapy”. Se seleccionaron 15 artículos que examinaron variables clínicas como la tasa de atracones o la patología de Trastorno por Atracón y variables subjetivas como la calidad de vida. El instrumento de evaluación más utilizado fue The Clinical Interview Eating Disorder Examination (EDE). Los resultados muestran la eficacia de los distintos formatos de aplicación de la terapia a estudio
Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
Get PDFProject-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
Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification
No full textClassification 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
The moderation effect of mentalization in the relationship between impulsiveness and aggressive behavior
No full textAggressive behavior is a growing problem across many contexts. Thus, searching for its predictors is important. The aim of the current study was to analyze the moderator effect of mentalization in the relationship between impulsiveness and both verbal and physical aggressive behavior, using a sample of 583 participants gathered from the general Spanish population (MAge = 34.60, SDAge = 12.99). In our sample, 182 were male and 401 were female. The results showed significant bivariate relationships among aggression, impulsiveness, and mentalization. Moderation structural equation modeling (MSEM) showed a significant moderation effect, so whereas the value of mentalization is not relevant in cases of people with low impulsiveness, high mentalization abilities allow those people with high impulsiveness to behave less aggressively than people with high impulsiveness and low mentalization abilities. Practical implications and limitations of the study are discussed
Síntomas depresivos en estudiantes universitarios de la Carrera de Medicina en la Universidad Técnica de Manabí, Ecuador
No full textIntroducción: La depresión es una enfermedad disruptiva del estado de ánimo con morbilidad asociada donde se experimenta tristeza recurrente y persistente que suele cronificarse con el tiempo, pérdida del interés por las cosas que antes le resultaban placenteras; afectando las áreas vitales. La presente investigación surge de la necesidad de indagar la manifestación clínica de síntomas depresivos en los estudiantes en formación profesional en medicina, con el fin de corroborar la problemática biopsicosocial que viven los estudiantes por los altos niveles de estrés que se encuentran enfrentando diariamente. Objetivo: El objetivo de la investigación es evaluar los síntomas depresivos en los estudiantes de sexto, séptimo y octavo semestre de la carrera de Medicina de la Universidad Técnica de Manabí, Ecuador. Metodología: Para ello se realizó un estudio con diseño observacional, exploratorio y descriptivo, de corte transversal; obteniendo así representaciones importantes de las distintas categorías presentes en el Inventario de Depresión de Beck BDI-II. Resultados: En los resultaron se evidenciaron el predominio del sexo femenino con un 57% y el estado civil de soltero es la mayoría de los estudiantes encuestados con un 96%, la edad predominante es entre los 22 y 24 años con un 61%. Los ítems de Tristeza, Sentimientos de culpa, Pérdida de interés, Pérdida de energía, Autocrítica, Cambios en los hábitos de sueño, Cansancio o fatiga; fueron los que presentaron respuestas más críticas; con porcentajes entre 32% y 50%. La prueba de homogeneidad de varianza mediante el estadístico de Levene, se cumple, a excepción de los ítems 05 (sentimientos de culpa) y 15 (pérdida de energía), por tanto, para los ítems 05 y 15 se lleva a cabo la prueba no paramétrica de Kruskall Wallis. Para comprobación de la normalidad está la prueba de Kolmogorov-Smirnov, en el cual todos los p-valores fueron muy cercanos a cero lo que indican el no cumplimiento de la normalidad, sin embargo, no es un requisito fundamental para los ANOVAS. En lo Anovas se probó la siguiente hipótesis, que el puntaje promedio de los estudiantes del sexto, séptimo y octavo curso no tiene diferencias significativas en cuanto a cada ítem. Conclusión: Atendiendo al diseño de investigación, se pudo observar que la mitad de los estudiantes no revelan sintomatología depresiva; sin embargo, la otra mitad si arroja un nivel de depresión con riesgo moderado de 22%, depresión leve 15% y depresión severa con el 13%; encontrándose que la mitad de la población estudiada si se adolece con la expresión de los síntomas que señala el inventario BDI-II
Emprendimiento basado en el liderazgo: diagnóstico de las habilidades de liderazgo entre los estudiantes universitarios
No full textBetalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet
No full textBetalains are water-soluble, nitrogen-containing vacuolar pigment and can be divided into two subclasses: the yellow – orange betaxanthins and the red – violet betacyanin. These pigments can be found mainly in Latin America, but also in some parts of Asia, Africa, Australia and in the Mediterranean area. In this work an overview related with the status of research about betalains extracted from Opuntia spp and the enforces made to evaluate their positive incidence in the human body is provided. Several studies enhance their anticancer, anti-inflammatory and antioxidant properties. They also exhibit antimicrobial and antidiabetic effect. Taking into account these properties, betalains seem to be a promising natural alternative as a colorant to replace the synthetic ones in the food additive industry. In addition, the use of Opuntia spp fruits as possible colorant sources in the Food Industry, may contribute positively to the sustainable development in semi-arid regions