Interfacial tension behavior of binary and ternary mixtures of partially miscible Lennard-Jones fluids: a molecular dynamics simulation

By means of extensive equilibrium molecular dynamics simulations we have investigated, the behavior of the interfacial tension $\gamma$ of two immiscible symmetrical Lennard-Jones fluids. This quantity is studied as function of reduced temperature $T^{*}={{k_{_B} T}\over \epsilon}$ in the range $0.6 \leq T^{*} \leq 3.0$. We find that, unlike the monotonic decay obtained for the liquid-vapor interfacial tension, for the liquid-liquid interface, $\gamma (T)$ has a maximum at a specific temperature. We also investigate the effect that surfactant-like particles has on the thermodynamic as well as the structural properties of the liquid-liquid interface. It is found that $\gamma$ decays monotonically as the concentration of the surfactant-like particles increases.Comment: LaTeX-Revtex file with 7 encapsulated postscript figures. Accepted for publication in Journal of Chemical Physic

Software tools for conducting bibliometric analysis in science: An up-to-date review

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims

Método análisis envolvente de datos y redes neuronales en la evaluación y predicción de la eficiencia técnica de pequeñas empresas exportadoras

In this research, a method was developed to evaluate and predict the efficiency of small exporting companies taking as input or asset variables the total assets, equity, total liabilities, operating expenses, sales costs and as output or result variables. net sales, net income and operating income. For this, the envelopment data analysis was used in the evaluation of the efficiency, the discriminant analysis in the evaluation of the classification of efficient and inefficient companies and the artificial neural networks to evaluate its capacity of classification prediction in 90 companies of the sector of the city of Barranquilla-Colombia. The results allowed to classify the companies according to level of efficiency showing an average technical efficiency of 41.38% of the sector with 11 representative companies of efficiency. The results show the relevance of the proposed methodology to correctly classify and forecast technical efficiency in small exporting companies. © Centro de Informacion Tecnologica. All rights reserved

Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación

A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were used. The methodology first relates the users according to the study variables, to then implement a cluster analysis that identifies the formation of groups. Finally uses a machine learning algorithm to classify the users according to their level of knowledge. The results show how the time a student stays in the platform is not related to belonging to the high knowledge group. Three categories of users were identified, applying the Fuzzy K-means methodology to determine transition zones between levels of knowledge. The k nearest neighbor algorithm presents the best prediction results with 91%. © 2019 Centro de Informacion Tecnologica. All Rights Reserved

HIPS: A new hippocampus subfield segmentation method

[EN] The importance of the hippocampus in the study of several neurodegenerative diseases such as Alzheimer's disease makes it a structure of great interest in neuroimaging. However, few segmentation methods have been proposed to measure its subfields due to its complex structure and the lack of high resolution magnetic resonance (MR) data. In this work, we present a new pipeline for automatic hippocampus subfield segmentation using two available hippocampus subfield delineation protocols that can work with both high and standard resolution data. The proposed method is based on multi-atlas label fusion technology that benefits from a novel multi-contrast patch match search process (using high resolution T1-weighted and T2-weighted images). The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The method has been evaluated on both high and standard resolution images and compared to other state-of-the-art methods showing better results in terms of accuracy and execution time.This research was supported by Spanish UPV2016-0099 and TIN2013-43457-R grants from UPV and the Ministerio de Economia y Competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "Defi imag'In". We also want to thank Javier Juan Albarracin for his valuable contribution to the development of this method.Romero Gómez, JE.; Coupe, P.; Manjón Herrera, JV. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage. 163:286-295. https://doi.org/10.1016/j.neuroimage.2017.09.049S28629516

Design Thinking (DT) in Engineering Education (EE): A Systematic Literature Review (SLR)

Trabajo presentado en el “13th International Conference on Industrial Engineering and Industrial Management/XXIII Congreso en Ingeniería de Organización (CIO 2019)”, Gijon (Asturias, Spain) , 11 y 12 de julio de 2019[EN] Design Thinking not only is a well-known technique for user-oriented product design, but also is an education technique in Higher Education. Design thinking is increasingly used as an innovative educational tool to promote in engineering student transversal skills as critical thinking, creativity, and teamwork. However, despite its popularity, the teaching community has implemented it in many different ways focusing on specific aspects without taking in notice of previous experiences. The aim of this work is to analyze the literature published about Design Thinking experience in Engineering Education through a systematic literature review. Our conclusions will contribute to this educational area pointing the state of the art and the future lines of this educational methodolog

Methodology of classification, forecast and prediction of healthcare providers accredited in high quality in Colombia

This research presents a methodology for classification, forecasting and prediction of healthcare providers accredited in Colombia. For this purpose, a quantitative, descriptive and predictive analysis was carried out of 27 institutions accredited in Colombia by 2016. Consequently, the machine learning techniques cluster analysis and artificial neural networks were used to define business profiles of the institutions under study. The method classifying, forecasting and predicting the membership of a healthcare provider to a business profile, previously created based on the high-quality patterns of accreditation. The input variables were assets, account receivable, inventory, property and equipment and the output variables health service sales and net profit. The cluster analysis defined two main groups. 1) accredited institutions in the process of financial consolidation; 2) accredited institutions financially sound. The process of forecasting and prediction through the creation of an artificial neural network yielded a 95% CI (088, 0.9975) precision in the classification, and 100% and 80% for sensitivity and specificity values respectively. The results evidence the capacity of the proposed methodology to recognise the characteristics and association patterns of HCP accredited in high quality

Metodología de Análisis Envolvente de Datos (DEA) - GLMNET para la Evaluación y Pronóstico de Eficiencia Financiera en una Zona Franca Industrial - Colombia.

This research work proposes a methodology for evaluation and forecasting for companies located in the Industrial Port Zone of the city of Barranquilla, Colombia. Based on an empirical and rational analysis, supported by the concepts of technical efficiency, purely technical efficiency, additive efficiency, efficiency of scale and of mixing, as well as in the algorithm for machine learning GLMNET. Work was done with 29 companies that presented their complete financial statements for the year 2017 in the Chamber of Commerce of Barranquilla - Colombia. As a result, it was found an average technical efficiency of 72.79%, a purely technical efficiency of 82.54% and an additive efficiency of 59.45%. In addition, the projections required to make inefficient organizations achieve efficiency are contributed. From the study, it can also be observed that 11 companies were constituted as benchmarks to measure the companies of the Free Zone of the Port of Barranquilla. It is noteworthy that the algorithm GLMNET delivered a good result in the prediction of group membership of efficient and inefficient enterprises, with an accuracy of 93.1%. © 2019 Centro de Informacion Tecnologica. All rights reserved.Department of Environmental Affairs, DEATeniendo en cuenta que las exportaciones generan un incremento de la productividad como lo señalan diferentes autores (Pardo y García, 1999), esta variable se estudia en esta investigación toda vez que se aportan las directrices para que las empresas ineficientes de la zona franca en estudio alcancen la eficiencia empresarial. En términos financieros con esta investigación se analiza las magnitudes en términos de ingresos y utilidad neta de tal forma que las empresas ineficientes incrementen su eficiencia frente a los recursos que utilizan. Lo anterior permite generar una serie de decisiones en el contexto analizado que permite dinamizar y direccionar las acciones para alcanzar los resultados que permita a las organizaciones ineficientes ser competitivas en el sector. Existen investigaciones previas sobre el uso del análisis DEA y el aprendizaje automático, en (Hong et al., 1999) clasifican empresas eficientes y no eficientes utilizando DEA para luego predecir el estado de eficiencia de nuevas empresas, por otro lado (Lin, Hu, y Tsai, 2012) revisan en un periodo de cinco años la implementación del aprendizaje automático y los modelos DEA para la modelación del riesgo de quiebra en las empresas, a su vez otras investigaciones realizan procesos de clasificación paralelas utilizando la técnica de Support Vector Machines (Yeh, Chi, y Hsu, 2010), además de valoraciones empresariales en sectores específicos, como cadenas de suministro ( Wong y Wong, 2007) y desempeño corporativo (Mirhedayatian, Azadi y Saen, 2014)

Herramienta interactiva en la comprensión del límite de una función

El propósito del trabajo es dar a conocer las bondades que tiene el uso de las tecnologías a través de la interactividad. Para ello, se realizó un estudio correlacional con un diseño cuasi-experimental con pre test y post test. Además se tuvo una población de 45 estudiantes que cursaban la asignatura de matemáticas II distribuidos en dos grupos. Se utilizó la prueba t de Student para verificar si existía diferencia entre el grupo experimental y de control sobre el grado de comprensión del límite de forma gráfica. De acuerdo a los resultados obtenidos se pudo constatar que existe diferencia estadística significativa entre los grupos (experimental y control), lo cual indica que la herramienta interactiva ayuda a la compresión del límite de una función. Por otra parte, fue considerada por los estudiantes como atractiva y educativa, esto pone en manifiesto que el uso de las tecnologías en la educación matemática puede potenciar la comprensión de los conceptos y algoritmos matemáticos de forma interactiva

Lifespan Changes of the Human Brain In Alzheimer's Disease

[EN] Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.This work benefited from the support of the project DeepVolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX- 03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria y Competitividad. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #s HHSN275200900018C). A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the NIH Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centers.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. 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