174 research outputs found

    Drops on soft solids: Free energy and double transition of contact angles

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    The equilibrium shape of liquid drops on elastic substrates is determined by minimising elastic and capillary free energies, focusing on thick incompressible substrates. The problem is governed by three length scales: the size of the drop RR, the molecular size aa, and the ratio of surface tension to elastic modulus ő≥/E\gamma/E. We show that the contact angles undergo two transitions upon changing the substrates from rigid to soft. The microscopic wetting angles deviate from Young's law when ő≥/Ea‚Čę1\gamma/Ea \gg 1, while the apparent macroscopic angle only changes in the very soft limit ő≥/ER‚Čę1\gamma/ER \gg 1. The elastic deformations are worked out in the simplifying case where the solid surface energy is assumed constant. The total free energy turns out lower on softer substrates, consistent with recent experiments

    Sistema de Recomendación de Asignaturas en el Proceso de Registro y Matrícula de Estudiantes Universitarios

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    Currently, recommendation systems are widely used to analyze user preferences and suggest related items. At the university level, the moment in which a subject is chosen by a student for his next educational stage, is monitored by an Academic Counselor, who according to the student record, and comparing similar profiles throughout his career, must recommend which subjects could contribute to student performance and learning. The present work represented an effort to design a recommender that is based on the modeling of the causal relationships existing between the subjects of the curricular curriculum of a university career, using fuzzy cognitive maps and OWA aggregation operators. The workflow of the proposed model and its implementation were applied through the computer tool (FCMDecision). A case study with the student records of a university in Guayaquil was developed, and an experiment was also carried out to test interpretability results with other existing models. Among the main results are the reliability of the metrics for the static analysis of fuzzy maps, the similarity with respect to a target student, and the importance that each subject represents in a new record.Actualmente, los sistemas derecomendaci√≥n son ampliamente utilizados paraanalizar preferencias de usuarios y sugerirles√≠tems afines. En el √°mbito universitario, elmomento en el cual una asignatura es elegidapor un estudiante para su siguiente etapaeducativa, es monitoreado por un ConsejeroAcad√©mico, quien de acuerdo con el r√©cordestudiantil, y comparando perfiles similaresa lo largo de su carrera, debe recomendarcu√°les asignaturas, podr√≠an contribuir alrendimiento y aprendizaje del estudiante. Elpresente trabajo represent√≥ un esfuerzo pordise√Īar un recomendador que se apoya en elmodelado de las relaciones causales existentesentre las asignaturas del p√©nsum curricular deuna carrera universitaria, empleando mapascognitivos difusos y operadores de agregaci√≥nOWA. Se aplic√≥ el flujo de trabajo del modelopropuesto y su implementaci√≥n, a trav√©s de laherramienta inform√°tica (FCM-Decision). Sedesarroll√≥ un estudio de caso con los r√©cordsestudiantiles de una universidad en Guayaquil,y adem√°s, se realiz√≥ un experimento paraprobar los resultados de interpretabilidad conotros modelos existentes. Entre los principalesresultados est√°n la fiabilidad de las m√©tricaspara el an√°lisis est√°tico de mapas difusos, lasimilitud respecto a un estudiante objetivo, y laimportancia que cada asignatura representa enun nuevo registro

    Decision making in inventory control by using artificial neural networks

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    The purpose of this work is to increase the sales of a store devoted to the purchase and sale of soft drinks, even though the store's inventory is overstocked. This occurs as a result of the business's lack of an effective management system that controls product ordering. Additionally, there is no analysis of future sales owing to the variations that may occur because of unforeseen occurrences. The main criterion was that the proprietors of the business submit monthly records from 2017 to July 2019. To accomplish this objective completely, we used the Monte Carlo simulation method to obtain data from August to December 2019; and neural networks to obtain data for all monthly periods in the years 2020, 2021, and 2022, which enabled us to generate records of demand and stock for each of the products. Finally, it was shown that the application of neural networks enables the solution of vehicle control issues, resulting in a maximization of more than 22% of sales, thus achieving the goal and giving an optimum solution to the company

    Correction to: Capillary Interaction and Self-Assembly of Tilted Magnetic Ellipsoidal Particles at Liquid Interfaces ((2018) 3:11 (14962?14972) DOI: 10.1021/acsomega.8b01818)

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    © 2019 American Chemical Society. We would like to correct the following minor errors in the paper: Figure 3 legend: Blue data points should be ss sims, red data points should be tt sims, blue line should be ss elliptical, and red line should be tt elliptical. Figure 3 caption, lines 4 and 5 should read, ...for the side-to-side configuration (blue) and the tip-to-tip configuration (red). p 14965, column 2, paragraph 2, lines 8-10 should read, ...has a higher energy compared to the 1/r12 2 power law, whereas the tip-to-tip configuration has a lower energy... The above corrections do not change any of the conclusions of the paper

    Capillary interaction and self-assembly of tilted magnetic ellipsoidal particles at liquid interfaces

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    Copyright © 2018 American Chemical Society. Magnetic ellipsoidal particles adsorbed at a liquid interface provide exciting opportunities for creating switchable functional materials, where self-assembly can be switched on and off using an external field [Davies et al., Adv. Mater., 2014, 26, 6715]. In order to gain a deeper understanding of this novel system in the presence of an external field, we study the capillary interaction and self-assembly of tilted ellipsoids using analytical theory and finite element simulations. We derive an analytical expression for the dipolar capillary interaction between tilted ellipsoids in elliptical polar coordinates, which exhibits a 1/r2 power law dependence in the far field (i.e., large particle separations r) and correctly captures the orientational dependence of the capillary interactions in the near field. Using this dipole potential and finite element simulations, we further analyze the energy landscape of particle clusters consisting of up to eight tilted ellipsoids in contact. For clusters of two particles, we find that the side-to-side configuration is stable, whereas the tip-to-tip configuration is unstable. However, for clusters of more than three particles, we find that circular loops of side-to-side particles become globally stable, whereas linear chains of side-to-side particles become metastable. Furthermore, the energy barrier for the linear-to-loop transition decreases with increasing particle number. Our results explain both thermodynamically and kinetically why tilted ellipsoids assemble side-to-side locally but have a strong tendency to form loops on larger length scales

    Think S-ICD first: The time has come

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    Health Technology Assessment on the use of the Wearable Cardioverter Defibrillator in Patients with Myocardial Infarction and with ICD Explant

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    The objective of the present work is to conduct a Health Technology Assessment (HTA) on the use of the Wearable Cardioverter Defibrillator (WCD) in patients at risk of Sudden Cardiac Arrest (SCA) following Myocardial Infarction (MI) or with an explanted Implantable Cardioverter Defibrillator (ICD)

    Predicting the Effectiveness of Rapid Tests Performed to Patients with COVID-19 through Linear Regression and Random Forest

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    En el transcurso del tiempo el mundo ha necesitado del conocimiento y la perseverancia de los seres humanos para poder solucionar cualquier problem√°tica que se le presente. Como, por ejemplo, ¬Ņen qu√© proporci√≥n est√° el aumento de infectados por COVID-19 en todo el planeta? Con la ayuda de enfoques no cl√≠nicos y tecnolog√≠as modernas como la miner√≠a de datos, inteligencia aumentada y t√©cnicas de inteligencia artificial, se ha logrado agilizar la enorme carga de trabajo en los sistemas de salud y al mismo tiempo brindar los mejores medios posibles para el diagn√≥stico y pron√≥stico de pacientes con covid-19 de manera efectiva. En este estudio, se implement√≥ un modelo matem√°tico para la predicci√≥n de la efectividad de las pruebas r√°pidas a las que se someten las personas posiblemente infectadas y definir cu√°l es el comportamiento epidemiol√≥gico causado por SARS-CoV2 (COVID-19). Para determinar el tipo de modelo a utilizar se aplicaron dos algoritmos, el de Regresi√≥n Lineal y el de Bosque Aleatorio o Random Forest, a un conjunto de datos utilizando el lenguaje de programaci√≥n Python. Posteriormente se realizar√°n las pruebas necesarias para verificar la efectividad de cada una de ellos. Una vez definido el modelo y despu√©s de haber realizado el debido entrenamiento de este, se realizar√° la predicci√≥n de un n√ļmero m√≠nimo y m√°ximo de las pruebas r√°pidas utilizadas en los pacientes que se encuentran infectados con COVID-19, identificando cu√°l de las pruebas r√°pidas es la m√°s utilizada y cu√°l es la m√°s efectiva. The rapid spread of SARS-CoV2 (COVID-19) has caused a collapse of health systems worldwide, so a strategy to control the spread is the timely detection of the virus through rapid tests, which allows acting and thus giving a timely treatment that reduces its spread. With the help of artificial intelligence techniques, within the subfield of machine learning or machine learning, there have been significant advances that allow speeding up the analysis of large volumes of data. This study aims to determine the effectiveness of rapid tests in detecting covid-19, using machine learning, applying a methodology that involves the creation of linear regression and Random Forest models with the Python programming language. In the methodology used, the models were created, which were then defined and trained, and after performing the tests and predictions, the validation metrics determined the precision and effectiveness of these models. From the results obtained, it is concluded that the random forest model is good since it provided a precision of 61%, but with the linear regression model, it was determined that it has a precision level of approximately 90%, so finally, with these results, health professionals will be able to make reliable predictions regarding the effectiveness of rapid tests as a mechanism that will help to quickly detect the presence of the virus and thus reduce the spread of the virus

    Investigation of the dynamics of 3-D flocs with complex morphology via Stokesian dynamics simulations

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    Understanding the transport behaviors of suspended particulate matter (SPM) is crucial for analyzing the impact and the flux of sediment in natural aquatic environment. SPM usually exists in form of flocs which are fragile and loosely bound aggregates characterized by highly irregular 3D shape, low effective densities and high porosity [1]. Previous studies of the physical characteristics of flocs are often based on simplified 2-D geometries of complex 3-D shapes. With the availability of 3-D sampling data of flocs, we employ Stokesian dynamics simulations to investigate the vertical or horizontal transport behaviors of flocs, e.g. settling under gravity or movement under shear flows. The correlations between the floc shapes, the transport behaviors and the floc internal stresses imposed by surrounding fluid are investigated. 3-D voxel-based datasets of the flocs are generated by conducting non-destructive 3-D X-ray computed tomography imaging on the stabilized floc samples, following the preparation protocol described in Wheatland et al. [2]. Based on the resulting voxel-based images of the flocs, the structure of each individual floc is modelled as an assembly of identical solid spheres and the velocity of the assembly is solved via Stokesian dynamics [3,4]. An automated process of predicting the dynamics of a floc in liquid environment from its voxel image is established. The entire modelling approach can serve as a powerful tool for analyzing the parameters determining the flocs transport behaviors and possibly provide inputs for modelling sediment bed growth rate at macro scales. Acknowledgements: The funding support from NERC project NE/N011678/1 has been acknowledged. References: [1] Droppo (2001) Hydrol. Process. 15:1551-1564; [2] Wheatland et al. (2017) Environ. Sci. Technol. 51:8917-8925; [3] Bossis et al. (1991) J. Chem. Phys. 94:5064-5070; [4] Swan et al. (2011) Phys. Fluids 23:071901. [5] Brady et al. (1988) Annu. Rev. Fluid Mech. 20.1:111-157
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