Resiliencia

Finalista (puesto 10º). Modalidad senio

Chapter Distribution Diagrams and Graphical Methods to Determine or to Use the Stoichiometric Coefficients of Acid-Base and Complexation Reactions

Computer networking & communication

Computer-aided sketching: incorporating the locus to improve the three-dimensional geometric design

This article presents evidence of the convenience of implementing the geometric places of the plane into commercial computer-aided design (CAD) software as auxiliary tools in the computer-aided sketching process. Additionally, the research considers the possibility of adding several intuitive spatial geometric places to improve the efficiency of the three-dimensional geometric design. For demonstrative purposes, four examples are presented. A two-dimensional figure positioned on the flat face of an object shows the significant improvement over tools currently available in commercial CAD software, both vector and parametric: it is more intuitive and does not require the designer to execute as many operations. Two more complex three-dimensional examples are presented to show how the use of spatial geometric places, implemented as CAD software functions, would be an effective and highly intuitive tool. Using these functions produces auxiliary curved surfaces with points whose notable features are a significant innovation. A final example provided solves a geometric place problem using own software designed for this purpose. The proposal to incorporate geometric places into CAD software would lead to a significant improvement in the field of computational geometry. Consequently, the incorporation of geometric places into CAD software could increase technical-design productivity by eliminating some intermediate operations, such as symmetry, among others, and improving the geometry training of less skilled usersPostprint (published version

Construction of an Artificial Neural Network-Based Method to Detect Structural Damage

This chapter shows the framework used to obtain data with which the artificial neural network (ANN) was developed. It describes its geometry, properties of the material, sections of structural elements, and loads used. Then, the numerical model of the framework under study is developed in structural analysis using SAP2000® software in order to obtain its modal parameters. In addition, a program made in MATLAB® is shown, from which data with and without damage to the framework under study were obtained, and with which the ANN was developed. Data from the numerical model were used to corroborate data obtained with MATLAB®. The neural model used in this work to detect structural damage is described. Data on damage were obtained simulating a plastic hinge in various elements of a test framework, varying the position of the hinge. The above resulted in obtaining various damage conditions for the same framework, which data thus obtained were used to develop the network. Damage conditions were hierarchized based on their fundamental periods in order to know where is more damage, depending on location of the hinge within the framework. Upon completion of the research, we have concluded that the methodology implemented to detect structural damage is rather simple. It was carried out in four steps

Dispersive solid phase extraction based on butylamide silica for determination of sulfamethoxazole in milk samples by capillary electrophoresis

A new method based on the combination of dispersive solid-phase extraction and capillary electrophoresis is proposed for the determination of sulfamethoxazole in milk samples. Butylamide silica was synthesized and used as extractant. Factors involved in sample treatment method such as: butylamide silica amount, NaOH concentration in methanol, sample volume, and dispersion time were evaluated using a Taguchi parameter design. Under optimal conditions, average recoveries ranged from 73 to 85% with a limit of detection of 0.05 mg L 1 were achieved. The proposed method is a useful technique for cleanup milk samples.Junta de Castilla y León Proyecto VA171U1

Las comunidades profesionales de aprendizaje. El caso de una institución educativa privada del distrito de San Isidro.

El objetivo de esta investigación fue describir las dimensiones de las comunidades profesionales de aprendizaje presentes en una Institución Educativa Privada del distrito de San Isidro. La producción corresponde al enfoque cualitativo trabajándose con tres categorías de análisis: la primera, la visión y valores compartidos; la segunda, liderazgo compartido y de soporte; y la tercera; aprendizaje colectivo y aplicado. En referencia al recojo de información se hizo uso de la entrevista semiestructurada como técnica de investigación y la guía de preguntas como instrumento, el cual fue aplicado a seis docentes del nivel secundario bajo criterios de inclusión/exclusión. A continuación, se construyó una matriz de análisis para organizar y analizar la información recopilada por medio de la técnica del Open Coding. El respectivo análisis de los hallazgos permitió concluir que las dimensiones de las comunidades profesionales de aprendizaje se describen, en primer lugar, a través de una visión que se hace visible al promover en el actuar diario el sentido de apoyo y familia, y al vivirse los valores por medio de diferentes acciones, estando así presentes en todas las actividades. También, cuando la distribución de responsabilidades busca ser horizontal, promover el liderazgo, generar la participación transversal y desarrollar una política de puertas abiertas. Por último, al generar el aprendizaje colectivo a través del coaching entre pares y la formación continua interna y externa, viéndose reflejado en la práctica en aula, construcción compartida de instrumentos, promoción de la evaluación formativa y aumento del compromiso docente

Waist Circumference as a Preventive Tool of Atherogenic Dyslipidemia and Obesity-Associated Cardiovascular Risk in Young Adults Males: A Cross-Sectional Pilot Study

Although the correlation coefficient between body mass index (BMI) and poor lipid profile has been reported, representing a cardiovascular risk, the need to find new early detection markers is real. Waist circumference and markers of atherogenic dyslipidemia are not usually measured in medical review appointments. The present study aimed to investigate the relationship between central adiposity and cardiovascular risk. This was a cross-sectional pilot study of 57 young males (age: 35.9 ± 10.85, BMI: 32.4 ± 6.08) recruited from community settings and allocated to non-obese or obese attending to their waist circumference. Total cholesterol (TC), high-density lipoproteins (HDL-C), and low-density lipoproteins (LDL-C) cholesterol and triglycerides (TG) were measured from plasma samples. Patients with at least 100 cm of waist circumference had significantly increased TC, LDL-C, non-HDL-C, and triglycerides and lower levels of HDL-C. The three atherogenic ratios TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C were all optimal in non-obese patients. LDL-C/HDL-C and TG/HDL-C were significantly higher and over the limit when assessing for atherogenic dyslipidemia. The number of patients at risk for cardiovascular events increases 2.5 folds in obese compared to non-obese. Measurement of waist circumference could be adopted as a simpler valid alternative to BMI for health promotion, to alert those at risk of atherogenic dyslipidemia

Geometrical correlation indices using homological constructions on manifolds

Abstract The course of dimensionality is a common problem in statistics and data analysis. Variable sensitivity analysis methods are a well studied and established set of tools designed to overcome these sorts of problems. However, as this work shows, these methods fail to capture relevant features and patterns hidden within the geometry of the enveloping manifold projected into a variable. We propose an index that captures, reflects and correlates the relevance of distinct variables within a model by focusing on the geometry of their projections. The analysis was made with an original R-package called TopSA, short for Topological Sensitivity Anal- ysis. The TopSA R-package is available on the site https://github.com/maikol- solis/TopSA.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Matemáticas Puras y Aplicadas (CIMPA)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de investigaciones Matemáticas y Metamatemáticas (CIMM

Geometric goodness of fit measure to detect patterns in data point clouds

The curse of dimensionality is a commonly encountered problem in statistics and data analysis. Variable sensitivity analysis methods are a well studied and established set of tools designed to overcome these sorts of problems. However, as this work shows, these methods fail to capture relevant features and patterns hidden within the geometry of the enveloping manifold projected onto a variable. Here we propose an index that captures, reflects and correlates the relevance of distinct variables within a model by focusing on the geometry of their projections. We construct the 2-simplices of a Vietoris-Rips complex and then estimate the area of those objects from a data-set cloud. The analysis was made with an original R-package called TopSA, short for Topological Sensitivity Analysis. The TopSA R-package is available at the site https://github.com/maikol-solis/TopSA.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de investigaciones Matemáticas y Metamatemáticas (CIMM)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Matemáticas Puras y Aplicadas (CIMPA