Enfoque metodológico en las estrategias de aprendizaje de conceptos matemáticos usados por estudiantes de la carrera de ingeniería civil

En este trabajo reportamos las herramientas arqueológicas desde un enfoque metodológico en las estrategias de aprendizaje de los conceptos matemáticos, con la finalidad de favorecer el aprendizaje de los alumnos de la carrera de ingeniería civil (IC) de la Facultad de ingeniería de la Universidad Autónoma de Chiapas (UNACH). El objetivo es caracterizar e identificar los patrones y semejanzas de las estrategias de aprendizaje, sean residuales o no, que han adquirido los alumnos desde su contexto y visión del mundo, a fin de conocer las habilidades y destreza en la construcción de conceptos matemáticos. A partir de ellos, creemos que se puede desarrollar un conjunto de acciones que permitan a los alumnos, desestructurar y desarmar las estrategias adquiridas, reestructurarlas o rearmarlas, deshaciéndose de las ineficientes, para dar mejores respuestas al aprendizaje de conceptos y soluciones en los problemas matemáticos de la carrera de ingeniería civil, en la Facultad de Ingeniería de la UNACH

Lexical and phonological processing in visual word recognition by stuttering children: Evidence from Spanish

A number of studies have pointed out that stuttering-like disfluencies could be the result of failures in central and linguistic processing. The goal of the present paper is to analyze if stuttering implies deficits in the lexical and phonological processing in visual word recognition. This study compares the performance of 28 children with and without stuttering in a standard lexical decision task in a transparent orthography: Spanish. Word frequency and syllable frequency were manipulated in the experimental words. Stutterers were found to be considerably slower (in their correct responses) and produced more errors than the non- stutterers (?(1) = 36.63, p and lt;.001, ?2 =.60). There was also a facilitation effect of syllable frequency, restricted to low frequency words and only in the stutterers group (t1(10) = 3.67, p and lt;.005; t2(36) = 3.10, p and lt;.001). These outcomes appear to suggest that the decoding process of stutterers exhibits a deficit in the interface between the phonological-syllabic level and the word level. Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2014

La exitosa respuesta de los consolareños a la COVID-19 en la comunidad Camilo Cienfuegos

Introduction: the emergence of Covid-19 pandemic resulted in more than 180 countries being affected today. Cuba has not been exempt from this situation and its first epidemiological event took place in Camilo Cienfuegos community, Consolacion del Sur municipality, Pinar del Río province, which tested the mobilization capacity of the medical personnel, and of the sectors that in one way or another collaborated in the period of quarantine between March 31 and May 1, in order to avoid the spread and the onset of new cases.Objective: to characterize the behavior of Camilo Cienfuegos community during Covid-19 period of quarantine.Methods: empirical methods such as observation, documentary review and interviews with members of the medical team and community leaders were applied.Development: a characterization of the community in the quarantine process was accomplished complying with the indications of the Municipal Defense Council to cope with and prevent the onset of Covid-19 new cases.Conclusions: the historical characterization of Camilo Cienfuegos community allowed the Municipal Defense Council to have a testimonial tool on the treatment of Covid-19, which facilitated the further requests or enquiries of posterior episodic situations of this disease in the province and the country, as well as its importance for decision making in coping with this pandemic. Introducción: la aparición de la pandemia de la COVID-19 trajo como resultado que más de 180 países estén afectados. Cuba no ha estado ajena a esta situación. El primer evento epidemiológico registrado en la comunidad Camilo Cienfuegos, municipio Consolación del Sur, provincia Pinar del Río, que puso a prueba la capacidad de movilización del personal médico, y de los sectores que de una u otra forma colaboraron en la puesta en cuarentena entre el 31 de marzo y el 1º de mayo, para evitar la propagación.Objetivo: caracterizar el comportamiento de la comunidad Camilo Cienfuegos durante la cuarentena por la COVID-19.Métodos: se utilizaron métodos empíricos, la observación, revisión documental, entrevistas a miembros del equipo médico y a dirigentes de la comunidad.Desarrollo: se logró una caracterización de la comunidad en el proceso de la cuarentena a partir de su indicación por el Consejo de Defensa Municipal, para el enfrentamiento y la prevención del surgimiento de nuevos casos de la enfermedad.Conclusiones: la caracterización histórica de la comunidad Camilo Cienfuegos permitió al Consejo de Defensa Municipal disponer de una herramienta testimonial sobre el tratamiento de la COVID-19, lo que facilitó su aplicación o consulta a posteriores situaciones episódicas de la enfermedad en la provincia y el país. Así como su importancia para la toma de decisiones en el enfrentamiento a la pandemia

A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes

In this paper, a novel learning architecture based on neural networks is used for temperature inverse modeling in microwave-assisted drying processes. The proposed design combines the accuracy of the radial basis functions (RBF) and the algebraic capabilities of the matrix polynomial structures by using a two-level structure. This architecture is trained by temperature curves, TcðtÞ; previously generated by a validated drying model. The interconnection of the learning-based networks has enabled the finding of electric field (E) optimal values which provide the TcðtÞ curve that best fits a desired temperature target in a specific time slo

Improving the signal detection accuracy of functional Magnetic Resonance Imaging

Available online 12 April 2018A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.See https://github.com/iamnielsjanssen/slice-based for a full analysis script using the Slice-Based method. This work was supported by The Spanish Ministry of Economy and Competitiveness (RYC2011-08433 and PSI2013-46334 to NJ)

A novel predictive architecture for microwave-assisted drying processes based on neural networks

In this contribution, a novel learning architecture based on the interconnection of two different learning-based neural networks has been used to both predict temperature and drying curves and solve inverse modelling equations in microwave-assisted drying processes. In this way, a neural model that combines the accuracy of neural networks based on Radial Basis Functions (RBF) and the algebraic capabilities of the matrix polynomial structures is presented and validated. The architecture has been trained by temperature (Tc(t)) and moisture content (Xt(t)) curves, which have been generated by a previously validated drying model. The results show that the neural model is able to very accurately predict both kind of curves for any combination of the considered input variables (electric field and air temperature) provided that an appropriate training process is performed. The proposed configuration also permits the solution of the inverse problem in the drying process by finding the optimal value for the electric field. This provides Tc(t) or Xt(t) curves that fit to a desired drying condition in a specific time slot.This work was supported in part by the SENECA Fundation (Spain) PCMC75/ 00078/FS/02, and the Spanish Science & Technology Ministry (MCYT) under TIC 2003-08164-C03-03 research project

Multiple-Input Multiple-Output Analyzer

Número de publicación: EP 2 325 662 B1. Numero de solicitud: HU2009E811143. Número de publicación internacional: WO 2010/026274Wireless systems that incorporate multiple inputs and multiple outputs (MIMO) for the signal are based upon turning multipath propagation, initially seen as a problem, into a solution to the limited transmission capacities. In this way, parallel transmission channels are available by increasing the number of transmitting and/or receiving antennas. Thus, high spectrum efficiencies can be achieved over the radio channel, that is, it constitutes a very competent way to satisfying the high requirements of the forthcoming Fourth Generation (4G) mobile communication systems. When the transmission channel transports energy instead of information by using high power supplies, other effects such as heating, drying and curing of materials, can be obtained.EMITE Ingeniería, S.L

Functional connectivity of the hippocampus and its subfields in resting-state networks

First published: 30 March 2021Many neuroimaging studies have shown that the hippocampus participates in a resting-state network called the default mode network. However, how the hippocampus connects to the default mode network, whether the hippocampus connects to other resting-state networks and how the different hippocampal subfields take part in resting-state networks remains poorly understood. Here, we examined these issues using the high spatial-resolution 7T resting-state fMRI dataset from the Human Connectome Project. We used data-driven techniques that relied on spatially-restricted Independent Component Analysis, Dual Regression and linear mixed-effect group-analyses based on participant-specific brain morphology. The results revealed two main activity hotspots inside the hippocampus. The first hotspot was located in an anterior location and was correlated with the somatomotor network. This network was subserved by co-activity in the CA1, CA3, CA4 and Dentate Gyrus fields. In addition, there was an activity hotspot that extended from middle to posterior locations along the hippocampal long-axis and correlated with the default mode network. This network reflected activity in the Subiculum, CA4 and Dentate Gyrus fields. These results show how different sections of the hippocampus participate in two known resting-state networks and how these two resting-state networks depend on different configurations of hippocampal subfield co-activity.Agencia Canaria de Investigación, Innovación y Sociedad de la Información; Ministerio de Ciencia, Innovación y Universidades, Grant/Award Number: PSI2017-84933- P, PSI2017-91955- EXP and TEC2016-80063- C3- 2- R; NIH Blueprint for Neuroscience Research, Grant/Award Number: 1U54MH091657; McDonnell Center for Systems Neuroscience; European Social Fund (ESF