El role-playing, de la Escuela a la vida

Trabajo de investigación acerca del role-playing, en la actualidad considerada como una de las principales técnicas psicodramáticas. Su ámbito de aplicación es inmenso, por lo que me he ceñido a su aplicación dentro del aula, concretamente dirigiéndonos al alumnado de Educación Primaria. He elegido distintas formas de aplicación de esta técnica en un alumnado dispar. Por un lado, alumnado con altas capacidades, alumnado con Necesidades Educativas Especiales, y alumnado de entornos educativos diferenciados.Grado en Educación Primari

A Discussion of Thin Client Technology for Computer Labs

Computer literacy is not negotiable for any professional in an increasingly computerised environment. Educational institutions should be equipped to provide this new basic training for modern life. Accordingly, computer labs are an essential medium for education in almost any field. Computer labs are one of the most popular IT infrastructures for technical training in primary and secondary schools, universities and other educational institutions all over the world. Unfortunately, a computer lab is expensive, in terms of both initial purchase and annual maintenance costs, and especially when we want to run the latest software. Hence, research efforts addressing computer lab efficiency, performance or cost reduction would have a worldwide repercussion. In response to this concern, this paper presents a survey on thin client technology for computer labs in educational environments. Besides setting out the advantages and drawbacks of this technology, we aim to refute false prejudices against thin clients, identifying a set of educational scenarios where thin clients are a better choice and others requiring traditional solutions

Evolutionary topology optimization of continuum structures under uncertainty using sensitivity analysis and smooth boundary representation

This paper presents an evolutionary approach for the Robust Topology Optimization (RTO) of continuum structures under loading and material uncertainties. The method is based on an optimality criterion obtained from the stochastic linear elasticity problem in its weak form. The smooth structural topology is determined implicitly by an iso-value of the optimality criterion field. This iso-value is updated using an iterative approach to reach the solution of the RTO problem. The proposal permits to model the uncertainty using random variables with different probability distributions as well as random fields. The computational burden, due to the high dimension of the random field approximation, is efficiently addressed using anisotropic sparse grid stochastic collocation methods. The numerical results show the ability of the proposal to provide smooth and clearly defined structural boundaries. Such results also show that the method provides structural designs satisfying a trade-off between conflicting objectives in the RTO problem.The authors would like to thank Dr. Francisco Periago for constructive suggestions and discussions. This work has been partially supported by the AEI/FEDER and UE under the contract DPI2016-77538-R and by the “Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia” under the contract 19274/PI/14

GPU acceleration for evolutionary topology optimization of continuum structures using isosurfaces

Evolutionary topology optimization of three-dimensional continuum structures is a computationally demanding task in terms of memory consumption and processing time. This work aims to alleviate these constraints proposing a well-suited strategy for Graphics Processing Unit (GPU) computing. Such a proposal adopts a fine-grained GPU instance of matrix-free iterative solver for structural analysis and an efficient GPU implementation for isosurface extraction and volume fraction calculation. The performance of the solving stage is evaluated using two preconditioning techniques, including the comparison with the sparse-matrix CPU implementation. The proposal is evaluated using topology optimization problems for real-world applications.We gratefully acknowledge the support of NVIDIA Corporation with the donation of some of the GPUs used for this research. Such a work has also been supported by the research support programmes of Ministry of Economy and Competitiveness under the contract DPI2016-77538-R and \Fundación Séneca Agencia de Ciencia y Tecnología de la Región de Murcia" under the contract 19274/PI/14

Estimating adaptive setpoint temperatures using weather stations

Reducing both the energy consumption and CO 2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.Spanish Ministry of Science, Innovation and Universities 00064742/ITC-20133094Spanish Ministry of Economy, Industry and Competitiveness BIA 2017-85657-

Quantum electrodynamics with magnetic textures

Coherent exchange between photons and different matter excitations (like qubits, acoustic surface waves or spins) allows for the entanglement of light and matter and provides a toolbox for performing fundamental quantum physics. On top of that, coherent exchange is a basic ingredient in the majority of quantum information processors. In this work, we develop the theory for coupling between magnetic textures (vortices and skyrmions) stabilized in ferromagnetic nanodiscs and microwave photons generated in a superconducting circuit. Within this theory we show that this hybrid system serves for performing broadband spectroscopy of the magnetic textures. We also discuss the possibility of reaching the strong coupling regime between these texture excitations and a single photon residing in a microwave superconducting cavity

An upper bound for the magnetic force gradient in graphite

Cervenka et al. have recently reported ferromagnetism along graphite steps. We present Magnetic Force microscopy (MFM) data showing that the signal along the steps is independent of an external magnetic field. Moreover, by combining Kelvin Probe Force Microscopy (KPFM) and MFM, we are able to separate the electrostatic and magnetic interactions along the steps obtaining an upper bound for the magnetic force gradient of about16 microN/m, a figure six times lower than the lowest theoretical bound reported by Cervenka et al. Our experiments suggest absence of MFM signal in graphite at room temperature.Comment: 14 pages, including supplemetary informatio

Multi-GPU acceleration of large-scale density-based topology optimization

This work presents a parallel implementation of density-based topology optimization using distributed GPU computing systems. The use of multiple GPU devices allows us accelerating the computing process and increasing the device memory available for GPU computing. This increment of device memory enables us to address large models that commonly do not fit into one GPU device. The most modern scientific computers incorporate these devices to design energy-efficient, low-cost, and high-computing power systems. However, we should adopt the proper techniques to take advantage of the computational resources of such high-performance many-core computing systems. It is well-known that the bottleneck of density-based topology optimization is the solving of the linear elasticity problem using Finite Element Analysis (FEA) during the topology optimization iterations. We solve the linear system of equations obtained from FEA using a distributed conjugate gradient solver preconditioned by a smooth aggregation-based algebraic multigrid (AMG) using GPU computing with multiple devices. The use of aggregation-based AMG reduces memory requirements and improves the efficiency of the interpolation operation. This fact is rewarding for GPU computing. We evaluate the performance and scalability of the distributed GPU system using structured and unstructured meshes. We also test the performance using different 3D finite elements and relaxing operators. Besides, we evaluate the use of numerical approaches to increase the topology optimization performance. Finally, we present a comparison between the many-core computing instance and one efficient multi-core implementation to highlight the advantages of using GPU computing in large-scale density-based topology optimization problems.This work has been supported by the AEI/FEDER and UE under the contract DPI2016-77538-R, and by the “Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia” of Spain under the contract 20911/PI/18