Resonant behaviour of the barrier of YBa2Cu3O7 grain boundary Josephson junctions fabricated on bicrystalline substrates with different geometries

We have analyzed a resonant behavior in the dielectric constant associated to the barrier of YBa2Cu3O7 (YBCO) grain boundary Josephson junctions (GBJJs) fabricated on a wide variety of bicrystalline substrates: 12° [0 0 1] tilt asymmetric, 24° [0 0 1] tilt asymmetric, 24° [0 0 1] tilt symmetric, 24° [1 0 0] tilt asymmetric, 45° [1 0 0] tilt asymmetric and 24° [0 0 1] tilt symmetric +45° [1 0 0] tilt asymmetric bicrystals. The resonance analysis allows us to estimate a more appropriate value of the relative dielectric constant, and so a more adequate value for the length L of the normal N region assuming a SNINS model for the barrier. In this work, the L dependence on the critical current density Jc has been investigated. This analysis makes possible a single representation for all the substrate geometries independently on around which axes the rotation is produced to generate the grain boundary. On the other hand, no clear evidences exist on the origin of the resonance. The resonance frequency is in the order of 1011 Hz, pointing to a phonon dynamic influence on the resonance mechanism. Besides, its position is affected by the oxygen content of the barrier: a shift at low frequencies is observed when the misorientation angle increases.

Optimisation of algorithms to compute information theoretic indexes

Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document. Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document.

Habitabilidad básica latinoamericana: estándares de urbanización y edificación popular, de bajo coste y bioclimática. El caso de los asentamientos humanos populares de la ciudad de Río de Janeiro, Brasil

Habitabilidad básica latinoamericana: estándares de urbanización y edificación popular, de bajo coste y bioclimática. El caso de los asentamientos humanos populares de la ciudad de Río de Janeiro, Brasil