Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

[EN]This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic Rössler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Molecular Antennas and Photoactive Nanomaterials based on Energy Transfer Processes

110 p.

Implementation of the nursing process in a health area: models and assessment structures used

Objective: to analyze what nursing models and nursing assessment structures have been used in the implementation of the nursing process at the public and private centers in the health area Gipuzkoa (Basque Country). Method: a retrospective study was undertaken, based on the analysis of the nursing records used at the 158 centers studied. Results: the Henderson model, Carpenito's bifocal structure, Gordon's assessment structure and the Resident Assessment Instrument Nursing Home 2.0 have been used as nursing models and assessment structures to implement the nursing process. At some centers, the selected model or assessment structure has varied over time. Conclusion: Henderson's model has been the most used to implement the nursing process. Furthermore, the trend is observed to complement or replace Henderson's model by nursing assessment structures.

Dehydration for a solid coordination network based on CuII-(py)2C(OH)2 trimers

Abstract de congreso: Póster presentado en 12th International Conference on Materials Chemistry (MC12), 20 - 23 July 2015, York, United Kingdom

Metalloporphyrin-based MOFs: First Cobalt Based TPPS-bipy Coordination Network

Poster presentado en el congreso: Third International Conference on Multifunctional, Hybrid and Nanomaterials (3-7 March 2013, Sorrento, Italy)

Luminescence and Magnetic Properties of Two Three -Dimensional Terbium and Dysprosium MOFs Based on Azobenzene-4,4 '-Dicarboxylic Linker

We report the in situ formation of two novel metal-organic frameworks based on terbium and dysprosium ions using azobenzene-4,4-dicarboxylic acid (H(2)abd) as ligand, synthesized by soft hydrothermal routes. Both materials show isostructural three-dimensional networks with channels along a axis and display intense photoluminescence properties in the solid state at room temperature. Textural properties of the metal-organic frameworks (MOFs) have been fully characterized although no appreciable porosity was obtained. Magnetic properties of these materials were studied, highlighting the dysprosium material displays slightly frequency-dependent out of phase signals when measured under zero external field and under an applied field of 1000 Oe.