Coordination and crystallisation molecules: Their interactions affecting the dimensionality of metalloporphyrinic SCFs

Synthetic metalloporphyrin complexes are often used as analogues of natural systems, and they can be used for the preparation of new Solid Coordination Frameworks (SCFs). In this work, a series of six metalloporphyrinic compounds constructed from different meso substituted metalloporphyrins (phenyl, carboxyphenyl and sulfonatophenyl) have been structurally characterized by means of single crystal X-ray diffraction, IR spectroscopy and elemental analysis. The compounds were classified considering the dimensionality of the crystal array, referred just to coordination bonds, into 0D, 1D and 2D compounds. This way, the structural features and relationships of those crystal structures were analyzed, in order to extract conclusions not only about the dimensionality of the networks but also about possible applications of the as-obtained compounds, focusing the interest on the interactions of coordination and crystallization molecules. These interactions provide the coordination bonds and the cohesion forces which produce SCFs with different dimensionalities.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Report on the workshop of tuna-dolphin interactions

ENGLISH: In April 1981 the IATTC convened a working group of scientists in Managua, Nicaragua to discuss the tuna-dolphin association and to suggest priorities for future research which would enable the effects of any interaction to be detected or quantified. The yellowfin tuna, Thunnus albacares, fishery in the eastern tropical Pacific is unique in that a significant proportion of the catch is of fish found in association with one or more species of dolphins. This association has never been fully understood but for many years tuna fishermen have used the more visible and more easily herded dolphin schools to help them locate and capture the tuna. In recent years, the concept of managing renewable resources in relation to their environments has been more fully developed. Any renewable resource is closely linked to other components in its general system and it is becoming increasingly more apparent that the harvesting of one resource affects another. This is the case with yellowfin tuna and dolphins in the eastern tropical Pacific, although the dolphins are killed incidental to the fishery and are not harvested. There would seem to be obvious advantages in managing the tuna-dolphin complex as a whole. To do this it is necessary to understand the effect that tuna and dolphins have on each other and the causal mechanisms of the interactions. SPANISH: En abril de 1981, la CIAT convoco un grupo de trabajo de investigadores en Managua (Nicaragua), para deliberar sobre la asociación atún-delfín e indicar prioridades referentes a una investigación futura que pueda facilitar la cuantificación o el reconocimiento de los efectos de cualquier interacción. La pesca del atún aleta amarilla Thunnus albacares en el Pacifico oriental tropical, es única, ya que una proporción importante de su captura es de peces encontrados en asociación con una o mas especies de delfines. No se ha logrado comprender cabalmente esta asociación, pero por varios anos los pescadores atuneros han utilizado los cardúmenes de delfines que son mas visibles y que pueden agruparse mas fácilmente para poder localizar y capturar los atunes. En los últimos anos, el concepto de administrar los recursos renovables con relación a su ambiente, ha tenido mas auge. Cualquier recurso renovable se vincula estrechamente a otros componentes en el sistema general y actualmente es mas evidente que la explotación de un recurso afecta otro. Este es el caso del atún aleta amarilla y de los delfines en el Pacifico oriental tropical aunque los delfines mueren incidentalmente con relación a la pesca y no son explotados. Parece que se obtendr1an ventajas evidentes si se administrara como un todo el conjunto atún-delfín. Para realizar esto es necesario comprender los efectos que tienen los atunes y delfines los unos sabre los otros y los mecanismos causantes de la interacción.

Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

Studies of magnetic interactions in Mn-doped β-Ga2O3 from first-principles calculations

The magnetic behavior of Mn-doped beta-Ga2O3 is Studied from first-principles calculations within the generalized gradient approximation method. Calculations show that ferromagnetic ordering is always favorable for configurations in which two Mn ions substitute either tetrahedral or octahedral sites, and the ferromagnetic ground state is also sometimes favorable for configurations where one Mn ion substitutes a tetrahedral site and another Mn ion substitutes an octahedral site. However, the configurations of the latter case are less stable than those of the former. (c) 2008 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Studies of magnetic interactions in Ni-doped ZnO from first-principles calculations

The magnetic interactions in Ni-doped ZnO are calculated using GGA and GGA + U method of density functional theory. The following three cases: (i) Ni-doped ZnO, (ii) (Ni, Al)-codoped ZnO, and (iii) (Ni, Li)-codoped ZnO are studied. The ferromagnetic ordering is always favorable for the three cases within GGA method. However, the ferromagnetic state is sometimes favorable after treating within the method of GGA + U. The GGA underestimates the correlated interactions especially when the Ni ions align directly to each other. (C) 2007 Elsevier B.V. All rights reserved.