A recursive algorithm to compute a basis of a similarity

Se definen conceptos y se aplica el teorema de Valverde para escribir un algoritmo que computa bases de similaridades. This paper studies sorne theory and methods to build a representation theorem basis of a similarity from the basis of its subsimilarities, providing an alternative recursive method to compute the basis of a similarity.

First steps in the discovery of patterns in the academic results of telecommunication engineering students in the subjects Analysis of Circuits and Mathematics

In this paper, the results of six years of research in engineering education, in the application of the European Higher Education Area (EHEA) to improve the performance of the students in the subject Analysis of Circuits of Telecommunication Engineering, are analysed taking into consideration the fact that there would be hidden variables that both separate students into subgroups and show the connection among several basic subjects such as Analysis of Circuits (AC) and Mathematics (Math). The discovery of these variables would help us to explain the characteristics of the students through the teaching and learning methodology, and would show that there are some characteristics that instructors do not take into account but that are of paramount importance

Discovery of ferromanganese hydrocarbon-related nodules associated with the Meknes mud volcano (Western Moroccan margin)

A suite of ferromanganese nodules were sampled during the MVSEIS-2008 cruise aboard of the R/V Hespérides in the flanks of Meknes mud volcano (Moroccan margin, NE Central Atlantic). The nodules were collected at water depths between 750-850 m within a seabed area characterized by high acoustic backscatter values. Debris of cold water corals and hydrocarbon-derived authigenic carbonate crusts were sampled at same time. The nodules show tabular morphology, up to 20 cm in maximum diameter and 2 kg of weight, brown-reddish external color and they are internally composed by a concentric to complex arrangement of laminae. The results of X-ray diffraction analysis show that these ferromanganese nodules are essentially composed of goethite and lepidocrocite, being Mn-oxides, silicates (quartz and clay minerals) and carbonates (calcite, dolomite and siderite) accessory to occasional minerals. All the samples display micritic to micro-sparitic mosaic under the petrographic microscope which forms massive, laminated or dendritic-mottled textures. The nodules show a high abundance of Fe, minor Mn and low contents of trace metals and REEs. Mature hydrocarbons, as n-alkanes derived from marine bacterial activity, and phenanthrene have been detected in all the ferromanganese nodules analyzed. These nodules display analogous characteristics (textural, mineralogical and geochemical) to the nodules studied by González et al (2009) in the carbonate mud-mounds in the Gulf of Cadiz, offshore Iberian margin. In this way, the same preliminary genetic model proposed for these nodules might be applicable to those find in the Meknes mud volcano. Therefore, the anaerobic oxidation of hydrocarbon-rich fluids within the mud-breccia sediments in the flanks of Meknes mud volcano would induce the formation of early diagenetic Fe-(Mn) carbonate nodules. Thus, the nodules were later exhumed by the erosive action of sea bottom currents generating the replacement of ferromanganese carbonates by Fe-Mn oxy-hydroxides. Thus, the hydrocarbon-rich fluid venting from deep seated reservoirs and erosive action of bottom currents must have been essential actors, as mineralization controls, for ferromanganese nodules generation and evolution. These findings imply that this type of nodules must be considered as new product as derived from the anaerobic/aerobic oxidation of hydrocarbons in areas of active seepages.

Discovery of a low-mass brown dwarf companion of the young nearby star G196-3.

A substellar-mass object in orbit at about 300 astronomical units from the young low-mass star G 196-3 was detected by direct imaging. Optical and infrared photometry and low- and intermediate-resolution spectroscopy of the faint companion, hereafter referred to as G 196-3B, confirm its cool atmosphere ?15 Jupiter masses. The separation and allow its mass to be estimated at 25?10 between the objects and their mass ratio suggest the fragmentation of a collapsing cloud as the most likely origin for G 196-3B, but alternatively it could have originated from a protoplanetary disc that has been dissipated. Whatever the formation process was, the young age of the primary star (about 100 million years) demonstrates that substellar companions can form on short time scales.

A framework for goal-oriented discovery of resources in the RESTful architecture

One of the challenges facing the current web is the efficient use of all the available information. The Web 2.0 phenomenon has favored the creation of contents by average users, and thus the amount of information that can be found for diverse topics has grown exponentially in the last years. Initiatives such as linked data are helping to build the Semantic Web, in which a set of standards are proposed for the exchange of data among heterogeneous systems. However, these standards are sometimes not used, and there are still plenty of websites that require naive techniques to discover their contents and services. This paper proposes an integrated framework for content and service discovery and extraction. The framework is divided into several layers where the discovery of contents and services is made in a representational stateless transfer system such as the web. It employs several web mining techniques as well as feature-oriented modeling for the discovery of cross-cutting features in web resources. The framework is used in a scenario of electronic newspapers. An intelligent agent crawls the web for related news, and uses services and visits links automatically according to its goal. This scenario illustrates how the discovery is made at different levels and how the use of semantics helps implement an agent that performs high-level tasks.

Species richness and similarity of vascular plants in the Spanish dehesas at two spatial scales

Aims of study: The goals of this paper are to summarize and to compare plant species richness and floristic similarity at two spatial scales; mesohabitat (normal, eutrophic, and oligotrophic dehesas) and dehesa habitat; and to establish guidelines for conserving species diversity in dehesas. Area of study: We considered four dehesa sites in the western Peninsular Spain, located along a climatic and biogeographic gradient from north to south. Main results: Average alpha richness for mesohabitats was 75.6 species, and average alpha richness for dehesa sites was 146.3. Gamma richness assessed for the overall dehesa habitat was 340.0 species. The species richness figures of normal dehesa mesohabitat were significantly lesser than of the eutrophic mesohabitat and lesser than the oligotrophic mesohabitat too. No significant differences were found for species richness among dehesa sites. We have found more dissimilarity at local scale (mesohabitat) than at regional scale (habitat). Finally, the results of the similarity assessment between dehesa sites reflected both climatic and biogeographic gradients. Research highlights: An effective conservation of dehesas must take into account local and regional conditions all along their distribution range for ensuring the conservation of the main vascular plant species assemblages as well as the associated fauna

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.