Estudio de los enlucidos de yeso de la iglesia de San Pedro de los Francos de Calatayud. Study of plasters finishes on San Pedro de los Francos church at Calatayud

El estudio de los enlucidos de la iglesia de San Pedro de los Francos de Calatayud ha permitido demostrar, en primer lugar, la secuencia estratigráfica mediante la lectura de la interfase de adherencia; en segundo lugar, la evolución de las técnicas de aplicación, desde el enlucido mudéjar, un fingido de piedra agramilado y pintado (siglo XV), hasta los enlucidos lisos y pinturas aplicados posteriormente. Y, finalmente, a través de los estudios de microscopía electrónica de barrido (SEM) y de difracción de rayos X (DRX), se ha determinado que son morteros en los que tanto el conglomerante como el árido son yeso y anhidrita, que correspondería a un yeso trasdicional multifase, cuyos granos más gruesos analizados como áridos proceden del producto obtenido artesanalmente.

Recuerdos de Pedro

Semblanza humana y de la obra de Pedro Suárez Bores, catedrático de Puertos de la Universidad Politécnica de Madrid desde 1968 a 1999 y profesor emérito de la citada universidad. Dos de las lunas de las hojas caídas del año dos mil diez fueron sombrías. En la primera de ellas, perdí a una de mis referencias humanas, un bastión en mi formación como persona, en mi forma de ser y comportarme, mi padre. En la otra, se fue alguien básico en mi profesión y en la forma de entender la ingeniería del mar, Pedro. Estas líneas pretenden ser un homenaje, una semblanza y un recordatorio de casi treinta años de conversaciones y vivencias entre los dos

Cooperativa agrícola de pequeños productores en la reserva del Merendón, San Pedro Sula, Honduras

El siguiente trabajo consiste en formar y estructurar una cooperativa agrícola en una Zona de Reserva muy cercana a la segunda ciudad más grande de Honduras (San Pedro Sula). Debido a los precios tan bajos que reciben los agricultores por la venta de productos en la ciudad, todo ello provocado entre otros factores por la independencia entre productores y la poca fuerza que pueden ejercer en el mercado, se crea la necesidad de actuar en conjunto. Para ello se forma la cooperativa COME (Cooperativa del Merendón) solo a 30 km de San Pedro Sula. Para realizar el proyecto se hicieron grupos de trabajo entre agricultores, entrevistas, visitas a sus parcelas, visitas a otras cooperativas y reuniones en la municipalidad, compras en los mercados locales y entrevistas en cuatro supermercados de la ciudad. Se ha diseñado la estrategia de la cooperativa, incluida la marca y logotipo, la política de ventas y las necesidades de producción. El presupuesto es de cien mil euros y el volumen de negocio de algo más de 125.000 euros anuales. Además de conseguir trabajo para las 32 familias involucradas se generaría empleo para 18 personas.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Parque eólico offshore en San Pedro de Alcántara

El principal motivo de implantar un parque eólico de estas características es responder a una necesidad palpable en el ámbito mundial como es el crecimiento en la demanda energética unido a un agotamiento de los recursos petrolíferos. Si a esto le sumamos la tendencia actual que han adquirido muchos países de oponerse al desarrollo de nuevas centrales nucleares tras el desastre ocurrido en Fukushima (Japón), y el aumento de los niveles de contaminación en las principales ciudades europeas, así como la sobreexplotación de los parques eólicos onshore en la Península Ibérica, configuran un marco perfecto para el despegue definitivo de la energía eólica offshore como alternativa perfecta al resto de fuentes de energía. Desde el punto de vista particular a nuestra localización, existe una demanda energética considerable, dado el carácter turístico de nuestro emplazamiento, que queda de manifiesto en el Plan General de Ordenación Urbana de Marbella, por lo que el objetivo de este proyecto será abastecer energía a San Pedro Alcántara y las poblaciones vecinas, a fin de que no se produzca ninguna carencia energética a lo largo de los periodos estivales, que serán los momentos de mayor demanda solicitada.

Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.

Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Los productos forestales no maderables como complemento económico en zonas forestales de países en desarrollo. Caso de El Merendón, en San Pedro Sula. Honduras. CA.

Se analizan las posibilidades que tiene el cultivo de mariposas y la utilización de las plantas en la alimentación, sanidad y otros usos en el medio rural de Honduras, como complemento para su desarrollo.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Mars: a personalised mobile activity recognition system

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

Pasarela sobre el jardín Turia entre la calle Pedro Aleixandre y la avenida de Francia nº1 (Valencia)

El proyecto de la pasarela sobre el cauce, hoy Parque del Turia, entre la Calle Pedro Aleixandre y Avda. de Francia nº1, queda enmarcado dentro de un proyecto mucho más ambicioso, que pretende revolucionar la ciudad de Valencia. La integración total del parque dentro de la ciudad sin que éste suponga una barrera. Por dicha razón su diseño y concepción debe englobarse dentro de un contexto determinado que sea coherente con todo lo proyectado y diseñado hasta el momento. En la actualidad el parque es un lugar de encuentro referente en Valencia con más de tres millones de visitantes anuales, es el más visitado y grande de España. El parque posee integrado en su interior la Ciudad de las Artes y las Ciencias, el Parque Gulliver, el Palau de la Música, el Parque de Cabecera, el Bioparc y el Zoo de Valencia. La pasarela será un paso de referencia para la gran cantidad de usuarios que cruzan a diario y especialmente los fines de semana, por esta zona de gran importancia por su valor turístico y comercial. Por tanto, será una infraestructura discreta pero estética, útil y confortable, que tiende a estar integrada con el entorno, manteniendo una inversión y mantenimiento adecuados.