Desenvolupament d'una aplicació informàtica per a la gestió d'activitats d'investigació

Aquest projecte es dirigeix a tots aquells docents que gestionen informació d'activitats dinvestigació des de diferents eines tecnològiques i que precisen de connexió a Internet i de diverses Webs. Per centralitzar aquestes dades, neix TeachPro, un software automatitzat que gestiona i emmagatzema informació sobre les activitats de gestió per tal que l'usuari en disposi de manera ràpida i organitzada. Tot això, sense connexió a Internet. Les dades són gestionades mitjançant una interfície a partir de la qual l'usuari podrà consultar, afegir, modificar, o eliminar tota aquella informació que desitgi. Totes aquestes dades seran emmagatzemades en una base de dades local.

Formation of disclinations line near a free nematic interface

We have studied the nucleation and the physical properties of a -1/2 wedge disclination line near the free surface of a confined nematic liquid crystal. The position of the disclination line has been related to the material parameters (elastic constants, anchoring energy, and favored anchoring angle of the molecules at the free surface). The use of a planar model for the structure of the director field (whose predictions have been contrasted to those of a fully three-dimensional model) has allowed us to relate the experimentally observed position of the disclination line to the relevant properties of the liquid crystals. In particular, we have been able to observe the collapse of the disclination line due to a temperature-induced anchoring-angle transition, which has allowed us to rule out the presence of a real disclination line near the nematic/isotropic front in directional growth experiments.

Desarrollo de una aplicación informática para la gestión de actividades docentes

Este proyecto consiste en el desarrollo de una aplicación informática que permite gestionar de forma automatizada y consistente los datos requeridos para la actividad docente de un profesor universitario. La aplicación permite gestionar: plan docente, asignaturas, horario docente, calendario de exámenes y proyectos final de carrera. Todas estas opciones tienen las funciones de, agregar, buscar, modificar y eliminar datos. Además tiene otras opciones como calendario docente y webs, cuya finalidad será consultar, de forma directa, páginas web de interés docente. Finalmente, la opción material docente tendrá como finalidad, crear, modificar y eliminar ficheros de diferente formato (word, excel, powerpoint, pdf) asociados a las asignaturas registradas en la aplicación. La aplicación se ha implementado en el sistema operativo Windows en el lenguaje de programación Java. Los datos utilizados se almacenan en la base de datos MySql Workbench. Para las validaciones de entrada de datos se ha utilizado JavaScript y JQuery. El diseño de la interfaz se ha llevado a cabo con Java Server Pages, Html, Css y framework Struts.

New reference tables and user-friendly Internet application for predicted heart weights.

BACKGROUND: Knowledge of normal heart weight ranges is important information for pathologists. Comparing the measured heart weight to reference values is one of the key elements used to determine if the heart is pathological, as heart weight increases in many cardiac pathologies. The current reference tables are old and in need of an update. AIMS: The purposes of this study are to establish new reference tables for normal heart weights in the local population and to determine the best predictive factor for normal heart weight. We also aim to provide technical support to calculate the predictive normal heart weight. METHODS: The reference values are based on retrospective analysis of adult Caucasian autopsy cases without any obvious pathology that were collected at the University Centre of Legal Medicine in Lausanne from 2007 to 2011. We selected 288 cases. The mean age was 39.2 years. There were 118 men and 170 women. Regression analyses were performed to assess the relationship of heart weight to body weight, body height, body mass index (BMI) and body surface area (BSA). RESULTS: The heart weight increased along with an increase in all the parameters studied. The mean heart weight was greater in men than in women at a similar body weight. BSA was determined to be the best predictor for normal heart weight. New reference tables for predicted heart weights are presented as a web application that enable the comparison of heart weights observed at autopsy with the reference values. CONCLUSIONS: The reference tables for heart weight and other organs should be systematically updated and adapted for the local population. Web access and smartphone applications for the predicted heart weight represent important investigational tools.

CleanEx: a database of heterogeneous gene expression data based on a consistent gene nomenclature.

The main goal of CleanEx is to provide access to public gene expression data via unique gene names. A second objective is to represent heterogeneous expression data produced by different technologies in a way that facilitates joint analysis and cross-data set comparisons. A consistent and up-to-date gene nomenclature is achieved by associating each single experiment with a permanent target identifier consisting of a physical description of the targeted RNA population or the hybridization reagent used. These targets are then mapped at regular intervals to the growing and evolving catalogues of human genes and genes from model organisms. The completely automatic mapping procedure relies partly on external genome information resources such as UniGene and RefSeq. The central part of CleanEx is a weekly built gene index containing cross-references to all public expression data already incorporated into the system. In addition, the expression target database of CleanEx provides gene mapping and quality control information for various types of experimental resource, such as cDNA clones or Affymetrix probe sets. The web-based query interfaces offer access to individual entries via text string searches or quantitative expression criteria. CleanEx is accessible at: http://www.cleanex.isb-sib.ch/.

Browsing a component library using Non-functional Information

This paper highlights the role of non-functional information when reusing from a component library. We describe a method for selecting appropriate implementations of Ada packages taking non-functional constraints into account; these constraints model the context of reuse. Constraints take the form of queries using an interface description language called NoFun, which is also used to state non-functional information in Ada packages; query results are trees of implementations, following the import relationships between components. We define two different situations when reusing components, depending whether we take the library being searched as closed or extendible. The resulting tree of implementations can be manipulated by the user to solve ambiguities, to state default behaviours, and by the like. As part of the proposal, we face the problem of computing from code the non-functional information that determines the selection process.

Yield and performance of sugarcane in on-farm interface with rubber in Brazil

Agroforestry systems are indicated as an alternative for sugarcane (Saccharum officinarum) cultivation in Piracicaba, SP, Brazil, however there are not many field experiments on plant performance under these conditions in the world. The objective of this work was to assess crop yield and partitioning in a sugarcane-rubber (Hevea brasiliensis) interface in on-farm conditions. The availability of irradiance for the crop along the interface was simulated and its effe ct over sugarcane dry matter production was tested. Crop yield was negatively affected by distance of the trees, but development and sucrose were not affected. Above ground dry matter increased from 16.6 to 51.5 t ha-1 from trees. Partitioning did not have a defined standard, as harvest index increased from 0.85 to 0.93, but specific leaf area was not significant along the transect, ranging from 13.48 to 15.73 m² kg-1. Light is the main factor of competition between the trees and the crop, but the relative importance of below ground interactions increases closer to the trees. Feasibility of the system depends on maturity of the trees and management strategies.

Remote collaborative multi-user informal learning experiences: design and evaluation

This paper presents a customizable system used to develop a collaborative multi-user problem solving game. It addresses the increasing demand for appealing informal learning experiences in museum-like settings. The system facilitates remote collaboration by allowing groups of learners tocommunicate through a videoconferencing system and by allowing them to simultaneously interact through a shared multi-touch interactive surface. A user study with 20 user groups indicates that the game facilitates collaboration between local and remote groups of learners. The videoconference and multitouch surface acted as communication channels, attracted students’ interest, facilitated engagement, and promoted inter- and intra-group collaboration—favoring intra-group collaboration. Our findings suggest that augmentingvideoconferencing systems with a shared multitouch space offers newpossibilities and scenarios for remote collaborative environments and collaborative learning.

Direct numerical simulations of interface dynamics to link capillary pressure and total surface energy

The flow of two immiscible fluids through a porous medium depends on the complex interplay between gravity, capillarity, and viscous forces. The interaction between these forces and the geometry of the medium gives rise to a variety of complex flow regimes that are difficult to describe using continuum models. Although a number of pore-scale models have been employed, a careful investigation of the macroscopic effects of pore-scale processes requires methods based on conservation principles in order to reduce the number of modeling assumptions. In this work we perform direct numerical simulations of drainage by solving Navier-Stokes equations in the pore space and employing the Volume Of Fluid (VOF) method to track the evolution of the fluid-fluid interface. After demonstrating that the method is able to deal with large viscosity contrasts and model the transition from stable flow to viscous fingering, we focus on the macroscopic capillary pressure and we compare different definitions of this quantity under quasi-static and dynamic conditions. We show that the difference between the intrinsic phase-average pressures, which is commonly used as definition of Darcy-scale capillary pressure, is subject to several limitations and it is not accurate in presence of viscous effects or trapping. In contrast, a definition based on the variation of the total surface energy provides an accurate estimate of the macroscopic capillary pressure. This definition, which links the capillary pressure to its physical origin, allows a better separation of viscous effects and does not depend on the presence of trapped fluid clusters.

Autonomous Measurements of Bridge Pier and Abutment Scour using Motion-Sensing Radio Transmitters, TR-595, 2010

Two portable Radio Frequency IDentification (RFID) systems (made by Texas Instruments and HiTAG) were developed and tested for bridge scour monitoring by the Department of Civil and Environmental Engineering at the University of Iowa (UI). Both systems consist of three similar components: 1) a passive cylindrical transponder of 2.2 cm in length (derived from transmitter/responder); 2) a low frequency reader (~134.2 kHz frequency); and 3) an antenna (of rectangular or hexagonal loop). The Texas Instruments system can only read one smart particle per time, while the HiTAG system was successfully modified here at UI by adding the anti-collision feature. The HiTAG system was equipped with four antennas and could simultaneously detect 1,000s of smart particles located in a close proximity. A computer code was written in C++ at the UI for the HiTAG system to allow simultaneous, multiple readouts of smart particles under different flow conditions. The code is written for the Windows XP operational system which has a user-friendly windows interface that provides detailed information regarding the smart particle that includes: identification number, location (orientation in x,y,z), and the instance the particle was detected.. These systems were examined within the context of this innovative research in order to identify the best suited RFID system for performing autonomous bridge scour monitoring. A comprehensive laboratory study that included 142 experimental runs and limited field testing was performed to test the code and determine the performance of each system in terms of transponder orientation, transponder housing material, maximum antenna-transponder detection distance, minimum inter-particle distance and antenna sweep angle. The two RFID systems capabilities to predict scour depth were also examined using pier models. The findings can be summarized as follows: 1) The first system (Texas Instruments) read one smart particle per time, and its effective read range was about 3ft (~1m). The second system (HiTAG) had similar detection ranges but permitted the addition of an anti-collision system to facilitate the simultaneous identification of multiple smart particles (transponders placed into marbles). Therefore, it was sought that the HiTAG system, with the anti-collision feature (or a system with similar features), would be preferable when compared to a single-read-out system for bridge scour monitoring, as the former could provide repetitive readings at multiple locations, which could help in predicting the scour-hole bathymetry along with maximum scour depth. 2) The HiTAG system provided reliable measures of the scour depth (z-direction) and the locations of the smart particles on the x-y plane within a distance of about 3ft (~1m) from the 4 antennas. A Multiplexer HTM4-I allowed the simultaneous use of four antennas for the HiTAG system. The four Hexagonal Loop antennas permitted the complete identification of the smart particles in an x, y, z orthogonal system as function of time. The HiTAG system can be also used to measure the rate of sediment movement (in kg/s or tones/hr). 3) The maximum detection distance of the antenna did not change significantly for the buried particles compared to the particles tested in the air. Thus, the low frequency RFID systems (~134.2 kHz) are appropriate for monitoring bridge scour because their waves can penetrate water and sand bodies without significant loss of their signal strength. 4) The pier model experiments in a flume with first RFID system showed that the system was able to successfully predict the maximum scour depth when the system was used with a single particle in the vicinity of pier model where scour-hole was expected. The pier model experiments with the second RFID system, performed in a sandbox, showed that system was able to successfully predict the maximum scour depth when two scour balls were used in the vicinity of the pier model where scour-hole was developed. 5) The preliminary field experiments with the second RFID system, at the Raccoon River, IA near the Railroad Bridge (located upstream of 360th street Bridge, near Booneville), showed that the RFID technology is transferable to the field. A practical method would be developed for facilitating the placement of the smart particles within the river bed. This method needs to be straightforward for the Department of Transportation (DOT) and county road working crews so it can be easily implemented at different locations. 6) Since the inception of this project, further research showed that there is significant progress in RFID technology. This includes the availability of waterproof RFID systems with passive or active transponders of detection ranges up to 60 ft (~20 m) within the water–sediment column. These systems do have anti-collision and can facilitate up to 8 powerful antennas which can significantly increase the detection range. Such systems need to be further considered and modified for performing automatic bridge scour monitoring. The knowledge gained from the two systems, including the software, needs to be adapted to the new systems.

Iowa Bridge Backwater Software User Manual, Version 2.0, TR-564, 2010

This manual describes how to use the Iowa Bridge Backwater software. It also documents the methods and equations used for the calculations. The main body describes how to use the software and the appendices cover technical aspects. The Bridge Backwater software performs 5 main tasks: Design Discharge Estimation; Stream Rating Curves; Floodway Encroachment; Bridge Backwater; and Bridge Scour. The intent of this program is to provide a simplified method for analysis of bridge backwater for rural structures located in areas with low flood damage potential. The software is written in Microsoft Visual Basic 6.0. It will run under Windows 95 or newer versions (i.e. Windows 98, NT, 2000, XP and later).

The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

A Framework for Building Hypercubes Using MapReduce

The European Space Agency's Gaia mission will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), by providing unprecedented position, parallax, proper motion, and radial velocity measurements for about one billion stars. The resulting catalogue will be made available to the scientific community and will be analyzed in many different ways, including the production of a variety of statistics. The latter will often entail the generation of multidimensional histograms and hypercubes as part of the precomputed statistics for each data release, or for scientific analysis involving either the final data products or the raw data coming from the satellite instruments. In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigmbut without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission. In addition, we put forward the advantages and shortcomings of the deployment of the framework on a public cloud provider, benchmark against other popular solutions available (that are not always the best for such ad-hoc applications), and describe some user experiences with the framework, which was employed for a number of dedicated astronomical data analysis techniques workshops.