Gestor d'expedients per l'Institut Municipal d'Hisenda

El projecte recull el treball portat a càrrec per l’anàlisi, disseny i implementació d’una eina per l’Institut Municipal D’Hisenda de l’Ajuntament de Barcelona que compleixi les necessitats d’un sistema d’informació capaç de gestionar els expedients que genera una sèrie de tributs, les sancions que comporten així com la documentació necessària per la comunicació amb el ciutadà. Per realitzar l’aplicació s’han utilitzat tecnologies que ens permeten treballar en l’entorn web, un nucli programat en llenguatge Java sobre la plataforma MVC de Struts, tot sobre un servidor d’aplicacions WebSphere i un motor de base de dades Oracle.

Niche conservatism in non-native birds in Europe: niche unfilling rather than niche expansion

Aim Niche conservatism, or the extent to which niches are conserved across space and time, is of special concern for the study of non-native species as it underlies predictions of invasion risk. Based on the occurrence of 28 non-native birds in Europe, we assess to what extent Grinnellian realized niches are conserved during invasion, formulate hypotheses to explain the variation in observed niche changes and test how well species distribution models can predict non-native bird occurrence in Europe. Location Europe. Methods To quantify niche changes, a recent method that applies kernel smoothers to densities of species occurrence in a gridded environmental space was used. This corrects for differences in the availability of environments between study areas and allows discrimination between 'niche expansion' into environments new to the species and 'niche unfilling', whereby the species only partially fills its niche in the invaded range. Predictions of non-native bird distribution in Europe were generated using several distribution modelling techniques. Results Niche overlap between native and non-native bird populations is low, but niche changes are smaller for species having a higher propagule pressure and that were introduced longer ago. Non-native birds in Europe occupy a subset of the environments they inhabit in their native ranges. Niche expansion into novel environments is rare for most species, allowing species distribution models to accurately predict invasion risk. Main conclusions Because of the recent nature of most bird introductions, species occupy only part of the suitable environments available in the invaded range. This signals that apart from purely ecological factors, patterns of niche conservatism may also be contingent on population-specific historical factors. These results also suggest that many claims of niche differences may be due to a partial filling of the native niche in the invaded range and thus do not represent true niche changes.

Agronomic and commercial performance of four Spanish carob cultivars

The carob tree (Ceratonia siliqua) shows interesting prospects for some coastal Mediterranean growing areas and is widely used for industrial, agricultural, and ornamental purposes. It can be an alternative crop adapted to part-time farming and can also be used to regenerate vegetation in areas with a mild climate and erosion problems. Four Spanish carob cultivars were examined (Banya de Cabra, Duraio, Matalafera, and Rojal) to determine the one that performed the best for planting new orchards in northeastern Spain (Catalonia). The trees in this rain-fed trial (average rainfall of 500 mm) were planted in 1986 using seedling rootstocks that were budded in 1987. The trees were trained using the free-vase system and were spaced 8 x 9 m (138 trees/ha including 12% pollinators). The results showed that ‘Rojal’ was the earliest bearing cultivar. However, no significant differences were observed for cumulative pod production 18 years after budding. With respect to cumulative seed yield, ‘Duraio’ had the highest production (95 kg/tree). The lowest tree vigor (trunk cross-section) was observed in ‘Matalafera’. ‘Rojal’ trees produced the largest pods (average fruit weight of 18.9 g) and lowest seed content (11.8%), while ‘Banya de Cabra’ and ‘Duraio’ produced the smallest fruit (weighing 15.3 and 16.2 g, respectively) with the highest seed content (15.2% and 17.3%, respectively). Gum content, expressed as a percentage of the dry weight, was highest in ‘Duraio’ (56.9%) and was lowest in ‘Rojal’ (54.1%). Thus, in terms of kernel and pod production, ‘Duraio’ appeared to be the best-performing female cultivar for planting new carob orchards

Avantpassats de plata

Treball de recerca realitzat per una alumna d'ensenyament secundari i guardonat amb un Premi CIRIT per fomentar l'esperit científic del Jovent l'any 2009. El treball es centra en conèixer la complexitat d’un estudi fotogràfic del s. XIX: l’estudi Napoleón. Per entendre tots els aspectes que implicava fer una fotografia en aquest estudi comença explicant com es van desenvolupar i descobrir les diferents tècniques fotogràfiques, després presenta l’estat de la fotografia a la Catalunya del s. XIX. El nucli del treball té diferents aspectes: per una banda s’investiga la història dels fundadors d’un dels estudis més importants a la Barcelona del s. XIX, per l’altra presenta com eren les sales, els decorats, els clients, la tipografia, les càmeres .... i per últim, porta a la pràctica tot allò necessari per a transformar un paper blanc en una fotografia fent servir els mètodes de l’època. Podríem dir que el treball es desenvolupa en tres àmbits: el primer sobre els fonaments tècnics i històrics de la fotografia, les fonts utilitzades per realitzar aquest apartat han estat fonamentalment bibliogràfiques; el segon fa referència a l’estudi fotogràfic dels Napoleón, en aquest cas, a part de les fonts bibliogràfiques, també ha estat de vital importància la informació aportada per un descendent de la família i finalment s’explica els procediments que es van fer servir per obtenir imatges durant el segle s.XIX i les reaccions químiques en les quals es fonamenten. Aporta també una part experimental que dóna un caire artístic i novedós al treball.

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

Thevetia peruviana (Family: Apocynaceae) in the control of slug and snail pests

An aqueous extract prepared from Kernels of the fruit of Thevetia peruviana (Pers.) Schumann (Family : Apocynaceae) was found under experimental conditions, to be toxic ti the slug Laevicaulis alte (Férussac) and the snail Achatina fulica Bowdich, the important agrihorticultural pests of Indo-Pacific countries. Concentrations as low as 1% (w/v) killed all the slugs exposed in less than 981.00 (± SD 22.76) min, and 2% of the extract killed 100% of the slugs L. alte and 50%, 50% and 30% of the snail A. fulica in between 92.34 (± SD 6.63) - 321.33 (± SD 4.14) and 271.20 (± SD 17.54) - 298.26 (± SD 16.69) min respectively. The most effective concentration of the extract was 20%; it killed 100% of exposed slugs and snails within a short time (40-50 and 90-1440 min respectively) when the extract was exposed on the soil in experimental trays or when it was applied to potato slices offered as food to the gastropods.

Tritordeum, wheat and triticale yield components under multi-local mediterranean drought conditions

The species x location interaction was of great importance in explaining the behaviour of genetic material. The study presented here shows, for the first time, the performance, under field conditions of the new tritordeum species, compared to wheat and triticale in a wide range of Mediterranean countries (Spain, Lebanon and Tunisia). The results obtained revealed that despite the diversity of environmental conditions, the main differences in yield were due to genotypes, especially to differences between species. The multi-local study with different growth conditions revealed important information about the water availability effect on yield. In the lowest yielding environments (Tunisia rainfed), Tritordeum and triticale yields were equivalent. However under better growth conditions (Spain), tritordeum yield was shown to be lower than wheat and triticale. Interestingly, when water limitation was extended during the pre-anthesis period, differences in tritordeum versus wheat-triticale yield rate were larger than when water stress occurred during anthesis. These variations were explained by the fact that kernel weight has been found as the limiting factor for yield determination in tritordeum, and a delay in the anthesis date may have been the cause for the low kernel weight and low yield under Mediterranean drought conditions. Such differences in yield between tritordeum and wheat or triticale could be explained by the fact that tritordeum is a relatively new species and far fewer resources have been devoted to its improvement when compared to wheat and triticale. Our results suggest that breeding efforts should be directed to an earlier anthesis date and a longer grain filling period. tritordeum proved to have possibilities to be grown under drought environments as a new crop, since its performance was quite close to wheat and triticale. Besides, it has qualitative added values that may improve farmers' income per unit land.

Desarrollo de software para el procesado numérico en tarjetas gráficas

Debido al gran número de transistores por mm2 que hoy en día podemos encontrar en las GPU convencionales, en los últimos años éstas se vienen utilizando para propósitos generales gracias a que ofrecen un mayor rendimiento para computación paralela. Este proyecto implementa el producto sparse matrix-vector sobre OpenCL. En los primeros capítulos hacemos una revisión de la base teórica necesaria para comprender el problema. Después veremos los fundamentos de OpenCL y del hardware sobre el que se ejecutarán las librerías desarrolladas. En el siguiente capítulo seguiremos con una descripción del código de los kernels y de su flujo de datos. Finalmente, el software es evaluado basándose en comparativas con la CPU.

Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Reduction theorems for operators on the cones of monotone functions

For a quasilinear operator on the semiaxis a reduction theorem is proved on the cones of monotone functions in Lp - Lq setting for 0 < q < ∞, 1<= p < ∞. The case 0 < p < 1 is also studied for operators with additional properties. In particular, we obtain critera for three-weight inequalities for the Hardy-type operators with Oinarov' kernel on monotone functions in the case 0 < q < p <= 1.