Regional income inequality in Italy in the long run (1871–2001). Patterns and determinants

The chapter presents up-to-date estimates of Italy’s regional GDP, with the present borders, in ten-year benchmarks from 1871 to 2001, and proposes a new interpretative hypothesis based on long-lasting socio-institutional differences. The inverted U-shape of income inequality is confirmed: rising divergence until the midtwentieth century, then convergence. However, the latter was limited to the centrenorth: Italy was divided into three parts by the time regional inequality peaked, in 1951, and appears to have been split into two halves by 2001. As a consequence of the falling back of the south, from 1871 to 2001 we record σ-divergence across Italy’s regions, i.e. an increase in dispersion, and sluggish β-convergence. Geographical factors and the market size played a minor role: against them are both the evidence that most of the differences in GDP are due to employment rather than to productivity and the observed GDP patterns of many regions. The gradual converging of regional GDPs towards two equilibria instead follows social and institutional differences − in the political and economic institutions and in the levels of human and social capital – which originated in pre-unification states and did not die (but in part even increased) in postunification Italy.

Let's K-Map Play: Juego de aprendizaje de Karnagh Maps para móvil

En este informe se describe el trabajo de fin de máster, centrado en el estudio de la gamificación como herramienta de aprendizaje aplicada a dispositivos móviles. Se ha realizado una revisión de los artículos científicos que tratan sobre el tema de la gamificación como herramienta educativa,para terminar el trabajo desarrollando un prototipo de juego para el aprendizaje de mapas de Karnaugh. Se ha optado por un desarrollo multiplataforma y se han revisado los frameworks de desarrollo más populares para desarrollo móvil multiplataforma así como los motores de juegos aplicables a este caso. Tras la implementación, se ha probado el prototipo en dos sistemas operativos móviles libres: Android y Firefox OS.

Global periodicity conditions for maps and recurrences via Normal Forms

We face the problem of characterizing the periodic cases in parametric families of (real or complex) rational diffeomorphisms having a fixed point. Our approach relies on the Normal Form Theory, to obtain necessary conditions for the existence of a formal linearization of the map, and on the introduction of a suitable rational parametrization of the parameters of the family. Using these tools we can find a finite set of values p for which the map can be p-periodic, reducing the problem of finding the parameters for which the periodic cases appear to simple computations. We apply our results to several two and three dimensional classes of polynomial or rational maps. In particular we find the global periodic cases for several Lyness type recurrences

Periodic orbits of integrable birational maps on the plane: blending dynamics and algebraic geometry, the Lyness' case

Contingut del Pòster presentat al congrés New Trends in Dynamical Systems

Tarracomap: Development of an archaeological application on Google Maps navigation System

This paper describes the result of a research about diverse areas of the information technology world applied to cartography. Its final result is a complete and custom geographic information web system, designed and implemented to manage archaeological information of the city of Tarragona. The goal of the platform is to show on a web-focused application geographical and alphanumerical data and to provide concrete queries to explorate this. Various tools, between others, have been used: the PostgreSQL database management system in conjunction with its geographical extension PostGIS, the geographic server GeoServer, the GeoWebCache tile caching, the maps viewer and maps and satellite imagery from Google Maps, locations imagery from Google Street View, and other open source libraries. The technology has been chosen from an investigation of the requirements of the project, and has taken great part of its development. Except from the Google Maps tools which are not open source but are free, all design has been implemented with open source and free tools.

Let's K-map play : Juego de aprendizaje de Karnaugh Maps para móvil

En este informe se describe el trabajo de fin de máster, centrado en el estudio de la gamificación como herramienta de aprendizaje aplicada a dispositivos móviles. Se ha realizado una revisión de los artículos científicos que tratan sobre el tema de la gamificación como herramienta educativa, para terminar el trabajo desarrollando un prototipo de juego para el aprendizaje de mapas de Karnaugh. Se ha optado por un desarrollo multiplataforma y se han revisado los frameworks de desarrollo más populares para desarrollo móvil multiplataforma, así como los motores de juegos aplicables a este caso. Tras la implementación, se ha probado el prototipo en dos sistemas operativos móviles libres: Android y Firefox OS.

Fluid geochemistry of the Acqui Terme-Visone geothermal area (Piemonte, Italy)

The main geothermal reservoir of Acqui Terme-Visone hosts Na-Cl waters, which are in chemical equilibrium at 120-130 degrees C with typical hydrothermal minerals including quartz, albite, K-feldspar, illite, chlorite (or smectite), anhydrite, calcite and an unspecified Ca-Al-silicate. In the Acqui Terme-Visone area, these geothermal waters ascend along zones of high vertical permeability and discharge at the surface almost undiluted or mixed with cold, shallow waters. To the SW of Acqui Terme, other ascending geothermal waters, either undiluted or mixed with low-salinity waters, enter relatively shallow secondary reservoirs, where they reequilibrate at 65-70 degrees C. Both chemical and isotopic data indicate that bacterial SO4 reduction affects all these waters, especially those discharged by the secondary reservoirs. Therefore, geothermal waters must get in contact with oil, acquiring the relatively oxidized organic substances needed by SO4-reducing bacteria. This oil-water interaction process deserves further investigations, for potential economic implications. (C) 2000 Elsevier Science Ltd. All rights reserved.

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.

On the preservation of combinatorial types for maps on trees

We study the preservation of the periodic orbits of an A-monotone tree map f:T→T in the class of all tree maps g:S→S having a cycle with the same pattern as A. We prove that there is a period-preserving injective map from the set of (almost all) periodic orbits of ƒ into the set of periodic orbits of each map in the class. Moreover, the relative positions of the corresponding orbits in the trees T and S (which need not be homeomorphic) are essentially preserved

Sierpinski curve Julia sets for quadratic rational maps

We investigate under which dynamical conditions the Julia set of a quadratic rational map is a Sierpiński curve.

Crystal size distribution of periclase in contact metamorphic dolomite marbles from the southern Adamello Massif, Italy

Crystal size distributions (CSD) of periclase in contact metamorphic dolomite marbles are presented for two profiles near the Cima Uzza summit in the southern Adamello Massif (Italy). The database was combined with geochemical and petrological information to deduce the controls on the periclase-forming reaction. The contact metamorphic dolomite marbles are exposed at the contact of mafic intrusive rocks and are partially surrounded by them. Brucite is retrograde and pseudomorphs spherical periclase crystals. Prograde periclase growth is the consequence of limited infiltration of water-rich fluid at T near 605C. Stable isotope data show depletion in (13)C and (18)O over a narrow region (40 cm) near the magmatic contact, whereas the periclase-forming reaction front extends up to 4 m from the contact. CSD analyses along the two profiles show that the median grain size of the periclase crystals does not change, but that there is a progressively greater distribution of grain sizes, including a greater proportion of larger grains, with increasing distance from the contact. A qualitative model, based on the textural and geochemical data, attributes these variations in grain size to changing reaction affinities along a kinetically dispersed infiltration front. This study highlights the need to invoke disequilibrium processes for metamorphic mineral growth and expands the use of CSDs to systems of mineral formation driven by fluid infiltration.

The thermal circuits of the Argentera Massif (western Alps, Italy): An example of low-enthalpy geothermal resources controlled by Neogene alpine tectonics

This work presents geochemistry and structural geology data concerning the low enthalpy geothermal circuits of the Argentera crystalline Massif in northwestern Italian Alps. I n this area some thermal springs (50-60 degreesC), located in the small Bagni di Vinadio village, discharge mixtures made up of a Na-Cl end-member and a Na-SO4 component. The latter is also discharged by the thermal springs of Terme di Valdieri located some kilometres apart within the same tectonic complex. Both end-members share the same meteoric origin and the same reservoir temperature, which is close to 150 degreesC. Explanations are thus required to understand how they reach the surface and how waters of the same origin and circulating in similar rocks can attain such different compositions. Sodium-sulphate waters discharged at both sites, likely represent the common interaction product of meteoric waters with the widespread granitic-migmatitic rocks of the Argentera Massif, whereas Na-CI waters originate through leaching of mineralised cataclastic rocks, which are rich in phyllosilicatic minerals and fluid inclusions, both acting as Cl- sources. Due to the relatively low inferred geothermal gradient of the region, -25C/km, meteoric waters have to descend to depths of 5.5-6 km to attain temperatures of similar to 150 degreesC. These relevant depths can be reached by descending meteoric waters, due to the recent extensional stress field, which allows the development of geothermal circulations at greater depths than in other sectors of the Alps by favouring a greater fractures aperture. The ascent of the thermal waters rakes place along brittle shear zones. In both sites, the thermal waters emerge at the bottoms of the valleys, close to either the lateral termination of a brittle shear zone at Terme di Valdieri, or a step-over between two en-echelon brittle shear zones at Bagni di Vinadio. These observations attest to a strong control operated on the location of outlet regions by both brittle tectonics and the minima in hydraulic potential inside the fractured massif.

Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?

We propose a multivariate approach to the study of geographic species distribution which does not require absence data. Building on Hutchinson's concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in Situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.