Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Mineral zoning and geochemistry of epithermal polymetallic Zn-Pb-Ag-Cu-Bi mineralization at Cerro de Pasco, Peru

The large Cerro de Pasco Cordilleran base metal deposit in central Peru is located on the eastern margin of a middle Miocene diatreme-dome complex and comprises two mineralization stages. The first stage consists of a large pyrite-quartz body replacing Lower Mesozoic Pucara carbonate rocks and, to a lesser extent, diatreme breccia. This body is composed of pyrite with pyrrhotite inclusions, quartz, and black and red chalcedony (containing hypogene hematite). At the contact with the pyrite-quartz body, the diatreme breccia is altered to pyrite-quartz-sericite-pyrite. This body was, in part, replaced by pipelike pyrrhotite bodies zoned outward to carbonate-replacement Zn-Pb ores hearing Fe-rich sphalerite (up to 24 mol % Fes). The second mineralization stage is partly superimposed on the first and consists of zoned east-west-trending Cu-Ag-(Au-Zn-Pb) enargite-pyrite veins hosted in the diatreme breccia in the western part of the deposit and well-zoned Zn-Pb-(Bi-Ag-Cu) carbonate-replacement orebodies; in both cases, sphalerite is Fe poor and the inner parts of the orebodies show typically advanced argillic alteration assemblages, including aluminum phosphate Sulfate (APS) minerals. The zoned enargite-pyrite veins display mineral zoning, from a core of enargite-pyrite +/- alunite with traces of Au, through an intermediate zone of tennantite, chalcopyrite, and Bi minerals to a poorly developed Outer zone hearing sphalerite-galena +/- kaolinite. The carbonate-hosted replacement ores are controlled along N 35 degrees E, N 90 degrees E, N 120 degrees E, and N 170 degrees E faults. They form well-zoned upward-flaring pipelike orebodies with a core of famatinite-pyrite and alunite, an intermediate zone with tetrahedrite-pyrite, chalcopyrite, matildite, cuprobismutite, emplectite, and other Bi minerals accompanied by APS minerals, kaolinite, and dickite, and an outer zone composed of Fe-poor sphalerite (in the range of 0.05-3.5 mol % Fes) and galena. The outermost zone consists of hematite, magnetite, and Fe-Mn-Zn-Ca-Mg carbonates. Most of the second-stage carbonate-replacement orebodies plunge between 25 degrees and 60 degrees to the west, suggesting that the hydrothermal fluids ascended from deeper levels and that no lateral feeding from the veins to the carbonate-replacement orebodies took place. In the Venencocha and Santa Rosa areas, located 2.5 km northwest of the Cerro de Pasco open pit and in the southern part of the deposit, respectively, advanced argillic altered dacitic domes and oxidized veins with advanced argillic alteration halos occur. The latter veins are possibly the oxidized equivalent of the second-stage enargite-pyrite veins located in the western part of the deposit. The alteration assemblage quartz-muscovite-pyrite associated with the pyrite-quartz body suggests that the first stage precipitated at slightly, acidic fin. The sulfide mineral assemblages define an evolutionary path close to the pyrite-pyrrhotite boundary and are characteristic of low-sulfidation states; they suggest that the oxidizing slightly acidic hydrothermal fluid was buffered by phyllite, shale, and carbonate host rock. However, the presence in the pyrite-quartz body of hematite within quartz suggests that, locally, the fluids were less buffered by the host rock. The mineral assemblages of the second mineralization stage are characteristic of high- to intermediate-sulfidation states. High-sulfidation states and oxidizing conditions were achieved and maintained in the cores of the second-stage orebodies, even in those replacing carbonate rocks. The observation that, in places, second-stage mineral assemblages are found in the inner and outer zones is explained in terms of the hydrothermal fluid advancing and waning. Microthermometric data from fluid inclusions in quartz indicate that the different ores of the first mineralization stage formed at similar temperatures and moderate salinities (200 degrees-275 degrees C and 0.2-6.8 wt % NaCl equiv in the pyrite-quartz body; 192 degrees-250 degrees C and 1.1-4.3 wt % NaCl equiv in the pyrrhotite bodies; and 183 degrees-212 degrees C and 3.2-4.0 wt % NaCl equiv in the Zn-Pb ores). These values are similar to those obtained for fluid inclusions in quartz and sphalerite from the second-stage ores (187 degrees-293 degrees C and 0.2-5.2 wt % NaCl equiv in the enargite-pyrite veins: 178 degrees-265 degrees C and 0.2-7.5 wt % NaCl equiv in quartz of carbonate-replacement orebodies; 168 degrees-999 degrees C and 3-11.8 wt % NaCl equiv in sphalerite of carbonate-replacement orebodies; and 245 degrees-261 degrees C and 3.2-7.7 wt % NaCl equiv in quartz from Venencocha). Oxygen and hydrogen isotope compositions oil kaolinite from carbonate-replacement orebodies (delta(18)O = 5.3-11.5%o, delta D = -82 to -114%o) and on alunite from the Venencocha and Santa Rosa areas (delta(18)O = 1.9-6.9%o, delta D = -56 to -73%o). Oxygen isotope compositions of quartz from the first and second stages have 6180 values from 9.1 to 1.7.8 per mil. Calculated fluids in equilibrium with kaolinite have delta(18)O values of 2.0 to 8.2 and delta D values of -69 to -97 per mil; values in equilibrium with alunite are -1.4 to -6.4 and -62 to -79 per mil. Sulfur isotope compositions of sulfides from both stages have a narrow range of delta(34)S values, between -3.7 and +4.2 per mil; values for sulfates from the second stage are between 4.2 and 31.2 per mil. These results define two mixing trends for the ore-forming fluids. The first trend reflects mixing between a moderately saline (similar to 10 wt % NaCl equiv) magmatic end member that had degassed (as indicated by the low delta D values) and meteoric water. The second mixing indicates condensation of magmatic vapor with HCl and SO(2) into meteoric water, which formed alunite. The hydrothermal system at Cerro de Pasco was emplaced at a shallow depth (similar to 500 m) in the epithermal and upper part of a porphyry environment. The similar temperatures and salinities obtained for the first stage and second stages, together with the stable isotope data, indicate that both stages are linked and represent successive stages of epithermal polymetallic mineralization in the upper part of a porphyry system.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Multivariable regression analysis and hot-spot stress approach into fatigue life estimations of welded joints

Kolmen eri hitsausliitoksen väsymisikä arvio on analysoitu monimuuttuja regressio analyysin avulla. Regression perustana on laaja S-N tietokanta joka on kerätty kirjallisuudesta. Tarkastellut liitokset ovat tasalevy liitos, krusiformi liitos ja pitkittäisripa levyssä. Muuttujina ovat jännitysvaihtelu, kuormitetun levyn paksuus ja kuormitus tapa. Paksuus effekti on käsitelty uudelleen kaikkia kolmea liitosta ajatellen. Uudelleen käsittelyn avulla on varmistettu paksuus effektin olemassa olo ennen monimuuttuja regressioon siirtymistä. Lineaariset väsymisikä yhtalöt on ajettu kolmelle hitsausliitokselle ottaen huomioon kuormitetun levyn paksuus sekä kuormitus tapa. Väsymisikä yhtalöitä on verrattu ja keskusteltu testitulosten valossa, jotka on kerätty kirjallisuudesta. Neljä tutkimustaon tehty kerättyjen väsymistestien joukosta ja erilaisia väsymisikä arvio metodeja on käytetty väsymisiän arviointiin. Tuloksia on tarkasteltu ja niistä keskusteltu oikeiden testien valossa. Tutkimuksissa on katsottu 2mm ja 6mm symmetristäpitkittäisripaa levyssä, 12.7mm epäsymmetristä pitkittäisripaa, 38mm symmetristä pitkittäisripaa vääntökuormituksessa ja 25mm/38mm kuorman kantavaa krusiformi liitosta vääntökuormituksessa. Mallinnus on tehty niin lähelle testi liitosta kuin mahdollista. Väsymisikä arviointi metodit sisältävät hot-spot metodin jossa hot-spot jännitys on laskettu kahta lineaarista ja epälineaarista ekstrapolointiakäyttäen sekä paksuuden läpi integrointia käyttäen. Lovijännitys ja murtumismekaniikka metodeja on käytetty krusiformi liitosta laskiessa.

Paediatric bi-ventricular external assist device based on artificial muscles

Objective: Existing VADs are single-ventricle pumps needing anticoagulation. We developed a bi ventricular external assist device that reproduces the physiological heart muscle movement completely avoiding anticoagulants. Methods: The device has a carbon fibre skeleton fitting a 30-40 kg patient's heart, to which a Nitinol based artificial muscle is connected. The artificial muscle wraps both ventricles. The strength of the Nitinol fibres is amplified by a pivot articulation in contact with the ventricle wall. The fibres are electrically driven and a dedicated control unit has been developed. We assessed hemodynamic performances of this device using a previously described dedicated bench test. Volume ejected and pressure gradient has been measured with afterload ranging from 25 to 50mmHg. Results: With anafterload of 50mmHg the system has an ejection fraction (EF) of 10% on the right side and 8% on the left side. The system is able to generate a systolic ejection of 5,5 ml on the right side and 4,4 ml on the left side. With anafterload of 25mmHg the results are reduced of about 20%. The activation frequency is 80/minute resulting in a total volume displacement of 440 ml/minute on the right side and 352 ml/minute on the left side. Conclusions: The artificial muscle follows Starling's law as the ejected volume increases when afterload increases. These preliminary studies confirmed the possibility of improving the EF of a failing heart using artificial muscle for external cardiac compression. This device could be helpful in weaning CPB and/or for short-term cardio-circulatory support in paediatric population with cardiac failure.

StopFlu: Sistema BI d'anàlisi predictiu contra la grip

Vivim, cada cop més, en un món tecnològic, on la vida diària es comparteix a les xarxes socials quasi sense adonar-nos-en. En aquest context, es generen quantitats ingents d'informació que, un cop tractades, poden ésser útils en estudis ben diversos com són la detecció de terratrèmols o la detecció prematura d'una epidèmia. En relació a aquest últim, el virus de la grip és un greu problema de salut pública ja que es destinen part dels recursos sanitaris durant un període de temps considerable i disminueix la productivitat laboral dels afectats que la pateixen. Davant d'aquesta situació, es planteja la realització d'un sistema de Business Intelligence que analitzi les dades extretes dels tweets de la plataforma Twitter en relació a les hospitalitzacions produïdes a un hospital de Catalunya, per tal de tenir un anàlisi predictiu de l'aparició d'un brot d'aquestes característiques. El treball va més enllà al emprar una tecnologia no convencional per la implementació del sistema BI. S'escull la dupla Elasticsearch i Kibana per tal d'aconseguir un sistema robust, distribuït, escalable i, sobretot, totalment personalitzable. Després d'un estudi d'aquestes dos solucions, incloent els plugins de monitoratge i càrrega de dades, s'ha elaborat un data warehouse complet i un quadre de comandament introductori. Es deixa, per futures línies de treball, l'anàlisi profund de les dades i la conseqüent extracció d'uns resultats que ens ajudin a predir amb una major antelació l'aparició d'un nou brot del virus de la grip.

StopFlu : Sistema BI d'anàlisi predictiva contra la grip

Realització d'un sistema de Business Intelligence que analitzi les dades extretes dels tweets de la plataforma Twitter en relació a les hospitalitzacions produïdes a un hospital de Catalunya, per tal de tenir una anàlisi predictiva de l'aparició d'un brot de grip. El treball va més enllà a l'emprar una tecnologia no convencional per la implementació del sistema BI. S'escull la dupla ElasticSearch i Kibana per tal d'aconseguir un sistema robust, distribuït, escalable i, sobretot, totalment personalitzable. Després d'un estudi d'aquestes dos solucions, incloent els plugins de monitoratge i càrrega de dades, s'ha elaborat un data warehouse complet i un quadre de comandament introductori.

Cultural Biases in Public Service Delivery : Evidence from a Regression Discontinuity Approach

What determines the share of public employment, at a given size of the State, in countries of similar levels of economic development? While the theoretical and empirical literature on this issue has mostly considered technical dimensions (efficiency and political considerations), this paper emphasizes the role of culture and quantifies it. We build a representative database for contracting choices of municipalities in Switzerland and exploit the discontinuity at the Swiss language border at identical actual set of policies and institutions to analyze the causal e↵ect of culture on the choice of how public services are provided. We find that French-speaking border municipalities are 50% less likely to contract with the private sector than their German-speaking adjacent municipalities. Technical dimensions are much smaller by comparison. This result points out that culture is a source of a potential bias that distorts the optimal choice for public service delivery. Systematic differences in the level of confidence in public administration and private companies potentially explain this discrepancy in private sector participation in public services provision.