Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Spatial-temporal variation of dissolved inorganic material in the Amazon basin

The Amazon River basin is important in the contribution of dissolved material to oceans (4% worldwide). The aim of this work was to study the spatial and the temporal variability of dissolved inorganic materials in the main rivers of the Amazon basin. Data from 2003 to 2011 from six gauging stations of the ORE-HYBAM localized in Solimões, Purus, Madeira and Amazon rivers were used for this study. The concentrations of Ca2+, Na+, K+, Mg2+, Cl-, SO4 -2, HCO3 - and SiO2 were analyzed. At the stations of Solimões and Amazon rivers, the concentrations of Ca2+, Mg2+, HCO3 - and SO4 -2 had heterogeneous distribution over the years and did not show seasonality. At the stations of Madeira river, the concentration of these ions had seasonality inversely proportional to water discharge (dilution-concentration effect). Similar behavior was observed for the concentrations of Cl- and Na+ at the stations of the Solimões, Amazon and Madeira rivers, indicating almost constant release of Cl- and Na+ fluxes during the hydrological cycle. K+ and SiO2 showed almost constant concentrations throughout the years and all the stations, indicating that their flows depend on the river discharge variation. Therefore, the temporal variability of the dissolved inorganic material fluxes in the Solimões and Amazon rivers depends on the hydro-climatic factor and on the heterogeneity of the sources. In the Madeira and Purus rivers there is less influence of these factors, indicating that dissolved load fluxes are mainly associated to silicates weathering. As the Solimões basin contributes approximately 84% of the total flux of dissolved materials in the basin and is mainly under the influence of a hydro-climatic factor, we conclude that the temporal variability of this factor controls the temporal variability of the dissolved material fluxes of the Amazon basin.

Modelo de Gestion Intersectorial para la formalización de villas, a partir de la revisión de normativa urbana, marco jurídico-político, y la innovación tecnológica de los recursos. Intersectorial management model for the formality of the illegal growth settles beginning from the revision of the urban norm, political - juridical, and the technological innovation of the resources.

El Proyecto que se presenta en esta convocatoria está relacionado con el diseño y desarrollo de procesos de gestión intersectorial para la reconversión formal del hábitat en asentamientos de crecimiento espontáneo e ilegal a partir de la la revisión de marcos legales y normativos respecto del derecho al uso de suelo urbano y la seguridad jurídica en la tenencia (regulación dominial) bajo un enfoque sustentable de los Derechos Humanos (DDHH) como así también a partir de la innovación tecnológica de los recursos para el mejoramiento habitacional en el marco de los aportes relacionados con la Responsabilidad Social Empresarial (RSE) .La originalidad, en este caso, se basa en la exploración de nuevos procesos de gestión mixta en los que intervienen diversos actores: ESTADO, SOCIEDAD, EMPRESA y COMUNIDAD CIENTIFICA, configurando un circuito intersectorial de gestión, capacitación y producción de hábitat que respondan al fortalecimiento del desarrollo comunitario, entendido como desarrollo económico con inclusión social y urbana.Los objetivos están relacionados con el fortalecimiento de la producción integral del hábitat social procurando el desarrollo genuino de las comunidades a partir de la expansión de sus capacidades y sus derechos.El presente proyecto de investigación explorará la producción de hábitat y de ciudad informal procurando reconvertirla hacia un modelo de gestión sustentable (normativo y tecnológico), basado en la necesidad de producir ciudadanía digna a partir de la construcción formal de hábitat.La propuesta del proyecto supone la participación intersectorial en la planificación formal y en la toma de decisiones para una inclusión social y económica, en el marco de un modelo de desarrollo de génesis inclusor, constituyéndose en un avance de conocimientos del propio campo disciplinar (hábitat) como así también de otros campos afines a los mismos (jurídico- social - económico- productivo- político ).

Clinical and epidemiological aspects of human parvovirus B19 infection in an urban area in Brazil (Niterói city area, State of Rio de Janeiro, Brazil)

This study was designed to analyse the clinical and epidemiological data from human parvovirus B19 cases in a six-year study of rash diseases conduct in an urban area in Brazil (Niterói city area, State of Rio de Janeiro). A total of 673 patients with acute rash diseases were seen at two primary health care units and at a general hospital. A clotted blood sample was collected from all subjects at the time of consultation. Forty-nine per cent (330 cases) of the patients were negative for dengue, rubella and measles IgM or for low avidity IgG to HHV-6. Of these 330, 105 (31.8%) were identified as IgM positive to parvovirus B19 by using an antibody capture EIA. During the study period, three distinct peaks of parvovirus infection were detected, suggesting that the disease appears to cycle in approximately 4-5 years. B19 infection was characterized by variable combinations of fever, flu-like symptoms, arthropathy, and gastrointestinal symptoms. Frequency of fever and arthropathy was substantially higher in adults, 75% [chi2 (1 D.F.) = 11.39, p = 0.0007] and 62.5% [chi2 (1 D.F.) = 29.89, p = 0.0000], respectively. "Slapped-cheek" appearance and reticular or lace-like rash were seen in only 30.1% of the children. No adult presented this typical rash. The lack of the typical rash pattern in a large proportion of parvovirus B19 and the similarity of clinical manifestations to other rash diseases, specially to rubella, highlight the difficulty of diagnosing B19 infection on clinical grounds alone.

Accurate memory for object location by individuals with intellectual disability: Absolute spatial tagging instead of configural processing?

Using head-mounted eye tracker material, we assessed spatial recognition abilities (e.g., reaction to object permutation, removal or replacement with a new object) in participants with intellectual disabilities. The "Intellectual Disabilities (ID)" group (n=40) obtained a score totalling a 93.7% success rate, whereas the "Normal Control" group (n=40) scored 55.6% and took longer to fix their attention on the displaced object. The participants with an intellectual disability thus had a more accurate perception of spatial changes than controls. Interestingly, the ID participants were more reactive to object displacement than to removal of the object. In the specific test of novelty detection, however, the scores were similar, the two groups approaching 100% detection. Analysis of the strategies expressed by the ID group revealed that they engaged in more systematic object checking and were more sensitive than the control group to changes in the structure of the environment. Indeed, during the familiarisation phase, the "ID" group explored the collection of objects more slowly, and fixed their gaze for a longer time upon a significantly lower number of fixation points during visual sweeping.

Are commuting and residential mobility decisions simultaneous? : the case of Catalonia (Spain)

In this paper we study the commuting and moving decisions of workers in Catalonia (Spain) and its evolution in the 1986-1996 period. Using a microdata sample from the 1991 Spanish Population Census, we estimate a simultaneous, discrete choice model of commuting and moves, thus indirectly addressing the home and job location decisions. The econometrical framework is a simultaneous, binary probit model with a commute equation and a move equation

Are commuting and residential mobility decisions simultaneous? : the case of Catalonia (Spain)

In this paper we study the commuting and moving decisions of workers in Catalonia (Spain) and its evolution in the 1986-1996 period. Using a microdata sample from the 1991 Spanish Population Census, we estimate a simultaneous, discrete choice model of commuting and moves, thus indirectly addressing the home and job location decisions. The econometrical framework is a simultaneous, binary probit model with a commute equation and a move equation

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

A Comparative Study: Service Animals and Emotional Support Animals under the Fair Housing Act and the Americans with Disabilities Act & An Overview of Assistance Animal Laws of Select States, April 18, 2010

The Americans with Disabilities Act (ADA) and the Fair Housing Act (FHA) prohibit discrimination on the basis of disability and govern the use of service or emotional support animals in places where pets may not be permitted. However, courts have been struggling with how to define and treat animals that qualify for protection under each law. This has created confusion as to what rights and duties are owed disabled persons and the animals that live with or accompany them. This essay attempts to clarify these two federal laws with regard to service or emotional support animals and the differing parties‘ rights and interests. It also includes an overview of select state laws that govern assistance animals of all types and our recommendations for enhancing the Iowa Civil Rights Act.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.