Sentando las bases políticas para la integración económica: el Grupo de Río y la concertación regional

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Aplicação da eletrorresistividade na caracterização do meio hidrogeológico na cidade de JI-paraná (RO)

The Ji-Paraná city (RO) it doesn't possess public system of collection and treatment of sewers, being the waters residuárias produced by the local population thrown at sewages. Traditionally, many inhabitants use wells amazon extracted underground water or tubular shallow in the urban zone. The study accomplished in the Nova Brasília neighborhood for Silva (2009) revealed that the local aquifer is strongly contaminated for nitrate, originated of the decomposition of the organic matter deposited at the sewages local maidservants. With the objective of detecting areas with high concentrations originating from organic compositions of septic sewages, geophysical risings were accomplished, later related with analyses physical-chemistries in samples of groundwaters obtained in several wells installed in the Nova Brasília neighborhood, besides of soil samples descriptions in zone not saturated obtained in wells. The results obtained by the geophysical rehearsals they reveal that the polluting feather not migrates through the zone saturated, arriving with relative easiness to the aquifer, reaching in some points, superior depth to 34 m reached by the geoelectrical profiling.

Zoneamento geoambiental aplicado ao planejamento da gestão ambiental e territorial do município de Colorado D'Oeste, RO

This research is based on the physical characterization of the environment to support definition of the best land use for the county of Colorado D'Oeste, in State of Rondônia, Brazil. Remote sensing and geoprocessing techniques were applied to support the preparation of a Geoenvironmental Zoning, which was used to define strategies of territorial and environmental management in that county. Digital and analogical remote sensing products, acquired by satellites, and additional cartographic and thematic maps allowed a morphostructural analysis to define low and high structural associated study site tectonic. Subsequently, this information was used to support analysis of the physiographic compartmentation of the study area. Based on this study information, it is possible to define geoenvironmental subzones and local hidrological regime, soils, mineral components, texture, color, and sedimentary materials. By integrating previous described information, a synthesis cartographic map generated. Accordingly, this Cartographic Sheet spatially defined the best land use over the study area, indicates zones for conservation, agricultural, and regeneration (areas that should be recovered). Finally, the results of this research can contribute and support governmental and non-governmental organization and local communities could improve land use and soil management, avoiding natural resource destruction and future land scarcity in the county of Colorado D'Oeste.

Formación de la familia y marginalidad urbana en Río de Janeiro

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La movilidad social y la fecundidad en Río de Janeiro

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Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Satisfacción de la demanda de especialistas de nivel superior: provincia de Pinar del Río al 2005

Trabajo de carácter práctico, intenta guiar el desarrollo perspectivo de la formación de especialistas requeridos.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Ruedas de negocios y cooperación empresaria en el MERCOSUR: análisis y seguimiento de las Ruedas de Negocios organizadas por el SEBRAE-Río de Janeiro en Buenos Aires

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The use and production of numerically controlled machine tools in Argentina

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The adoption of machine tabulation for national foreign trade statistics: the case of Ecuador

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