844 resultados para Data mining and knowledge discovery


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The graphical representation of spatial soil properties in a digital environment is complex because it requires a conversion of data collected in a discrete form onto a continuous surface. The objective of this study was to apply three-dimension techniques of interpolation and visualization on soil texture and fertility properties and establish relationships with pedogenetic factors and processes in a slope area. The GRASS Geographic Information System was used to generate three-dimensional models and ParaView software to visualize soil volumes. Samples of the A, AB, BA, and B horizons were collected in a regular 122-point grid in an area of 13 ha, in Pinhais, PR, in southern Brazil. Geoprocessing and graphic computing techniques were effective in identifying and delimiting soil volumes of distinct ranges of fertility properties confined within the soil matrix. Both three-dimensional interpolation and the visualization tool facilitated interpretation in a continuous space (volumes) of the cause-effect relationships between soil texture and fertility properties and pedological factors and processes, such as higher clay contents following the drainage lines of the area. The flattest part with more weathered soils (Oxisols) had the highest pH values and lower Al3+ concentrations. These techniques of data interpolation and visualization have great potential for use in diverse areas of soil science, such as identification of soil volumes occurring side-by-side but that exhibit different physical, chemical, and mineralogical conditions for plant root growth, and monitoring of plumes of organic and inorganic pollutants in soils and sediments, among other applications. The methodological details for interpolation and a three-dimensional view of soil data are presented here.

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Quantitative information from magnetic resonance imaging (MRI) may substantiate clinical findings and provide additional insight into the mechanism of clinical interventions in therapeutic stroke trials. The PERFORM study is exploring the efficacy of terutroban versus aspirin for secondary prevention in patients with a history of ischemic stroke. We report on the design of an exploratory longitudinal MRI follow-up study that was performed in a subgroup of the PERFORM trial. An international multi-centre longitudinal follow-up MRI study was designed for different MR systems employing safety and efficacy readouts: new T2 lesions, new DWI lesions, whole brain volume change, hippocampal volume change, changes in tissue microstructure as depicted by mean diffusivity and fractional anisotropy, vessel patency on MR angiography, and the presence of and development of new microbleeds. A total of 1,056 patients (men and women ≥ 55 years) were included. The data analysis included 3D reformation, image registration of different contrasts, tissue segmentation, and automated lesion detection. This large international multi-centre study demonstrates how new MRI readouts can be used to provide key information on the evolution of cerebral tissue lesions and within the macrovasculature after atherothrombotic stroke in a large sample of patients.

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[cat] Analitzem una economia amb dues característiques principals: la mobilitat dels treballadors implica transferència de coneixement i la productivitat de l’empresa augmenta amb l’intercanvi de coneixement. Cada empresa desenvolupa un tipus de coneixement que serà trasmès a la resta de la indústria mitjançant la mobilitat de treballadors. Estudiem dues estructures de mercat laboral i utilitzant un anàlisi comparatiu derivem les implicacions del model. Els resultats revelen com la mobilitat de treballadors depèn en la varietat i nivell del coneixement, la presència de costos de mobilitat, les institucions, la capacitat d’absorvir coneixement per part de les empreses i la mida de la indústria. Els resultats no depenen de l’estructura del mercat laboral.

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O assunto Brasil foi analisado na base de teses francesas DocThèses, compreendendo os anos de 1969 a 1999. Utilizou-se a técnica de Data Mining como ferramenta para obter inteligência e conhecimento. O software utilizado para a limpeza da base DocThèses foi o Infotrans, e, para a preparação dos dados, empregou-se o Dataview. Os resultados da análise foram ilustrados com a aplicação dos pressupostos da Lei de Zipf, classificando-se as informações em trivial, interessante e ruído, conforme a distribuição de freqüência. Conclui-se que a técnica do Data Mining associada a softwares especialistas é uma poderosa aliada no emprego de inteligência no processo decisório em todos os níveis, inclusive o nível macro, pois oferece subsídios para a consolidação, investimento e desenvolvimento de ações e políticas.

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The Powell Basin is a small oceanic basin located at the NE end of the Antarctic Peninsula developed during the Early Miocene and mostly surrounded by the continental crusts of the South Orkney Microcontinent, South Scotia Ridge and Antarctic Peninsula margins. Gravity data from the SCAN 97 cruise obtained with the R/V Hespérides and data from the Global Gravity Grid and Sea Floor Topography (GGSFT) database (Sandwell and Smith, 1997) are used to determine the 3D geometry of the crustal-mantle interface (CMI) by numerical inversion methods. Water layer contribution and sedimentary effects were eliminated from the Free Air anomaly to obtain the total anomaly. Sedimentary effects were obtained from the analysis of existing and new SCAN 97 multichannel seismic profiles (MCS). The regional anomaly was obtained after spectral and filtering processes. The smooth 3D geometry of the crustal mantle interface obtained after inversion of the regional anomaly shows an increase in the thickness of the crust towards the continental margins and a NW-SE oriented axis of symmetry coinciding with the position of an older oceanic spreading axis. This interface shows a moderate uplift towards the western part and depicts two main uplifts to the northern and eastern sectors.

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This article describes a method for determining the polydispersity index Ip2=Mz/Mw of the molecular weight distribution (MWD) of linear polymeric materials from linear viscoelastic data. The method uses the Mellin transform of the relaxation modulus of a simple molecular rheological model. One of the main features of this technique is that it enables interesting MWD information to be obtained directly from dynamic shear experiments. It is not necessary to achieve the relaxation spectrum, so the ill-posed problem is avoided. Furthermore, a determinate shape of the continuous MWD does not have to be assumed in order to obtain the polydispersity index. The technique has been developed to deal with entangled linear polymers, whatever the form of the MWD is. The rheological information required to obtain the polydispersity index is the storage G′(ω) and loss G″(ω) moduli, extending from the terminal zone to the plateau region. The method provides a good agreement between the proposed theoretical approach and the experimental polydispersity indices of several linear polymers for a wide range of average molecular weights and polydispersity indices. It is also applicable to binary blends.

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The overall system is designed to permit automatic collection of delamination field data for bridge decks. In addition to measuring and recording the data in the field, the system provides for transferring the recorded data to a personal computer for processing and plotting. This permits rapid turnaround from data collection to a finished plot of the results in a fraction of the time previously required for manual analysis of the analog data captured on a strip chart recorder. In normal operation the Delamtect provides an analog voltage for each of two channels which is proportional to the extent of any delamination. These voltages are recorded on a strip chart for later visual analysis. An event marker voltage, produced by a momentary push button on the handle, is also provided by the Delamtect and recorded on a third channel of the analog recorder.

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A review of the Iowa Department of Transportation's field data collection and reporting system has been performed. Included were several systems used by the Office of Construction and Local Jurisdictions. The entire field data collection and reporting systems for asphalt cement concrete (ACC) paving, portland cement concrete (PCC) paving, and PCC structures were streamlined and computerized. The field procedures for materials acceptance were also reviewed. Best practices were identified and a method was developed to prioritize materials so transportation agencies could focus their efforts on high priority materials. Iowa State University researchers facilitated a discussion about Equal Employment Opportunity (EEO) and Affirmative Action (AA) procedures between the Office of Construction field staff and the Office of Contracts. A set of alternative procedures was developed. Later the Office of Contracts considered these alternatives as they developed new procedures that are currently being implemented. The job close-out package was reviewed and two unnecessary procedures were eliminated. Numerous other procedures were reviewed and flowcharted. Several changes have been recommended that will increase efficiency and allow staff time to be devoted to higher priority activities. It is estimated the improvements in ACC paving, PCC paving and structural concrete will by similar to three full time equivalent (FTE) positions to field construction, field materials and Office of Materials. Elimination of EEO interviews will be equivalent to one FTE position. It is estimated that other miscellaneous changes will be equivalent to at least one other FTE person. This is a total five FTEs. These are conservative estimates based on savings that are easily quantified. It is likely that total positive effect is greater when items that are difficult to quantify are considered.

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We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

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Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.