939 resultados para Active joint count of zero
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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources for the year ended June 30, 2012
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Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petro-physical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine the parameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petro-physical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.
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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources for the year ended June 30, 2013
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Joint inversion of crosshole ground-penetrating radar and seismic data can improve model resolution and fidelity of the resultant individual models. Model coupling obtained by minimizing or penalizing some measure of structural dissimilarity between models appears to be the most versatile approach because only weak assumptions about petrophysical relationships are required. Nevertheless, experimental results and petrophysical arguments suggest that when porosity variations are weak in saturated unconsolidated environments, then radar wave speed is approximately linearly related to seismic wave speed. Under such circumstances, model coupling also can be achieved by incorporating cross-covariances in the model regularization. In two case studies, structural similarity is imposed by penalizing models for which the model cross-gradients are nonzero. A first case study demonstrates improvements in model resolution by comparing the resulting models with borehole information, whereas a second case study uses point-spread functions. Although radar seismic wavespeed crossplots are very similar for the two case studies, the models plot in different portions of the graph, suggesting variances in porosity. Both examples display a close, quasilinear relationship between radar seismic wave speed in unconsolidated environments that is described rather well by the corresponding lower Hashin-Shtrikman (HS) bounds. Combining crossplots of the joint inversion models with HS bounds can constrain porosity and pore structure better than individual inversion results can.
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Audit report on the Iowa Water Pollution Control Works Financing Program and the Iowa Drinking Water Facilities Financing Program, joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources, for the year ended June 30, 2014
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Image registration has been proposed as an automatic method for recovering cardiac displacement fields from Tagged Magnetic Resonance Imaging (tMRI) sequences. Initially performed as a set of pairwise registrations, these techniques have evolved to the use of 3D+t deformation models, requiring metrics of joint image alignment (JA). However, only linear combinations of cost functions defined with respect to the first frame have been used. In this paper, we have applied k-Nearest Neighbors Graphs (kNNG) estimators of the -entropy (H ) to measure the joint similarity between frames, and to combine the information provided by different cardiac views in an unified metric. Experiments performed on six subjects showed a significantly higher accuracy (p < 0.05) with respect to a standard pairwise alignment (PA) approach in terms of mean positional error and variance with respect to manually placed landmarks. The developed method was used to study strains in patients with myocardial infarction, showing a consistency between strain, infarction location, and coronary occlusion. This paper also presentsan interesting clinical application of graph-based metric estimators, showing their value for solving practical problems found in medical imaging.
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Audit report on the Iowa Water Pollution Control Works Financing Program (Clean Water Program) and the Iowa Drinking Water Facilities Financing Program (Drinking Water Program), joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources, for the year ended June 30, 2005
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Audit report on the Iowa Water Pollution Control Works Financing Program (Clean Water Program) and the Iowa Drinking Water Facilities Financing Program (Drinking Water Program), joint programs of the Iowa Finance Authority and the Iowa Department of Natural Resources, for the year ended June 30, 2004
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Eighty-Sixth General Assembly Joint Rules of the House and Senate (House Concurrent Resolution 6), House adopted 2-3-2015, Senate adopted 2-4-2015
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The Quaternary Active Faults Database of Iberia (QAFI) is an initiative lead by the Institute of Geology and Mines of Spain (IGME) for building a public repository of scientific data regarding faults having documented activity during the last 2.59 Ma (Quaternary). QAFI also addresses a need to transfer geologic knowledge to practitioners of seismic hazard and risk in Iberia by identifying and characterizing seismogenic fault-sources. QAFI is populated by the information freely provided by more than 40 Earth science researchers, storing to date a total of 262 records. In this article we describe the development and evolution of the database, as well as its internal architecture. Aditionally, a first global analysis of the data is provided with a special focus on length and slip-rate fault parameters. Finally, the database completeness and the internal consistency of the data are discussed. Even though QAFI v.2.0 is the most current resource for calculating fault-related seismic hazard in Iberia, the database is still incomplete and requires further review.
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Different compounds have been reported as biomarkers of a smoking habit, but, to date, there is no appropriate biomarker for tobacco-related exposure because the proposed chemicals seem to be nonspecific or they are only appropriate for short-term exposure. Moreover, conventional sampling methodologies require an invasive method because blood or urine samples are required. The use of a microtrap system coupled to gas chromatography–mass spectrometry analysis has been found to be very effective for the noninvasive analysis of volatile organic compounds in breath samples. The levels of benzene, 2,5-dimethylfuran, toluene, o-xylene, and m- p-xylene have been analyzed in breath samples obtained from 204 volunteers (100 smokers, 104 nonsmokers; 147 females, 57 males; ages 16 to 53 years). 2,5-Dimethylfuran was always below the limit of detection (0.005 ppbv) in the nonsmoker population and always detected in smokers independently of the smoking habits. Benzene was only an effective biomarker for medium and heavy smokers, and its level was affected by smoking habits. Regarding the levels of xylenes and toluene, they were only different in heavy smokers and after short-term exposure. The results obtained suggest that 2,5-dimethylfuran is a specific breath biomarker of smoking status independently of the smoking habits (e.g., short- and long-term exposure, light and heavy consumption), and so this compound might be useful as a biomarker of smoking exposure