876 resultados para Capitol costs
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Item 1005-C
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"Report to the State University of New York."
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"HUD-PDR-708"--P. [4] of cover.
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Mode of access: Internet.
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Mode of access: Internet.
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"GAO/HRD-82-92"--Prelim. p.
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"B-223880"--P. [1].
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Imprint on label mounted on t. p.: Washington, Oliphant Washington Service.
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Thesis (Master's)--University of Washington, 2016-06
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Thesis (Master's)--University of Washington, 2016-06
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Thesis (Master's)--University of Washington, 2016-06
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We compared the costs incurred by families attending outpatient appointments at the Royal Children's Hospital (RCH) in Brisbane with those incurred by families who had a consultation via videoconference in their regional area. In each category 200 families were interviewed. The median time spent travelling for videoconferences was 30 min compared with 80 min for face-to-face appointments. Families interviewed in the outpatient department had travelled a median distance of 70 km, while those who had a videoconference at the local hospital had travelled only 20 km. It cost these families much more to attend an appointment at the RCH than to attend a videoconference. Ninety-six per cent of families (193) reported at least one of the following types of expense: 150 families had expenses related to parking (median A$10), 156 had fuel expenses (median A$10) and 122 reported costs related to meals purchased at the RCH (median A$10). Only 21 families who had their appointment via local videoconference reported any additional costs. Specialist appointments via videoconference were a more convenient and cheaper option for families living in regional areas of Queensland than the conventional method of attending outpatient appointments at the specialist hospital in Brisbane.
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The present paper argues that the costs of climate change are primarily adjustment costs. The central result is that climate change will reduce welfare whenever it occurs more rapidly than the rate at which capital stocks (interpreted broadly to include natural resource stocks) would naturally adjust through market processes. The costs of climate change can be large even when lands are close to their climatic optimum, or evenly distributed both above and below that optimum.
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Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.