859 resultados para Measures and largenesses


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The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied.

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This paper decomposes the conventional measure of selection bias in observational studies into three components. The first two components are due to differences in the distributions of characteristics between participant and nonparticipant (comparison) group members: the first arises from differences in the supports, and the second from differences in densities over the region of common support. The third component arises from selection bias precisely defined. Using data from a recent social experiment, we find that the component due to selection bias, precisely defined, is smaller than the first two components. However, selection bias still represents a substantial fraction of the experimental impact estimate. The empirical performance of matching methods of program evaluation is also examined. We find that matching based on the propensity score eliminates some but not all of the measured selection bias, with the remaining bias still a substantial fraction of the estimated impact. We find that the support of the distribution of propensity scores for the comparison group is typically only a small portion of the support for the participant group. For values outside the common support, it is impossible to reliably estimate the effect of program participation using matching methods. If the impact of participation depends on the propensity score, as we find in our data, the failure of the common support condition severely limits matching compared with random assignment as an evaluation estimator.

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The main contribution to the radiological impact from natural radiation received by general population is due to the emission of radon (222Rn). The objective of this project is the study of radon gas as a radioactive element in our buildings (existing and future constructions) to avoid its influence in interior rooms. The proposed methodology reflects different aspects of natural radioactivity in buildings, their sources, their control criteria and regulatory framework; aspects related to the presence of radon in our constructions, entryways, measurement methodology for indoor environmental concentration are studied; other protection solutions and remediation measures in both existing buildings and new construction projects are analyzed. In conclusion, the paper presents previous evaluation tools, the analysis of existing concentration and the choice of the most appropriate mitigation / remediation measures depending on each case, through the establishment of different architectural proposals to plan actions against radon where necessary.

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Mode of access: Internet.

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"Health and Safety."

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Transportation Department, Washington, D.C.

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ASTIA document no. AD215 482.