896 resultados para Team Evaluation Models


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With the advances in technology, seismological theory, and data acquisition, a number of high-resolution seismic tomography models have been published. However, discrepancies between tomography models often arise from different theoretical treatments of seismic wave propagation, different inversion strategies, and different data sets. Using a fixed velocity-to-density scaling and a fixed radial viscosity profile, we compute global mantle flow models associated with the different tomography models and test the impact of these for explaining surface geophysical observations (geoid, dynamic topography, stress, and strain rates). We use the joint modeling of lithosphere and mantle dynamics approach of Ghosh and Holt (2012) to compute the full lithosphere stresses, except that we use HC for the mantle circulation model, which accounts for the primary flow-coupling features associated with density-driven mantle flow. Our results show that the seismic tomography models of S40RTS and SAW642AN provide a better match with surface observables on a global scale than other models tested. Both of these tomography models have important similarities, including upwellings located in Pacific, Eastern Africa, Iceland, and mid-ocean ridges in the Atlantic and Indian Ocean and downwelling flows mainly located beneath the Andes, the Middle East, and central and Southeast Asia.

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Based on the scaling criteria of polymer flooding reservoir obtained in our previous work in which the gravity and capillary forces, compressibility, non-Newtonian behavior, absorption, dispersion, and diffusion are considered, eight partial similarity models are designed. A new numerical approach of sensitivity analysis is suggested to quantify the dominance degree of relaxed dimensionless parameters for partial similarity model. The sensitivity factor quantifying the dominance degree of relaxed dimensionless parameter is defined. By solving the dimensionless governing equations including all dimensionless parameters, the sensitivity factor of each relaxed dimensionless parameter is calculated for each partial similarity model; thus, the dominance degree of the relaxed one is quantitatively determined. Based on the sensitivity analysis, the effect coefficient of partial similarity model is defined as the summation of product of sensitivity factor of relaxed dimensionless parameter and its relative relaxation quantity. The effect coefficient is used as a criterion to evaluate each partial similarity model. Then the partial similarity model with the smallest effect coefficient can be singled out to approximate to the prototype. Results show that the precision of partial similarity model is not only determined by the number of satisfied dimensionless parameters but also the relative relaxation quantity of the relaxed ones.

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According to the Millennium Ecosystem Assessment’s chapter “Coastal Systems” (Agardy and Alder 2005), 40% of the world population falls within 100 km of the coast. Agardy and Alder report that population densities in coastal regions are three times those of inland regions and demographic forecasts suggest a continued rise in coastal populations. These high population levels can be partially traced to the abundance of ecosystem services provided in the coastal zone. While populations benefit from an abundance of services, population pressure also degrades existing services and leads to increased susceptibility of property and human life to natural hazards. In the face of these challenges, environmental administrators on the coast must pursue agendas which reflect the difficult balance between private and public interests. These decisions include maintaining economic prosperity and personal freedoms, protecting or enhancing the existing flow of ecosystem services to society, and mitigating potential losses from natural hazards. (PDF contains 5 pages)