			Home About us Contact

Model Type (model + type)

Distribution by Scientific Domains

Earth and Environmental Science	40%
Medical Sciences	20%
Life Sciences	20%

Selected Abstracts

An enhanced method for source parameter imaging of magnetic data collected for mineral exploration

GEOPHYSICAL PROSPECTING, Issue 5 2005
Richard S. Smith
ABSTRACT We have developed a method for imaging magnetic data collected for mineral exploration to yield the following structural information: depth, model type (structural index) and susceptibility. The active nature of mineral exploration data requires we derive the structural information from a robust quantity: we propose that the first- or second-order analytic-signal amplitude is suitably stable. The procedure is to normalize the analytic-signal amplitude by the peak value and then use non-linear inversion to estimate the depth and the structural index for each anomaly. In our field example, different results are obtained depending on whether we inverted for the first- or second-order analytic-signal amplitude. This is probably because the two-dimensional contact, thin sheet or horizontal cylinder models we have assumed are not appropriate. In cases such as these, when our model assumptions are not correct, the results should not be interpreted quantitatively, but they might be useful for giving a qualitative indication of how the structure might vary. With a priori information, it is possible to assume a model type (i.e. set the structural index) and generate estimates of the depth and susceptibility. These data can then be gridded and imaged. If a contact is assumed, the susceptibility contrast is estimated; for the dike model, the susceptibility-thickness is estimated; for the horizontal cylinder, the susceptibility-area is estimated. To emphasize that the results are dependent on our assumed model, we advocate prefixing any derived quantity by the term ,apparent'. [source]

Different methods for modelling the areal infiltration of a grass field under heavy precipitation

HYDROLOGICAL PROCESSES, Issue 7 2002
Bruno Merz
Abstract The areal infiltration behaviour of a grass field is studied using a data set of 78 sprinkler infiltration experiments. The analysis of the experimental data shows a distinct event dependency: once runoff begins, the final infiltration rate increases with increasing rainfall intensity. This behaviour is attributed to the effects of small-scale variability. Increasing rainfall intensity increases the ponded area and therefore the portion of the plot which infiltrates at maximum rate. To describe the areal infiltration behaviour of the grass field the study uses two different model structures and investigates different approaches for consideration of subgrid variability. It is found that the effective parameter approach is not suited for this purpose. A good representation of the observed behaviour is obtained by using a distribution function approach or a parameterization approach. However, it is not clear how the parameters can be derived for these two approaches without a large measurement campaign. The data analysis and the simulations show the great importance of considering the effects of spatial variability for the infiltration process. This may be significant even at a small scale for a comparatively homogeneous area. The consideration of heterogeneity seems to be more important than the choice of the model type. Furthermore, similar results may be obtained with different modelling approaches. Even the relatively detailed data set does not seem to permit a clear model choice. In view of these results it is questionable to use very complex and detailed simulation models given the approximate nature of the problem. Although the principle processes may be well understood there is a lack of models that represent these processes and, more importantly, there is a lack of techniques to measure and parameterize them. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Conceptions of Dementia in a Multiethnic Sample of Family Caregivers

JOURNAL OF AMERICAN GERIATRICS SOCIETY, Issue 8 2005
Ladson Hinton MD
Understanding variability in conceptions of dementia in multiethnic populations is important to improve care and guide research. The objectives of this study were to describe caregiver conceptions of dementia using a previously developed typology and to examine the correlates of conceptions of dementia in a multiethnic sample. This is a cross-sectional study conducted in Boston and the San Francisco Bay area. Participants were a convenience sample of 92 family dementia caregivers from four ethnic/racial groups: African-American, Anglo European-American, Asian-American, and Latino. In-depth, qualitative interviews explored the caregivers' ideas about the nature and cause of dementia (i.e., explanatory models). Explanatory models of caregivers were categorized as biomedical, folk, or mixed (folk/biomedical). Quantitative analyses examined the association between ethnicity and other caregiver characteristics, and explanatory model type. Overall, 54% of caregivers, including 41% of Anglo European Americans, held explanatory models that combined folk and biomedical elements (i.e., mixed models). For example, many families attributed Alzheimer's disease and related dementias to psychosocial stress or normal aging. Ethnicity, lower education, and sex were associated with explanatory model type in bivariate analyses. In multiple logistic regression analysis, minority caregivers (P<.02) and those with less formal education (P<.02) were more likely to hold mixed or folk models of dementia. Although minority and nonminority caregivers often incorporated folk models into their understanding of dementia, this was more common in minority caregivers and those with less formal education. Further research on cross-ethnic differences in a larger, more-representative sample is needed. [source]

Organizational context and taxonomy of health care databases

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 5 2001
Deborah Shatin PhD
Abstract An understanding of the organizational context and taxonomy of health care databases is essential to appropriately use these data sources for research purposes. Characteristics of the organizational structure of the specific health care setting, including the model type, financial arrangement, and provider access, have implications for accessing and using this data effectively. Additionally, the benefit coverage environment may affect the utility of health care databases to address specific research questions. Coverage considerations that affect pharmacoepidemiologic research include eligibility, the nature of the pharmacy benefit, and regulatory aspects of the treatment under consideration. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Modelling suppressed and active convection.

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 626 2007
Comparing a numerical weather prediction, cloud-resolving, single-column model
Abstract This paper describes the design of and basic results from a case study to compare simulations of convection over the Tropical West Pacific. Simulations are carried out using a cloud-resolving model (CRM), a global numerical weather prediction (NWP) model and a single-column version of the NWP model (SCM). The experimental design for each model type is discussed and then results are compared. The periods simulated each include a regime with strong convective activity, a much more suppressed regime with far less convection, as well as the transition between these regimes. The description of the design and basic results from this study are given in some detail, as a study including all these model types is relatively new. Comparing the local forcing due to the dynamics in the NWP model with the observed forcing used to drive the CRM and SCM it is found that there is good agreement for one period chosen but significant differences for another. This is also seen in fields such as rain rate and top-of-atmosphere radiation. Using the period with good agreement we are able to identify examples of biases in the NWP model that are also reproduced in the SCM. Also discussed are examples of biases in the NWP simulation that are not reproduced in the SCM. It is suggested that understanding which biases in the SCM are consistent with the full NWP model can help focus the use of an SCM in this framework. © Crown Copyright 2007. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd [source]

Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors

ECOGRAPHY, Issue 6 2009
Alexandra D. Syphard
Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate. [source]

Interpreting temporal variation in omnivore foraging ecology via stable isotope modelling

FUNCTIONAL ECOLOGY, Issue 4 2009
Carolyn M. Kurle
Summary 1The use of stable carbon (C) and nitrogen (N) isotopes (,15N and ,13C, respectively) to delineate trophic patterns in wild animals is common in ecology. Their utility as a tool for interpreting temporal change in diet due to seasonality, migration, climate change or species invasion depends upon an understanding of the rates at which stable isotopes incorporate from diet into animal tissues. To best determine the foraging habits of invasive rats on island ecosystems and to illuminate the interpretation of wild omnivore diets in general, I investigated isotope incorporation rates of C and N in fur, liver, kidney, muscle, serum and red blood cells (RBC) from captive rats raised on a diet with low ,15N and ,13C values and switched to a diet with higher ,15N and ,13C values. 2I used the reaction progress variable method (RPVM), a linear fitting procedure, to estimate whether a single or multiple compartment model best described isotope turnover in each tissue. Small sample Akaike Information criterion (AICc) model comparison analysis indicated that 1 compartment nonlinear models best described isotope incorporation rates for liver, RBC, muscle, and fur, whereas 2 compartment nonlinear models were best for serum and kidney. 3I compared isotope incorporation rates using the RPVM versus nonlinear models. There were no differences in estimated isotope retention times between the model types for serum and kidney (except for N turnover in kidney from females). Isotope incorporation took longer when estimated using the nonlinear models for RBC, muscle, and fur, but was shorter for liver tissue. 4There were no statistical differences between sexes in the isotope incorporation rates. I also found that N and C isotope incorporation rates were decoupled for liver, with C incorporating into liver tissue faster than N. 5The data demonstrate the utility of analysing isotope ratios of multiple tissues from a single animal when estimating temporal variation in mammalian foraging ecology. [source]

Forty years of numerical climate modelling

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 9 2001
K. McGuffie
Abstract Climate modelling is now a mature discipline approaching its fortieth birthday. The need for valid climate forecasts has been underlined by the recognition that human activities are now modifying the climate. The complex nature of the climate system has resulted in the development of a surprisingly large array of modelling tools. Some are relatively simple, such as the earth systems and energy balance models (EBMs), while others are highly sophisticated models which challenge the fastest speeds of the most powerful supercomputers. Indeed, this discipline of the latter half of the twentieth century is so critically dependent on the availability of a means of undertaking powerful calculations that its evolution has matched that of the digital computer. The multi-faceted nature of the climate system demands high quality, and global observations and innovative parameterizations through which processes which cannot be described or calculated explicitly are captured to the extent deemed necessary. Interestingly, results from extremely simple, as well as highly complex and many intermediate model types are drawn upon today for effective formulation and evaluation of climate policies. This paper discusses some of the important developments during the first 40 years of climate modelling from the first models of the global atmosphere to today's models, which typically consist of integrated multi-component representations of the full climate system. The pressures of policy-relevant questions more clearly underline the tension between the need for evaluation against quality data and the unending pressure to improve spatial and temporal resolutions of climate models than at any time since the inception of climate modelling. Copyright © 2001 Royal Meteorological Society [source]

Performance and numerical behavior of the second-order scheme of precise time-step integration for transient dynamic analysis

NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS, Issue 6 2007
Hang Ma
Abstract Spurious high-frequency responses resulting from spatial discretization in time-step algorithms for structural dynamic analysis have long been an issue of concern in the framework of traditional finite difference methods. Such algorithms should be not only numerically dissipative in a controllable manner, but also unconditionally stable so that the time-step size can be governed solely by the accuracy requirement. In this article, the issue is considered in the framework of the second-order scheme of the precise integration method (PIM). Taking the Newmark-, method as a reference, the performance and numerical behavior of the second-order PIM for elasto-dynamic impact-response problems are studied in detail. In this analysis, the differential quadrature method is used for spatial discretization. The effects of spatial discretization, numerical damping, and time step on solution accuracy are explored by analyzing longitudinal vibrations of a shock-excited rod with rectangular, half-triangular, and Heaviside step impact. Both the analysis and numerical tests show that under the framework of the PIM, the spatial discretization used here can provide a reasonable number of model types for any given error tolerance. In the analysis of dynamic response, an appropriate spatial discretization scheme for a given structure is usually required in order to obtain an accurate and meaningful numerical solution, especially for describing the fine details of traction responses with sharp changes. Under the framework of the PIM, the numerical damping that is often required in traditional integration schemes is found to be unnecessary, and there is no restriction on the size of time steps, because the PIM can usually produce results with machine-like precision and is an unconditionally stable explicit method. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007 [source]