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Very General (very + general)
Selected AbstractsParaxial ray methods for anisotropic inhomogeneous mediaGEOPHYSICAL PROSPECTING, Issue 1 2007Tijmen Jan Moser ABSTRACT A new formalism of surface-to-surface paraxial matrices allows a very general and flexible formulation of the paraxial ray theory, equally valid in anisotropic and isotropic inhomogeneous layered media. The formalism is based on conventional dynamic ray tracing in Cartesian coordinates along a reference ray. At any user-selected pair of points of the reference ray, a pair of surfaces may be defined. These surfaces may be arbitrarily curved and oriented, and may represent structural interfaces, data recording surfaces, or merely formal surfaces. A newly obtained factorization of the interface propagator matrix allows to transform the conventional 6 × 6 propagator matrix in Cartesian coordinates into a 6 × 6 surface-to-surface paraxial matrix. This matrix defines the transformation of paraxial ray quantities from one surface to another. The redundant non-eikonal and ray-tangent solutions of the dynamic ray-tracing system in Cartesian coordinates can be easily eliminated from the 6 × 6 surface-to-surface paraxial matrix, and it can be reduced to 4 × 4 form. Both the 6 × 6 and 4 × 4 surface-to-surface paraxial matrices satisfy useful properties, particularly the symplecticity. In their 4 × 4 reduced form, they can be used to solve important boundary-value problems of a four-parametric system of paraxial rays, connecting the two surfaces, similarly as the well-known surface-to-surface matrices in isotropic media in ray-centred coordinates. Applications of such boundary-value problems include the two-point eikonal, relative geometrical spreading, Fresnel zones, the design of migration operators, and more. [source] An introduction to the European Terrestrial Ecosystem Modelling ActivityGLOBAL ECOLOGY, Issue 6 2001Martin T. Sykes Abstract The objective of the European Terrestrial Ecosystem Modelling Activity (ETEMA) was to address some of the major challenges in developing generalized models to examine responses of natural and seminatural ecosystems to environmental change at the regional to European scale. The approach described herein was to break down the totality of ecosystem functioning into its key components, each with its characteristic spatial and temporal scales. A conceptual framework was developed describing the configuration of these components as modules within a generalized simulation model. The framework describes the key inputs, outputs and state variables, their spatial and temporal contexts, and information flows between modules. The ,backbone' of the model is a system of nested timing loops corresponding to the disparate time scales at which different ecosystem processes occur. The framework is a theoretical construct into which ecosystem models at levels of complexity ranging from the very general to the highly detailed can be mapped, and thus provides a guide for development of models for novel, particularly regional-scale, applications. A number of subsystem studies of the major components of ecosystem functioning, i.e. modules of the conceptual framework, are briefly introduced herein. The general aim of the subsystem studies was to identify the key alternative formulations (as opposed to minor variants) and test these against observational data. The various subsystem studies concern planetary boundary layer,ecosystem interactions, ecosystem CO2 and H2O fluxes, vegetation physiology and phenology, biogeography and vegetation dynamics, detritus and SOM dynamics, soil moisture and human and natural disturbances and, as individual papers, they complete this special ETEMA issue. [source] Exact multivariate tests for brain imaging dataHUMAN BRAIN MAPPING, Issue 1 2002Rita Almeida Abstract In positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data sets, the number of variables is larger than the number of observations. This fact makes application of multivariate linear model analysis difficult, except if a reduction of the data matrix dimension is performed prior to the analysis. The reduced data set, however, will in general not be normally distributed and therefore, the usual multivariate tests will not be necessarily applicable. This problem has not been adequately discussed in the literature concerning multivariate linear analysis of brain imaging data. No theoretical foundation has been given to support that the null distributions of the tests are as claimed. Our study addresses this issue by introducing a method of constructing test statistics that follow the same distributions as when the data matrix is normally distributed. The method is based on the invariance of certain tests over a large class of distributions of the data matrix. This implies that the method is very general and can be applied for different reductions of the data matrix. As an illustration we apply a test statistic constructed by the method now presented to test a multivariate hypothesis on a PET data set. The test rejects the null hypothesis of no significant differences in measured brain activity between two conditions. The effect responsible for the rejection of the hypothesis is characterized using canonical variate analysis (CVA) and compared with the result obtained by using univariate regression analysis for each voxel and statistical inference based on size of activations. The results obtained from CVA and the univariate method are similar. Hum. Brain Mapping 16:24,35, 2002. © 2002 Wiley-Liss, Inc. [source] Edge feeding of circular patch microstrip antennasINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 2 2001B. S. Rao Abstract Edge feeding of a circular patch antenna by a microstrip line has been achieved by utilizing the circumferential variation of the field which is fixed by loading the patch. However, loading splits the degenerate modes and gives rise to cross-polarization. Three different techniques are presented to analyze the loaded circular patch antenna. Comparison with the measured results for input impedance and radiation patterns shows good agreement. The concept used here is very general and can be applied to the edge feeding of any ring antenna. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 74,85, 2001. [source] Weighting hyperspectral image data for improved multivariate curve resolution resultsJOURNAL OF CHEMOMETRICS, Issue 9 2008Howland D. T. Jones Abstract The combination of hyperspectral confocal fluorescence microscopy and multivariate curve resolution (MCR) provides an ideal system for improved quantitative imaging when multiple fluorophores are present. However, the presence of multiple noise sources limits the ability of MCR to accurately extract pure-component spectra when there is high spectral and/or spatial overlap between multiple fluorophores. Previously, MCR results were improved by weighting the spectral images for Poisson-distributed noise, but additional noise sources are often present. We have identified and quantified all the major noise sources in hyperspectral fluorescence images. Two primary noise sources were found: Poisson-distributed noise and detector-read noise. We present methods to quantify detector-read noise variance and to empirically determine the electron multiplying CCD (EMCCD) gain factor required to compute the Poisson noise variance. We have found that properly weighting spectral image data to account for both noise sources improved MCR accuracy. In this paper, we demonstrate three weighting schemes applied to a real hyperspectral corn leaf image and to simulated data based upon this same image. MCR applied to both real and simulated hyperspectral images weighted to compensate for the two major noise sources greatly improved the extracted pure emission spectra and their concentrations relative to MCR with either unweighted or Poisson-only weighted data. Thus, properly identifying and accounting for the major noise sources in hyperspectral images can serve to improve the MCR results. These methods are very general and can be applied to the multivariate analysis of spectral images whenever CCD or EMCCD detectors are used. Copyright © 2008 John Wiley & Sons, Ltd. [source] Asthma Hospitalization Rates Among Children, and School Building Conditions, by New York State School Districts, 1991-2001JOURNAL OF SCHOOL HEALTH, Issue 8 2006Erin Belanger This study examined patterns of asthma hospitalization and possible factors contributing to asthma hospitalizations, including sociodemographics and school environmental factors, among school-age children (5-18 years) in New York State (NYS) over an 11-year period (1991-2001). Asthma hospitalization data from the Statewide Planning and Research Cooperative System and the 1990 and 2000 census population files were geocoded into NYS school districts statewide, and school district asthma hospitalization rates were then calculated. Building Condition Survey for each school was then utilized to create summary measures of school building conditions for each school district. Hospitalization rates were linked to district school building conditions by using logistic regression analysis that controlled for poverty. Calculation of time trends revealed overall declines in asthma rates among school-age children for NYS from 1991 to 2001. This general decline was found in each sociodemographic group. The mean rate for NYS from 1991 to 2001 was 27/10,000. Poorly rated building systems that were significantly associated with increased school district asthma hospitalization rates were roofing (odds ratio [OR] = 1.76; 95% confidence interval [CI95] = 1.13-2.74), windows (OR = 1.66; CI95= 1.08-2.54), exterior walls (OR = 2.24; CI95= 1.31-3.83), floor finishes (OR = 1.75; CI95= 1.14-2.69), and boiler/furnace (OR = 1.71; CI95= 1.99-2.94). This does not indicate a definite link between these systems and asthma hospitalizations since the available building system information is very general and crude. (J Sch Health. 2006;76(8):408-413) [source] MACROECONOMIC UNCERTAINTY AND MACROECONOMIC PERFORMANCE: ARE THEY RELATED?THE MANCHESTER SCHOOL, Issue 2005DON BREDIN We use a very general bivariate generalized autoregressive conditional heteroskedasticity-in-mean model and G7 monthly data covering the 1957,2003 period to test for the impact of real and nominal macroeconomic uncertainty on inflation and output growth. Our evidence supports a number of important conclusions. First, in most countries output growth uncertainty is a positive determinant of the output growth rate. Second, there is mixed evidence regarding the effect of inflation uncertainty on inflation and output growth. Hence, contrary to popular belief, uncertainty about the inflation rate is not necessarily detrimental to economic growth but in some cases it may also enhance growth. Finally, there is mixed evidence on the effect of output uncertainty on inflation. In sum, our results indicate that macroeconomic uncertainty may even improve macroeconomic performance. [source] | ||||||||||