178,227 research outputs found
Erythria iranica Dlabola et Jankovic 1981
No full textErythria iranica Dlabola et Jankovic, 1981 Erythria iranica Dlabola et Jankovic, 1981: 76. Erythria iranica Dlabola, 1981: Dlabola & Jankovic (1981). Recorded distribution in Iran: South of central region of Alborz mountains and northwest of Iran.Published as part of Mozaffarian, Fariba & Wilson, Michael R., 2016, A checklist of the leafhoppers of Iran (Hemiptera: Auchenorrhyncha: Cicadellidae), pp. 1-63 in Zootaxa 4062 (1) on page 44, DOI: 10.11646/zootaxa.4062.1.1, http://zenodo.org/record/25679
Trauma and suicidality in war affected communities
No full textAvailable online 15 September 2012 as article in press. Cite as: Jankovic J, et al. Trauma and suicidality in war affected communities. European Psychiatry (2012),
http://dx.doi.org/10.1016/j.eurpsy.2012.06.001Journal homepage: http://www.elsevier.com/journals/european-psychiatry/0924-933
Effective conductivity of an anisotropic heterogeneous medium of random conductivity distribution
No full text"\"The paper deals with the effective conductivity tensor K(ef) of anisotropic random media subject to mean uniform flux. The hydraulic conductivity K field is modeled as a collection of spheroidal disjoint inclusions of different, isotropic and independent Y = ln K; the latter is a random variable with given distribution of variance sigma(2)(Y). Inclusions are embedded in homogeneous background of anisotropic conductivity K(0). The K field is anisotropic, characterized by the anisotropy ratio f, ratio of the vertical and horizontal integral scales of K. We derive K(ef) by accurate numerical simulations; the numerical model for anisotropic media is presented here for the first time, and it generalizes a previously developed model for isotropic formations [I. Jankovic, A. Fiori, and G. Dagan, Multiscale Model. Simul., 1 (2003), pp. 40-56]. The numerical model is capable of solving complex three-dimensional flow problems with high accuracy for any configuration of the spheroidal inclusions and arbitrary K distribution. The numerically derived K(ef) for a normal Y is compared with its prediction by (i) the self-consistent solution K(sc), (ii) the first-order approximation in sigma(2)(Y), and (iii) the exponential conjecture [L. J. Gelhar and C. L. Axness Water. Resour. Res., 19 (1983), pp. 161-180]. It is found that the self-consistent solution K(sc) is very accurate for a broad range of the values of the parameters sigma(2)(Y), f and for the densest inclusions packing. In contrast, the first-order solution strongly deviates from K(ef) for large sigma(2)(Y), as expected, and the exponential conjecture is generally unable to correctly represent the effective conductivity. The numerical solution for the potential is expressed as an infinite series of spheroidal harmonics, attached to the interior and exterior of each inclusion, which accounts for the nonlinear interaction between neighboring inclusions.\"
New Directions for Recognizing visual Patterns in Medical Imaging
No full textNew study directions are focused on the extraction and recognition of visual patterns from different types of medical images
Arousal and Valence Have Dissociable Effects on Responses to Schematic Emotional Faces
No full textDatasets in zipped file for Booy, R. B., Jankovic, N., & Spalek, T. M., (submitted). "Arousal and Valence Have Dissociable Effects on Responses to Schematic". Canadian Journal of Experimental Psychology
Flow and transport in highly heterogeneous formations: 1. Conceptual framework and validity of first-order approximations RID A-2321-2010
No full text[1] Flow of uniform mean velocity U takes place in a formation of spatially variable, random conductivity K(x). Advective transport of a plume of an inert solute is investigated by the Lagrangean approach. The aim of the study is to determine the spatial moments of the plume, i.e., of fluid particle trajectories, for highly heterogeneous aquifers, for which sigma(Y) > 1, where Y = ln K. A multi-indicator model of the permeability structure, which is different from the common multi-Gaussian one, is proposed: the formation is modeled as a collection of N blocks of different K-(j). The structure is defined by the distribution of K-(j), the blocks' shape, and the coordinates of their centroids. The following simplifications are adopted: the blocks are inclusions of a regular shape (circles, spheres for isotropic media investigated here) defined by the radius A, and the inclusions are not overlapping, and their centroids are distributed uniformly and independently in space. At the continuous limit the model is characterized by the joint pdf f(Y, A). The model is shown to be quite general and to comprise binary, bimodal, indicator variograms and unimodal distributions of Y as particular cases. The study is focused on the latter case, with Y normal N [[Y], sigma(Y)(2)] and stationary covariance of given integral scale I-Y; these are the parameters commonly estimated for sedimentary formations. This leaves still freedom in selecting the pdf f(A). The simple model selected for semianalytical and numerical analysis is that of inclusions of radius R and volume fraction n, submerged in a matrix of effective conductivity K-ef. The latter represents the effect of inclusions of much smaller radius, which appear as a nugget in the log conductivity two-point covariance. An approximate analytical solution of the flow is obtained by using a self-consistent approximation, while a fully numerical one is derived in part 3 [Jankovic et al., 2003a]. Transport is solved by particle tracking, and the time-dependent spatial moments (trajectories variance, skewness, kurtosis) are presented in part 2 [Fiori et al., 2003]. In the self-consistent approximation the asymptotic longitudinal macrodispersivity alpha(L), which is a function of Y, shows strong nonlinear effects: inclusions of large positive Y lead to a finite alpha(L), whereas alpha(L) grows unbounded for those of negative Y. This effect is not captured by the common first-order approximation in sigma(Y), which is symmetrical and overestimates alpha(L) for Y > 0 and underestimates it for Y < 0. As a result, the second spatial moment is predicted accurately by the first-order approximation, by cancellation of errors, provided that f(Y) is symmetrical. However, the transient regime and higher-order moments are not captured by the first-order approximation
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Identification of heterogeneous aquifer transmissivity using an AE-based method RID A-2321-2010
No full textDetermination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T( x, y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis ( 1985) identified two types of the logtransmissivity Y = In( T) variations: correlated variations with variance sigma(Yc)(2) and correlation scale, I-Y, on the order of kilometers, and uncorrelated variations with variance sigma(Yn)(2). Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma(Yc)(2), I-Y, and sigma(Yn])(2)) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations ( carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west- central Nevada for which abundant transmissivity and head measurements are available
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