Development and Preliminary Validation of the Physiological Hyperarousal Scale for Children.

Considerable empirical support exists for the positive affect and negative affect components of the tripartite model of anxiety and depression proposed by L. A. Clark and D. Watson (1991); however, less attention has been paid to the physiological hyperarousal component of the model. The development of the Physiological Hyperarousal Scale for Children (PH-C; J. Laurent, S. J. Catanzaro, & T. E. Joiner Jr., 1995) is described. The psychometric properties of items are examined using students in Grades 6-12 (N = 398). Initial scale validation includes a joint factor analysis with the Positive and Negative Affect Scale for Children (PANAS-C; J. Laurent et al., 1999; J. Laurent, K. Potter, & S. J. Catanzaro, 1994). The relationship between the PH-C and existing measures that tap related constructs is examined. Together, the PH-C and PANAS-C provide a means to assess tripartite model constructs useful in differentiating anxiety and depression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the dimensionality of multi-view hyperspectral measurements of vegetation

This note investigates the structure of two data sets of highly resolved directional-reflectance of vegetation canopies, obtained with a spectroradiometer mounted on a goniometer. One canopy was a grass lawn (largely erectophile) and the other was of watercress (largely planophile). The data sets consist of radiance measurements in 356 spectral bands in visible and near-infrared wavelengths, and in either 61 or 358 different directions, but avoiding directions very close to the hot-spot. The singular value decompositions of the two-dimensional data sets are used to investigate their intrinsic dimensionality, and so also the redundancy contained in the data. A single directional function and a single spectral function together fit the data quite well in a least squares sense, with the root mean square residual amounting to one part in a hundred of the total sum of squares of the data. However, the residuals indicate that one or two further pairs of functions are needed to characterize the systematic variation of spectral reflectance with direction (or equivalently, of the angular reflectance distribution with wavelength). Three such pairs of functions are found to fit the data to better than one part in a thousand, with the residuals then showing no systematic structure. The consequences for atmospheric correction of multi-view remote sensing data are discussed.     

The effect of anisotropic reflectance on imaging spectroscopy of snow properties

How does snow's anisotropic directional reflectance affect the mapping of snow properties from imaging spectrometer data? This sensitivity study applies two spectroscopy models to synthetic images of the spectral hemispherical-directional reflectance factor (HDRF) with prescribed snow-covered area and snow grain size. The MEMSCAG model determines both sub-pixel snow-covered area and the grain size of the fractional snow cover. The Nolin/Dozier model analyzes the ice absorption feature that spans wavelength λ≅1.03 μm to estimate snow grain radius when the pixel is fully snow-covered. Retrievals of subpixel snow-covered area with MEMSCAG are progressively more sensitive to the HDRF as grain size decreases, solar zenith angle increases, and fractional snow cover increases. The model overestimates snow cover in the forward reflectance angles by up to +20% and underestimates it in the backward reflectance angles by as much as −15%. Grain size retrievals from both MEMSCAG and Nolin/Dozier are more sensitive to anisotropy as grain size and solar zenith angle increase. MEMSCAG retrievals of grain size are insensitive to snow-covered area. The largest inferred grain sizes occur around a peak in the backward reflectance angles and the smallest generally occur at the largest view angles in the forward direction. Retrievals of albedo from MEMSCAG and Nolin/Dozier are similarly sensitive to anisotropy, with albedo errors up to 5% for a 30° solar zenith angle and up to 10% at 60°. The albedo differences between the two models are less than 0.015 for all grain sizes and solar zenith angles.     

Applications of percolation theory to fungal spread with synergy

There is increasing interest in the use of the percolation paradigm to analyse and predict the progress of disease spreading in spatially structured populations of animals and plants. The wider utility of the approach has been limited, however, by several restrictive assumptions, foremost of which is a strict requirement for simple nearest-neighbour transmission, in which the disease history of an individual is influenced only by that of its neighbours. In a recent paper, the percolation paradigm has been generalized to incorporate synergistic interactions in host infectivity and susceptibility, and the impact of these interactions on the invasive dynamics of an epidemic has been demonstrated. In the current paper, we elicit evidence that such synergistic interactions may underlie transmission dynamics in real-world systems by first formulating a model for the spread of a ubiquitous parasitic and saprotrophic fungus through replicated populations of nutrient sites and subsequently fitting and testing the model using data from experimental microcosms. Using Bayesian computational methods for model fitting, we demonstrate that synergistic interactions are necessary to explain the dynamics observed in the replicate experiments. The broader implications of this work in identifying disease-control strategies that deflect epidemics from invasive to non-invasive regimes are discussed.     

Does Kripke’s Argument Against Functionalism Undermine the Standard View of What Computers Are?

Kripke’s argument against functionalism extended to physical computers poses a deep philosophical problem (not previously addressed in the literature) for understanding the standard view of what computers are. The problem puts into jeopardy the definition in the standard view that computers are physical machines for performing physical computations. Indeed, it is entirely possible that, unless this philosophical problem is resolved, we will never have a good understanding of computers and may never know just what they are.