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Based on the near real-time Global Positioning System (GPS) precise pointing positioning (PPP)-inferred water vapor system recently developed at the University of Calgary, Calgary, Alberta, an Ordinary Kriging procedure has been developed to predict the local and regional precipitable water vapor (PWV) and describe its distribution over Canada using limited available data. The Ordinary Kriging procedure includes five steps: (1) quantifying PWV spatial structure by calculating an experimental semivariogram; (2) fitting semivariogram models (spherical, exponential, and Gaussian) with nonlinear, weighted least squares; (3) determining the best-fitted model with cross-validation analysis; (4) estimating whole PWV maps by Ordinary Kriging interpolation; and (5) outputting kriging standard error maps. The 24 h variogram analysis shows that the correctly calculated experimental semivariogram is essential to the accuracy of the kriged maps, which depends on the configuration of the sites, lag step, and lag tolerance. The optimal lag step and lag tolerance for the current Canadian GPS network configuration are 5° and 2.5°, respectively. Among the three semivariogram models, the spherical model fails in its performance most of the time, and the best hourly fitted semivariogram model is either the exponential (90%) or the Gaussian (10%) model. The Gaussian-model-based Ordinary Kriging process produces more detailed maps. The surface maps of the kriging standard errors indicate that the area between longitudes –125° and –60° and latitudes 44° and 54° has higher accuracy due to higher availability of data. [ABSTRACT FROM AUTHOR]

Abstract Module coupling is an important criterion for evaluating the quality of a software design. While the benefits of reduced module coupling are widely agreed upon, it has been difficult to measure coupling and thus understand it empirically. This study argues the definition of coupling, defines a set of coupling metrics based on the measurement of connections of a module within its running environment, and validates the set using principal component analysis. In an empirical study, the results indicate that these coupling metrics capture three distinct attributes of module coupling. These three attributes represent sources of variation not accounted for in the set of metric primitives and are appropriate for evaluating the coupling complexity of software. This study provides a set of validated measurements of the coupling complexity of software and a new way to evaluate module coupling measurements. [ABSTRACT FROM AUTHOR]