PubMed · 16335607
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Keith Hurst. 2005. Quality assurance frameworks.. https://doi.org/10.1108/ijhcqa.2005.06218faa.001
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The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks, elements of the synaptic connectivity matrix can be chosen randomly from appropriate distributions, making results from random matrix theory highly relevant. Unfortunately, classic results on the eigenvalue spectra of random matrices do not apply to synaptic connectivity matrices because of the constraint that individual neurons are either excitatory or inhibitory. Therefore, we compute eigenvalue spectra of large random matrices with excitatory and inhibitory columns drawn from distributions with different means and equal or different variances.
Wettability is a dominant parameter governing spontaneous imbibition. However less attention has been paid to the effect of wettability on the scaling of spontaneous imbibition data. Actually few models can include wettability in scaling of spontaneous imbibition data. To this end, a scaling model has been developed for NAPL (oil)-saturated porous media with different wettability based on the fluid flow mechanisms in porous media. Relative permeability, capillary pressure, initial water saturation, and wettability are considered in the scaling model. Theoretically this scaling model is suitable for both cocurrent and countercurrent spontaneous imbibition. The experimental data of countercurrent spontaneous water imbibition at different wettability cannot be scaled using the frequently used scaling model but can be scaled satisfactorily using the scaling model developed in this study. An analytical solution to the relationship between recovery and imbibition time for linear spontaneous imbibition has also been derived in the case in which gravity is ignored. The analytical solution predicts a linear correlation between the recovery by spontaneous water imbibition and the square root of imbibition time, which has been verified against experimental data.
A deterministic model was developed to predict pollutant mass first flush and to utilize it for better design of best management practices (BMPs) that focus on treating the first flush. The model used the kinematic wave equation to calculate flow and mass transport, and erosion equations to calculate pollutant concentrations, which were assumed to be from a short and a long term source. The model parameters were calibrated with a parameter estimation procedure using three years' monitoring data from a highway runoff site in west Los Angeles. The simulation results showed that there exists an optimum watershed size to maximize first flush. Contours of watershed length, developed from simulations for different conditions of rainfall and watershed geometry, can be used to design runoff collection systems for highways and parking lots to maximize first flush.