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An improved estimation of the value ξ= 2 originally proposed by Halpin and Tsai is given through a set of finite element analyses that consider randomly distributed unidirectional fibers for different volume fractions.
For generality, we consider randomly deployed sensor networks and formulate constrained optimization techniques in a Bayesian experimental design framework to obtain the best knowledge about a given state-of-nature represented by a finite number of parameters, and to make the optimal decisions for statistical classification problems based on the Neyman-Pearson fundamental lemma.
Since we consider randomly occurred paint defects, the problem is modelled by stochastic programming.
The emergence of pathogen population structure is robust to alternative formulations of our multi-locus model that do not consider sequential expression, or formulations that consider randomly ordered sequential expression.
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Considering randomly arriving objects on a conveyor, this task becomes even more challenging.
For this reason, each set of simulations considers randomly selected meteorological situations (to avoid possible subjective bias in the date selection) representative of the Antarctic summer 19822, S-82) and winter periods (1995, W-95).
Level of artifacts in spectra obtained by Multidimensional Fourier Transform has been studied, considering randomly sampled signals of high dimensionality and long evolution times.
Mode-I, Mode-II and mixed mode subcritical crack growth mechanisms considering randomly distributed initial microcracks have been used in the calibrated time-independent model.
For statistical purposes, a Monte Carlo simulation was adopted, where for all the presented results we studied 10,000 random topologies considering randomly generated users' positions.
Though some crystals are small enough to be considered randomly oriented, big crystals such as cerussite and hydrocerussite are bigger than the beam spot used obtaining a diffraction pattern different from that of the standards and thus difficult to be identified.
These samples can be considered randomly sampled with respect to TNS.
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