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Figure 8.3.5 Pair distribution functions 9ij (r) for media with particles of three different sizes as a function of r normalized by 2al. The simulation parameters are N = 481, N 1 = 324, N2 = 125, N3 = 32, a3 = 1.5al, a2 = 1.2al, h = 0.15, 12 = 0.1, 13 = 0.05, and b. = 0.03167. The data points are Metropolis Monte Carlo simulation results, and the curves are obtained from Percus-Yevick calculations.

a mixture of three particle sizes with a3 > a2 > al. The particle si2es and volume fractions are (i) a3 = 1.5al, a2 = 1.2al, h = 0.15, 12 = 0.1, and 1:3 = 0.05 in Fig. 8.3.5 and (ii) a3 = 2al, a2 = 1.2al, h = 0.12, 12 = 0.09, and 13 = 0.05 in Fig. 8.3.6. We observe that it usually requires more particles in the simulation processes to obtain better results when the ratios of particle size are increased. The maxima of pair distribution functions for multiple size cases are at the separation equal to the sum of the radii of two particles. Thus gij(r) is maximum at r = ai + aj' We note that gij(r) exhibits the most oscillation when both i and j are of the largest size, while there is less oscillation in gij (r) for smaller sizes. This shows that in a dense medium with particles of different sizes, the larger particles show stronger correlation and less freedom than smaller particles in positioning themselves in the presence of all other particles. Generally, the features of the function of gij are between gii and gjj' The results in Figs. 8.3.3-8.3.6 are in good agreement with the Percus-Yevick approximation. In Fig. 8.3.7, we use the sequential addition method to generate random distributions of particles and re-examine the cases of the particles of same size for (i) f = 0.2, N = 400, and (ii) f = 0.3, N = 600. The simulated pair distribution functions obtained are averaged over T = 30 realizations. The

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One of the easiest ways to build your test project is to build it using the Code Editor after you have developed the code to test You can use this technique to automatically build unit test methods for every method in every class in every project of your application To build a test project based on existing code using the Code Editor: 1 Right-click in the Code Editor and select Create Unit Tests from the context menu The Create Unit Tests dialog is displayed, as shown in Figure 612

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From a business perspective, the major concerns of countries in the Pa ci c Rim and elsewhere that are devoting signi cant resources to develop ing biotech parks is the timetable for technology developments and the sociopolitical landscape that will de ne the business environment once the technology is ready for consumption. What is the best mix of initiatives pharmaceutical, military, or some other area that will best provide the likelihood of economic success over the next ve years and into the next decade. Corporate and political leaders are faced with the challenge of fa cilitating simultaneous progress in complementary areas, such as healthcare and tissue engineering or computing and bioinformatics. Furthermore, it s essential for investors in biotech to develop a realistic timeline for use ful and pro table developments in each of these elds.

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Figure 8.3.6 Pair distribution functions 9ij (r) for media with particles of three different sizes as a function of r normalized by 2al. The simulation parameters are N = 1284, Nl = 864, N2 = 375, N3 = 45, a3 = 2al, a2 = 1.2al, h = 0.12, 12 = 0.09, 13 = 0.05, and Ll = 0.03213. The data points are Metropolis Monte Carlo simulation results, and the curves are obtained from Percus-Yevick calculations.

Figure 612 The Create Unit Tests dialog allows you to generate unit tests for the class members you specify 2 Check the desired classes, properties, and methods to include in the test The unit testing feature generates a unit test method for each property and method that you select

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