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The values that are checked by default depend on where you were in the Code Editor when you selected the Create Unit Tests option If you were on the class de nition, the class and all its members are selected If you were on a speci c member, only that member is selected 3 Select the desired Output project For a new test, select the default Create a new Visual Basic test project option If you already created a test project, you can add to it by selecting the project from this drop-down list 4 Click OK The New Test Project dialog is displayed (see Figure 613) for entry of the test project name

Figure 8.3.7 Pair distribution functions g(r) for media with particles of single size as a function of r normalized by 2a. The simulation parameters are (i) N = 400, f = 0.2 and (ii) N = 600, f = 0.3, and T = 30 realizations. The data points are Monte Carlo simulation results using sf'quential addition method, and the curves are obtained from Perc us- Yevick calculations.

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simulated pair distribution functions compare well with the Percus-Yevick approximation. This example shows that both the numbers of required realizations and the number of particles may be small if using the method of sequential random addition. Both Metropolis shuffling approach and sequential deposition method have been used to create ensembles of randomly distributed spherical particles. Generally speaking, Metropolis method is a good candidate for creating high volume fraction configurations. Because of the rising rate of overlapping, the sequential deposition method will become difficult by depositing particles one by one into the test volume when the density of particles becomes higher (> 30%).

he prospect of producing personalized, designer pharmaceuticals, or high-protein, vitamin-enriched rice to feed malnourished millions in In dia, Africa, and other developing countries, and the prospect of cloning an endless supply of healthy replacement organs and tissues for aging baby boomers in the United States seem like lofty goals with obvious bene ts to society. Similarly, the means to cure all genetic disease and extend the hu man lifespan by several decades are, at least super cially, intuitively valu able. After all, what parents would turn down a guarantee their children or grandchildren would not inherit a gene for a potentially lethal disease, such as breast cancer or cystic brosis Furthermore, what heart attack victim wouldn t give his life savings for an injection of embryonic stem cells if the treatment could repair heart tissue following an event that would otherwise be fatal Although these and other biotechnologies may be fascinating to the medical research community, the concern, from a practical, economic per spective, is the value proposition associated with each of them. For exam ple, high-protein, vitamin enriched rice is of no value to people in underdeveloped countries when the underlying issue is poverty. Most peo ple who are malnourished in Africa can t afford to buy enough food to eat, regardless of the nutrient density of the available agricultural products. What s more, even if the genetically engineered nutrient-packed food were donated by a biotech company, government public health of cials might re ject the aid because of their concerns regarding the long-term effects of food on the health of their country s citizens. Alternately, they may resent being used as Third World guinea pigs for an experimental crop developed by some multinational agricultural corporation.

In this section we consider particle with surface adhesion. This means that when the particles are randomly packed together, they have a tendency to form clusters and bonds with each other. This is an appropriate model for certain types of geophysical media and composite materials. For example, terrain snow consists of ice grains that form bridges as metamorphism occurs. The clustering potential is modeled in the Percus-Yevick pair distribution formulation with spheres displaying surface adhesion. The adhesive force is parameterized using a variable T which governs the degree of clustering. The structure factor has a closed form analytical solution.

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