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When the volume additivity assumption is employed for calculating solution densities where experimental does not exist, for example with many organic solutions, then it is of course consistent to use (6.38). Figure 6.12 shows the Kelvin factor as a function of aerosol wet size for various inorganic compositions. It serves to illustrate that as the aerosol size decreases the Kelvin factor increases exponentially. Similarly, it is sensitive to the chemical composition of the droplet, thus requiring models for the surface tension that can capture this variability. Again, inorganic systems are relatively well understood. For single-salt systems, empirical ts are commonly used, though there can be inconsistencies between different ts that inevitably introduce sensitivities to growth factor calculations (see [182]). Indeed, Hameri et al. [182] found that below 10 nm, the variation in surface tension for (NH4 )2 SO4 derived from different parameterisations produced a noticeable difference in predicted water uptake behaviour. Above 10 nm, the difference was negligible. For multicomponent systems, three groups of calculation have been presented in the literature [183]. These include simple additive methods (e.g. [184 187]) and conventional methods based on the Gibbs adsorption equation [181]. It is well known that some organic compounds are surface active and their presence in solution can signi cantly affect the surface tension of cloud droplets [166, 188, 189]. Whereas for non-aqueous solutions the mixture surface tension in some cases can be approximated by a linear dependence, aqueous systems show pronounced non-linear characteristics. This is typical of organic/aqueous systems, in which small concentrations of the organic material may signi cantly affect the mixture surface tension [190]. Whilst predictive methods are available,

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Agatisa, P. K., 537 Agnew, H. W., 318, 337 Agurs-Collins, T. D., 546, 559 Ahern, D., 299, 313, 347, 357, 359 Ahima, R. S., 126, 138 Ahlbom, A., 342, 360 Ahles, T. A., 175, 185, 252, 262, 263 Ahluwalia, J. S., 552, 559 Ahmed, M., 388 Ahmed, S. H., 152, 162 Ahn, D. K., 546, 557, 561, 567 Aho, K., 171, 185, 188 Aiken, L. S., 385, 454, 459 Aikens, J. E., 196, 210 Aisquith, B. F., 155, 163 Aitchison, T., 592, 612 Aiuti, F., 73 Akerstedt, T., 369, 384 Akil, H., 181, 185 Akman, D., 408 Aksit, B., 42, 47 Alarcon, G. S., 185, 188 Albain, K. S., 501, 506 Albert, A., 204, 217 Albert, S. G., 196, 211 Alboukrek, D., 189 Albrecht, R. J., 130, 138 Albu, J., 206, 210 Alcaraz, J. E., 144 Aldag, J. C., 178, 190 Aldenkamp, A. P., 476, 482 Aldwin, C., 44, 45, 496, 507, 508 Alexander, B. K., 152, 162 Alexander, C. J., 419, 430, 435 Alexander, C. N., 596, 605, 608, 609 Alexander, F., 102, 115, 354, 357, 557, 559 Alexander, R. W., 185, 188 Alexander, S. D., 332, 334 Alferi, S. M., 65, 68 Al ci, S., 174, 185 Al eri, T., 56, 71 Ali, A., 413 Ali, N., 279, 286 Alkan, N., 42, 47 Allan, R., 339, 349, 357 Allard, M., 500, 507 Allemand, H., 399, 409 Allen, G. M., 107, 115, 539 Allen, J., 451, 461 Allen, K. A., 61, 71 Allen, K. D., 260, 262, 471, 482 Allen, S. M., 281, 286 Allender, P. S., 355, 357 Allerton, M., 225, 241

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Additive noise channel. Find the channel capacity of the following discrete memoryless channel:

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GIS, it retrieves the GUID for each global table and column referenced in the query as part of its validation and disambiguation process. These GUIDs are then added to the fully quali ed version of the query (along with the other quali cation details). Using a GUID value as a unique identi er for GIS constructs (and LDS constructs) is not a new idea it is somewhat analogous to the version identi er ( serialVersionUID ) used by Serializable classes in Java, and also similar to the various identi ers stored in the registry by the Microsoft Common Object Model (COM) the ancestor of Microsoft .NET Framework. While those two environments were initially implemented as centralized programming environments, many distributed computing mechanisms also use GUID values for this same purpose namely, identifying when a deployed or distributed version of something matches the expected version. There are many examples of environments using this approach, such as the Common Object Resource Broker Architecture (CORBA), Microsoft .NET Framework, and Java s Remote Method Invocation. This enables components that are disconnected from each other most of the time to verify that they are synchronized each time they dynamically connect to each other.

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