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where + and are the activity coef cients of a cation and anion, respectively, V + and V the corresponding stoichiometric coef cients. However, the range of use is restricted by the maximum molality at which the data were obtained. Empirical mixing rules are available, such as Bromley s (1973) or Kusik and Meisner s rule (1978). (The equations are not presented here, but can be found in the literature, e.g. [95].) These schemes have been used extensively in atmospheric models, and shown to accurately predict activity coef cients over a range of molalities. The disadvantage of such techniques is the lack of being able to treat the system on a more fundamental level. That is to say, the techniques of Pitzer [96] and Clegg et al. [24, 97], hereafter referred to as the CSB approach following Clegg and Seinfeld [29], use binary and ternary interaction parameters derived from numerous systems, whereas the above techniques rely solely on data from binary mixtures. Carslaw et al. [98] also notes that since these rules do not account directly for speci c interactions between solution components, in a study regarding HNO3 H2 SO4 H2 O mixtures, the modelled activity of pure aqueous H2 SO4 had to be adjusted in order to reproduce the measured vapour pressures. Following this, the use of these rules often results in the employment of similar mixing rules used to calculate the water content. Metzger et al. [91] formulated empirical equations, designed speci cally to improve computational performance, for the activity coef cients of solutes as a function of RH. Although noted to be applicable to mixed solutions, and comparable to other mixing rules (see [93]), Metzger et al. [93] note how these equations break down for a sulphuric acid solution. Similarly, this formulation is based on semi-ideality in that interactions between different solutes are not treated explicitly. As stated by Zaveri et al. [99], as this scheme simply assumes that the multicomponent activity coef cient of each electrolyte is equal to its mean binary activity coef cient, which may be reasonable for dilute solutions (RH > 80%), it generally tends to break down for concentrated solutions. More recently, Zaveri et al. [99] developed the multicomponent Taylor expansion method, based on the series expansion rule used for dilute alloy solutions. Here the authors extend this to aqueous electrolyte solutions at any concentration, using the ZSR technique for calculating the water content and the CSB model for deriving most of the important model parameters at 298.15 K.
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have an impact beyond the narrow con nes of academic political psychology, it will need to account, at a minimum, for the patterning of personality variables across the entire matrix of the person (Millon & Davis, 2000, pp. 2, 65). Only then will political personality assessment provide an adequate basis for explaining, predicting, and understanding political outcomes. Moreover, political personologists will need to advance an integrative theory, not only of personality and of political leadership, but also of the personality-politics nexus. In The Psychological Assessment of Presidential Candidates (1996b), Stanley Renshon provides a partial blueprint for this daunting task. Inadequate Progress From Description of Observable Phenomena to Theoretical Systematization Ultimately, scholarly progress in personality-in-politics inquiry hinges on its success in advancing from the natural history stage of inquiry to a stage of deductively formulated theory (Northrop, 1947). The intuitive psychologist s ability to sense the correctness of psychological insight (chapter by Millon in this volume) presents an easily overlooked obstacle to progress in political-personological inquiry. Early in the development of a scienti c discipline, according to philosopher of science Carl Hempel (1965), investigators primarily strive to describe the phenomena under study and to establish simple empirical generalizations concerning them, using terms that permit the description of those aspects of the subject matter which are ascertainable fairly directly by observation (p. 140). Hermann s (1974, 1980) early work illustrates this initial stage of scienti c development. In the words of Hempel (1965),
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Behan, W. M. H., 386 Beilin, L. J., 602, 611, 612 Beiser, M., 42, 47 Beitchman, J. H., 399, 408 Belanger, A. M., 364 Belar, C. D., 1, 2, 22 Belcher, L., 227, 239 Beliard, D., 136, 139 Belilos, E., 518, 519, 537 Belknap, J. K., 163 Bell, D. S., 196, 210, 372, 390 Bell, J., 107, 119 Bell, R. A., 551, 565 Bell, R. M., 155, 162, 163 Bellinger, D. L., 76, 93, 181, 186 Bellodi, L., 88 Belsky, J., 447, 459 Belzer, M. E., 479, 482 Bem, S. L., 533, 537 Bendel, R., 557, 562 Bender, B., 78, 88, 104, 109, 115 Bendtsen, L., 251, 265 Bendtsen, P., 59, 68 Benefante, R., 495, 503 Bengtsson, C., 563 Benjamin, C., 188, 413 Bennett, B., 374, 386 Bennett, D. A., 496, 510 Bennett, E. J., 399, 408 Bennett, P., 6, 15, 21, 127, 143, 405, 408, 518, 538, 550, 565 Bennett, R. M., 172, 181, 183, 186, 190 Benotsch, E. G., 234, 240 Benowitz, N. L., 147, 162 Benschop, R. J., 94 Ben-Shlomo, Y., 503, 506 Bensing, J., 373, 389 Benson, H., 254, 262, 332, 335, 353, 359, 406, 408 Benson, L. J., 559 Benson, P., 280, 287 Bentall, R. P., 379, 389 Bentley, G., 295, 314 Benyamini, Y., 498, 506 Benz, R. L., 323, 336 Ben-Zur, H., 38, 42, 44, 45 Beresford, T., 159, 167 Berg, C. J., 543 Berg, P. A., 182, 187 Berg, S., 500, 507 Berga, S. L., 93 Berge, K., 364 Berger, M., 600, 613, 614 Bergeron, A. M., 594, 607 Bergman, L. H., 457, 460 Bergman, R., 217 Bergman, W., 73 Bergmann, B. M., 319, 336 Bergner, M., 306, 313 Berkbigler, D., 109, 118 Berkey, S. C., 358 Berkman, A. H., 419, 431 Berkman, L., 59, 61, 63, 68, 73, 82, 88, 323, 337, 342, 357, 498, 503 Berkowitz, J., 314
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