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Shallow, fresh groundwaters in recharge areas in a wide range of aquifer types Typical of the leading-edge of sea water intrusion into shallow uncon ned aquifer Shallow, fresh groundwaters in aquifers composed (at least in part) of dolomite Shallower portions of regional con ned aquifers; waters deduced to have been affected by ion exchange (see Section 4.4.2) Gypsum-bearing sedimentary aquifers, and groundwaters affected by oxidation of pyrite and other sul de minerals Normally the result of mixing of two or more different facies Rare; can result from mixing of ancient Na-rich groundwaters with pyrite oxidation waters||; also formed by intensive evaporation of waters which have previously lost their Ca and HCO3 to calcite precipitation, Associated with the invasion of shallow, fresh aquifers by sea water ; less commonly, can form by dissolution of the very rare evaporite mineral tachyhydrite (CaCl2) Mixing of fresh and saline waters ; possible in uence also of reverse ion exchange In uence of sea water, ancient saline groundwaters, or dissolution of halite (NaCl)

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P2P DDBEs, 529 Global query, 173 Global timestamp, 271 Global Transaction Manager (GTM), 21, 510 Global transaction, 199, 239, 253, 271, 352 Global wait-forgraph (GWFG), 262, 271 Globally unique identi ers (GUIDs), 520 521, 533 Graph Sending Rule, 267 Greedy algorithm, 143, 173 Group, 437, 447 Grouping AT, 397, 420 Grouping, in vertical fragmentation, 49 Growing phase, 240

Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

18. 19. 20.

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smaller than .05, while SRMSD(X) has to be only 1.11 to obtain a p value smaller than .05 with n = 100. Illustration 1 The data set in Bland and Altman (1999, pp. 137 138) is analyzed for the purpose of illustration. The data set consists of systolic blood pressure measures, where simultaneous measurements were made by three methods. The stability between the first measures of Method J (J1) and Method R (R1), as well as the stability between the first measures of J1 and Method S (S1), are examined. Repeatedmeasure ANOVA tables are presented in Tables 34.3 and 34.4. Correlation between the two measures, SRMSD, SSD, and corresponding p value are summarized in Table 34.5. The limits of agreement, 95% confidence interval (CI ) for the limits and 95% CI for the mean differences, are included in the table, based on Bland and Altman s method, as well as the mean and the SD of score differences. The stability between J1 and S1 was also examined by Bland and Altman (1999). Consequently, they reported 95% CI for limits of agreement, the same as the results in Table 34.5. Based on these values, Bland and Altman conTable 34.3 Repeated-Measure ANOVA Method J versus Method S in Bland and Altman s (1999) Data Source of Variation Between people Within people Between measures Residual Sum of Squares 165,060.800 192.000 3.388 188.612 df 84 85 1 84 Mean Square 1,965.010 2.259 3.388 2.245

2. 3. 4.

many different kinds of individual and group trajectories over age or time, including the addition of covariates. All these features are apparent without directly writing or specifying a model for the full trajectory over time. Results From Fitting Dynamic Growth Models To illustrate some of these dynamic growth models, the Bradway-McArdle Non-Verbal data were tted (using Mx and NLMIXED). A new model (M4) based on the partial adjustment model (as described in Equation 18.26) required four free parameters with individual differences in the initiallevel variance (an asymptote) and in the latent slope (the distance from asymptote) parameters. This model requires all loadings to have be an exponential function formed from a single rate parameter (estimated at = 0.16), and the resulting expected trajectory is drawn as the solid line in Figure 18.9. In contrast to the shape of previous latent basis model, this is an exponential shape that rises rapidly and then stays fairly constant at the asymptote (or equilibrium point) from age 42 to age 65.

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Note. Detailed discussions of these three perspectives are provided by Cramer (2000), Ihilevich & Gleser (1986, 1991), and Vaillant (1986).

Step 3: Schema Integration Schema integration is the last step in the generation of the GCS from its component export schemas. The challenges in this step are due to overlaps, similarities, differences, con icts, inconsistencies, and interpretations of each export schema that is to be integrated. This is the most time-consuming and errorprone step in the process. The dif culty of this step does not have a technical source but stems from not understanding the contents, the intentions, and the semantics of structures contained in each export schema. That is why this step cannot be completely automated. Stepwise re nement of the GCS is required with the DBAs and the DDBE designers working together to make sure that the outcome is correct. The integration step is divided into four substeps.

Following is a function that can have any number of single-value arguments (it doesn t work with multicell range arguments). It simply returns the sum of the arguments.

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