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Observations and modelling of the variation of aerosol properties as a function of temperature. In (a), the grey line represents the equilibrium composition of aerosol for 4.8 ppmv H2 O, 8.1 ppbv HNO3 , 0.5 ppbv H2 SO4 , at a pressure of 50 hPa, and demonstrates how aerosol composition changes with temperature. The temperature scale used is the amount of cooling below the nitric acid trihydrate stability temperature (T TNAT ). (a) also shows measured NO y content of the aerosol phase (black crosses). The measurements were taken as air parcels were being cooled rapidly by a mountain wave, and may be compared to microphysical model simulations (grey crosses) and the equilibrium curve (grey line). Although the measured behaviour follows the equilibrium line approximately, there is signi cant deviation due to lag and hysteresis in the absorption of nitric acid. The change in composition is accompanied by an increase in condensed volume, which is exempli ed by the change in the visible-light backscattering coef cient in (b) (black crosses). Again the measurements may be compared to microphysical model simulations (grey crosses). (This gure is a black-and-white version of Figure 9 in Lowe et al. [33]. For the rst observations of this temperature-dependent composition and volume change at polar stratospheric cloud temperatures, see Dye et al. [34].)



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A uthorization (Real Time)

taking the examination at the same time. Others choose to borrow materials from a colleague who recently took the examination. While the biannual national examination is renewed and updated each time it is given, certain areas of psychology such as statistics do not change radically between administrations. Some choose not to use any of these testing materials at all. With adequate preparation, many successfully pass the written examination. States do vary in designating what constitutes a passing score. Some states consider a fixed number of correct items such as 150 out of 200 as the criterion for passing, while others use the national median, which changes with each testing administration. Approximately 2,200 people take the examination across the United States each time it is given. An applicant may take the exam again if not passed on the first (or subsequent) attempt(s).





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As the name implies, with substitution-based stego you go in and substitute data for information that is already in the file, often referred to as overwriting. This method may seem straightforward, but you have to be very careful that you don t overwrite data in a way that would make the file unusable or visually flawed. The trick is to find insignificant information in a file information that can be overwritten without having any impact. For example, you can take the Word example discussed in the previous section and use a substitution-based technique instead of an insertion-based technique. In this case, any data that is between end-of-text and beginning-of-text markers has minimal impact on the document and could be overwritten. The difference is that with substitution, the size of the file has not changed. There is a limit to how much data you can hide with this approach because you are limited to the amount of insignificant data in the file.

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Note that the decision to discard the variable is made here in the modeling phase rather than the EDA phase. We should not discard variables in the EDA (data understanding) phase due simply to lack of apparent pairwise relationship with the response. One never knows what relationships exist in higher dimensions, and we should allow the models to decide which models should and should not be retained, as we have done here. The exclusion of lifestyle cluster has improved the performance of the logistic regression model from $2.90 to an estimated $2.96 pro t per customer. This $0.06 represents an improvement of 18% over the model that retained the variable, compared to the baseline model. Therefore, in this case, less is more. On the other hand, the CART model is completely unaffected by the exclusion of lifestyle cluster, since the variable did not make it into the model to begin with. The performance of both the C5.0 model and the neural network model degraded slightly with the exclusion of the variable. However, our best model so far remains the logistic regression model using 80% 20% balancing, no principal components, and excluding the lifestyle cluster eld. Note that without the application of overbalancing as a surrogate for misclassi cation costs, we would not have had access to a helpful logistic regression model. This model provides an estimated pro t per customer of $2.96, which represents a solid improvement of 45% over the models that applied misclassi cation costs ($2.81) directly as compared to the baseline benchmark of $2.63 [i.e., ($2.96 $2.81)/($2.96 $2.63) = 45%].

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