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Of course the magnitude of the collateral effects of using ground or surface waters varies greatly from one situation to another Nevertheless, acknowledgment that ground and surface waters constitute a single resource is the rst step towards developing a mature approach to their management We must recognize that any use of a natural water resource, surface or subsurface, is a choice, and a choice which inevitably limits the scope for making other possible choices concerning the same overall water resource system Of course on short time-scales the limitations imposed by making one choice may not be very obvious, and may well be reversible But cumulatively, drop-bydrop so to speak, we gradually drain the cup of our water resource options with each additional use.

Having accepted that resource use involves making a choice, a plethora of questions immediately arise: Who makes the choice For whom is the choice made Is the choice we ve made the best option from an economic, ecological or social perspective This book provides an introduction to the scienti c insights and techniques needed to develop well-informed responses to these and related questions It is hoped that the reader will be inspired to pursue the subject in greater depth, in which case they will be able to.

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Table 3.1. Populations (log10 CFU/cm2) of indicator organisms and pathogens before and after application of decontamination interventions on hides, beef carcasses and beef or pork trimmings. (cont.) Surface Interventions in sequence 1st Ozonated water (1%, 15 min, 7.2 C) 2nd CPCe (0.5%, 15 min, 7.2 C) NA 3rd NA 4th E. coli Coliforms APC Salmonella Typhimurium ClO2 (200 ppm, 15 min, 7.2 C) Trisodium phosphate (10%, 15 min, 7.2 C) NA NA E. coli Microorganism(s) Log10 CFU/cm2 or g Before 6.8 6.0 7.1 5.8 6.8 After 5.1 4.1 5.6 4.0 6.2 Reference Acetic acid (5%, 3 min) CPC (0.5%, 3 min) CPC (0.5%, 3 min) Trisodium phosphate (10%, 3 min) NA NA Coliforms APC Salmonella Typhimurium APC Salmonella Typhimurium APC Salmonella Typhimurium APC Salmonella Typhimurium 6.0 7.1 5.8 7.1 5.8 7.1 5.8 7.1 5.8 5.7 6.8 5.5 5.3 3.8 5.9 4.4 6.2 4.6 Pohlman et al. (2002b) ClO2 (200 ppm, 3 min) NA NA CPC (0.5%, 3 min) NA NA

Deg = Application.WorksheetFunction.Degrees(3.14)

Automation reduces cost. On average, we had two people in each of the ten different departments spending all of their time manually processing and crunching numbers. We wanted to leverage technology to introduce greater efficiency. Dashboard deployment helped achieve greater automation and reduced the number to six people who now maintain the process for all ten departments. The automation has led to far fewer errors as well.

Column view has a trick to get a column just wide enough to display all its items. If you double-click the column divider control, the column snaps instantly (or not so instantly, if there are a lot of items in the column) to exactly the right width for showing everything it contains. Note that there s some inconsistency in what happens when you drag different user interface elements in the Finder. The sticky sidebar dragging trick described earlier in Make Perfect Sidebars Every Time would be handy here, but it doesn t work; double-clicking the divider control performs a very different action than double-clicking the sidebar dot.

Some of the differences between ActionScript and JavaScript are as follows:

(rule center closest to the new data point). We return to our step list: Step 6: Recursive calculation of the consequent parameters by modi ed RLS algorithm for globally optimal parameters or by modi ed wRLS for locally optimal parameters [4]. When the number of rules increases or decreases, the number of parameters to be optimized increases or decreases as well, and for RLS special care must be taken with the covariance matrix that changes its dimensions in these cases. Firstorder Takagi-Sugeno rules are used and the number of parameters increases quickly with the number of rules. Step 7: Prediction of the model output for the next time step. With the rule base as it is, after updating, predict the output for a certain future instant. The algorithm continues from stage 2 at the next time step (theoretically, an in nite loop in case of online learning). The prediction error will be measured when the future instant arrives and will then be used in the quadratic criterion of the recursive least squares for consequent parameter learning.

Change the captions .PivotFields( Sum of BUDGET ).Caption = Budget ($) .PivotFields( Sum of ACTUAL ).Caption = Actual ($) .PivotFields( Sum of Variance ).Caption = Variance ($) End With Application.ScreenUpdating = True End Sub

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