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(2002) and Crawley et al (2001) report on the evolution of the programs BLAST and DOE-2, both of which contained in exible generic plant and controls model options, into a new modular program EnergyPlus The program has evolved as an integrated stand-alone modeller, as well as permitting links to third-part software (Fig79) Besides the facility to link the zonal ventilation program COMIS (Feustel 1999) and EnergyPlus (Crawley et al 2001) see Fig79, Dorer & Weber 1999 describe the development of a linkage mechanism with the modular simulation program TRNSYS (undated) within a shared graphical user-interface by translating COMIS as a TRNSYS Type For higher levels of energy and ventilation modelling sophistication, the coupling and integration of energy simulation and computational uid dynamics (CFD) has received widespread attention (Bartak et al 2002; Beausoleil-Morrison 2002; Zhai & Chen 2003; Negr o 1998; Zhai et al.

The way in which cattle are fed markedly affects the CLA content of their meat and milk; those that graze upon pasture have more CLA than those that are fed upon grain and commercial cattle foods It is possible to manipulate the CLA content of milk fat by changing the diet of cows (reviewed by Lawson et al 2001) In some non-ruminant species CLA precursors can be produced by their gut bacteria and these are converted into CLA after absorption In general, it is thought that.

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2002; Kim et al 2001; Murakami et al 2001) Bartak et al and Beausoleil-Morrison focus on integrative methods within the program ESP-r whilst Zhai et al focus on linkage mechanisms between energy simulation programs Though surface conditions and air diffusion are interrelated and it is therefore necessary to call the CFD calculations at each simulation time step, this is computationally very expensive Figure 711 illustrates a bypass mechanism which therefore permits CFD calculations to be called at periods greater than the simulation time step in effect a quasi-steady-state method as far as the CFD calculations are concerned (Zhai et al 2002) Kim et al (2001) and.

One approach is to gain access to the costing data and directly alter the per-unit costs of all or selected inventory items If the changes made are extremely small, such as tenths of a cent, the differences may appear so insignificant that an auditor will not bother to trace why there is such a tiny difference in the computer s recorded cost and the cost on the supplier s invoice However, when there are many units of a particular item in stock, that small incremental change in the cost can result in a significant alteration in the value of the inventory, and so it is worth the effort for a fraudulent manager to undertake The best way to spot this problem is to use an accounting system that records and reports on all transactions in the system.

19 14.63 13 0 0 0 0 13628.8 0 13628.8 A0 = 800 P&L = 3200.8 P&L1 = 11244.8 P&L2 = 12828.8 IM = 800 MM = 600 INDIVIDUAL TRADES 0 (0,1) P&L=175.6 SIZE=1 P&L/SIZE=175.6 EQ=201.6 COST=26 1 (1,2) P&L=175.6 SIZE=-1 P&L/SIZE=175.6 EQ=201.6 COST=26 2 (2,5) P&L=164.4 SIZE=1 P&L/SIZE=164.4 EQ=190.4 COST=26 3 (5,6) P&L=276.4 SIZE=-1 P&L/SIZE=276.4 EQ=302.4 COST=26 4 (6,9) P&L=63.6 SIZE=1 P&L/SIZE=63.6 EQ=89.6 COST=26 5 (9,12) P&L=328.8 SIZE=-2 P&L/SIZE=164.4 EQ=380.8 COST=52 6 (12,13) P&L=1000.8 SIZE=2 P&L/SIZE=500.4 EQ=1052.8 COST=52 7 (13,14) P&L=1501.2 SIZE=-3 P&L/SIZE=500.4 EQ=1579.2 COST=78 8 (14,17) P&L=4518 SIZE=5 P&L/SIZE=903.6 EQ=4648 COST=130 9 (15,17) P&L=1232.4 SIZE=3 P&L/SIZE=410.8 EQ=1310.4 COST=78 10 (16,17) P&L=75.2 SIZE=4 P&L/SIZE=18.8 EQ=179.2 COST=104 11 (17,18) P&L=3316.8 SIZE=-12 P&L/SIZE=276.4 EQ=3628.8 COST=312 STATISTICS OF TRADES Total P&L = 12828.8 Total P&L/unit = 3630.4 Gross profit = 12828.8 Gross profit/unit = 3630.4 Gross loss = 0 Gross loss/unit = 0 Total number of trades = 12 Number of winning trades = 12 Number of losing trades = 0 Average profit = 1069.06667 Average profit/unit = 302.533333 Average loss = 0 Average loss/unit = 0 Largest winning trade = 4518 Largest winning trade/unit = 903.6 Largest losing trade = 0 Largest losing trade/unit = 0 Max number of consecutive wins = 12 Max number of consecutive losses = 0 Maximum consecutive profit = 12828.8 Maximum consecutive profit/unit = 3630.4 Maximum consecutive loss = 0 Maximum consecutive loss/unit = 0 PL distribution 0 (0, 3000] 10 1 (3000, 6000] 2 PL/unit distribution 0 (0, 300] 8 1 (300, 600] 3

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