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2 Divide each item s total contribution margin by the average contribution margin to arrive at the profit factor For example, in Exhibit 65, the first menu item shows a total contribution margin of $2,466 in column L This figure, divided by the average of $2,235 from step 1, results in a profit factor of 110, which is recorded in column T It is wrong to assume that if an item has a very high profit factor this is good Because of the way in which profit factors are calculated, the average of all profit factors is 10 This means that any profit factors higher than 10 have to be balanced by other profit factors lower than 10 In other words, the higher some items profit factors are, the lower others will be.

No structure from component schemas is lost in the integrated schema. This requirement is known as the completeness of the integrated schema. The integrated schema must not have any duplicate information. This requirement is known as the minimality of the integrated schema. The integrated schema must be easily understood.

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a major consequence of their rather fixed system of interpreting child development Recently, this position has begun to change, and homosexuality in some psychoanalytic centers is now perceived as one of many possible types of normal sexual adaptation Other psychoanalytic theories have also begun to change during the past decade (Gabbard, 2000; Kandel, 1999) Some nonanalyst psychiatrists and psychologists have employed a concept of normality analogous to that of the psychoanalysts They tended to focus on individuals who functioned very well on a wide variety of tasks: While most people have problems in at least a few of the variables under study (eg, to be an astronaut, an individual had to function well in many areas; he or she was in effect in a class of supernormals ), only a few individuals adapt extraordinarily well Normality was perceived by some investigators as fully functioning behavior.

22 K P Ajoy, D Popovic, Transparent Fuzzy/Neuro-Fuzzy Modelling in Computational Intelligence in Time Series Forecasting, Theory and Engineering Applications, London: Springer, 2005, pp 275 303 23 Plamen Angelov, Edwin Lughofer, Data-Driven Evolving Fuzzy Systems Using eTS and FLEXFIS: Comparative Analysis, Int J General Systems, 37(1): 45 67, 2008 24 J Victor, A Dourado, Evolving Takagi-Sugeno Fuzzy Models, Technical Report, CISUC, 2003, http://cisucdeiucpt/acg 25 B Scholkopf, A Smola, Learning with Kernels Cambridge, MA: MIT Press, 2001 26 V Vapnik, The Nature of Statistical Learning Theory Springer, 1995 27 J Victor, A Dourado, Online Interpretability by Rule Base Simpli cation and Reduction Proc Eunite Symposium, Aachen, 2004 28 J-SR Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference Systems, IEEE Trans Syst Man Cybern, 23: 665 685, 1993 29 O Nelles, LOLIMOT Local Linear Model Trees for Nonlinear Dynamic System Identi cation, Automatisierungstechnik, 45(4): 163 174, April 1997 30.

Thus, the menu will not be a balanced menu, which it would be if all menu items differ only slightly from the average of 10 Items that have very high profit factors have to be offset by items with very low profit factors The operating expenditures for the very low profit factor menu items are generally considered as being wasted Such expenditures are for purchasing, receiving, storing, issuing, preparation, and service However, this point of view is far from correct from a marketing point of view It is important not to lose sight of this; the variance and availability of a balanced menu is not insignificant from the viewpoint of customers Stars Stars are menu items that the restaurant manager would prefer to sell whenever possible These items should be left on the menu unless there is a good reason to remove them.

R Babuska, Fuzzy Modeling for Control Boston: Kluwer Academic Publishers, 1998 31 P Angelov, C Xydeas, Fuzzy Systems Design: Direct and Indirect Approaches, Soft Computing: A Fusion of Foundations, Methodologies and Applications, 10: 836 849, Springer Verlag, 2006 32 M Setnes, R Babuska, U Kaymak, H R van Nauta Lemke, Similarity Measures in Fuzzy Rule Base Simpli cation, IEEE Trans on Systems, Man, and Cybernetics Part B: Cybernetics, 28(3): 376 386, 1988 33 Y Jin, Fuzzy Modeling of High-Dimensional Systems: Complexity Reduction and Interpretability Improvement, IEEE Trans on Fuzzy Systems, 8(2): 212 221, 2000 34 A Bordes, S Ertekin, J Weston, L on Bottou, Fast Kernel Classi ers with Online and Active e Learning, J Machine Learning Research, 6: 1579 1619, 2005 35 G Cauwenberghs, T Poggio, Incremental and Decremental Support Vector Machine Learning In Advances in Neural Processing Systems, vol 10, Cambridge, MA: MIT Press, USA, 2001 36 C.

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