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information on hygienic status or spoilage tendency, but a too high initial count may indicate problems of a microbiological nature. Depending on the initial microbial load, on the use of lactic starter, and on the technological parameters (temperature, relative humidity, and air velocity), undesired microorganisms are outgrown through inhibition by useful microorganisms, if necessary measures are taken during processing at the right time and to the right extent. As a result of a combination of low temperature, lowered pH, drying (lowered aw), and competition with added starter cultures, if applicable, bacteria that are sensitive to the factors mentioned above change in their number and die off. Pseudomonads cannot survive longer than a few days; enteric bacteria do not survive either; clostridia and staphylococci are inhibited; and even listeriae and EHEC strains can be inhibited in their growth. Cocci and lactobacilli are more likely to grow, the latter being less sensitive to low pH. As drying proceeds, a lactobacilli-dominance can be observed, and lactobacilli account for the majority of microorganisms in the nal product. As a result of salt, soluble protein goes into solution and forms a net among meat and fat particles, which is geli ed by lactic acid or by concentrating salt. This matrix ensures good adhesion between meat and fat particles and also good sliceability; binding and structure is formed this way (van t Hooft 1999; Incze 2007). During ripening, tissue and bacterial enzymic activity causes the breakdown of protein (proteolysis) and lipids (lipolysis). Muscle tissue enzymes (endoenzymes) play a role in dry sausages during the rst part of ripening, and enzymes of microorganisms are responsible mainly for proteolysis and lipolysis. As a result of this metabolism, myo brillar and sarcoplasmic proteins are partly broken down and nonprotein nitrogen

The utility of psychological test data can be attenuated not only by guarded respondents, who seek to restrict the amount of information they provide and thereby limit the examiner s ability to form impressions of them, but also by respondents who strive purposefully to create certain impressions of themselves. Impression management typically takes the form of attempts to malinger psychological disorder (faking bad) or of efforts to present a deceptively positive picture of psychological capability and well-being (faking good). Efforts to fake good sometimes result in test protocols that resemble the undependable records produced by guarded respondents who are seeking to conceal what they are like as people. There is nevertheless a subtle difference between the motivations of reluctant respondents, who aim to deny their imperfections and prevent the examiner from forming any clear impression of them, and deceptive (fake good) respondents, who extol their virtues and want the examiner to form a definite and distinctly positive impression of them. The relatively direct and obvious item content of SRIs makes them generally more susceptible than PBMs to malingering and deception. Respondents who want to appear psychologically better or worse off than they are can usually decide with little difficulty whether to answer true or false on self-report items such as Most of the time I feel blue.

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Table 7.12 did not contain either a neural network model or a logistic regression model, since Clementine does not have an explicit method for applying misclassi cation costs for these algorithms. Nevertheless, there is an alternative method for achieving decision effects similar to those provided by the misclassi cation costs. This alternative method is overbalancing. Table 7.13 contains the performance results for a series of neural network models run, using no principal components, for various levels of balancing. For the rst model there is no balancing; for the second model the target variable is balanced 50% 50%; for the third model the target variable is overbalanced, about 65% responders and 35% nonresponders; for the fourth model the target variable is overbalanced, about 80% responders and 20% nonresponders; and for the fth model the target variable is overbalanced, about 90% responders and 10% nonresponders. Note that the three models that have been overbalanced each outperform the baseline send to everyone model, even though none of these models applied misclassi cation costs directly. Thus, overbalancing, properly applied, may be used as a surrogate for misclassi cation costs. The optimal performance by the neural network was obtained using the 80% 20% overbalancing ratio. Let s compare this performance against the other three algorithms using the same ratio. Table 7.14 shows the performance results for all four algorithms, using the 80% 20% overbalancing ratio.

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

[Eswaren74] Eswaren, K., Placement of Records in a File and File Allocation in a Computer Network, in Proceedings of Information Processing Conference, Stockholm, pp. 304 307, 1974. [Hammer79] Hammer, M., and Niamir, B., A Heuristic Approach to Attribute Partitioning, in Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 93 101, Boston, May 1979. [Hoffer75] Hoffer, H., and Severance, D., The Use of Cluster Analysis in Physical Database Design, in Proceedings of the First International Conference on Very Large Databases, Framingham, MA, pp. 69 86, September 1975. [McCormick72] McCormick, W., Schweitzer, P., and White T., Problem Decomposition and Data Reorganization by a Clustering Technique, Operations Research, Vol. 20, No. 5, pp. 993 1009, 1972. [Navathe84] Navathe, S., Ceri, S., Wiederhold, G., and Dou, J., Vertical Partitioning Algorithms for Database Design, ACM Transactions on Database Systems, Vol. 9, No. 4, pp. 680 710, December 1984. [Ozsu99] Ozsu, M., and Valduriez, P., Principles of Distributed Database Systems, Prentice Hall, Englewood Cliffs, NJ, 1999. [Sacca85] Sacca, D., and Wiederhold, G., Database Partitioning in a Cluster of Processors, ACM Transactions on Database Systems, Vol. 10, No. 1, pp. 29 56, October 1985.

Unary operator can be factored with respect to some binary operation:

experience the double-occupancy rate is 40 percent. To determine the rates, we pick a spread of $10 between the single and the double rates. To clarify, the method of determining a double occupancy percentage is shown as follows: 50 Rooms 70% 365 140% 17,885 total guests

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