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1. A. EI-Hoiydi, Interference between Bluetooth networks Upper bound on the packet error rate, IEEE Commun. Lett. 5, 245 247 (2001). 2. T.-Y. Lin, Y.-K. Liu, and Y.-C. Tseng, An improved packet collision analysis for multi-Bluetooth piconets considering frequency-hopping guard time effect, IEEE J. Sel. Areas Commun. 22, 2087 2094 (2004). 3. K. Naik, D. S. L. Wei, Y. T. Su, and N. Shiratori, Analysis of packet interference and aggregated throughput in a cluster of Bluetooth piconets under different traf c conditions, IEEE J. Sel. Areas Commun. 23, 1205 1218 (2005). 4. I. Howitt, Mutual interference between independent Bluetooth piconets, IEEE Trans. Vehic. Technol. 52(3), 708 718 (2003). 5. N. Golmie and F. Mouveaux, Interference in the 2.4 GHz ISM band: impact on the Bluetooth access control performance, in Proc. IEEE Int. Conf. on Communications, Vol. 8, Helsinki, Finland, June 2001, pp. 2540 2545. 6. O. Karjalainen, S. Rantala, and M. Kivikoski, The performance of Bluetooth system in the presence of WLAN interference in an of ce environment, in Proc. 8th IEEE Int. Conf. on Communication Systems, Vol. 2, Singapore, Nov. 2002, pp. 628 631. 7. J. Lansford, A. Stephens, and R. Nevo, Wi-Fi (802.11b) and Bluetooth: Enabling coexistence, IEEE Network 15(5), 20 27 (2001). 8. I. Howitt, WLAN and WPAN coexistence in UL band, IEEE Trans. Vehic. Technol. 50(4), 1114 1124 (2001).

If the workbook has not been saved, attempting to access the Last Save Time property will generate an error. The On Error statement causes this error to be ignored. The If-Then-Else structure checks the value of the SaveTime variable and displays the appropriate message. If this variable is empty, it means the file was not saved. Figure 11-9 shows an example of this procedure s result.

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range of intensity Interestingly, Yerkes and Dodson (1908) were not sure if the levels of intensity of the stimulus used in their experiments with mice were most favorable The problem becomes even more complicated when separate and interactive effects of different components of anxiety, or multiple pleasant and unpleasant emotions, are examined Applied research and practice in high-achievement sport, however, require a more individualized approach that can predict an individual performance One strategy to solve the problem was proposed by Hanin (1978, 1995, 1997, 2000), who argued that it is unproductive to focus only on actual anxiety and corresponding levels of performance, matters that are difficult to compare across athletes For instance, what one athlete would consider a good or even excellent performance could be perceived by another athlete as poor.

Therefore, the emphasis should be on analysis of past performance history and estimation of intensity of emotions accompanying individually successful and unsuccessful performances Because the moderate anxiety for all assumption did not work in practice, a more intraindividual focus and individualized criteria in the evaluation of current anxiety intensity were needed (Hanin, 1978, 1995; Raglin & Hanin, 2000) Several studies have reported the percentage of athletes performing their best when experiencing high, moderate, or low anxiety (see Jokela & Hanin, 1999) The distribution of athletes in these categories was surprisingly well balanced across different studies: high (M = 342; 26% to 50%), moderate (M = 346; 22% to 44%), and or low (M = 35; 25% to 48%) Moreover, Jokela and Hanin (1999) were unable to identify a single study in their metaanalysis that demonstrated that different athletes had the same (or similar) optimal levels of anxiety.

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The individual-oriented strategy proposed by Hanin (1978, 1986, 1989) to predict the effects of anxiety on athletic performance emphasized a need to analyze an athlete s past performance history to identify emotions accompanying individually best performances The main emphasis in this approach is on predicting individual performance by contrasting, for instance, current anxiety level with the previously established success-related anxiety level ( high, moderate, or low) The concept of zones of optimal functioning (ZOF) initially proposed in precompetition anxiety research was a tentative optimal range of intensity scores predicting individually successful performance Later, the ZOF concept, extended to pleasant and unpleasant emotions, was later termed IZOF (individual zones of optimal functioning) to emphasize the withinindividual focus of the model (Hanin, 1995, 1997, 2000)..