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This means that an athletic career does not put people at a disadvantage Alfermann et al report that the high educational level of their sample of German former elite athletes (many of them from track and field or handball) contributed to successful occupational careers more often for males than females Hackfort et al and Nagel found that the occupational careers of former athletes in most cases correspond to their occupational training The group of amateurs in the study by Hackfort et al showed neither social downward nor upward mobility In consequence, no proof of a correlation between social mobility and involvement in elite sports could be found (p 126) The pattern of results was different for the professionals: They were more at risk for downward social mobility than amateurs due to their exclusive involvement in sport Conzelmann et al.

(2001) and Nagel (2002) investigated 616 German former successful Olympic athletes who had participated in the Olympics between 1960 and 1992 and who had reached at least the top six (individual sports) or four (team sports) places in their respective sport The overwhelming majority of these athletes made an occupational career that mirrored their educational and training level A minority showed upward mobility, which means that they had a higher position than would be expected from their education An even smaller minority showed downward mobility, having less qualified positions than would be expected from their educational level These results corroborate earlier findings from American College Sport showing that elite sport does not undermine an individual s occupational development and life satisfaction (Greendorfer & Blinde, 1985) Nevertheless, the high involvement in elite sport may prolong an athlete s personal growth and occupational development Studies with college athletes in the United.

Effectiveness of athletic performance is usually related to the amount of available energy and its efficient use Different athletes can be successful by using different resources In other words, the same level of performance may be achieved either through the increase of total effort or via skillful (smart) utilization of available resources (efficiency) However, usually optimal emotion regulatory function is manifested in an athlete s efficient recruitment (effort) and utilization (skill) of available resources, resulting in energizing and organizing effects on performance In contrast, emotion dysfunction in self-regulation usually ref lects a failure to recruit resources and their inefficient utilization, resulting in de-energizing and disorganizing effects of emotion on athletic performance.

on whatever the participants see t for their current purposes. In this respect, there may be an irresolvable discrepancy between mainstream developers way of seeing context and the way humans actually see it [10]. 2. Even if One had a Machine that could Correctly Tell what the Users Need, the Users may not Like it While liking technology that eases everyday activities [7], the need for control is stressed repeatedly by users:

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Optimality of emotions, then, is related to their mobilizing function and getting ready for a task at hand by using either normal resources, as in the case of pleasant optimal (P+) emotions, or emergency resources, as in the case of unpleasant optimal (N+) strong emotions In contrast, dysfunctional emotions ( both unpleasant, N , and pleasant, P ) are signals of inability to effectively use available resources or to compensate for their situational depletion Too much satisfaction or celebration of intermediate success can be really distracting and demobilizational Therefore, both positive and negative emotions can produce adaptive and maladaptive outcomes Apparently, total effects depend on the interaction of mixed (pleasant and unpleasant) emotions and their ratio (of positive and negative).

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kicks back in but starts up another algorithm as well (known as congestion control). When congestion occurs, a comparison is made between the congestion window size and the TCP advertised window size. Whichever is smaller, this number is then halved and saved in a variable known as the slow-start threshold. This value must be at least 2 segments unless the congestion was a time-out and then the congestion window is set to 1 (slow start). The TCP sender then can either start up slow start or congestion avoidance. Once ACKs are received, the congestion window is increased. Once the congestion window matches the value saved in the slow-start threshold, the slow-start algorithm stops and the congestion avoidance algorithm starts. This algorithm allows for more controlled (linear, not exponential like the slow-start algorithm) growth in transmission in that it multiples the segment size by 2, divides this value by the congestion window size, and then continually increases the rate based on this algorithm each time an ACK is received. This allows for growth on the TCP connection but at a more controlled rate The effects of these algorithms were dramatic on the Internet. All versions of TCP/IP software now include these algorithms and their effects are not only based on remote connections. These algorithms are placed into action between two stations on a local subnet as well.