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Picture it: A PGA (Professional Golfers Association) tour player is on the final hole of a major tournament. All he needs to do to win the tournament is sink a simple 5-foot putt on a flat, straight green. This is a putt he has sunk so many times in practice he has lost count. This is a putt he knows, he understands, he can execute without a second thought. But, on this day, on this putt, there are other thoughts. He notices the crowd, the leader board, and thinks about how many people are counting on him to make the shot and win the tournament. He thinks about all those individuals, including himself, who expect him to finish at the top. Thus, this putt is not exactly the same as all of the simple 5-foot putts he has taken in practice. And when he steps up to the ball, performs his preshot routine, and executes his shot it becomes apparent how different this pressure-filled putt actually is. Our golfer does the unthinkable, the unexpected, and the unwarranted given his ability: He misses the putt, he loses the tournament, he chokes under the pressure. We have all heard the term choking before. In the sports arena, we talk about the bricks in basketball, when the game-winning free throw manifests itself as an air ball, or we speak of the yips in golf, when an easy putt to win the tournament stops short. In more academic domains, we refer to cracking in important test-taking situations, when a test score much lower than expected results in a failing course grade or prevents the all-important college admission. But what exactly do these terms mean, and, more important, why do less-than-optimal performances

These resistances are slightly more accurate than the expressions given by Barrett and Clement [14], as they give the correct limits in the four cases of large or small R, and large or small vapour density, ve , or, equivalently pressure, pve . However, we have not included here correction terms arising when vapour pressures or densities are not very small compared to those of the gas, air in our case, as they are not generally relevant to environmental problems. The dimensionless ratios of the conductive heat current to the latent heat carried by the mass current have been termed surface condensation numbers [24] in the continuum region, and are given by the inverse of the corresponding resistance ratios: Cns = RMD /RHC = kT ( )2 /(L D ve (T ( )) (3.50)

Jump to 37 and learn about bit depth in colors. Proceed to 29 and learn about formatting paragraphs.

knoppmyth nodma The nodma setting turns off direct memory access (DMA) for drives that do not support this feature. If you re not sure what this does, leave it alone it actually helps performance when it s enabled on drives that support this feature, and hardware detection usually gets this right. knoppmyth vsync=85 hsync=78 The vsync and hsync settings correspond to vertical and horizontal synchronization, respectively, and enable you to fine-tune monitor settings. Use these with caution, because the wrong settings can be potentially harmful, both to you (by provoking excessive eyestrain) and to the monitor (by causing it to run at incorrect frequencies). knoppmyth 2 This option sets KnoppMyth to run-level 2 (text mode) for experi-

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It is worth noting that there is an interesting interplay between the amount of loss in data aggregation and the amount of energy expended to effect it. As we are about to show, if we are willing to expend slightly more energy, lossless data aggregation can be achieved. The corresponding trade-off is interesting in its own right, being characteristic of choices that present themselves in the design of protocols for wireless sensor networks. For illustration purposes, assume that it is necessary to determine the maximum of the bit codes stored by the sensors in Ai, j and refer to Figure 4.6. To solve this problem, all the sensors in Ai, j that have collected relevant information engage in the following protocol, which is guaranteed to aggregate the values into the maximum. Assume that each sensor stores a d-bit code for the range.

Model Evaluation In this section we examine three aspects of model evaluation. First, we discuss the problem of assessing t in a SEM model. We then present several popular t indices. The section concludes with a discussion of evaluating direct and indirect parameter estimates.

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