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Lagally, McCaw, Young, Medema, and Thomas (2004) found no difference in RPE between novice and recreationally trained lifters during resistance exercise In a study designed to explicitly examine the influence of athletic experience on perceived effort and effort tolerance, Tenenbaum et al (2001) compared the perceived and sustained effort of individual-sport athletes (swimmers, cyclists, triathletes, and long-distance runners), team sport athletes ( basketball, hockey, and soccer players), and untrained participants on two fatiguing tasks: an isometric handgrip task and a treadmill running task Results indicated that individual-sport athletes rated perceived effort consistently lower than did their team sport and untrained counterparts throughout both tasks The three groups were also significantly different on effort tolerance On average, individual-sport, team sport, and untrained participants.

This procedure will be called from the main macro, and will do the actual updating of the progress indicator.

To make this process even easier, click the small vertical column of dots located on the right side of the Quick Launch and drag it to the right (see Figure 6-3). This increases the size of your Quick Launch toolbar and gives you more room for dragging and dropping.

GaAs HBT E B diode strings TLP I V characteristics demonstrate a turn-on voltage, a linear dynamic on-resistance region, a saturation region, followed by avalanche breakdown and a reverse resistance region. GaAs HBT structures using emitter base (E B) diode strings with an isolated collector regions can provide noise isolation from the substrate region.

The construction is really straightforward: 1. Acquire the right USB sync cable. Figure 15-11 shows the packaging for the sync cable used here, and Figure 15-12 shows what the cable looks like. It s very important to get Cable 22, which has the bulge in the middle, and not one of the other cables. Other cables can be used, but you ll have to figure out the pinouts.

Therefore the combination of lognormal and heavy-tail distributions captures the full range of Web content size, with the former modeling the body, and the latter modeling the tail Content Popularity The relative popularity of Web content has been studied extensively The almost universal consensus is that content popularity follows a Zipf-like distribution where the popularity of the ith most popular le is proportional to 1/i a The value of a depends on where the traces were collected The estimates of a range from 05 to 1 in the Web proxy logs [9,14,24] and Web client logs [7,12]; and range from 1 to 2 in the Web server logs [4,26] A larger a implies that accesses are more concentrated on a small set of popular documents For example, a proxybased trace study [9] reports that 25 40% of pages account for 70% of the client accesses.

Most important, iSweep removes duplicates for you automagically. You can ask iSweep to delete duplicates from your disk and remove them from your library, or if you want to be a little more cautious, iSweep will move the duplicates to another location rather than deleting them. You can also instruct it to be sure not to remove any songs that are part of a playlist. Getting rid of duplicates is the reason to buy iSweep, but it has a few other useful features as well. If you have music scattered around your disk and not included in your iTunes library, you can ask iSweep to find these stray tunes for you and even consolidate them in one place. If you re not sure whether you want to buy iSweep, you can download and try it out for free. The limited tryout version lets you find and list duplicates, but not delete or move them.

convex closure, 538 convex families, 655 convex hull, 526, 530, 532, 534 536, 538, 543, 544, 565, 591, 594, 595 convex set, 330, 534 convexi cation, 598 convexity, 26, 28, 32, 42, 432, 616, 657, see also concavity capacity region, broadcast channel, 598 multiple access channel, 534 rate distortion function, 316 strict, 26 convolution, 270, 674 convolutional code, 215 interleaved, 215 cookie-cutting, 240 copper, 274 Coppersmith, D., 689 correlated random variables, coding, see Slepian-Wolf coding correlation, 46, 252, 258, 294, 295, 593 Costa, M.H.M., 593, 694, 703 Costello, D.J., 708 counterfeit, 45 covariance matrix, 249, 277, 279, 280, 284, 292, 397, 416, 417, 681 Cover, T.M., xxi, 69, 158, 182, 240, 299, 425, 508, 575, 593, 609 611, 626, 656, 687, 690, 691, 694 699, 701, 712, 717 CPU, 465 Cram r, H., 696 e Cram r-Rao inequality, 392, 395, 396, 399 e with bias, 402 crosstalk, 273 cryptography, 171 Csisz r, I., 55, 325, 332, 334, 335, 346, a 347, 358, 408, 461, 610, 696, 697 Csisz r-Tusn dy algorithm, 335 a a cumulative distribution function, 127, 128, 130, 243, 437, 439 cut set, 512, 589, 597 D-adic distribution, 112 D-ary alphabet, 103, 109 Dantzig, G.B., 697 Darroch, J.N., 697 Dar czy, Z., 690 o