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Table 7.3. Methods to Assess Meat Emulsion Properties Principle Water holding capacity (WHC) Force is applied, by centrifugation without any heating (at 4 C or 15 C) or by compression, to remove unbound or loosely bound water. WHC expressed in g H2O absorbed/g meat A destabilizing treatment combining heating at 70 C during a given time and centrifugation is applied. % total expressible uid (TEF) = [(weight of centrifuge tube and sample) (weight of centrifuge tube and pellet)]/ sample weight 100 Calculated after the supernatant was dried for 16 h at 103 C and expressed as a % of sample weight Fat released: weight on heating the exudate. Water released: difference between total uid released and fat released. calculated as weight loss during standard heat processing and eventually smoking. expressed as % of initial sample weight calculated after heat processing and cold storage, according to the same way as cooking loss Determined after batter samples were heated (35 min, core temperature about 90 C), cooled (4 C for 24 h) and reheated in cans (45 C for 1 h). The uid jelly and fat, separated in a volumetric cylinder, are measured in ml calculated as % of the original weight of batter Determined on cooked product during cold storage Calculated as weight difference between the beginning and the end of the storage (at least 7 days at 4 C) Observations and sources Mittal & Barbut 1994; Candogan & Kolsarici 2003; Desmond and Kenny 1998; Lin & Huang 2003.

1080::5698::9887:1234 The algorithm that runs the expansion for the address would get confused. How many 0s go in each of the colon-compressed slots The correct way to compress this address would be to compress one or the other sides.

If p k 1 < r, then a new hidden neuron is generated (each hidden neuron corresponds to each center) and m1 m1 1 where r is a selected radius, r 2 0; 1 . Once a new hidden neuron is generated, the next step is to assign initial centers and widths of the network. A new density with value 1 is generated for this hidden neuron: c1im1 1 k xi k w1im1 1 k rand 2 2; 4 v1im1 1 k yi k dm1 1 k 1 If p k 1 ! r, then a hidden neuron is not generated. If z1j k 1 p k 1 , we have the winner neuron j . The winner neuron is a neuron that increments its importance in the algorithm. Then its population density [3] must be increased and is updated as: dj k dj k 1 8:21 8:20

Essential Windows CE Application Programming by Robert Burdick Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471327476 Pub Date: 03/01/99

MMSE, 266 Mobile user (MU), 28, 557 559, 562 564, 566 576, 581, 585, 587, 590 Modi ed CCB algorithm, 292 Moment-generating function, 404 Monte Carlo simulation, 173, 182, 280, 291 Most signi cant bit (MSB), 113, 117, 122, 356, 358 Moving Pictures Experts Group (MPEG), 82, 242 MPDU, 7 11, 96, 147, 327 329 MPIB, 383 M/M/c AU1 queuing model, 242 MR, 435 MRC, 264, 267 MRR, 499, 501, 512 MSC, 131 MSDU, 8 12, 14 15, 242, 327 329, 333, 489 MSE, 142, 158 MSI, 56 58, 302 305, 307 315 MSI-based scheme, 303, 309, 311 313 MSR, 499 502, 512 MT, 82, 86 87, 243 MTR, 437 MTS, 194 MTT, 437 438 MTU, 129, 132 Multiagent system (MAS), 27, 29, 48 49, 51, 53, 55, 57 59, 61 Multiband orthogonal frequency division multiplexing, 240, 299 Multicast, 241, 415, 419 420, 429 438, 482, 484, 504, 506, 591 592 Multicast polling, 504, 506 Multicast routing, 415, 420, 429 Multicluster sensor network, 389 Multidomain WLAN, 30 33, 35, 41 Multihop, 65, 72 78, 244, 246, 251 253, 257, 315, 350, 374, 439, 443 445, 452, 464, 469, 471, 515 517, 521 524, 556, 569 Multihop ad hoc/sensor networks, 521 Multihop diversity, 522 Multihop relay networking, 522 Multihop wireless communication, 516 Multi-input multi-output (MIMO), 16, 23 24, 588 Multiple access time division multiple access (DAMA TDMA), 589 Multiple Bluetooth piconets, 151, 154, 156, 169, 179, 182 Multiple-input multiple-output (MIMO), 16, 23 24, 588 Multirate support, 192, 250

In both cases, the online collected data were stored onto hard-disc in the same order as they were recorded; so a valid simulation of the online mode is the case when sending sample per sample into the evolution of the fuzzy models and taking the label entries as a simulation of online operator feedback Both data sets were split into a training data set (two-thirds of the data) and a test data set (one-third of the data) The training data was split into two halves, one taken for initial of ine training, the other for further evolution of the classi ers The test data set is for the purpose of evaluating the evolved classi ers on new unseen data Table 53 shows the performance of evolving classi ers obtained by FLEXFIS-Class MM (see Algorithm 2) vs.

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