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ture to the contraction and likely hardening of lamentous materials present within the meat. These results showed that two reactions with opposite effects were taking place, one producing tenderization and the other increasing hardness of the samples. Many researchers have observed an increased shear force with increasing internal temperature, followed by a decrease in shear
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Several years later, consensus on what is meant by the term collective efficacy exists within groups of researchers (e.g., Paskevich and his colleagues tend to emphasize components of Zaccaro s definition, and Feltz and her colleagues tend to emphasize components of Bandura s definition), yet remains elusive between these groups. This elusiveness unnecessarily bogs down the processing of related manuscripts, and therefore knowledge development, too frequently. Given that neither definition can be right if a construct does not truly exist, a more important question is which definition of collective efficacy leads to the development of measurements that most accurately predict what we want to predict about group behavior (Maddux, 1999, p. 225). MEASURING COLLECTIVE EFFICACY Before reviewing the relationship between various measures of collective efficacy, and other theoretically relevant external variables, an in-depth examination of how these measures have been constructed is warranted. Assessment Methods As the field has transitioned to include the study of collective efficacy in sport, multiple methods to assess collective
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For example, while evolving fuzzy rule-based models [1 3], use the data density (informative potential) of the new data sample as a trigger to update the rule base. Knowledge-based evolving intelligent systems (EISs) should be able to do the following [23, 24]: 1. 2. 3. 4. 5. 6. 7. Learn fast from a large amount of data (using fast training). Adapt incrementally in an online mode. Dynamically create new modules with open structure. Memorize information that can be used at a later stage. Interact continuously with the environment in a lifelong learning mode. Deal with knowledge (e.g., rules) as well as with data. Adequately represent space and time.
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1.5.1.5 Zeng et al. s Approximated Traffic Model (Any Dimensional) Zeng et al. s model is based on Xie and Kuek s traffic model [9]. Using Little s formula, when the blocking probability of originating calls and the forced termination probability of handoff calls are small, the average numbers of occupied channels E[C] is approximated by E[C]
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The Viterbi algorithm (VA) performs maximum likelihood decoding. It is applied to the trellis of a convolutional code whose properties are conveniently used to implement this algorithm. As explained above, one of the main problems that faces maximum likelihood decoding is the number of calculations that have to be done over all the possible code sequences. The VA reduces this complexity of calculation by avoiding having to take into account all the possible sequences. The decoding procedure consists of calculating the cumulative distance between the received sequence at an instant ti at a given state of the trellis, and each of all the code sequences that arrive at that state at that instant ti . This calculation is done for all states of the trellis, and for successive time instants, in order to look for the sequence with the minimum cumulative distance. The sequence with the minimum cumulative distance is the same as the sequence with the highest probability of being alike to the received sequence if transmission is done over the AWGN channel. The following example illustrates the application of the Viterbi decoding algorithm. The distance used as a measure of the decoding procedure is the Hamming distance; that is, the distance between any two sequences is de ned as the number of differences between these two sequences.
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