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Table 2. (continued)
"... In PAGE 6: ... Sixteen of these strategies were triggered by one or more of the poles that form the focus of this paper. In Table2 , we show these sixteen strategies. For each strategy, we show the number of PMs alluding to that strategy.... In PAGE 6: ... We also list the poles which triggered the PMs to mention that strategy. Table2 . Number of PMs espousing each coping-strategy No.... In PAGE 7: ...7 Table2 . (continued) 3 If there is no committed/enthusiastic project owner , find /demand one (maybe an influential, enthusiastic user).... In PAGE 9: ...9 Table2 . (continued) 16 Ensure that project success measures, including acceptance-testing criteria, are well-defined and agreed in advance.... ..."
Table 6. Influential fuzzy if-then rules of Player 1 during the game-playing against the other 99 players with the optimal strategy for the previous actions.
"... In PAGE 25: ... Antecedent part Number of rounds 1 q 2 q 3 q 4 q 5 q Consequent k c Certainty k CF 499 L H L H H Market 3 1 494 H L H L L Market 5 1 4 H H H L L Market 5 1 We also monitored the fuzzy reasoning process in each fuzzy rule-based approximation system for Player 1 when Player 1 with the fuzzy rule-based approximation strategy played against the other 99 players with the optimal strategy for the previous actions. In the same manner as in Section 5, we show influential fuzzy if-then rules in Table6 . As in Table 5, the two patterns of the market price vector can be observed from the most influential fuzzy if-then rules in Table 6.... In PAGE 25: ... In the same manner as in Section 5, we show influential fuzzy if-then rules in Table 6. As in Table 5, the two patterns of the market price vector can be observed from the most influential fuzzy if-then rules in Table6 . We can also see that the fuzzy rule-based approximation systems learned the market selection knowledge that can make use of the synchronized oscillation of selected markets.... In PAGE 25: ... We can also see that the fuzzy rule-based approximation systems learned the market selection knowledge that can make use of the synchronized oscillation of selected markets. That is, each fuzzy if-then rule in Table6 says that high payoff will be obtained in the current round from markets where the previous market prices were low. Player 1 with the trained fuzzy rule-based approximation systems in Table 6 chooses a market with a lower previous market price between Market 3 and Market 5.... In PAGE 25: ... That is, each fuzzy if-then rule in Table 6 says that high payoff will be obtained in the current round from markets where the previous market prices were low. Player 1 with the trained fuzzy rule-based approximation systems in Table6 chooses a market with a lower previous market price between Market 3 and Market 5. As shown in Table 5 and Table 6, the two fuzzy rule-based strategies learned the same market selection knowledge through different learning schemes.... In PAGE 29: ...able 5 in Subsection 6.1. We also examined the five fuzzy rule-based approximation systems for Player 1 when the fuzzy rule-based approximation strategy was assigned to this player. The final five fuzzy rule-based approximation systems were almost the same as Table6 in Subsection 6.1.... ..."
Table 2. Probabilities for being a case when showing none, one, or both of the influential interactions and the mean number of cases and controls over the 50 data sets from Simulation 2
TABLE 8. Expected inbreeding values1 for influential bulls when different numbers of sample animals were selected for the base popu- lation from which expected inbreeding was estimated. Expected inbreeding values (%)
Table 5. Percentage of changes in labor supply caused by each influential factor (one factor develops, based on the moderate scenario, while the others remain at the 1995 level).
Table 2: Consensus clusters and influential attributes identified for the MYOEPITHELIAL collection. The format is the same as Table 1. Complete information on the genes in the consensus clusters and full lists of attributes can be found in Additional Files 3 and 4.
2006
Table 3 Discriminant analysis of SMS users with unwillingness-to-communicate, shortcomings in text messaging, and demographics predictorsa (N = 532)
in in SMS
2006
"... In PAGE 10: ... Discriminant analysis was run using these three aspects as predictors. Table3 shows that SMS student users were more likely to be male with a high family household income and were well aware of the shortcomings inherent in SMS text messaging, such as the confusing acronyms and the unclear message intention. When compared to non-users, SMS users were more socially anxious or felt less valued in face-to-face communication but not significantly linked to UCS-R.... ..."
Table 2 Examples of augmentations and diminishments Focal Products
2004
"... In PAGE 15: ... The PromoPad can replace the surrounding competition with complementary settings of the product or specific suggested complementary products. Table2 lists several possible examples of augmentations and diminishments of the focal products from Table 1. ... ..."
Cited by 2
Table 7. An Example of Application Port Table
2004
"... In PAGE 14: ...able 6. Notations for FRM...................................................................................43 Table7 .... In PAGE 61: ...Table 7. An Example of Application Port Table We also record the communication type of each application according to the classification in Table7 . We use this communication type information in the FRM process to finalize the grouping of flows.... ..."
Table III: Standardized discriminant function and correlation coefficient Discriminating variables Discriminant
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