### Table 1. Test words and contexts used in the present experiment. Words in focus are given in upper-case letters.

2000

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### Table 15. Shutdown characteristics for VVER-440 equilibrium cycles

"... In PAGE 7: ...VER-440 equilibrium cycles............................................................................................ 73 Table15 .... In PAGE 74: ... At high burn-ups, spectral hardening causes the reactivity worth of control rods to decrease in magnitude. This can be seen for the VVER-440 equilibrium cycles in the third column of Table15 , which gives the EOC HZP trip reactivity worth. The lower trend line in Figure 19 shows the same data plotted versus average discharge burn-up.... In PAGE 74: ... The lower trend line in Figure 19 shows the same data plotted versus average discharge burn-up. The corresponding shutdown margin from hot full power to hot zero power at EOC (fourth column in Table15 and middle trend line in Figure 19) decreases with burn-up (the same trend is also seen in PWRs [35]), though for VVER-440 it remains within acceptable bounds. However, the ... In PAGE 75: ... Trip reactivity worth and shutdown margins in VVER-440 equilibrium cycles as a function of average discharge burn-up shutdown margin during refuelling with 13.5 g/kg soluble boron (fifth column in Table15 and upper trend line in Figure 19) shows a very adverse trend (due to the combined effect of reduced control rod reactivity worth and smaller soluble boron reactivity worth) and eventually goes outside acceptable bounds in the absence of burnable poisons. Similar results would be expected for PWR cores.... In PAGE 75: ... Similar results would be expected for PWR cores. Table15 also shows the re-criticality temperature for EOC HFP core conditions. This is the temperature at which the shutdown margin is eroded in a cool-down fault and the core becomes critical again.... ..."

### Table 2 presents the quantities, Lagrange multiplier, lower bound and upper bound values after each iteration.

"... In PAGE 9: ... Table2 : Iterative values of convergence test The table shows that the lower bound is monotonically increasing and the upper bound is monotonically decreasing. After 20 iterations the lower bound and the upper bound converge and provide the optimal solution.... ..."

### Table 2 presents the quantities, Lagrange multiplier, lower bound and upper bound values after each iteration.

2005

"... In PAGE 32: ... All variables are expressed as diffusion indexes. Since the selection strategy employed here requires knowledge of characteristic roots and characteristic vectors of the correlation matrix of the five PMI variables, the associated characteristic roots and vectors of the five PMI variables are reported in Table2 . It can be seen from the entries in the table that the first principal component accounts for approximately 75 percent of the variation in the five variables of PMI.... In PAGE 32: ... 5 There were breaks in the data during the World War II. 6 Pelaez (2003a,b, Table2 ) also reported similar correlations using data for the period Jan. 1950 to Jan.... In PAGE 33: ...Table2 : Results of PCA of the five PMI components using their correlation matrix Principal component First Second Third Fourth Fifth Characteristic root 3.... In PAGE 33: ...010 Note: All the variables are expressed as diffusion indexes. The values of the characteristic vector associated with the first principal component of the five PMI variables, reported in Table2 , show that the first principal component PMI series could be computed as PMI3A = 0.... In PAGE 34: ... Inclusion of all five variables in the PMI formula, however, necessitates data for computing all the five variables, which in reality can be time- consuming and costly to collect. It is easy to verify from the entries in Table2 that only one characteristic root, derived from the correlation matrix of the five diffusion indexes, is greater than 0.70, suggesting that only one of the five diffusion indexes be retained for purposes of computing a simpler PMI.... In PAGE 34: ...0 20.0 Time Since the decision as to which of the five variables should be retained requires information on the correlations (or loadings) between the variables and the principal components, these correlations are also reported in the bottom row of Table2 . It can be seen from the entries in the table that the employment diffusion index (IEmploy) has the highest correlation (i.... In PAGE 35: ...Table2 in regards to the number of PMI variables to be included in the model is inclusion of the second principal component in the model, Inventory in this case, as the characteristic root of the second principal component is very close to 0.70.... In PAGE 35: ...his case, as the characteristic root of the second principal component is very close to 0.70. One advantage of this strategy is that we can increase the percentage of variance in the PMI index explained by the independent variables through the addition of the second PMI variable. As previously mentioned with the entries in Table2 , the employment diffusion index (IEmploy) has the highest correlation (i.... In PAGE 73: ... Table2 : Iterative values of convergence test The table shows that the lower bound is monotonically increasing and the upper bound is monotonically decreasing. After 20 iterations the lower bound and the upper bound converge and provide the optimal solution.... In PAGE 86: ...The Vietnamese manufacturing industry, though still weak, is growing strongly at the average of 14% in the last several years ( Table2 ). It also receives increasing attention and investment from the government as well as foreign investors.... In PAGE 100: ... Boctor, 1993). We have considered four construction methods, each depending on a priority rule, as defined in Table2 . The P-MIN LST heuristic, as one knows, is equivalent to the P-MIN SLK heuristic.... In PAGE 100: ... The P-MIN LST heuristic, as one knows, is equivalent to the P-MIN SLK heuristic. Table2 - Priority Rule Used by Construction Methods Scheduling Priority rule Priority Value Priority scheme name selected value Parallel (P) Minimum SLacK (SLK) time P-MIN SLK MIN LST(j)-t Parallel (P) Latest Finish Time (LFT) P-MIN LFT MIN LFT(j)-t Serial (S) Latest Finish Time (LFT) S-MIN LFT MIN LFT(j) Serial (S) Latest Start Time (LST) S-MIN LST MIN LST(j) ... ..."

### Table 2: Results per target presentation mode. Upper half: best results are in bold type, and lowest ones are underscored. Lower half: significant statistical results are in bold type.

2005

"... In PAGE 12: ... These differences are statistically significant. Table2 also shows that multimodal presentations of targets reduced both selection times compared to oral presentations, and error rates in comparison with visual presentations, both results being statistically significant. These results are in ... ..."

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### Table 2: Results per target presentation mode. Upper half: best results are in bold type, and lowest ones are underscored. Lower half: significant statistical results are in bold type.

2005

"... In PAGE 12: ... These differences are statistically significant. Table2 also shows that multimodal presentations of targets reduced both selection times compared to oral presentations, and error rates in comparison with visual presentations, both results being statistically significant. These results are in ... ..."

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### Table 10 presents a breakdown of the types of limb injuries sustained. Upper and lower limbs are

"... In PAGE 24: ...1 Total 100.0 Source: MAA Claims Register June 1995 Table10 . Types of Limb Injury Type of Injury Percent of Upper Limb Injuries Percent of Lower Limb Injuries Percent of All Limb Injuries Joint injury 55.... ..."

### Table 6.2: Bounds on the layout area obtained by application of the presented methods. Upper bound for the wire length of product network Factor Lower Bisector Bisector Bifurcator Bifurcator network bounds (r = 2) (r gt; 2) (r = 2) (r gt; 2) Collinear

### Table 1, HSM needs much smaller numbers of iterations for reaching a stable state than CS. Moreover, the time needed for each iteration is much shorter in HSM than in CS. All of the following data indicate that HSM converges quickly, and the states of neurons in each hill climbing application arrive at a stable state in less than 10 iterations. Each test run uses hill climbing at most 20 times. Guy [4] presented an upper bound of the crossing number for the complete graphs:

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