### Table 1 shows the four highest loadings on the last factor in the case of extracting 13, 14, or 15 factors, respectively. This confirms that the quality of the factors declines considerably after extracting 14 factors. The fourteen-factor solution explains 51.8% of the variance of the matrix in the citing projection, and 47.9% of the variance in the cited projection.

"... In PAGE 6: ....669 0.687 0.472 Table1 : Highest factor loadings on the last factor in a 13-, 14-, and 15-factor solution, respectively. The factor loadings for the 172 categories on the fourteen factors in the citing dimension are provided in Appendix I.... ..."

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### Table 1: Condition Factors for Water Demand Prediction.

1995

"... In PAGE 3: ...2 Condition Attributes Fourteen factors have been identified which may affect the daily consumption of water in a city. They are listed in Table1 . The first factor is day of the week, which is chosen based on the observation that on weekends the daily total distribution flows are usually less than those on weekdays.... ..."

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### Table 3: First vehicle then crew scheduling

1999

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### Table 3: First vehicle then crew scheduling

1999

Cited by 1

### Table 4 Classification results with different reducts 1: Number of rules; 2: Classification accuracy POSAR CEAR DISMAR GAAR PSORSAR

"... In PAGE 25: ... So, all the particles have a powerful search capability, which can help the swarm avoid dead ends. The comparison of the number of decision rules and the classification accuracy with different reducts are shown in Table4... ..."

### Table 5: Computational results for Example 1 for constant processing times. MILP STN Model MILP/CP Hybrid Scheme

2004

"... In PAGE 28: ...ith 13 time points, while instances Ms2 and Ms3 were solved for various time grids (i.e. no of time points) but no integer feasible solution was found in 36,000 CPU seconds. The computational statistics reported in Table5 , correspond to the MILP with the minimum number of time points that can represent the optimal solution found by the hybrid algorithm. The proposed algorithm, on the other hand, obtained the optimal solution for all instances of both objectives.... ..."

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### Table 2: Results for 30 di erent test problems.

"... In PAGE 15: ... i = 0; Stop := False; repeatfold B := fB, fold I := fI; do x 2 Neighbors(xi) evaluate x ! f(x); if f(x) gt; fB and x is feasible thenfB := f(x), xB := x; elseif f(x) gt; max(fB; fI) thenfI := f(x), xI := x; enddo if fB gt; fold B then xi+1 = xB; elseif fI gt; max(fold B ; fold I ) then xi+1 = xI; else Stop := True; i := i + 1; until Stop = True; if fB gt; 0 then Feasible Local Optimum found else No feasible solution found end.From the 30 test cases that are listed in Table2 , the restart from the best infeasible neighbor turned out to be helpful in almost half of the cases, as is shown in Table 1. On the other hand it shows that in a few cases it caused a longer search without improving the objective function.... In PAGE 16: ...04667 54 1.04670 62 Table 1: Test problems from Table2 where infeasible neighbors were explored. lowed is then gradually decreased, until only feasible patterns can be selected as new parents.... In PAGE 19: ... PI: Pairwise interchange from an arbitrary starting point. The results are listed in Table2 . Computation times are in seconds on an HP 9000/720 workstation.... ..."

### Table 3 Results for 30 di erent test problems.

"... In PAGE 19: ...04667 54 1.04670 62 Table 2 Test problems from Table3 where infeasible neighbors were explored. Of course, there are several other options possible to take advantage of infea- sible patterns.... In PAGE 21: ...2 Computational results We compared 4 algorithms: (1) DC: DICOPT running under the modeling language GAMS; (2) CR: CONOPT, followed by the simple rounding procedure; (3) CRP: As CR, but then followed by Pairwise Interchange; (4) PI: Pairwise interchange from an arbitrary starting point. The results are listed in Table3 . Computation times are in seconds on an HP 9000/720 workstation.... ..."

### Table 1. Summary of various shape metric values for invalid elements.

"... In PAGE 7: ... The solutions from these three cases must be thrown away. Table1 summaries the values of the various shape metrics for negative Jacobians at Gauss points. From Table 1 and Figures 17 and 18, it can be seen that for invalid elements, the values of the aspect ratio as well as warping factor did not show particularly different.... In PAGE 7: ... Table 1 summaries the values of the various shape metrics for negative Jacobians at Gauss points. From Table1 and Figures 17 and 18, it can be seen that for invalid elements, the values of the aspect ratio as well as warping factor did not show particularly different. In other words, these two shape measures cannot identify invalid elements.... ..."

### Table 1. Results for test set 1

1993

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