### Table 1: General Model Parameters Parameter Description Typical Values

"... In PAGE 14: ... The following sections consider the communication cost implications of both mesh-based and bus-based architectures. 6 Model Parameters Table1 summarizes the model parameters used in the sequential and concurrent models. For each parameter, representative values are listed.... ..."

### Table 7 Maximum likelihood estimates general models imitation treatments

"... In PAGE 23: ... Thus the models are related to each other via a nested structure. Table7... ..."

### Table 5. Analytical results vs. simulation for a more general model

"... In PAGE 7: ... 4-Node DM architecture LA LC l 1 l 3 l 2 LB Figure 4. Queueing model of the architecture Table5 compares the analytical approximations to simulation results for this model, for several combinations of arrival rates. Although the errors are slightly higher for this model than for our standard example, the analytical predictions still remain within approximately 10% of the simulation results.... ..."

### Table 2. General dynamic models with behavioral relations

"... In PAGE 13: ... The early models were designed in the United States while the Europeans caught up in the 1980s and now seem to dominate the work with static behavioral models. Table2 lists general dynamic models with behavioral relations. General here means two things.... ..."

### Table 3: Local search for the general shop model

"... In PAGE 24: ... Again, we can state that the neighbor selection process has no great influ- ence on the quality of the solutions, however, for rst- t the corresponding computation times are shorter. Comparing the results of Table3 with those of the columns CONV of Table 2, we see that the tabu search approach for the general shop model again reduces the objective values signi cantly. For the two relevant practical instances the best found value is below or close to the (not good) lower bound of the time-lag model (which is used in prac- tice).... ..."

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### Table 3: Local search for the general shop model

"... In PAGE 24: ... Again, we can state that the neighbor selection process has no great influ- ence on the quality of the solutions, however, for rst- t the corresponding computation times are shorter. Comparing the results of Table3 with those of the columns CONV of Table 2, we see that the tabu search approach for the general shop model again reduces the objective values signi cantly. For the two relevant practical instances the best found value is below or close to the (not good) lower bound of the time-lag model (which is used in prac- tice).... ..."

Cited by 1

### Table 1: alternative general prediction models

in Production planning in batch process industries: comparing regression analysis and neural networks

"... In PAGE 4: ... Especially when problems of multicollinearity arise, the final regression model found by stepwise regression might not be the most suitable. The alternative prediction models are presented in Table1 . Given are the number of the model, the number of regressor variables, the regressor variables, and the R2pre.... In PAGE 5: ...Table1 the P-value was equal to 0.00.... In PAGE 5: ... This results in model RM10. Models RM8 and RM9 are not included in Table1 , because they include non-significant variables in addition to the variables also included in model RM10. Including two-way interactions results in an increase in the explained variation.... ..."

### Table 1: alternative general prediction models

in Production Planning in Batch Process Industries: Comparing Regression Analysis and Neural Networks

"... In PAGE 4: ... Especially when problems of multicollinearity arise, the final regression model found by stepwise regression might not be the most suitable. The alternative prediction models are presented in Table1 . Given are the number of the model, the number of regressor variables, the regressor variables, and the R2pre.... In PAGE 5: ...Table1 the P-value was equal to 0.00.... In PAGE 5: ... This results in model RM10. Models RM8 and RM9 are not included in Table1 , because they include non-significant variables in addition to the variables also included in model RM10. Including two-way interactions results in an increase in the explained variation.... ..."

### Table 5. Generalized Exponential Model Relationships

"... In PAGE 24: ... ) is the software hazard rate function; t is a time or resource variable for measuring the progress of the project; K is a constant of proportionality denoting the failures per unit of t ; E0 is the initial number of faults in the software; and Ec is the number of faults in the software that have been found and corrected after t units have been expended. Table5 reflects how this model is related to some of the models in the literature. Table 5.... ..."