### Table 1. Factors defining the system environment System Environment Factor Levels

"... In PAGE 4: ...olving the corresponding mixed integer programming model provided in section 2.3. 3.1 Design of Experiments The factors that define system environments used in the experiments are summarized in Table1 . We simulated arrivals of order requests in various environments defined by a complete combination of all factor levels.... In PAGE 4: ... Total number of order arrivals generated during the forecast period is determined by the demand load multiplied by length of forecast horizon divided by the average order size. Arrival times and sizes of the orders are generated from the distributions defined in Table1 over a 90-period forecast horizon. Four different distributions of arrival times are included in the study, uniform arrivals and three other arrivals with seasonality near the beginning (LT), the middle (MT), and the end (RT) of the forecast horizon respectively.... ..."

### Table 3 COMBINATIONS OF FACTORS TO DEFINE THE EXPERIMENTAL MATRICES

"... In PAGE 5: ... It was decided that in order to have more consistent information from the welding process, three matrix experiments were to be run. Table3 lists all three combinations to produce the different experimental matrices. This can be interpreted as running a full factorial experiment, however it is not quite the case, since it is about three different fractional factorial experiments that provide the most information possible in only nine runs per experiment.... ..."

### Table 3 Experiment 2: Four conditions defined by two factors

2006

"... In PAGE 12: ... Two between-subject factors define the four between-subject conditions: sharing information of auto- mation performance and sharing reliance on automation. Table3 lists the four conditions and describes how they were implemented within the model. When two operators share information regarding automation performance, CPA(nC01) will be available to update the BA(n)ofone operator (see Eq.... ..."

### Table 2: Scenarios are based on scenario 1 as defined above. Factors f1-f3 are incrementally removed.

### Table 12. Top 5 virtual community success factors defined by the three groups in roundtable workshop.

"... In PAGE 9: ...able 11. Cross-case comparison of case evaluations. .............................................................. 82 Table12 . Top 5 virtual community success factors defined by the three groups in roundtable workshop.... ..."

### Table 4: Some of the performance factors on three imple- mentations of matrix multiply, determined experimentally. E is the execution time of each implementation. Section 4 defines the other factors.

1999

"... In PAGE 8: ... f5: number of instructions in the implementation In this paper, we explore the effect of nt, f1, f2, and f3 on performance. Table4 shows the values of these factors on several implementations of matrix multiply; Table 5 does likewise for FFT. nt f1 f2 f3 MM -0.... ..."

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### Table 4: Some of the performance factors on three imple- mentations of matrix multiply, determined experimentally. E is the execution time of each implementation. Section 4 defines the other factors.

1999

"... In PAGE 8: ... #0F f 5 : number of instructions in the implementation In this paper, we explore the effect of nt, f 1 , f 2 ,andf 3 on performance. Table4 shows the values of these factors on several implementations of matrix multiply; Table 5 does likewise for FFT. nt f 1 f 2 f 3 MM -0.... ..."

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### Table 2 SIEG and 11 factors for gait in each defined group (n=96). Mean (S. D) Test groups

"... In PAGE 3: ...Results Table2 shows the experimental results of the gait analysis in each defined group. Most of the factors present a tendency to decline or incline with regard to severity, and the SIEG was found to be significantly different among each of the groups.... ..."

### Table 4 - Factor Map Group Defining Terms (Combined 2001-04 IEEE Proceedings)

2005

"... In PAGE 6: ... Therefore, the co-chair messages were removed from the files before Table 3 was derived. However, Table4 shows in what groups the co-chair messages clustered during initial analyses. This knowledge helps verify the process for using the NLP-extracted entities to derive the factor groupings.... In PAGE 6: ... This knowledge helps verify the process for using the NLP-extracted entities to derive the factor groupings. During the first iteration, eleven factor groups were derived, as shown in the 2nd and 5th columns of Table4 and preceded by Map: . The remaining terms in the 2nd and 5th columns are the other terms of the respective factor group.... In PAGE 9: ...However, observing the clustering of the messages in the other ten factor groups helps validate the NLP entities extraction and standard factor map process used to cluster the abstracts. Viewing Table4 , the trust factor, defined by the terms: control, security, privacy, trust, access control and authentication, had the highest loading abstract (i.... ..."

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