"... In PAGE 31: ... SCM tools and other optimization tools reside in the third quadrant. Based on the ERP evolution map, a contribution factor matrix is developed in Table6 . It is not possible to map every detailed functionality or enabling characteristics provided by a specific ERP package.... In PAGE 82: ...01) is better in the merchandising department than the sales (distribution) department (see Table 6). Table6 : Utilization Level of POS System by User Department User department Levels Utilization levels of POS system Merchand izing Sales t-value Check on over-stocked and out-of stock items 4.68 4.... In PAGE 95: ...3% 239,647 +74% 282,103 +17.7% Table6 : Commercial Vehicle Population Country 1997 Market Growth (%) 1998 Market Growth (%) 1999 Market Growth (%) 2000 Market Growth (%) Thailand (RHD) 231,096 -44.5% 97,765 -57.... In PAGE 113: ... In contrast, remarkable growth rates of export earnings were posted for concentrates, preserves, and other edible parts, while modest growth were achieved in frozen, dried and juice. The export values of all mango product forms posted declining growth rates in major traditional markets such as Hong Kong, Japan, and the United States ( Table6 ). Table 6: Mango Exports: All Products, Value by Country, 1996-2000 Country Growth Rate (%) % Share TOTAL (3.... In PAGE 113: ... The export values of all mango product forms posted declining growth rates in major traditional markets such as Hong Kong, Japan, and the United States (Table 6). Table6 : Mango Exports: All Products, Value by Country, 1996-2000 Country Growth Rate (%) % Share TOTAL (3.32) 100.... ..."

"... In PAGE 5: ...10/3. Table3 shows the classification results for the individual base classifiers. The classification accuracy of each individual classifier ranges between 30% and 50%.... ..."

"... In PAGE 2: ... Since DUC-2002 data and TREC Novelty track data include extracts, we used them for setting pa- rameters. Table1 shows the contribution of each feature that was the basis of a scoring function. The greater the standard deviation is, the greater effect the scoring function has for the score of each sentence, and our system multiplies val- ues of each scoring function by given parameter weights to calculate the score of each sentence.... ..."

"... In PAGE 11: ....0001 for the largest nuclear area and was 0.0092 for the number of metastatic axillary lymph nodes. An analysis of deviance 16 was performed in order to estimate the importance and interdependence of nuclear features relative to lymph node status ( Table2 ). For a single variable model, Worst Area is a better prognosticator than is lymph node status (Table 2, rows 1 and 2).... In PAGE 11: ... An analysis of deviance 16 was performed in order to estimate the importance and interdependence of nuclear features relative to lymph node status (Table 2). For a single variable model, Worst Area is a better prognosticator than is lymph node status ( Table2 , rows 1 and 2). ... In PAGE 12: ...given in Table2 , row 3. Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5).... In PAGE 12: ...given in Table 2, row 3. Adjusting for Worst Area causes a large effect ( Table2 , row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another.... In PAGE 12: ...given in Table 2, row 3. Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less ( Table2 , row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another.... In PAGE 12: ... Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers ( Table2 , rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another. When machine learning with leave-one-out cross validation was used to select the optimal number of features for prognostic models, computer-derived nuclear Worst Radius was used 84.... In PAGE 12: ... Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones ( Table2 , rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another. When machine learning with leave-one-out cross validation was used to select the optimal number of features for prognostic models, computer-derived nuclear Worst Radius was used 84.... ..."

"... In PAGE 11: ....0001 for the largest nuclear area and was 0.0092 for the number of metastatic axillary lymph nodes. An analysis of deviance 16 was performed in order to estimate the importance and interdependence of nuclear features relative to lymph node status ( Table2 ). For a single variable model, Worst Area is a better prognosticator than is lymph node status (Table 2, rows 1 and 2).... In PAGE 11: ... An analysis of deviance 16 was performed in order to estimate the importance and interdependence of nuclear features relative to lymph node status (Table 2). For a single variable model, Worst Area is a better prognosticator than is lymph node status ( Table2 , rows 1 and 2). ... In PAGE 12: ...given in Table2 , row 3. Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5).... In PAGE 12: ...given in Table 2, row 3. Adjusting for Worst Area causes a large effect ( Table2 , row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another.... In PAGE 12: ...given in Table 2, row 3. Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less ( Table2 , row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another.... In PAGE 12: ... Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers ( Table2 , rows 4 and 5) are about the same as are the unadjusted ones (Table 2, rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another. When machine learning with leave-one-out cross validation was used to select the optimal number of features for prognostic models, computer-derived nuclear Worst Radius was used 84.... In PAGE 12: ... Adjusting for Worst Area causes a large effect (Table 2, row 4), where as the effect of adjusting for lymph node status is less (Table 2, row 5). The adjusted numbers (Table 2, rows 4 and 5) are about the same as are the unadjusted ones ( Table2 , rows 1 and 2 respectively), indicating that the contributions by Worst Area and lymph node status are independent of one another. When machine learning with leave-one-out cross validation was used to select the optimal number of features for prognostic models, computer-derived nuclear Worst Radius was used 84.... ..."

"... In PAGE 6: ...able 2 - Attributes for detailed view.......................................................................................................... 15 Table3 - Contributers.... ..."

"... In PAGE 4: ...3 Clusters of interest All received submissions [16] were double reviewed by the organisers. By compiling all contributions in a so-called contribution matrix (see Table1 ), and comparing the addressed topics, the organisers were able to divide the contributions into three major clusters of interest. The first relates to the possible links between the original problem domain and the software architecture(s) that can be related to it.... ..."

"... In PAGE 6: ... Observation 2b (within subjects): In the last period before changing groups subjects contribute less than in the previous period and less than in the next period (in the new group). Table2 displays the proportion of contributions in the different situations. Subjects who will leave their group contribute in 25% of the cases, and the members who will stay behind in 35% of the cases, a statistically significant difference (Wilcoxon test with sessions as observations, p lt;0.... ..."

"... In PAGE 8: ...3 Clusters of interest All received submissions [16] were double reviewed by the organisers. By compiling all contributions in a so-called contribution matrix (see Table1 ), and comparing the addressed topics, the organisers were able to divide the contributions into three major clusters of interest. The first relates to the possible links between the original problem domain and the software architecture(s) that can be related to it.... ..."