### Table 9: The results of the estimation for the combination of RMRC crossover and point mutation for di erent K-values.

1996

"... In PAGE 19: ... For K=50 and 95 with random interactions, and K=95 with nearest neighbor interactions, all partial autocorrelation are within the bound (as are the autocorrelations for those cases), suggesting a white noise model. Estimation The results of the estimation of the identi ed models are presented in Table9 (t-statistics shown between parentheses). Again all parameters appear to be signi cant.... ..."

Cited by 37

### Table 3: The K-value of the best hybrid without barriers. ? Data Block Size (in bytes) ?! mesh

1996

"... In PAGE 7: ... 32 32 mesh ? Data Block Size (in bytes) ?! ts=tp 8 32 128 1024 4096 16384 150 2 3 4 5 5 5 1000 1 2 3 4 5 5 7000 0 1 2 3 4 5 Results without barrier The performance of the hybrids in the absence of barriers in both the CE and DE components is investigated in this section. Table3 presents the K for the best hybrid without barriers for three di erent mesh sizes and startup ratios. Though all the combinations of factors were studied, we present the data for only some selected combinations.... ..."

Cited by 6

### Table 2-2: Computed K values for soils in Madison County, Arkansas

in Soil Erosion

"... In PAGE 6: ... This will provide representative examples of soil erosion predictive models, and from these examples, the model will be chosen for implementation with GRASS. To that end, a classification scheme in Table2 -1 is presented that seeks to represent the variety of soil erosion prediction models found in the current literature. The table shows four basic levels, with Level 1 being the most simple of the models to understand and implement and Level 4 being the most complex and difficult.... In PAGE 6: ... This research focuses on the most commonly used models because, quot;most models [soil erosion models] are sufficiently modular that component relationships can be changed to meet the specific needs of the user [11]. Models were chosen to cover in detail the four levels of mathematical difficulty and complexity (see Table2 -1). The models analyzed were the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), Meyer and Wischmeier apos;s Simulation of the Process of Soil Erosion By Water, the Nonpoint Source Pollutant Loading Model ... In PAGE 8: ... The methods that constitute Level 4 all attempt to model analytically each of the important steps in the erosion process. TABLE 2-1: Classification Scheme for Soil Erosion Prediction Models The levels of mathematical difficulty and complexity are shown in Table2 -1 [19]. Models will be examined from each of these four levels so that an accurate examination of soil erosion prediction models can be researched.... In PAGE 14: ... The factor seeks to measure the combined effect of all the interrelated cover and management variables. Table2 -3 shows the C factors for current land use and land cover [15]. Although the values for C are available for various farm and land use conditions, this study will focus primarily on those values that pertain to construction areas.... In PAGE 14: ...anagement variables. Table 2-3 shows the C factors for current land use and land cover [15]. Although the values for C are available for various farm and land use conditions, this study will focus primarily on those values that pertain to construction areas. Since part of this study evaluates the amount of soil erosion that takes place after construction modifications are made to the land area, the factor C for mulches in this study is included in the Table2... In PAGE 15: ... 15 mulches are used and construction work has removed all vegetation and the root zone of the soil which removes the residual effects of prior vegetation. Table2 -4 gives the Cover and Management ... In PAGE 16: ...1 Confined Animal Operations 0.15 Table2... In PAGE 18: ...02 100 quot; 25 34-50 0.02 75 Table2... In PAGE 22: ... Thus, once the improved model is finished, it should be relatively easy to use the RUSLE in GRASS and this research. Table2 -5 shows some of the major differences as well as similarities between the RUSLE and the USLE [21] . Table 2-5: USLE vs.... In PAGE 22: ... Table 2-5 shows some of the major differences as well as similarities between the RUSLE and the USLE [21] . Table2 -5: USLE vs. RUSLE: Similarities and Differences Factor Universal Soil Loss Equation (USLE) Revised Universal Soil Loss Equation (RUSLE) R Based on long-term rainfall conditions for specific geographic areas in the U.... In PAGE 23: ... P factor values are based on hydrologic groups, slope, row grade, ridge height, and the 10-year single storm index values. --------------------------------- RUSLE estimates of P factor may be higher or lower than estimates obtained through the USLE Table2 -5 (continued): USLE vs. RUSLE: Similarities and Differences Conclusions In terms of complexity the RUSLE would remain a Level 1 model because no delivery mechanism exits for the movement of sediment and water.... In PAGE 35: ... IRC, KK24 The interflow and groundwater recession parameters. Table2... In PAGE 36: ... 36 The major parameters of the LANDS subroutine are given in Table2 -4 [6]. The flowchart provided in Figure 2-4 shows the subprogram of LANDS.... In PAGE 46: ... 46 Stormwater Models Table2 -7, taken from the Virginia Department of Transportation Manual sums up the basic capabilities of the SWM models in the current literature [39]. Table 2-7: A Comparison of Stormwater Models Capability HSPF STORM SWMM Event (E) or (C) continuous (Event refers to a single rainfall event, while continuos refers to predictions based on a period of time such as a year or month.... In PAGE 46: ... 46 Stormwater Models Table 2-7, taken from the Virginia Department of Transportation Manual sums up the basic capabilities of the SWM models in the current literature [39]. Table2 -7: A Comparison of Stormwater Models Capability HSPF STORM SWMM Event (E) or (C) continuous (Event refers to a single rainfall event, while continuos refers to predictions based on a period of time such as a year or month.) E,C C E,C Infiltration loss techniques Stanford Watershed model, infiltration as function of soil moisture and permeability Runoff coefficient (1) Horton model (2) Modified Green-Ampt model Runoff modeling techniques Manning apos;s equation and storage routing Modified rational formula Storage routine using Manning apos;s equation and continuity equation Sewer routing Yes No Yes Non-point source pollutant accumulation and washoff modeling techniques Sediment detachment transport; pollutant is related to sediment.... In PAGE 61: ... Since GRASS can only store integer numbers in the data layers, these factors were multiplied by a factor of 100 for relative accuracy. The values used for K in this analysis were taken from Table2 -1 which can be found in the Soil Survey For Madison County in the state of Arkansas [28]. As an example, in the soil survey for Huntsville, AR, the K factor for the soil called Allen is 0.... In PAGE 62: ... The C factor is a required primary data layer and the value is between 0 and 1. These values can be found in Table2 -3 and Table 2-4 for the different types of cover and mulches that can be applied. The values for the C factor can be quite small and as a result a factor of 1000 was applied to the various C factors for relative accuracy.... ..."

### Table 4. Estimated optimal K value by different approaches

2007

"... In PAGE 6: ... In addition, the new consensus function in GCC performs better than those in existing consensus clustering methods due to the adoption of the normalized cut algo- rithm, which results in a more accurate partition of the consensus matrix. Table 5 lists the corresponding values of ARI with respect to the estimated K value in Table4 . The graph based consensus clustering approaches clearly outperform the consensus clustering approaches, especially in the Leukemia dataset and the Lung cancer dataset.... ..."

### Table 4. Optimal k-Values with R, Q and MSE for the IC Strategy

"... In PAGE 26: ... Second, removing a certain percentage of data from two data sets with different numbers of attributes but the same number of cases would result in different numbers of complete cases. Table 3 and Table4 show the observed optimal k-values for the CC strategy and the IC strategy, respectively, given the average number of complete cases for the simulated percentages. It can be seen that the optimal value of k for a certain number of neighbours is the same regardless of strategy.... ..."

### Table 3. Optimal k-Values with R, Q and MSE for the CC Strategy

"... In PAGE 26: ... Second, removing a certain percentage of data from two data sets with different numbers of attributes but the same number of cases would result in different numbers of complete cases. Table3 and Table 4 show the observed optimal k-values for the CC strategy and the IC strategy, respectively, given the average number of complete cases for the simulated percentages. It can be seen that the optimal value of k for a certain number of neighbours is the same regardless of strategy.... In PAGE 29: ... The diagram (for six attributes) can be seen in Fig. 5 (for the raw numbers, see Table3 and Table 4). Both neighbour strategies provide nearly maximum ability (R) up to around 30% missing data (when, on average, 88% of the cases are incomplete).... ..."

### Table 2. Some values of the function fSM(k), values of the best known lower and upper bounds, and examples of words of length fSM(k) + 1 with the same k-spectrums. k 2k ? 1 fSM(k) (k) ? 1 u;v

"... In PAGE 9: ... But since u 6 = v, we can choose a mapping m such that m(u) 6 = m(v). The results on the two-letter alphabet in Table2 were found by computer. Cases F; F; F ; F .... ..."

### Table 2.: Two different set of K-values (Note that not all combinations of impact need to be meaningful for all variables. Such K-values are left out and denoted by dots.)

"... In PAGE 9: ... K-values can therefore be higher than 1 only for variables which can reach levels higher than 1. In Table2 two possible sets of K-values are presented. The K-values in the left part of Table 2 imply a high impact on symptoms by family stress and work demands.... In PAGE 9: ... In Table 2 two possible sets of K-values are presented. The K-values in the left part of Table2 imply a high impact on symptoms by family stress and work demands. We assume that this is given in the group of severely impaired patients.... In PAGE 9: ... We assume that this is given in the group of severely impaired patients. The right part of Table2 shows an example of lower impact on the symptoms as it might be given in... In PAGE 10: ... The numbers under Kp, Kw and Kss are K-values. As example for the meaning of K-values see the last row left in Table2 .... In PAGE 10: ...xamples. For models with few variables calculations can be done manually. However help from computers is preferablei. Figure 4 shows the dynamics in our model of chronicity using the K-values for apos;high impact on symptoms apos; (see Table2 left part). It is a three-dimensional table with symptoms on y-axis, work and family stress on combined x- and z-axis.... In PAGE 12: ...followed by an employment but without additional interventions on P, W or SS, would be doomed to failure. In the second example, the assumed effects of the system (the matrix of interactions) are kept constant, but the power of the unwelcome effects on P (the K-values) are reduced, as shown in Table2 right part ( apos;lower impact on symptoms apos;). Given these K-values, the dynamics presented in Figure 3, right panel, will result: The person circles within four states: the state apos;no work, mild symptoms, high family stress apos; is followed by the state apos;work, mild symptoms, high family stress apos;, i.... In PAGE 13: ... We first introduce an intervention on the work site and go then to family intervention. First type of intervention: sheltered job As a starting point we take our model of chronicity (Figure 2) with the K-values denoting a lower impact on the symptoms ( Table2 ), which results in chronicity as cyclic attractor (Figure 3, right panel). As mentioned above, this seems realistic for many patients applying for vocational rehabilitation.... ..."

### Table 2 Some values of the function fSM(k), values of the best known lower and upper bounds, and examples of words of length fSM(k) + 1 with the same k-spectrums.

### Table 1: Some vertex subset properties expressed as ( ; )-sets, with tractabil- ity/intractability cuto value for the partitioning problem (1 means tractable for all k values). Reference * if cuto value proved here.

1998

"... In PAGE 1: ... Gen- eralized dominating sets, introduced by Telle in [8] and de ned formally in the next section, are parameterized by two sets and of nonnegative integers. Many well-studied vertex subset properties with applications in facility location and network communication can be expressed as ( ; )-sets [8, 1, 2, 3], see Table1 . In this paper we present a systematic study of the complexity of partitioning vertices of a given graph G into k ( ; )-sets, for varying values of the parameters k; ; .... In PAGE 4: ... This is not surprising, as the number of partitions of n vertices into k classes is not polynomial in n for any k 2. 3 NP-completeness results The values of and used to describe the vertex subset properties listed in Table1 are con- ned to f0g; f0; 1g; f1g; N and P. For = f0g we know from Fact 5 that the corresponding partition problems are easy.... ..."

Cited by 17