### Table 1: The parameters of the optical tree crown model.

1997

"... In PAGE 3: ... They were used as ground truth in the experiments described here. The various parameters selected for the optical model are listed in Table1 . The tree crown shape parameters were sub- jectively estimated from a number of the available images of the selected stand.... ..."

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### Table 1: Optical parameters at the exit of the fringe field.

"... In PAGE 5: .... from which the optical parameters may be obtained using expression for the transformation of the Twiss parameter through a beam transfer section. 5 EXPERIMENTAL VERIFICATION The optical parameters at the exit of the field map derived from the foregoing calculations are shown in Table1 , along with, for comparison, the parameters given by the MAD modeling in which the fringe field of the CPS magnet is represented by dipole, quadrupole and sextupole field components distributed over the full magnet length [10]. Table 2 shows the computed optical parameters at the three beam profile monitors in the TT2 transfer line from CPS to SPS for the field map and the MAD models.... ..."

### Table 3. Parameters used in the CEMHYD3D v3.0 modeling of the hydration reactions of the five different CCRL cements. CCRL

"... In PAGE 5: ... The total alkali contents of the cements were taken directly from the CCRL summary reports. For three of the cements (see Table3 ), the readily soluble alkalis (sodium and potassium) were measured in the NIST laboratory for 1 h old filtered pore solutions; for the other two, the readily soluble alkalis were assumed to be 80 % of their respective total alkali values [13]. The measured and assumed activation energies for the hydration reactions for each cement are also provided in Table 3.... In PAGE 5: ....00035 [19]. This parameter was then adjusted using a spreadsheet to provide the best agreement between the model and experimentally measured chemical shrinkages in the 8 h to 10 h time range, as will be demonstrated in the Results section. The adjusted kinetics factors obtained in this manner are also provided in Table3 . This adjusted kinetics factor value was then used for the subsequent simulation of hydration under sealed conditions for the heat of hydration virtual test method (w/c=0.... In PAGE 9: ... The results obtained for the first case, where the complete characterization of the cement was employed, are provided in Figures 3 and 4. By calibrating the kinetics factor for each starting microstructure ( Table3 ), the obtained curves for predicted chemical shrinkage and heat release are quite similar amongst the three replicates. Three different random starting microstructures were also generated for the case where only the phase volume composition of the cement was provided.... ..."

### Table 2 summarizes the sampling distributions of the three estimators of the diffusion

"... In PAGE 20: ... If, however, the information about intraday volatility that is revealed by the range but not by absolute or squared returns is useful in the estimation of the model, the sampling properties of the range- based quasi-maximum likelihood estimator could well dominate the sampling properties of the exact maximum likelihood estimator for absolute returns. 14 Comparing the third row of each panel in Table2... In PAGE 21: ...bsolute return as volatility proxy. First, the range-based parameter estimates are more accurate. Second, even for the same parameters values, the approximate normality of the log range yields a more efficient volatility extraction. With this in mind, we summarize in the last two panels of Table2 (and in the last column of Figure 2) the sampling distributions of the average extraction error , which is 1 T j T t apos;1 ( ln Ft amp;ln Ft ) an estimator of the expected extraction error , and the average squared extraction E [ ln Ft amp;ln Ft ] error , which is an estimator of the expected squared extraction error 1 T j T t apos;1 ( ln Ft amp;ln... In PAGE 22: ... Now we discuss the results for a smaller sample size of T = 500 observations and a larger sample size of T = 5000 observations. We show the results for T = 500 in Table 3; they are qualitatively identical to those in Table2 . Quantitatively, however, the relative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy, which was already poor with T = 1000 observations, is much worse with T = 500 observations.... In PAGE 22: ... D We present the results for T = 5000 in Table 4. Qualitatively, they are again identical to the results in Table2 ; quantitatively, the comparative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy is improved in some respects,... ..."

### Table 2 summarizes the sampling distributions of the three estimators of the diffusion

"... In PAGE 18: ... If, however, the information about intraday volatility that is revealed by the range but not by absolute or squared returns is useful in the estimation of the model, the sampling properties of the range- based quasi-maximum likelihood estimator could well dominate the sampling properties of the exact maximum likelihood estimator for absolute returns. 14 Comparing the third row of each panel in Table2... In PAGE 19: ...bsolute return as volatility proxy. First, the range-based parameter estimates are more accurate. Second, even for the same parameters values, the approximate normality of the log range yields a more efficient volatility extraction. With this in mind, we summarize in the last two panels of Table2 (and in the last column of Figure 2) the sampling distributions of the average extraction error , which is 1 T j T t apos;1 ( ln Ft amp;ln Ft ) an estimator of the expected extraction error , and the average squared extraction E [ ln Ft amp;ln Ft ] error , which is an estimator of the expected squared extraction error 1 T j T t apos;1 ( ln Ft amp;ln... In PAGE 20: ... Now we discuss the results for a smaller sample size of T = 500 observations and a larger sample size of T = 5000 observations. We show the results for T = 500 in Table 3; they are qualitatively identical to those in Table2 . Quantitatively, however, the relative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy, which was already poor with T = 1000 observations, is much worse with T = 500 observations.... In PAGE 20: ... D We present the results for T = 5000 in Table 4. Qualitatively, they are again identical to the results in Table2 ; quantitatively, the comparative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy is improved in some respects,... ..."

### Table 2: Camera and optics model parameters

1995

"... In PAGE 14: ...obot block was modeled as a first order lag filter with a time constant of 0.0318 second. This was based on our knowledge of the current AESOP robot and its capabilities. The camera and optics model parameters are shown in Table2 and are typical of those used with certain laparoscopes. The image feedback rate was assumed to be at 0.... ..."

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### Table 5: Root Mean Squared Forecast Errors relative to a linear AR model (where the parameters have been estimated recursively using expanding sam- ples)

### Table 6: Root Mean Squared Cumulative Forecast Errors relative to a linear AR model (where the parameters have been estimated recursively using expanding samples)

### Table 2: Reaction rates for the model.

"... In PAGE 6: ...aking into account Eq.(8a) for X7 and using Eq.(6a) the concentration of the (APC/Axin) complex, X6, can be expressed as function of X11 and X12: X6 = K17X12AP C0 K7(K17 + X11): (11) Di erential Equations of the Independent Variables We rst consider the di erential equations for X2, X4, X9, and X10 which are not a ected by the rapid equilibrium approximations. These equations can be easily rewritten by introducing the rate equations given in Table2 and by eliminating the dependent variables using the Eqs. (5a) and (7) to (11).... In PAGE 7: ... (11) and introducing on the right hand side of Eq. (16) the rate equations from Table2 yields dP1 dt = dX12 dt 1 + AP C0K17 K7(K17 + X11) dX11 dt AP C0K17X12 K7(K17 + X11)2 = k3X2X4 k6 GSK30AP C0K17X12 K7(K17 + X11) + k 6X4 + V14 k15X12; (18) which depends solely on the independent variables. In an analogous way, we obtain dP2 dt = 1 + X11 K8 dX3 dt + X3 K8 dX11 dt = k4X4 k5X3 k9X3X11 K8 + k10X9 and (19) dP3 dt = dX11 dt 1 + X3 X8 + T CF 0K16 (K16 + X11)2 + AP C0K17 (K17 + X11)2 + X11 K8 dX3 dt = V12 k9X3 K8 + k13 X11: (20) In summary, the original system of 15 di erential equations involving 31 parameters (Table 1) is reduced to a system of 7 di erential equations (Eqs.... ..."

### Table 7: Parameter values for 3-D model

2000

"... In PAGE 3: ... Note that V P depends on P a o 2 and P a co 2 while V C depends on P B co 2 . Table7 at the end of this paper gives parameter values used in simulation studies of the model #281#29-#283#29 #28unless otherwise noted#29. 2.... In PAGE 11: ...11 and where A 1 = a 2 +a 3 #16 V; A 2 = a 3 #16 x #16 V x ; B 1 = b 2 +b 3 #16 V; B 2 = b 3 #16 y #16 V y ; C 1 = c 2 ; C 2 = a 3 #16 x #16 V z : Clearly jP#28i!#29j 2 ,jQ#28i!#29j 2 will take a complicated form from which it is di#0Ecult to extract a simple condition for stability.However, we can study the stability for parameter values which are physiological meaningful #28 Table7 #29. The expression F#28!#29=jP#28i!#29j 2 ,jQ#28i!#29j 2 is a six degree polynomial of the form F#28!#29 = ! 6 +k 1 ! 4 +k 2 ! 2 +k 3 : Now, we let ! 2 = v and de#0Cne ^ F#28v#29=v 3 +k 1 v 2 +k 2 v+k 3 : We #0Cnd, for our parameters, that this cubic has two negative roots and one positiveroot v o so that ! o = p v o is the only #28simple#29 positiverootofF#28!#29 #28see Figure 1#29.... In PAGE 12: ... We compare the two- dimensional model with the peripheral control only. Using the same parameter values #28from the three-dimensional model parameters, Table7 #29 we see from Table 2 that a model with the peripheral control only is much more unstable than the three-dimensional model incorporating both a peripheral control and central control. Note that for normal control gain the two-dimensional model predicted instabilityata#1Cmultiplier of 1.... ..."

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