"... In PAGE 7: ...able 1 Runway Configuration Usage according to weather conditions .................................... 13 Table2 Minimum Distance Between Aircraft Entering the Final Approach Corridor .... In PAGE 23: ... But since it is subject to measurement errors on the part of the air traffic controller, the actual buffer is assumed to be normally distributed with a mean of 2100 meters and a standard deviation of 1260 meters, but truncated two standard deviations at either side of the mean. The minimum distance between successive aircraft is shown in Table2 . When the trailing airplane is slower than the leading airplane, the minimum separation occurs when the airplane that follows enters the common approach path.... In PAGE 23: ... When the trailing airplane is faster than the leading airplane, the minimum separation occurs when the leading airplane crosses the runway threshold. In addition to the minimum distance, Table2 shows the necessary separation between aircraft when the trailing airplane enters the common approach path. In Table 2 and throughout this study, aircraft are classified according to the wake vortex criteria: light, medium and heavy.... In PAGE 23: ... In addition to the minimum distance, Table 2 shows the necessary separation between aircraft when the trailing airplane enters the common approach path. In Table2 and throughout this study, aircraft are classified according to the wake vortex criteria: light, medium and heavy. This classification is ... In PAGE 32: ... MinLeadTime is a property of Plane, which specifies the minimum separation time between the arriving plane and the next plane to arrive. This value is determined by looking up in Table2 (which is represented by a matrix in STROBOSCOPE), adding a stochastic buffer, and dividing by the approach speed. This is defined in the model with the following expression: ONRELEASE AP1 ASSIGN MinLeadTime apos;Min [ComAppL, (MatrixIJ[NextArvType==Heavy ?0:NextArvType==Medium?1:2, Type] +Max[MinBufferd,Min[sNormal[BufferDist,BuffDistsd,2],MaxBufferd]])]/AppSpeed; It is after this time has passed that a resource is released to AppSignals, thus preventing other airplanes that may be in AppR21L to begin approaching during that time.... ..."

"... In PAGE 4: ... A zero indicates that it distinguishes no features for that part-of-speech. However, as illustrated in Table3 below, interpretation of grammatical tokens suffers from a lack of classification universality and devices indicated as absent or... ..."

"... In PAGE 4: ... A zero indicates that it distinguishes no features for that part-of-speech. However, as illustrated in Table3 below, interpretation of grammatical tokens suffers from a lack of classification universality and devices indicated as absent or rare in a language may well exist. ... ..."

"... In PAGE 6: ... MinLeadTime is a property of Plane, which specifies the minimum separation time between the arriving plane and the next plane to arrive. This value is determined by looking up in Table2 (which is represented by a matrix in STROBOSCOPE), adding a stochastic buffer, and dividing by the approach speed. This is defined in the model with the following expression: ONRELEASE AP1 ASSIGN MinLeadTime apos;Min [ComAppL, (MatrixIJ[NextArvType==Heavy ?0:NextArvType==Medium?1:2, Type] +Max[MinBufferd,Min[sNormal[BufferDist,BuffDists d,2],MaxBufferd]])]/AppSpeed; It is after this time has passed that a resource is released to AppSignals, thus preventing other airplanes that may be in AppR21L to begin approaching during that time.... ..."

"... In PAGE 2: ...test 1 test 2 test 3 savg ayre 69 47 49 55 bell 74 76 84 78 coe 82 82 85 83 dare 58 56 90 68 eames 82 72 71 75 fixx 44 56 41 47 gore 81 59 91 77 tavg 70 64 73 69 Table1 : A display which can be a spreadsheet or a table. for doodling with data.... In PAGE 2: ... This is because a table represents a relation in the sense of the relational data model: each row is a tuple of the relation represented by the table and each column is an attribute. Consider as an example the display in Table1 , showing the result of seven students in three tests, together with averages per student and per test. If the display is a table, then it represents a relation.... In PAGE 2: ...uples. Thus we see that in the table the rows and columns play different roles. Also, the averages for the students (s-avg) are part of the relation, but those for the tests (t-avg) are not. If the display in Table1 is regarded as a spreadsheet, we do not make such distinctions: rows and columns have the same status. Spreadsheets got to computers before tables did, exploding into the vacuum of the initially empty niche for exploratory computer use.... In PAGE 3: ... If this partial query succeeds (and it must, if the entire query is to succeed), then there are answer substitutions, and these are transmitted through that channel. Let us illustrate the dataflow model with an example based on the data in Table1 . When we view these data as a relational table, then the relation has five arguments: name, testl, test2, test3, and s-avg.... In PAGE 3: ... We call these binary relations tl, relating the name to testl; t2 and t3 are similarly defined. With these relations we can build up a table as in Table1 step by step by means of a query with several goals; see Figure 1 for an example.... In PAGE 5: ... We give examples to illustrate how joins, selections, and projections can be done in logic programming. The examples are based on the data introduced in Table1 , assuming that the available relations are the binary tl, t2, and t3 used in Figure 1 and Figure 2.... ..."

"... In PAGE 6: ... This follows from the definition of skill learning as being the improvement in performance on unique stimuli over blocks. What can we say about performance on repeating stimuli? Data pertinent to this question come, once again, from Kirsner and Speelman (1996), who showed that power functions also provided an excellent fit to performance on the repeating words and non- words in their lexical decision task (Kirsner amp; Speelman, 1996, Figure 5, and Table3 ). Further evidence that multiple repetitions give rise to a reaction time function that follows the power law comes from Logan (1990, Table 1, and Figures 5 and 6).... In PAGE 25: ..., 1999, Experiment 1). Parameters of the best-fit power functions are shown in the upper half of Table3 , along with two goodness-of-fit measures, root-mean-squared deviation (RMSD) and r2, which show that these power functions provide a good fit to performance on unique stimuli and performance on repeating stimuli in the digit-entering task. For IRP, we found that a linear power function provided as good a fit in terms of RMSD and r2 as did the best fitting nonlinear power function.... In PAGE 25: ... For IRP, we found that a linear power function provided as good a fit in terms of RMSD and r2 as did the best fitting nonlinear power function.8 This best linear fit is also graphed in Figure 13a, and its parameters are shown in the upper half of Table3 . These fits to empirical data from the digit-entering task exhibit the same pattern as that obtained by Kirsner and Speelman (1996): Perfor- mance on unique stimuli and performance on repeating stimuli follow the nonlinear power law, but repetition priming is linear.... In PAGE 25: ... For IRP, however (again, as with the empirical data), a linear power function provided as good a fit as did the best fitting nonlinear power function. The parameters of these fitted functions are shown in the lower half of Table3 , along with the goodness-of-fit measures. Even though these measures arise from a single learning mechanism in the model, they exhibit the same pattern of dissociation as in the digit-entering task empirical re- sults, viz.... In PAGE 26: ...974 and .950, respectively ( Table3 ). Similarly, the RMSD and r2 values for the comparison between empirical and simulated performance on repeating stimuli curves, Figure 13a vs.... ..."