"... In PAGE 7: ... year(2,[pls60,pls61,pls62]). Table4 shows a code extract showing how we flag illegal unit combinations. For example, the first code extract reads a student who has already taken the modules listed in AttendedBefore may not take module X if module PreReq, which must precede X, is not listed therein .... ..."

"... In PAGE 6: ... Our soft spot analysis, on the other hand, promptly pinpointed the most vulnerable nodes. Table2 shows a breakdown of time taken in each step. Note that the processing time of each node is comparable to the design DEC, indicating that our methodology is able to scale approximately linearly as the design complexity increases.... ..."

"... In PAGE 5: ...4 Table 11: How did the recent World Summit on Sustainable Development (WSSD) change the approach to using an EIS? Table 12: The importance of linking an EIS and a Geographical Information System (GIS) Table 13: Implementing an EIS provides financial benefits Table 14: Implementing an EIS improves legal compliance Table15 : Implementing an EIS makes environmental reports simpler Table 16: Implementing an EIS reduces environmental risks Table 17: Implementing an EIS improves environmental strategy Table 18: Implementing an EIS aids ISO 14001 accreditation Table 19: Implementing an EIS improves managing environmental auditing data Table 20: Environmental legal register is an essential component in EIS Table 21: Health and safety information forms an integral part of an EIS Table 22: The role that corporate executive management plays in the implementation of an EIS Table 23: Corporate reporting functionality is an essential component in an EIS Table 24: Web based reporting of environmental data should be a basic output of an effective EIS ... In PAGE 44: ...5% 51.6% Table15 below indicates that across all size of mine, there is agreement to the statement that implementing an EIS makes environmental reports simpler. Although small, medium and large mines did have neutrality percentage figures, these are outweighed by the larger percentage figures in the agree and strongly agree columns.... ..."

"... In PAGE 4: ...able 5-2: Pure Variants Feature Model for MobilePhoto ............................................... 35 Table6 -1: MobilePhoto Jar Size Comparison.... In PAGE 4: ...able 6-1: MobilePhoto Jar Size Comparison.................................................................. 40 Table6 -2: MobilePhoto Heap Memory Comparison .... In PAGE 4: ...able 6-2: MobilePhoto Heap Memory Comparison ....................................................... 41 Table6 -3: MobilePhoto Source Code Metrics Comparison.... In PAGE 4: ...able 6-3: MobilePhoto Source Code Metrics Comparison............................................. 42 Table6 -4: Modified Classes for Custom Blackberry AspectJ Runtime.... In PAGE 46: ... Our evaluation of the AspectJ implementation and the object-oriented implementation of MobilePhoto shows that the application size will always be larger when using AspectJ because you must include the AspectJ runtime classes with your application jar file. Table6 -1 compares the size of the jar files for each implementation when only the base components are used, and when all the components are compiled into the application. Table 6-1: MobilePhoto Jar Size Comparison MobilePhoto AOP (Base) MobilePhoto OOP (Base) MobilePhoto AOP (All)* MobilePhoto OOP (All)* Final app size [jar] 200 KB jar 165 KB jar 251 KB jar 213 KB jar The aspect-oriented implementation jars listed in table 6-1 were created by including the classes found in the standard AspectJ runtime without modification.... In PAGE 46: ... Table 6-1 compares the size of the jar files for each implementation when only the base components are used, and when all the components are compiled into the application. Table6 -1: MobilePhoto Jar Size Comparison MobilePhoto AOP (Base) MobilePhoto OOP (Base) MobilePhoto AOP (All)* MobilePhoto OOP (All)* Final app size [jar] 200 KB jar 165 KB jar 251 KB jar 213 KB jar The aspect-oriented implementation jars listed in table 6-1 were created by including the classes found in the standard AspectJ runtime without modification. The size of this jar (aspectjrt.... In PAGE 47: ... The size of our modified (and obfuscated) AspectJ runtime jar was 29 Kb44. Table6 -2 shows measurements of heap memory use45 at specified intervals during the MobilePhoto application lifecycle. It compares equivalent product instances for the aspect-oriented and object-oriented implementations after invoking various commands in the application.... In PAGE 48: ... Table 6-3 below provides details of MobilePhoto source code metrics. Table6 -3: MobilePhoto Source Code Metrics Comparison MobilePhoto AOP (Base) MobilePhoto OOP (Base) MobilePhoto AOP (All)46 MobilePhoto OOP (All)46 # build script tasks 7+ 6+ 7+ 6+ # Classes ( amp; Aspects) 16 (no aspects) 16 41 (35 classes + 6 aspects) 41 # Lines of Code 550 565 1481 (227 from Aspect code) 1524 Build Time 19 Seconds (to run Ant build with ajc) 18 Seconds (to run Ant build with javac) 31 Seconds (to run Ant build, no conversions) 29 Seconds (to run Ant build, no conversions) The most significant finding of the quantitative analysis is that any J2ME and AspectJ implementation will increase your application by approximately 29 Kb. This is important when developing for devices with tight restrictions on the size of application.... In PAGE 54: ... These changes did not affect the AspectJ usage in MobilePhoto, and slightly reduced the size of the runtime. Table6 -4 provides a summary of the AspectJ runtime source files that were modified, and notes the non-J2ME references that had to be removed. Table 6-4: Modified Classes for Custom Blackberry AspectJ Runtime Modified AspectJ Runtime Class Non-J2ME references removed org.... In PAGE 54: ... Table 6-4 provides a summary of the AspectJ runtime source files that were modified, and notes the non-J2ME references that had to be removed. Table6 -4: Modified Classes for Custom Blackberry AspectJ Runtime Modified AspectJ Runtime Class Non-J2ME references removed org.... ..."

"... In PAGE 2: ... Table 1 shows the resulting ranked list. In the remainder of this article, we will focus on the top 10 CSFs, which are italicised in Table1 . The choice for precisely this number was somewhat arbitrary, but worked out well in our analyses, as will become apparent later on.... In PAGE 10: ... Proposition 1: The list of critical success factors as compiled by Nelson and Somers (2001), and more specifically the top 10 of that list, can adequately explain both success and failure of specific ERP implemen- tation projects. As the previous section has illustrated, the top 10 of the list of CSFs from Table1 suffices to explain broadly what went wrong in the particular ERP implementation investigated, and also why performance went up after the project crisis. This is not to say that there is no additional detail possible.... ..."

"... In PAGE 10: ...xample of a multi-level data structure, which we will discuss in more detail in Section 5.1. The best-known data structures for orthogonal range searching are by Chazelle [52, 55], who used compressed range trees and other techniques to improve the storage and query time. His results in the plane, under various models of computation, are summarized in Table1 ; the preprocessing time of each data structure is O(n log n). If the query rectangles are \three-sided rectangles quot; of the form [a1; b1] [a2; 1], then one can use a priority search tree of size O(n) to answer a planar range-reporting query in time O(log n + k) [188].... In PAGE 10: ... If the query rectangles are \three-sided rectangles quot; of the form [a1; b1] [a2; 1], then one can use a priority search tree of size O(n) to answer a planar range-reporting query in time O(log n + k) [188]. All the results mentioned in Table1 can be extended to higher dimensions at a cost of logd?2 n factor in the preprocessing time, storage, and query-search time. The query time (or the query-search time in the range-reporting case) can be reduced to O((log n= log log n)d?1) in the RAM model by increasing the space to O(n logd?1+ quot; n).... In PAGE 18: ...17 objects are approximated by rectangles. Chazelle [55] has shown that the bounds mentioned in Table1 hold for this problem also. In practice, two general approaches are used to answer a query.... ..."

"... In PAGE 10: ...xample of a multi-level data structure, whichwe will discuss in more detail in Section 5.1. The best-known data structures for orthogonal range searching are by Chazelle [55, 58], who used compressed range trees and other techniques to improve the storage and query time. His results in the plane, under various models of computation, are summarized in Table1 ;; the preprocessing time of each data structure is O(n log n). If the query rectangles are \three-sided rectangles quot; of the form [a 1 ;;b 1 ] [a 2 ;; 1], then one can use a priority search tree of size O(n) to answer a planar range-reporting query in time O(log n + k)[208].... In PAGE 10: ... Asymptotic upper bounds for planar orthogonal range searching, due to Chazelle [55, 58], in the random access machine (RAM), arithmetic pointer machine (APM), elementary pointer machine (EPM), and semigroup arithmetic models. Each of the two-dimensional results in Table1 can be extended to queries in R d at a cost of an additional log d;2 n factor in the preprocessing time, storage, and query-search time. For d 3, Subramanian and Ramaswamy [270] have proposed a data structure that can answer a range-reporting query in time O(log d;2 n log n + k) using O(n log d;1 n) space, and Bozanis et al.... In PAGE 18: ... Rectangle-rectangle searching is central to many applications because, in practice, polygonal objects are approximated by rectangles. Chazelle [58] has shown that the bounds mentioned in Table1 also hold for this problem. In practice, two general approaches are used to answer a query.... ..."

"... In PAGE 3: ... 1277 matches passed the given hard constraints, which were inserted into a new database. Table1 shows the four soft constraints applied to the matches that satisfied the hard constraints and the functions which implement them. These functions measure degrees of satisfaction of matches between sailors and jobs, each subject to one soft constraint.... ..."

"... In PAGE 9: ... 6.2 Array Bounds Checking and Heap Invariants Table2 shows the results of running C2bp on a set of toy illustrative examples. The program kmp is a Knuth-Morris- Pratt string matcher and qsort is an array implementation of quicksort, both examples used by Necula [26].... ..."

"... In PAGE 9: ... 6.2 Array Bounds Checking and Heap Invariants Table2 shows the results of running C2bp on a set of toy illustrative examples. The program kmp is a Knuth-Morris- Pratt string matcher and qsort is an array implementation of quicksort, both examples used by Necula [26].... ..."