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5614 Extended firing rule When a strategy has been selected, the firing rule for state classes as described in section 532 is easily adapted to extended interpretations The different enabling instances of each transition are simply associated with as many variables in the systems defining firing domains; the variable management strategy (we will remember here the FEFF strategy) determines the selection of variables exiting from domains To facilitate the calculation of new classes, variables of any system are indexed depending on the order in which they are introduced (and renumbered by preserving this order when placed in canonical form) In firing rule step (2b) of section 532, we eliminate transition instances conflicting with t by starting with the oldest instances, excluding any commonly fired instance In step (2c), all instances of transitions which remained enabled on firing of commonly fired instances are translated.

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In step (2d), a new variable is introduced for each instance of newly enabled transitions (indexed so that they appear as the newest instances) For the example shown in Figure 510, the class graph shown in Figure 511 is thereby achieved 5615 Boundedness Theorems 56, 57 and 58, which set out sufficient conditions for boundedness, are no longer applicable with the extended firing rule In fact, lemma 51 and theorem 55 are no longer verified, since the number of variables is no longer bounded by the number of transitions Lemma 51 and theorems 55, 56, 57 and 5.

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8, however, extend to nets referred to as T-bounded: a net is said to be T-bounded if there is an integer k 0 such that, for any reachable marking M, and any transition t, t is at most k times enabled by M (or q M qPre(t) q k) The number of variables associated with a transition (using the extended firing rule) in any firing domain of a T-bounded time Petri net is clearly bounded since being at most equal to the constant k; the outcome is that the number of constructible firing domains is finite When applied to T-bounded time Petri nets, theorems 56, 57 and 58 therefore provide sufficient conditions for boundedness..

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at random would be stable. In their words, they were concerned with an airport with 100 planes, slum areas with 104 persons or the human brain with 1010 neurons . . . [where] stability is of central importance . Forty years on and these questions are no less pertinent; indeed, we may add an order of magnitude to some of these problems and present entirely new problems for solution; for example, global access to the Internet. Gardner and Ashby showed that for small numbers of components (n; e.g. neurons or species) stability declines with connectance between components (Fig. 7.3). As the number of components increases, the system moves rapidly to a breakpoint situation when a small change in connectance will result in a switch from stability to complete instability. These results were developed by May (1972) in an ecological context. He concluded that increased numbers of species do not automatically imply community stability and in fact may produce just the opposite effect (see Jansen & Kokkoris 2003 and references therein for further debate on these results). Increased stability with complexity was promoted by Elton (1958), whose conclusions were partly based on detailed case studies of invasive species, such as the giant snail Achatina fulica into Hawaii and the red deer Cervus elaphus into New Zealand, which contributed to dramatic declines in the endemic species of those islands (see May 1984 and Pimm 1984 for a critique of Elton s views). May also demonstrated that a species that interacts widely with many other species (high connectance) does so weakly (small interaction coef cient) and, conversely, those that interact strongly with

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{Order Placement Module} if(obCount = 2) then sell short tomorrow at open; {We have entered OB twice} if(osCount = 2) then buy tomorrow at open; {We have entered OS twice} if(marketPosition = 1) then begin Sell ("longLoss")next bar at longProtStop on a stop; Sell ("longProfit")next bar at longProfitStop on a limit; end; if(marketPosition = 1) then begin BuyToCover ("shortLoss")next bar at shortProtStop on a stop; BuyToCover ("shortProfit")next bar at shortProfitStop on a limit; end;

Negative phase unbalance protection Double threshold independent time: time delay = Tst + several 1st threshold: I i = 0.2 I n , seconds time delay = 0.5 seconds 2nd threshold: I i = 0.4 I n , Dependent time: Protection operating curve above the point of co-ordinates I i = 0.3 I n and T = Tst

PointValue The dollar value per share of one increment on the price scale. Calculated as Big Point Value divided by the Price Scale using the values specified in the symbol dictionary.

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