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(1990) Foundations of Social Theory Cambridge, MA: Harvard University Press Collins, H (1993) The structure of knowledge Social Research, 60, 95 116 D Aveni, R (1994) Hypercompetition: Managing the Dynamics of Strategic Maneuvering New York: Free Press Davenport, T H & Prusak, L (1998) Working Knowledge: How Organisations Manage What They Know Boston, MA: Harvard Business School Press Davidow, W H & Malone, M S (1992) The Virtual Corporation: Structuring and Revitalising the Corporation for the 21st Century New York: Harper Collins Dierkes, M, Antal, A B Child, J & Nonaka, I (Eds) (2001) Handbook of Organizational Learning and Knowledge Oxford: Oxford University Press Drott, M C (2001) Personal knowledge, corporate information: the challenges for competitive intelligence Business Horizons, 44(2), 31 37 Easton, G & Araujo, L (1994) Market exchange, social structures and time European Journal of Marketing, 28(3), 72 84 Eisenhardt, K M (1989).

In modern times, with the use of poles made out of carbon- ber composite material, the current world record is nearly double that at 614 m (2008) Despite the re nement in the preparation for elite performances, the improvement in world records in the last 20 years has been moderate For example, the World Record in the marathon has improved by 2 min 17 s (18%), the 10,000 m and 5000 m track race times by 56 s (34%) and 22 s (30%), respectively, and the shot put distance has increased by 50 cm (22%) during this time..

Making fast strategic decisions in high-velocity environments Academy of Management Journal, 32, 543 576 Fenton, E M & Pettigrew, A (2000) Theoretical perspectives on new forms of organizing In A M Pettigrew & E M Fenton (Eds), The Innovating Organization London: Sage Galunic, D C & Rodin, S (1998) Resource recombinations in the rm: knowledge structures and the potential for Schumpeterian innovation Strategic Management Journal, 19(12), 1193 1201 Garud, R & Kumaraswamy, A (1995) Technological and organizational designs for realigning economies of substitution Strategic Management Journal, 16, 93 109 Grant, R M (1996) Toward a knowledge-based theory of the rm Strategic Management Journal, 17, 109 122 Greve, H R (1995) Jumping ship: the diffusion of strategy abandonment Administrative Science Quarterly, 40, 444 473..

DOS is dead (almost)

as it changes its state in response to the messages it receives. Thus, this is a behavioral diagram with hardly any structural content. 2.9.2 Putting Together a State Machine Diagram

// If it's the KERNEL32 heap, make the critical // all processes. if ( fShared && HKernelHeap ) MakeCriticalSectionGlobal( if ( 0 == ppCurrentProcessId ) _DebugOut( "HeapCreate: SLE_ERROR ); hHeap + 0x70 );

Watch Out!

Thus, genetic factors strongly in uence body form and physiology and, as a consequence, both human physical performance and risk of injury. The identi cation of speci c genetic elements of risk will lead to a greater understanding of the mechanisms through which physiologic performance is regulated, and how the health bene ts of exercise are derived. They may also help us understand the mechanisms predisposing to risk of sports-related injury in athletes, as well as to disease states in more sedentary individuals.

from R1, R2, R3, and R9 respectively, and delivers the task to R7 along the created path R0-R1-R7 This version of auction aggregation protocol will be designated as simple auction aggregation protocol (SAAP ) In case of limited ooding (only up to k -hop neighbors from the collector), it is called k-SAAP (Mezei et al, submitted for publication) The difference between k-SAAP and k-SAP (and similarly between SAP and SAAP ) is that individual bids are aggregated at intermediate nodes, instead of routing all of them back to the collector robot The algorithm can be further re ned by providing autonomy in retransmitting decisions to individual robots In k-SAAP , the receiving robot will retransmit only if it is at distance of <k hops from the bidding robot The k-hop auction aggregation protocol (k-AAP ) protocol (Mezei et al.

, submitted for publication) applies k -hop neighborhood around current robot Each robot is assumed to already know the position, cost, and availability of all its neighboring robots up to k hops away One simple way is periodic diffusion of its local information [including the status of its (k 1)-hop neighbors] to its neighbors ( hello message), or piggybacking it to data messages In k-AAP (Mezei et al, submitted for publication), the robot that received the bid (and the best learned cost C associated with previous senders on the path from the bidding robot to it) will compare C and the cost of providing service by its k -hop neighbors It will retransmit the message only if at least one of its own k -hop neighbors has cost <C .