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At each round of the algorithm execution, we denote by X a subset of the set of informed nodes and by Y a subset of the set of uninformed nodes The front in this round is the set F of pairs (x, y), such that x X, y Y, and x is a neighbor of y The covered front XF = {x X : (x, y) F, for some y Y} (or the uncovered front YF = {y Y : (x, y) F, for some x X}) is the set of informed (or uninformed) nodes that belong to a couple in the front F We define the spokesmen set S XF as the set of those informed nodes in the front that act as transmitters in the next round.

For any spokesmen set S, RS YF denotes the set of nodes that receive the message correctly when exactly nodes from S transmit Hence RS consists of those nodes in YF that have exactly one neighbor in S The main difficulty of the algorithm is to choose S at each round in such a way that RS is as large as possible and so that the choosing process is polynomial Clearly, inspection of all possible candidate sets is out of the question In [10], the following spokesmen election algorithm (SEA) is described Algorithm SEA Phase 1 Finding the size of the spokesmen set S For every 1 i n, let Si be the family of all i-element subsets of XF Let wi be the average size of sets RS over all S Si Let k be the index i for which this average is maximized.

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Let us consider the following example. Example Consider an LFSR 4, 1 + X3 + X4 . It induces the recurrence Dn = Dn 1 + Dn 4 .

Odd arrays were used as there are more variables per length, allowing better control of SD. Loss resistance of cylindrical or strip dipoles is easily computed: a dipole of length , radius a, made of material with surface resistance Rs, has a loss resistance Rloss of: Rloss = Rs (1 sin c k ) 1 4 a sin 2 k 2 (2.39)

This will be the size of the chosen set S Phase 2 Finding the elements of the spokesmen set S Elements of S are found one by one, in k iterations In the beginning S = 0, N = XF The / mth iteration, for 1 m k, starts with S containing m 1 nodes and N = XF \ S For each x N, we define FS,x as the family of all sets of the form S {x} P, where P is any (k m)-element subset of N \ {x} For any x N, let uS,x denote the average of |RT| over all sets T in the family FS,x The element x N for which uS,x is the largest is transfered from N to S, ie, S := S {x} and N := N \ {x}.

The algorithm ends after k iterations, with the set S of size k It is shown in [10] that the averages wi in Phase 1 and uS,x in Phase 2 can be computed in polynomial time, hence Algorithm SEA runs in polynomial time Moreover, the spokesmen set S obtained by this algorithm satisfies the property |RS | > |YF |/ln|XF | This means that the choice of S guarantees that at least a fraction 1/ln|XF | of nodes that can potentially receive the source message for the first time are actually informed in a given round Algorithm SEA is used as a subroutine in the main algorithm called wave expansion.

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cover both the preparatory stage and beginning of sport specialization in Stambulova s (1994) model The authors agree that during initiation children are engaged in fun, playful sport or exercise activities and perceive sport as merely playing a game Coaches and parents provide guidance and support for young athletes, helping them to test various sport disciplines and to find one or two that are the most suitable to their abilities and interests Children learn sport rules and might participate in their first competitions, but so far they do sports more for fun than for achieving specific sport goals Observing young athletes at this stage, it is difficult to predict who will build a career in sports, but it is already obvious that some children develop the social role of an athlete and an identity corresponding to this role.

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