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possible graphs are shown in Figure 7.8 (upper numbers). Obviously, graph 5 would be selected, which is indeed the graph that represents a decomposition of the distribution (cf. Section 3.4.2). Note that, as in the relational and the probabilistic case, the complete graph 8 always receives or shares the best assessment, because it consists of only one clique. As a consequence, it represents no real decomposition of the distribution. Note also that in order to rank two graphs (that is, in order to decide which of them is better) we need not compute the specificity divergences explicitly, but can confine ourselves to computing the nonspecificities of the distributions corresponding to the graphs, because the nonspecificity of the original distribution is fixed. This simplifies the assessment: find the graph among those of acceptable complexity (w.r.t. maximal clique size) for which the corresponding possibility distribution is most specific. Up to now we have assumed that the multidimensional distribution, for which a decomposition is desired, is given. However, as in the probabilistic setting, this is usually not the case in applications. Instead we are given a database of sample cases. Clearly, the situation parallels the probabilistic case: it is infeasible to estimate first the joint possibility distribution from the database, so that we can proceed as indicated above. In order to derive a feasible indirect method we may reason as follows: an approximation of a given possibility distribution may be compared to this distribution by integrating the differences in the number of tuples for each -cut over the possible values of (see above). However, this formula can be

#ifndef PLAYERSERVER_hh #define PLAYERSERVER_hh_ #include "example.hh" class PlayerServer : public virtual PlayerBOAImpl { private: char * the_player_name; public: PlayerServer(); virtual NameStr name(CORBA::Environment & env = CORBA::default_environment); virtual void set_name(char *); }; class PlayerFactoryServer:public virtual PlayerFactoryBOAImpl { virtual Player * createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment); }; #endif ///////////////////////////////////////////////////////////// // PlayerServer.cc // 20 March 1999 ///////////////////////////////////////////////////////////// #include "PlayerServer.hh" #include <iostream.h> PlayerServer::PlayerServer(){ the_player_name=NULL; }; void PlayerServer::set_name(char * name){ if (the_player_name != NULL) delete the_player_name; the_player_name = new char [strlen(name)+1]; strcpy(the_player_name,name); cout << "Created Player object for " << name << "\n"; }; NameStr PlayerServer::name(CORBA::Environment & env = CORBA::default_environment){ char * ret = new char[strlen(the_player_name)+1]; strcpy(ret,the_player_name); return(ret); }; Player * PlayerFactoryServer::createPlayer(NameStr name, CORBA::Environment & env = CORBA::default_environment){ PlayerServer * aPlayer = new PlayerServer; aPlayer->set_name(name);

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is to prepare the solution of a strong acid (e.g. HClO4) and that of a strong base (e.g. R4NOH) and measure the potentials of the pH sensor in the two solutions. Then, pKSH can be obtained approximately from the potential difference between the two solutions (DE, mV): pKSH ! DE=59:2 logfc strong acid c strong base g 6:7

This need to match the intervention to the indiduals readiness to hear the message is similar to what we recognise in the clinical domain (see 13 by Treasure & Bauer, this volume) Another dif culty, particularly with the widespread distribution of fact-oriented educational materials, relates to the risk of unwittingly teaching someone about eating disorder symptoms This effect may be augmented if the information appears to increase the interest in eating disorders as a hot and exciting topic, which is politically correct for the media, teachers, health care professionals, and others to engage in Young people in more or less dif cult life situations may view eating disorder symptoms as a gateway to get help and, hence, resort to symptom imitation (Bruch, 1985; Habermas, 1992).

where U is the electrochemical potential which, as a rst approximation, should be taken as N Snm m0 5:3:6 U Z qV kT ln N0 where V electric potential, V N0 equilibrium metal ion concentration at reference condition, number/m3 atomic volume, m3, Snm mechanical stress, N/m2 m0 reference chemical potential, J Substituting Eq. (5.3.6) in Eq. (5.3.5), we get ! ND kT Z qE rN r Snm Ja kT N

57 G S Prokop and R R Joseph, Electromigration Failure at Aluminium-Silicon Contacts, J Appl Phys, vol 43, no 6, pp 2595 2602, 1972 58 I A Blech and H Sello, The Failure of Thin Aluminium Current-Carrying Strips on Oxidized Silicon, Phys Failure Electron, vol 5, pp 496 505, June 1967 59 J Howard and R F Ross, Electromigration Effects in Aluminium on Silicon Substrates, Appl Phys Lett, vol 11, no 3, pp 85 87, Aug 1967 60 G J VanGurp, Electromigration in Aluminium Films Containing Si, Appl Phys Lett, vol 19, pp 476 479, 1971 61 A J Learn, Electromigration Effects in Aluminium Alloy Metallization, J Electron Materials, vol 3, no 2, pp 531 552, 1974 62 S Vaidya, D B Fraser, and W S Lindenberger, Electromigration in Fine-Line Sputtered Gun Al, J Appl Phys, vol 51, pp 4475 4482, 1980 63 J R.

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