Magnetic Rotation in the A = 80 Region: M1 Bands in Heavy Rb Isotopes.

We have studied the isotopes (82)Rb45, (83)Rb46, and (84)Rb47 to search for magnetic rotation which is predicted in the tilted-axis cranking model for a certain mass region around A = 80. Excited states in these nuclei were populated via the reaction (11)B + (76)Ge with E = 50 MeV at the XTU tandem accelerator of the LNL Legnaro. Based on a γ-coincidence experiment using the spectrometer GASP we have found magnetic dipole bands in each studied nuclide. The regular M1 bands observed in the odd-odd nuclei (82)Rb and (84)Rb include B(M1)/B(E2) ratios decreasing smoothly with increasing spin in a range of 13(-) ≤ J(π) ≤ 16(-). These bands are interpreted in the tilted-axis cranking model on the basis of four-quasiparticle configurations of the type [Formula: see text]. This is the first evidence of magnetic rotation in the A ≈ 80 region. In contrast, the M1 sequences in the odd-even nucleus (83)Rb are not regular, and the B(M1)/B(E2) ratios show a pronounced staggering.

An introduction to fuzzy systems.

We introduce the notion of fuzzy sets as a tool for modeling sets with ill-defined or flexible boundaries. Fuzzy sets naturally appear when describing the meaning of natural language words pertaining to quantitative scales, or when modelling the notion of typicality. The three main semantics for fuzzy sets are recalled: similarity, preference and uncertainty. Each semantics underlies a particular class of applications. Similarity notions are exploited in clustering analysis and fuzzy controllers. Uncertainty is captured by fuzzy sets in the framework of possibility theory. The membership function of a fuzzy set is also sometimes a kind of utility function that represents flexible constraints in decision problems. Fuzzy sets are acknowledged as a major tool in information engineering for the purpose of bridging the gap between human-originated formalized knowledge, and numerical data.

A computational approach to approximate and plausible reasoning with applications to expert systems.

The intended purpose of this paper is twofold: proposing a common basis for the modeling of uncertainty and imprecision, and discussing various kinds of approximate and plausible reasoning schemes in this framework. Together with probability, different kinds of uncertainty measures (credibility and plausibility functions in the sense of Shafer, possibility measures in the sense of Zadeh and the dual measures of necessity, Sugeno's g¿-fuzzy measures) are introduced in a unified way. The modeling of imprecision in terms of possibility distribution is then presented, and related questions such as the measure of the uncertainty of fuzzy events, the probability and possibility qualification of statements, the concept of a degree of truth, and the truth qualification of propositions, are discussed at length. Deductive inference from premises weighted by different kinds of measures by uncertainty, or by truth-values in the framework of various multivalued logics, is fully investigated. Then, deductive inferences from imprecise or fuzzy premises are dealt with; patterns of reasoning where both uncertainty and imprecision are present are also addressed. The last section is devoted to the combination of uncertain or imprecise pieces of information given by different sources. On the whole, this paper is a tentative survey of quantitative approaches in the modeling of uncertainty and imprecision including recent theoretical proposals as well as more empirical techniques such as the ones developed in expert systems such as MYCIN or PROSPECTOR, the management of uncertainty and imprecision in reasoning patterns being a key issue in artificial intelligence.