We investigate the doping effects of magnetic and nonmagnetic impurities injected to the honeycomb iridate sample of Na2IrO3 . Both the doping result in changing the ordering temperature as well as the Curie-Weiss temperature of the parent sample as
a consequence of enhancement of the lattice frustration, screening of the Ir atoms and spin-orbit effects that reflects in the susceptibility and specific heat measurements. Our findings are corroborated by a detailed comparative study of various magnetic and nonmagnetic impurity atoms that have notable effects on different electronic properties of the doped compounds.
We have investigated the magnetic response of La0.7Sr0.3MnO3/SrRuO3 superlattices to biaxial in-plane strain applied in-situ. Superlattices grown on piezoelectric substrates of 0.72PbMg1/3Nb2/3O3-0.28PbTiO3(001) (PMN-PT) show strong antiferromagnetic
coupling of the two ferromagnetic components. The coupling field of mu0HAF = 2.8 T is found to decrease by deltaHAF/delta epsilon ~ -520 mT %-1 under reversible biaxial strain mu0HAF at 80 K in a [La0.7Sr0.3MnO3(22)/SrRuO3(55)]15 superlattice. This reveals a significant strain effect on interfacial coupling. The applied in-plane compression enhances the ferromagnetic order in the manganite layers which are under as-grown tensile strain. It is thus difficult to disentangle the contributions from strain-dependent antiferromagnetic Mn-O-Ru interface coupling and Mn-O-Mn ferromagnetic double exchange near the interface, since the enhanced magnetic order of Mn spins leads to a larger net coupling of SrRuO3 layers at the interface. Strain-dependent orbital occupation in a single-ion picture cannot explain the sign of the observed strain dependence, whereas the enhanced Mn order at the interface is qualitatively in line with it.
In group decision making (GDM) problems fuzzy preference relations (FPR) are widely used for representing decision makers opinions on the set of alternatives. In order to avoid misleading solutions, the study of consistency and consensus has become a
very important aspect. This article presents a simulated annealing (SA) based soft computing approach to optimize the consistency/consensus level (CCL) of a complete fuzzy preference relation in order to solve a GDM problem. Consistency level indicates as experts preference quality and consensus level measures the degree of agreement among experts opinions. This study also suggests the set of experts for the necessary modifications in their prescribed preference structures without intervention of any moderator.
