Transition from direct to sequential two-proton decay in ss-dd shell nuclei

Transitions among different mechanisms of two-proton decay are studied in general. The introduced improved direct-decay model generalizes the semi-analytical models used before and provides flawless phenomenological description of three-body correlations in $2p$ decays. This is demonstrated by examples of the low-lying $^{16}$Ne state decays. Different forms of transition dynamic are shown to be highly probable beyond the proton dripline for the $s$-$d$ shell nuclei. It is demonstrated that transition dynamic of $2p$ emitters can provide means for extraction of a width of the ground-state resonance of a core+$p$ subsystem of the core+$2p$ system. Practical applicability of the method is demonstrated by properties of the $^{14}$F ground state derived from the ^{15}\mbox{Ne}\rightarrow ^{\,13\!\!}\mbox{O}+2p decay data and of the $^{29}$Cl ground state derived from the ^{30}\mbox{Ar}\rightarrow ^{\,28\!\!}\mbox{S}+2p decay data.Comment: 8 pages, 10 figure

Slowing-Down in the Radiator and Cherenkov Radiation Angular Distributions from Relativistic Heavy Ions at FAIR, SPS and LHC energies

An adaptive learning algorithm using laser velocity meter is proposed for the process control of tandem cold mills. This method is used for on-line adaptive learning calculation of deformation resistance of strip and rolling friction models. The reverse calculation values of deformation resistance and friction coefficient as variables of non-linear equations can be obtained by introducing the measured rolling force and forward slip into the calculation model individually. Using Newton-Raphson iterative calculation to solve the non-linear equations, the reverse calculation values can be calculated. The actual forward slip obtained from the measured strip rolling speed using laser meter makes it possible that the deformation resistance of strip and friction coefficient can be uncoupled for model adaptive learning calculation. The inverse calculation and adaptive learning can improve whole control system of tandem cold mills significantly