9,149 research outputs found
Formation of superheavy nuclei in cold fusion reactions
Within the concept of the dinuclear system (DNS), a dynamical model is
proposed for describing the formation of superheavy nuclei in complete fusion
reactions by incorporating the coupling of the relative motion to the nucleon
transfer process. The capture of two heavy colliding nuclei, the formation of
the compound nucleus and the de-excitation process are calculated by using an
empirical coupled channel model, solving a master equation numerically and
applying statistical theory, respectively. Evaporation residue excitation
functions in cold fusion reactions are investigated systematically and compared
with available experimental data. Maximal production cross sections of
superheavy nuclei in cold fusion reactions with stable neutron-rich projectiles
are obtained. Isotopic trends in the production of the superheavy elements
Z=110, 112, 114, 116, 118 and 120 are analyzed systematically. Optimal
combinations and the corresponding excitation energies are proposed.Comment: 18 pages, 8 figure
Incorporating a priori knowledge into initialized weights for neural classifier
Artificial neural networks (ANN), especially, multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge in the design of ANNs is still an open problem. The paper tries to give some insight on this topic emphasizing weight initialization from three perspectives. Theoretical analyses and simulations are offered for validatio
Comparison of ν-support vector regression and logistic equation for descriptive modeling of Lactobacillus plantarum growth
Due to the complexity and high non-linearity of bioprocess, most simple mathematical models fail to describe the exact behavior of biochemistry systems. As a novel type of learning method, support vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension and local minima. In this paper, we developed a ν-SVR model with genetic algorithms (GA) in the pre-estimate in Lactobacillus plantarum fermentation by comparing the predicting capability of logistic model and SVR model. 5-fold cross validation technique was applied in the SVR train to avoid over-fitting. The information of SVR parameters were obtained in the generation of 150 and the optimal parameters were C= 235.8935, σ= 8.3608, ν=0.7587. Correspondingly, the logistic model parameters μmax and xmax were estimated as 0.4791 and 0.3498, respectively. The experimental results demonstrated that, SVR model excelled the logistic model based on the normalized mean square error (NMSE), mean absolute percentage error (MAPE) and the Pearson correlation coefficient R. We found that the ν-SVR model optimized by genetic algorithms could be a potential monitoring method for prediction of biomass.Key words: Support vector regression, genetic algorithm, logistic model, prediction of biomass
Possible Way to Synthesize Superheavy Element Z=117
Within the framework of the dinuclear system model, the production of
superheavy element Z=117 in possible projectile-target combinations is analyzed
systematically. The calculated results show that the production cross sections
are strongly dependent on the reaction systems. Optimal combinations,
corresponding excitation energies and evaporation channels are proposed in this
letter, such as the isotopes ^{248,249}Bk in ^{48}Ca induced reactions in 3n
evaporation channels and the reactions ^{45}Sc+^{246,248}Cm in 3n and 4n
channels, and the system ^{51}V+^{244}Pu in 3n channel.Comment: 10 pages, 4 figures, 1 tabl
Hydrostatic pressure effects on the static magnetism in Eu(FeCo)As
The effects of hydrostatic pressure on the static magnetism in
Eu(FeCo)As are investigated by complementary
electrical resistivity, ac magnetic susceptibility and single-crystal neutron
diffraction measurements. A specific pressure-temperature phase diagram of
Eu(FeCo)As is established. The structural phase
transition, as well as the spin-density-wave order of Fe sublattice, is
suppressed gradually with increasing pressure and disappears completely above
2.0 GPa. In contrast, the magnetic order of Eu sublattice persists over the
whole investigated pressure range up to 14 GPa, yet displaying a non-monotonic
variation with pressure. With the increase of the hydrostatic pressure, the
magnetic state of Eu evolves from the canted antiferromagnetic structure in the
ground state, via a pure ferromagnetic structure under the intermediate
pressure, finally to a possible "novel" antiferromagnetic structure under the
high pressure. The strong ferromagnetism of Eu coexists with the
pressure-induced superconductivity around 2 GPa. The change of the magnetic
state of Eu in Eu(FeCo)As upon the application
of hydrostatic pressure probably arises from the modification of the indirect
Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between the Eu moments
tuned by external pressure.Comment: 9 pages, 6 figure
Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States
Combining first-principles calculations and spin- and angle-resolved
photoemission spectroscopy measurements, we identify the helical spin textures
for three different Dirac cone states in the interfaced systems of a 2D
topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The
spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or
Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by
Rashba effect, and the hybridized Dirac cone between the former two states.
Further orbit- and atom-resolved analysis shows that s and pz orbits have a
clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of
upper (lower) Dirac cone, while px and py orbits have an additional radial spin
component. The Dirac cone states may reside on different atomic layers, but
have the same spin texture. Our results suggest that the unique spin texture of
Dirac cone states is a signature property of spin-orbit coupling, independent
of topology
Phase diagram of Eu magnetic ordering in Sn-flux-grown Eu(FeCo)As single crystals
The magnetic ground state of the Eu moments in a series of
Eu(FeCo)As single crystals grown from the Sn flux has
been investigated in detail by neutron diffraction measurements. Combined with
the results from the macroscopic properties (resistivity, magnetic
susceptibility and specific heat) measurements, a phase diagram describing how
the Eu magnetic order evolves with Co doping in
Eu(FeCo)As is established. The ground-state magnetic
structure of the Eu spins is found to develop from the A-type
antiferromagnetic (AFM) order in the parent compound, via the A-type canted AFM
structure with some net ferromagnetic (FM) moment component along the
crystallographic direction at intermediate Co doping levels,
finally to the pure FM order at relatively high Co doping levels. The ordering
temperature of Eu declines linearly at first, reaches the minimum value of
16.5(2) K around = 0.100(4), and then reverses upwards with
further Co doping. The doping-induced modification of the indirect
Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between the Eu moments,
which is mediated by the conduction electrons on the (Fe,Co)As
layers, as well as the change of the strength of the direct interaction between
the Eu and Fe moments, might be responsible for the change of the
magnetic ground state and the ordering temperature of the Eu sublattice. In
addition, for Eu(FeCo)As single crystals with 0.10
0.18, strong ferromagnetism from the Eu
sublattice is well developed in the superconducting state, where a spontaneous
vortex state is expected to account for the compromise between the two
competing phenomena.Comment: 10 pages, 9 figure
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