Spectral Properties of an Energy-Dependent Hamiltonian

The three-dimensional Klein–Gordon oscillator exhibits an algebraic structure known from supersymmetric quantum mechanics. The supersymmetry is unbroken with a vanishing Witten index, and it is utilized to derive the spectral properties of the Klein–Gordon oscillator, which is closely related to that of the nonrelativistic harmonic oscillator in three dimensions. Supersymmetry also enables us to derive a closed-form expression for the energy-dependent Green’s function.

Download Full-text

SIGMAL: A Program for Calculating L-Shell lonization Cross-Sections

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097600 ◽

1981 ◽

Vol 39 ◽

pp. 198-199 ◽

Cited By ~ 3

Author(s):

R.F. Egerton

Keyword(s):

Cross Sections ◽

Fortran Program ◽

Absorption Data ◽

X Ray ◽

Atomic Electrons ◽

Energy Dependent ◽

Hydrogenic Model ◽

Electron Energy Loss ◽

X Ray Absorption ◽

Shell Ionization

SIGMAL is a short (∼ 100-line) Fortran program designed to rapidly compute cross-sections for L-shell ionization, particularly the partial crosssections required in quantitative electron energy-loss microanalysis. The program is based on a hydrogenic model, the L1 and L23 subshells being represented by scaled Coulombic wave functions, which allows the generalized oscillator strength (GOS) to be expressed analytically. In this basic form, the model predicts too large a cross-section at energies near to the ionization edge (see Fig. 1), due mainly to the fact that the screening effect of the atomic electrons is assumed constant over the L-shell region. This can be remedied by applying an energy-dependent correction to the GOS or to the effective nuclear charge, resulting in much closer agreement with experimental X-ray absorption data and with more sophisticated calculations (see Fig. 1 ).

Download Full-text

Freeze-Fracture Studies of Membranes in Developing Sieve Elements in a Plant Tissue Culture

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s1431927600002798 ◽

1980 ◽

Vol 38 ◽

pp. 636-637

Author(s):

R. D. Sjolund ◽

C. Y. Shih

Keyword(s):

Plant Tissue ◽

Cultured Cells ◽

Freeze Fracture ◽

Tissue Cultures ◽

Sieve Elements ◽

Intact Plants ◽

Plant Tissue Cultures ◽

Whole Plants ◽

Energy Dependent ◽

Similar Elements

The differentiation of phloem in plant tissue cultures offers a unique opportunity to study the development and structure of sieve elements in a manner that avoids the injury responses associated with the processing of similar elements in intact plants. Short segments of sieve elements formed in tissue cultures can be fixed intact while the longer strands occuring in whole plants must be cut into shorter lengths before processing. While iyuch controversy surrounds the question of phloem function in tissue cultures , sieve elements formed in these cultured cells are structurally similar to those of Intact plants. We are particullarly Interested In the structure of the plasma membrane and the peripheral ER in these cells because of their possible role in the energy-dependent active transport of sucrose into the sieve elements.

Download Full-text

Model of multicomponent narrow-band periodical non-stationary random signal

Information extraction and processing ◽

10.15407/vidbir2020.48.017 ◽

2020 ◽

Vol 2020 (48) ◽

pp. 17-24

Author(s):

I.M. Javorskyj ◽

◽

R.M. Yuzefovych ◽

P.R. Kurapov ◽

◽

...

Keyword(s):

Time Series ◽

Real Time ◽

Hilbert Transform ◽

Spectral Properties ◽

Narrow Band ◽

Analytic Signal ◽

Random Signal ◽

Hilbert Transformation ◽

The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

Download Full-text