A numerical comparison of spherical, spheroidal and ellipsoidal harmonic gravitational field models for small non-spherical bodies: examples for the Martian moons

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

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Evolution of collisional systems

International Astronomical Union Colloquium ◽

10.1017/s0252921100101411 ◽

1984 ◽

Vol 75 ◽

pp. 361-362

Author(s):

André Brahic

Keyword(s):

Gravitational Field ◽

Dynamical Evolution ◽

Oblate Planet

AbstractThe dynamical evolution of planetary discs in the gravitational field of an oblate planet and a satellite is numerically simulated.

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Exploring the Boundaries of the Number–Temporal Order Association

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000285 ◽

2015 ◽

Vol 62 (3) ◽

pp. 198-205 ◽

Cited By ~ 3

Author(s):

Dana Ganor-Stern

Keyword(s):

Embodied Cognition ◽

Mental Representation ◽

Temporal Order ◽

Past Research ◽

Numerical Comparison ◽

Negative Number ◽

Comparison Task ◽

Negative Numbers ◽

The Past

Past research has shown that numbers are associated with order in time such that performance in a numerical comparison task is enhanced when number pairs appear in ascending order, when the larger number follows the smaller one. This was found in the past for the integers 1–9 ( Ben-Meir, Ganor-Stern, & Tzelgov, 2013 ; Müller & Schwarz, 2008 ). In the present study we explored whether the advantage for processing numbers in ascending order exists also for fractions and negative numbers. The results demonstrate this advantage for fraction pairs and for integer-fraction pairs. However, the opposite advantage for descending order was found for negative numbers and for positive-negative number pairs. These findings are interpreted in the context of embodied cognition approaches and current theories on the mental representation of fractions and negative numbers.

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Sedimentation of charged colloids in the gravitational field : Relaxation toward equilibrium

Journal de Physique IV (Proceedings) ◽

10.1051/jp4:2000513 ◽

2000 ◽

Vol 10 (PR5) ◽

pp. Pr5-109-Pr5-112

Author(s):

J.-F. Dufrêche ◽

J.-P. Simonin ◽

P. Turq

Keyword(s):

Gravitational Field ◽

Charged Colloids

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Pair Production in a Field of Heavy Nuclei and in a Gravitational Field

Uspekhi Fizicheskih Nauk ◽

10.3367/ufnr.0105.197112ag.0780 ◽

1971 ◽

Vol 105 (12) ◽

pp. 780-781 ◽

Cited By ~ 7

Author(s):

Ya.B. Zel'dovich ◽

Lev P. Pitaevskii ◽

Valentin S. Popov ◽

Aleksei A. Starobinskii

Keyword(s):

Gravitational Field ◽

Pair Production ◽

Heavy Nuclei

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On the discovery of the gravitational field equations by Einstein and Hilbert: new materials

Uspekhi Fizicheskih Nauk ◽

10.3367/ufnr.0171.200112d.1347 ◽

2001 ◽

Vol 171 (12) ◽

pp. 1347 ◽

Cited By ~ 4

Author(s):

Vladimir P. Vizgin

Keyword(s):

Gravitational Field ◽

New Materials ◽

Field Equations ◽

Gravitational Field Equations

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Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2002.7.1.15200 ◽

2002 ◽

Vol 7 (1) ◽

pp. 31-42

Author(s):

J. Šaltytė ◽

K. Dučinskas

Keyword(s):

Spatial Correlation ◽

Random Fields ◽

Data Augmentation ◽

Gaussian Random Fields ◽

Classification Rule ◽

Numerical Comparison ◽

First Order ◽

Bayesian Risk ◽

Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

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