475 research outputs found
Kajian Psikologi Wanita Tokoh Utama Novel Air Mata Tuhan Karya Aguk Irawan M.n.
A novel entitled Air Mata Tuhan as a study of woman psychology was focused on the main character. This research aimed to know the forms of women's personality, and to describe the linkage between structural elements and psychology aspect contained on the main character in the Novel entitled Air Mata Tuhan written by Aguk Irawan M.N. The used research method was qualitative by using literature study. The data contained in the Novel entitled Air Mata Tuhan were also analyzed. The research result indicated that structural elements included title, theme, characterization, character, and conflict has the linkage. The analysis of woman psychology showed that (1) the woman characteristic was beauty included beauty of inside and outside, tenderness, humility, nurturing, easily disappointed and rise up again; (2) woman and family, included woman as a wife, woman as a partner of life; and (3) woman and depression. Those characteristics were belonged to the main character in the Novel entitled Air MataTuhan named Fisha
Ultra--Planck Scattering in D=3 Gravity Theories
We obtain the high energy, small angle, 2-particle gravitational scattering
amplitudes in topologically massive gravity (TMG) and its two non-dynamical
constituents, Einstein and Chern--Simons gravity. We use 't Hooft's approach,
formally equivalent to a leading order eikonal approximation: one of the
particles is taken to scatter through the classical spacetime generated by the
other, which is idealized to be lightlike. The required geometries are derived
in all three models; in particular, we thereby provide the first explicit
asymptotically flat solution generated by a localized source in TMG. In
contrast to =4, the metrics are not uniquely specified, at least by naive
asymptotic requirements -- an indeterminacy mirrored in the scattering
amplitudes. The eikonal approach does provide a unique choice, however. We also
discuss the discontinuities that arise upon taking the limits, at the level of
the solutions, from TMG to its constituents, and compare with the analogous
topologically massive vector gauge field models.Comment: 20 pages, preprint BRX TH--337, DAMTP R93/5, ADP-93-204/M1
Data-driven simulation and characterisation of gold nanoparticle melting
The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers
Caracterização anatômica dos anéis de crescimento de espécies arbóreas de Florestas Ombrófila das Terras Baixas e Ombrófila Mista, no Estado do Paraná.
Organizado por Patricia Póvoa de Mattos, Celso Garcia Auer, Rejane Stumpf Sberze, Katia Regina Pichelli e Paulo César Botosso
Relativistic Chasles' theorem and the conjugacy classes of the inhomogeneous Lorentz group
This work is devoted to the relativistic generalization of Chasles' theorem,
namely to the proof that every proper orthochronous isometry of Minkowski
spacetime, which sends some point to its chronological future, is generated
through the frame displacement of an observer which moves with constant
acceleration and constant angular velocity. The acceleration and angular
velocity can be chosen either aligned or perpendicular, and in the latter case
the angular velocity can be chosen equal or smaller than than the acceleration.
We start reviewing the classical Euler's and Chasles' theorems both in the Lie
algebra and group versions. We recall the relativistic generalization of
Euler's theorem and observe that every (infinitesimal) transformation can be
recovered from information of algebraic and geometric type, the former being
identified with the conjugacy class and the latter with some additional
geometric ingredients (the screw axis in the usual non-relativistic version).
Then the proper orthochronous inhomogeneous Lorentz Lie group is studied in
detail. We prove its exponentiality and identify a causal semigroup and the
corresponding Lie cone. Through the identification of new Ad-invariants we
classify the conjugacy classes, and show that those which admit a causal
representative have special physical significance. These results imply a
classification of the inequivalent Killing vector fields of Minkowski spacetime
which we express through simple representatives. Finally, we arrive at the
mentioned generalization of Chasles' theorem.Comment: Latex2e, 49 pages. v2: few typos correcte
Building nonparametric -body force fields using Gaussian process regression
Constructing a classical potential suited to simulate a given atomic system
is a remarkably difficult task. This chapter presents a framework under which
this problem can be tackled, based on the Bayesian construction of
nonparametric force fields of a given order using Gaussian process (GP) priors.
The formalism of GP regression is first reviewed, particularly in relation to
its application in learning local atomic energies and forces. For accurate
regression it is fundamental to incorporate prior knowledge into the GP kernel
function. To this end, this chapter details how properties of smoothness,
invariance and interaction order of a force field can be encoded into
corresponding kernel properties. A range of kernels is then proposed,
possessing all the required properties and an adjustable parameter
governing the interaction order modelled. The order best suited to describe
a given system can be found automatically within the Bayesian framework by
maximisation of the marginal likelihood. The procedure is first tested on a toy
model of known interaction and later applied to two real materials described at
the DFT level of accuracy. The models automatically selected for the two
materials were found to be in agreement with physical intuition. More in
general, it was found that lower order (simpler) models should be chosen when
the data are not sufficient to resolve more complex interactions. Low GPs
can be further sped up by orders of magnitude by constructing the corresponding
tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte
Machine-learning of atomic-scale properties based on physical principles
We briefly summarize the kernel regression approach, as used recently in
materials modelling, to fitting functions, particularly potential energy
surfaces, and highlight how the linear algebra framework can be used to both
predict and train from linear functionals of the potential energy, such as the
total energy and atomic forces. We then give a detailed account of the Smooth
Overlap of Atomic Positions (SOAP) representation and kernel, showing how it
arises from an abstract representation of smooth atomic densities, and how it
is related to several popular density-based representations of atomic
structure. We also discuss recent generalisations that allow fine control of
correlations between different atomic species, prediction and fitting of
tensorial properties, and also how to construct structural kernels---applicable
to comparing entire molecules or periodic systems---that go beyond an additive
combination of local environments
- …