4,638 research outputs found
Cosmology and thermodynamics of FRW universe with bulk viscous stiff fluid
We consider a cosmological model dominated by stiff fluid with a constant
bulk viscosity. We classify all the possible cases of the universe predicted by
the model and analyzing the scale factor, density as well as the curvature
scalar. We find that when the dimensionless constant bulk viscous parameter is
in the range the model began with a Big Bang, and make a
transition form the decelerating expansion epoch to an accelerating epoch, then
tends to the de Sitter phase as . The transition into the
accelerating epoch would be in the recent past, when For
the model doesn't have a Big Bang and suffered an increase in the
fluid density and scalar curvature as the universe expands, which are
eventually saturates as the scale factor in the future. We have
analyzed the model with statefinder diagnostics and find that the model is
different from CDM model but approaches CDM point as We have also analyzed the status of the generalized second law of
thermodynamics with apparent horizon as the boundary of the universe and found
that the law is generally satisfied when and for
the law is satisfied when the scale factor is larger than a
minimum value
THE CAUSAL STRUCTURE OF LAND PRICE DETERMINANTS
This paper investigates causation contemporaneously and over time to elucidate the persistent lack of agreement about what "causes" changes in farmland prices. Using recently developed causal modeling framework of directed acyclic graphs (DAGs) and cointegrated (VAR) techniques, the assumed causal structures of existing structural and empirical models are tested directly. The results validate concerns about the nonstationarity of these series. Land price changes are found to respond to a small subset of the oft-cited causes of price change, including macroeconomic variables.Land Economics/Use,
Asymptotic normalization of mirror states and the effect of couplings
Assuming that the ratio between asymptotic normalization coefficients of
mirror states is model independent, charge symmetry can be used to indirectly
extract astrophysically relevant proton capture reactions on proton-rich nuclei
based on information on stable isotopes. The assumption has been tested for
light nuclei within the microscopic cluster model. In this work we explore the
Hamiltonian independence of the ratio between asymptotic normalization
coefficients of mirror states when deformation and core excitation is
introduced in the system. For this purpose we consider a phenomenological rotor
+ N model where the valence nucleon is subject to a deformed mean field and the
core is allowed to excite. We apply the model to 8Li/8B, 13C/13N, 17O/17F,
23Ne/23Al, and 27Mg/27P. Our results show that for most studied cases, the
ratio between asymptotic normalization coefficients of mirror states is
independent of the strength and multipolarity of the couplings induced. The
exception is for cases in which there is an s-wave coupled to the ground state
of the core, the proton system is loosely bound, and the states have large
admixture with other configurations. We discuss the implications of our results
for novae.Comment: 8 pages, 2 figures, submitted to PR
Efficient Learning of a One-dimensional Density Functional Theory
Density functional theory underlies the most successful and widely used
numerical methods for electronic structure prediction of solids. However, it
has the fundamental shortcoming that the universal density functional is
unknown. In addition, the computational result---energy and charge density
distribution of the ground state---is useful for electronic properties of
solids mostly when reduced to a band structure interpretation based on the
Kohn-Sham approach. Here, we demonstrate how machine learning algorithms can
help to free density functional theory from these limitations. We study a
theory of spinless fermions on a one-dimensional lattice. The density
functional is implicitly represented by a neural network, which predicts,
besides the ground-state energy and density distribution, density-density
correlation functions. At no point do we require a band structure
interpretation. The training data, obtained via exact diagonalization, feeds
into a learning scheme inspired by active learning, which minimizes the
computational costs for data generation. We show that the network results are
of high quantitative accuracy and, despite learning on random potentials,
capture both symmetry-breaking and topological phase transitions correctly.Comment: 5 pages, 3 figures; 4+ pages appendi
Foreign political instability and U.S. agricultural exports: evidence from panel data
The intent of this paper is to examine the impact of political instability in importing nations on U.S. agricultural trade. A panel data set representing eighty-seven importing countries covering the 1990-2000 period was used to investigate how the degree of democratic practices and three types of political instability (violent, social, and elite) affect U.S agricultural exports. The empirical results suggest that political instability do have a statistically significant effect on U.S. agricultural export demand.agricultural trade
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