8,852 research outputs found
The Need for Agricultural Data in South Africa: With Specific Reference to the Western Cape Province
In order for public and private decision-makers in the agricultural sector to use agricultural information for decision-making, solving problems or increasing their knowledge, the necessary data on agriculture must be available. As a result of the deregulation of the agricultural marketing sector in South Africa, the supply of this type of data has decreased. Also, the need for data on agriculture by the various decision-makers, i.e. the policy-makers, researchers, agricultural service industries as well as the farmers and extension officers, has changed. Since information systems are always based on the needs of the decision-makers, the need for data on agriculture should be determined before either existing methodologies are improved or new methodologies are introduced, to increase the supply of data on agriculture in South Africa. The objective of this paper is to identify the agricultural data needs and supply for South Africa with specific reference to the Western Cape Province. This was done by means of postal surveys and informal methods. Recommendations for an effective information system have also be concluded.Research and Development/Tech Change/Emerging Technologies,
Incorporation of Spacetime Symmetries in Einstein's Field Equations
In the search for exact solutions to Einstein's field equations the main
simplification tool is the introduction of spacetime symmetries. Motivated by
this fact we develop a method to write the field equations for general matter
in a form that fully incorporates the character of the symmetry. The method is
being expressed in a covariant formalism using the framework of a double
congruence. The basic notion on which it is based is that of the geometrisation
of a general symmetry. As a special application of our general method we
consider the case of a spacelike conformal Killing vector field on the
spacetime manifold regarding special types of matter fields. New perspectives
in General Relativity are discussed.Comment: 41 pages, LaTe
The damping of gravitational waves in dust
We examine a simple model of interaction of gravitational waves with matter
(primarily represented by dust). The aim is to investigate a possible damping
effect on the intensity of gravitational wave when passing through media. This
might be important for gravitational wave astronomy when the sources are
obscured by dust or molecular clouds.Comment: 7 pages, accepted to Phys. Sc
On the Asymptotic Stability of De-Sitter Spacetime: a non-linear perturbative approach
We derive evolution and constraint equations for second order perturbations
of flat dust homogeneous and isotropic solutions to the Einstein field
equations using all scalar, vector and tensor perturbation modes. We show that
the perturbations decay asymptotically in time and that the solutions converge
to the De-Sitter solution. By induction, this result is valid for perturbations
of arbitrary order. This is in agreement with the cosmic no-hair conjecture of
Gibbons and Hawking.Comment: 11 pages, 2 figure
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful
application-specific design to ensure that the most informative relationships
in the data are learnable. In this paper, we apply deep neural networks to the
nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We
consider problems of estimating macroscopic quantities (e.g., the queue at an
intersection) at a lane level. First-principles modeling at the lane scale has
been a challenge due to complexities in modeling social behaviors like lane
changes, and those behaviors' resultant macro-scale effects. Following domain
knowledge that upstream/downstream lanes and neighboring lanes affect each
others' traffic flows in distinct ways, we apply a form of neural attention
that allows the neural network layers to aggregate information from different
lanes in different manners. Using a microscopic traffic simulator as a testbed,
we obtain results showing that an attentional neural network model can use
information from nearby lanes to improve predictions, and, that explicitly
encoding the lane-to-lane relationship types significantly improves
performance. We also demonstrate the transfer of our learned neural network to
a more complex road network, discuss how its performance degradation may be
attributable to new traffic behaviors induced by increased topological
complexity, and motivate learning dynamics models from many road network
topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation
System
A Characterisation of the Weylian Structure of Space-Time by Means of Low Velocity Tests
The compatibility axiom in Ehlers, Pirani and Schild's (EPS) constructive
axiomatics of the space-time geometry that uses light rays and freely falling
particles with high velocity, is replaced by several constructions with low
velocity particles only. For that purpose we describe in a space-time with a
conformal structure and an arbitrary path structure the radial acceleration, a
Coriolis acceleration and the zig-zag construction. Each of these quantities
give effects whose requirement to vanish can be taken as alternative version of
the compatibility axiom of EPS. The procedural advantage lies in the fact, that
one can make null-experiments and that one only needs low velocity particles to
test the compatibility axiom. We show in addition that Perlick's standard clock
can exist in a Weyl space only.Comment: to appear in Gen.Rel.Gra
- …