93,900 research outputs found
Off-Shell NN Potential and Triton Binding Energy
The NONLOCAL Bonn-B potential predicts 8.0 MeV binding energy for the triton
(in a charge-dependent 34-channel Faddeev calculation) which is about 0.4 MeV
more than the predictions by LOCAL NN potentials. We pin down origin and size
of the nonlocality in the Bonn potential, in analytic and numeric form. The
nonlocality is due to the use of the correct off-shell Feynman amplitude of
one-boson-exchange avoiding the commonly used on-shell approximations which
yield the local potentials. We also illustrate how this off-shell behavior
leads to more binding energy. We emphasize that the increased binding energy is
not due to on-shell differences (differences in the fit of the NN data or phase
shifts). In particular, the Bonn-B potential reproduces accurately the
mixing parameter up to 350 MeV as determined in the recent
Nijmegen multi-energy NN phase-shift analysis. Adding the relativistic effect
from the relativistic nucleon propagators in the Faddeev equations, brings the
Bonn-B result up to 8.2 MeV triton binding. This leaves a difference of only
0.3 MeV to experiment, which may possibly be explained by refinements in the
treatment of relativity and the inclusion of other nonlocalities (e.~g.,
quark-gluon exchange at short range). Thus, it is conceivable that a realistic
NN potential which describes the NN data up to 300 MeV correctly may explain
the triton binding energy without recourse to 3-N forces; relativity would play
a major role for this result.Comment: 5 pages LaTeX and 2 figures (hardcopies, available upon reqest),
UI-NTH-940
Quantum correction for electron transfer rates. Comparison of polarizable versus nonpolarizable descriptions of solvent
The electron transfer rate constant is treated using the spin-boson Hamiltonian model. The spectral density is related to the experimentally accessible data on the dielectric dispersion of the solvent, using a dielectric continuum approximation. On this basis the quantum correction for the ferrous–ferric electron transfer rate is found to be a factor 9.6. This value is smaller than the corresponding result (36) of Chandler and co-workers in their pioneering quantum simulation using a molecular model of the system [J. S. Bader, R. A. Kuharski, and D. Chandler, J. Chem. Phys. 93, 230 (1990)]. The likely reason for the difference lies in use of a rigid water molecular model in the simulation, since we find that other models for water in the literature which neglect the electronic and vibrational polarizability also give a large quantum effect. Such models are shown to overestimate the dielectric dispersion in one part of the quantum mechanically important region and to underestimate it in another part. It will be useful to explore a polarizable molecular model which reproduces the experimental dielectric response over the relevant part of the frequency spectrum
Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses
Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production
Multi-modal Image Processing based on Coupled Dictionary Learning
In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing
Advances In Wireless Communication
The self-trapped hole in caesium halides
The equilibrium lattice configuration, electronic excitation energies and activation energies for hopping motion are calculated for a self-trapped hole in simple cubic CsCl, CsBr and CsI. The defect is regarded as a X2- molecular ion (X=Cl, Br, I) whose bond-length has been modified by the crystalline environment. Agreement with the experimental ultraviolet transition energies is good. Excitation energies deduced from measurement of g-shifts in CsBr and CsI are too low, a feature common to all alkali bromides and iodides, and attributed to the approximations involved in their deviation. The initial calculations predict lower activation energies of 90 degrees jumps than for 180 degrees jumps, in contrast with what is observed in CsI. An alternative model is presented, which reproduces the correct trend. Comparison of the actual numbers with experiment is hampered by the fact that the latter are done at low temperature (60-90K), the calculations being done in the high-temperature limit
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