506 research outputs found
Nuclear Parton Densities and Structure Functions
We calculate nuclear parton distribution functions (PDFs), using the
constituent quark model. We find the bounded valon distributions in a nuclear
to be related to free valon distributions in a nucleon. By using improved
bounded valon distributions for a nuclear with atomic number and the
partonic structure functions inside the valon, we can calculate the nuclear
structure function in space. The results for nuclear structure-function
ratio at some values of are in good agreement with the
experimental data.Comment: To be published in Int. Journal of Modern Phys.
Polarized Deeply Inelastic Scattering (DIS) Structure Functions for Nucleons and Nuclei
We extract parton distribution functions (PDFs) and structure functions from
recent experimental data of polarized lepton-DIS on nucleons at next-to-leading
order (NLO) Quantum Chromodynamics. We apply the Jacobi polynomial method to
the DGLAP evolution as this is numerically efficient. Having determined the
polarized proton and neutron spin structure, we extend this analysis to
describe 3He and 3H polarized structure functions, as well as various sum
rules. We compare our results with other analyses from the literature.Comment: LaTeX, 12 pages, 11 figures, 6 tables. Update to match published
versio
Next-to-Leading order approximation of polarized valon and parton distributions
Polarized parton distributions and structure functions of the nucleon are
analyzed in the improved valon model. The valon representation provides a model
to represent hadrons in terms of quarks, providing a unified description of
bound state and scattering properties of hadrons. Polarized valon distributions
are seen to play an important role in describing the spin dependence of parton
distributions in the leading order (LO) and next-to-leading order (NLO)
approximations. In the polarized case, a convolution integral is derived in the
framework of the valon model. The Polarized valon distribution in a proton and
the polarized parton distributions inside the valon are necessary to obtain the
polarized parton distributions in a proton. Bernstein polynomial averages are
used to extract the unknown parameters of the polarized valon distributions by
fitting to the available experimental data. The predictions for the NLO
calculations of the polarized parton distributions and proton structure
functions are compared with the LO approximation. It is shown that the results
of the calculations for the proton structure function, , and its first
moment, , are in good agreement with the experimental data for a
range of values of . Finally the spin contribution of the valons to the
proton is calculated.Comment: 22 pages, 7 figures. Published in Journal of High Energy Physics
(JHEP
Optimized superpixel and AdaBoost classifier for human thermal face recognition
Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%
Pion mass dependence of the semileptonic scalar form factor within finite volume
We calculate the scalar semileptonic kaon decay in finite volume at the
momentum transfer , using chiral perturbation
theory. At first we obtain the hadronic matrix element to be calculated in
finite volume. We then evaluate the finite size effects for two volumes with and and find that the difference between the finite
volume corrections of the two volumes are larger than the difference as quoted
in \cite{Boyle2007a}. It appears then that the pion masses used for the scalar
form factor in ChPT are large which result in large finite volume corrections.
If appropriate values for pion mass are used, we believe that the finite size
effects estimated in this paper can be useful for Lattice data to extrapolate
at large lattice size.Comment: 19 pages, 5 figures, accepted for publication in EPJ
Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer
© 2019, Springer-Verlag London Ltd., part of Springer Nature. Existing classification techniques that are proposed previously for eliminating data inconsistency could not achieve an efficient parameter reduction in soft set theory, which effects on the obtained decisions. Meanwhile, the computational cost made during combination generation process of soft sets could cause machine infinite state, which is known as nondeterministic polynomial time. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of searching domain space using a developed Markov chain model. Furthermore, this study introduces an efficient soft set reduction-based binary particle swarm optimized by biogeography-based optimizer (SSR-BPSO-BBO) algorithm that generates an accurate decision for optimal and sub-optimal choices. The results show that the decision partition order technique is performing better in parameter reduction up to 50%, while other algorithms could not obtain high reduction rates in some scenarios. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms the other optimization algorithms in achieving high accuracy percentage of a given soft dataset. On the other hand, the proposed Markov chain model could significantly represent the robustness of our parameter reduction technique in obtaining the optimal decision and minimizing the search domain.Published versio
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