1,705 research outputs found
The contributions of components to the axial charges of proton and its resonances
We calculate the axial charges of the proton and its resonances in the
framework of the constituent quark model, which is extended to include the
components. If 20% admixtures of the components in
the proton are assumed, the theoretical value for the axial charge in our model
is in good agreement with the empirical value, which can not be well reproduced
in the traditional constituent quark model even though the symmetry breaking or relativistic effect is taken into account. We also
predict an unity axial charge for with 30%
components constrained by the strong and electromagnetic decays.Comment: 4 pages, 4 table
Entropy and Its Quantum Thermodynamical Implication for Anomalous Spectral Systems
The state function entropy and its quantum thermodynamical implication for
two typical dissipative systems with anomalous spectral densities are studied
by investigating on their low-temperature quantum behavior. In all cases it is
found that the entropy decays quickly and vanishes as the temperature
approaches zero. This reveals a good conformity with the third law of
thermodynamics and provides another evidence for the validity of fundamental
thermodynamical laws in the quantum dissipative region.Comment: 10 pages, 3 figure
Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity
Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD
Proton strangeness form factors in (4,1) clustering configurations
We reexamine a recent result within a nonrelativistic constituent quark model
(NRCQM) which maintains that the uuds\bar s component in the proton has its
uuds subsystem in P state, with its \bar s in S state (configuration I). When
the result are corrected, contrary to the previous result, we find that all the
empirical signs of the form factors data can be described by the lowest-lying
uuds\bar s configuration with \bar s in P state that has its uuds subsystem in
state (configuration II). Further, it is also found that the removal of the
center-of-mass (CM) motion of the clusters will enhance the contributions of
the transition current considerably. We also show that a reasonable description
of the existing form factors data can be obtained with a very small probability
P_{s\bar s}=0.025% for the uuds\bar s component. We further see that the
agreement of our prediction with the data for G_A^s at low-q^2 region can be
markedly improved by a small admixture of configuration I. It is also found
that by not removing CM motion, P_{s\bar s} would be overestimated by about a
factor of four in the case when transition dominates over direct currents.
Then, we also study the consequence of a recent estimate reached from analyzing
the existing data on quark distributions that P_{s\bar s} lies between 2.4-2.9%
which would lead to a large size for the five-quark (5q) system, as well as a
small bump in both G^s_E+\eta G^s_M and G^s_E in the region of q^2 =< 0.1
GeV^2.Comment: Prepared for The Fifth Asia-Pacific Conference on Few-Body Problems
in Physics 2011 in Seoul, South Korea, 22-26 August 201
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