247 research outputs found
Theoretical prediction of spectral and optical properties of bacteriochlorophylls in thermally disordered LH2 antenna complexes
A general approach for calculating spectral and optical properties of
pigment-protein complexes of known atomic structure is presented. The method,
that combines molecular dynamics simulations, quantum chemistry calculations
and statistical mechanical modeling, is demonstrated by calculating the
absorption and circular dichroism spectra of the B800-B850 BChls of the LH2
antenna complex from Rs. molischianum at room temperature. The calculated
spectra are found to be in good agreement with the available experimental
results. The calculations reveal that the broadening of the B800 band is mainly
caused by the interactions with the polar protein environment, while the
broadening of the B850 band is due to the excitonic interactions. Since it
contains no fitting parameters, in principle, the proposed method can be used
to predict optical spectra of arbitrary pigment-protein complexes of known
structure.Comment: ReVTeX4, 11 pages, 9 figures, submitted to J. Chem. Phy
Kinetic Monte Carlo and Cellular Particle Dynamics Simulations of Multicellular Systems
Computer modeling of multicellular systems has been a valuable tool for
interpreting and guiding in vitro experiments relevant to embryonic
morphogenesis, tumor growth, angiogenesis and, lately, structure formation
following the printing of cell aggregates as bioink particles. Computer
simulations based on Metropolis Monte Carlo (MMC) algorithms were successful in
explaining and predicting the resulting stationary structures (corresponding to
the lowest adhesion energy state). Here we present two alternatives to the MMC
approach for modeling cellular motion and self-assembly: (1) a kinetic Monte
Carlo (KMC), and (2) a cellular particle dynamics (CPD) method. Unlike MMC,
both KMC and CPD methods are capable of simulating the dynamics of the cellular
system in real time. In the KMC approach a transition rate is associated with
possible rearrangements of the cellular system, and the corresponding time
evolution is expressed in terms of these rates. In the CPD approach cells are
modeled as interacting cellular particles (CPs) and the time evolution of the
multicellular system is determined by integrating the equations of motion of
all CPs. The KMC and CPD methods are tested and compared by simulating two
experimentally well known phenomena: (1) cell-sorting within an aggregate
formed by two types of cells with different adhesivities, and (2) fusion of two
spherical aggregates of living cells.Comment: 11 pages, 7 figures; submitted to Phys Rev
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems
and other components of the Internet of Things continues to increase annually.
In this work, an algorithm selection framework is employed on the NSL-KDD data
set and a novel paradigm of machine learning taxonomy is presented. The
framework uses a combination of user input and meta-features to select the best
algorithm to detect cyber attacks on a network. Performance is compared between
a rule-of-thumb strategy and a meta-learning strategy. The framework removes
the conjecture of the common trial-and-error algorithm selection method. The
framework recommends five algorithms from the taxonomy. Both strategies
recommend a high-performing algorithm, though not the best performing. The work
demonstrates the close connectedness between algorithm selection and the
taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2
Analysis of Transient Processes in a Radiophysical Flow System
Transient processes in a third-order radiophysical flow system are studied
and a map of the transient process duration versus initial conditions is
constructed and analyzed. The results are compared to the arrangement of
submanifolds of the stable and unstable cycles in the Poincare section of the
system studied.Comment: 3 pages, 2 figure
Symmetries and Fixed Point Stability of Stochastic Differential Equations Modeling Self-Organized Criticality
A stochastic nonlinear partial differential equation is built for two
different models exhibiting self-organized criticality, the Bak, Tang, and
Wiesenfeld (BTW) sandpile model and the Zhang's model. The dynamic
renormalization group (DRG) enables to compute the critical exponents. However,
the nontrivial stable fixed point of the DRG transformation is unreachable for
the original parameters of the models. We introduce an alternative
regularization of the step function involved in the threshold condition, which
breaks the symmetry of the BTW model. Although the symmetry properties of the
two models are different, it is shown that they both belong to the same
universality class. In this case the DRG procedure leads to a symmetric
behavior for both models, restoring the broken symmetry, and makes accessible
the nontrivial fixed point. This technique could also be applied to other
problems with threshold dynamics.Comment: 19 pages, RevTex, includes 6 PostScript figures, Phys. Rev. E (March
97?
Statistics of extremal intensities for Gaussian interfaces
The extremal Fourier intensities are studied for stationary
Edwards-Wilkinson-type, Gaussian, interfaces with power-law dispersion. We
calculate the probability distribution of the maximal intensity and find that,
generically, it does not coincide with the distribution of the integrated power
spectrum (i.e. roughness of the surface), nor does it obey any of the known
extreme statistics limit distributions. The Fisher-Tippett-Gumbel limit
distribution is, however, recovered in three cases: (i) in the non-dispersive
(white noise) limit, (ii) for high dimensions, and (iii) when only
short-wavelength modes are kept. In the last two cases the limit distribution
emerges in novel scenarios.Comment: 15 pages, including 7 ps figure
A Method for Determining the Transient Process Duration in Dynamic Systems in the Regime of Chaotic Oscillations
We describe a method for determining the transient process duration in a
standard two-dimensionaldynamic system with discrete time (Henon map),
occurring in the regime of chaotic oscillationsComment: 4 pages, 2 figure
Simulation study of the inhomogeneous Olami-Feder-Christensen model of earthquakes
Statistical properties of the inhomogeneous version of the
Olami-Feder-Christensen (OFC) model of earthquakes is investigated by numerical
simulations. The spatial inhomogeneity is assumed to be dynamical. Critical
features found in the original homogeneous OFC model, e.g., the
Gutenberg-Richter law and the Omori law are often weakened or suppressed in the
presence of inhomogeneity, whereas the characteristic features found in the
original homogeneous OFC model, e.g., the near-periodic recurrence of large
events and the asperity-like phenomena persist.Comment: Shortened from the first version. To appear in European Physical
Journal
Effect of Trends on Detrended Fluctuation Analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
estimate long-range power-law correlation exponents in noisy signals. Many
noisy signals in real systems display trends, so that the scaling results
obtained from the DFA method become difficult to analyze. We systematically
study the effects of three types of trends -- linear, periodic, and power-law
trends, and offer examples where these trends are likely to occur in real data.
We compare the difference between the scaling results for artificially
generated correlated noise and correlated noise with a trend, and study how
trends lead to the appearance of crossovers in the scaling behavior. We find
that crossovers result from the competition between the scaling of the noise
and the ``apparent'' scaling of the trend. We study how the characteristics of
these crossovers depend on (i) the slope of the linear trend; (ii) the
amplitude and period of the periodic trend; (iii) the amplitude and power of
the power-law trend and (iv) the length as well as the correlation properties
of the noise. Surprisingly, we find that the crossovers in the scaling of noisy
signals with trends also follow scaling laws -- i.e. long-range power-law
dependence of the position of the crossover on the parameters of the trends. We
show that the DFA result of noise with a trend can be exactly determined by the
superposition of the separate results of the DFA on the noise and on the trend,
assuming that the noise and the trend are not correlated. If this superposition
rule is not followed, this is an indication that the noise and the superimposed
trend are not independent, so that removing the trend could lead to changes in
the correlation properties of the noise.Comment: 20 pages, 16 figure
Effect of nonstationarities on detrended fluctuation analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
quantify long-range power-law correlations in signals. Many physical and
biological signals are ``noisy'', heterogeneous and exhibit different types of
nonstationarities, which can affect the correlation properties of these
signals. We systematically study the effects of three types of
nonstationarities often encountered in real data. Specifically, we consider
nonstationary sequences formed in three ways: (i) stitching together segments
of data obtained from discontinuous experimental recordings, or removing some
noisy and unreliable parts from continuous recordings and stitching together
the remaining parts -- a ``cutting'' procedure commonly used in preparing data
prior to signal analysis; (ii) adding to a signal with known correlations a
tunable concentration of random outliers or spikes with different amplitude,
and (iii) generating a signal comprised of segments with different properties
-- e.g. different standard deviations or different correlation exponents. We
compare the difference between the scaling results obtained for stationary
correlated signals and correlated signals with these three types of
nonstationarities.Comment: 17 pages, 10 figures, corrected some typos, added one referenc
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