784 research outputs found
Longitudinal Losses Due to Breathing Mode Excitation in Radiofrequency Linear Accelerators
Transverse breathing mode oscillations in a particle beam can couple energy
into longitudinal oscillations in a bunch of finite length and cause
significant losses. We develop a model that illustrates this effect and explore
the dependence on mismatch size, space-charge tune depression, longitudinal
focusing strength, bunch length, and RF bucket length
Glycine/Glycolic acid based copolymers
Glycine/glycolic acid based biodegradable copolymers have been prepared by ring-opening homopolymerization of morpholine-2,5-dione, and ring-opening copolymerization of morpholine-2,5-dione and glycolide. The homopolymerization of morpholine-2,5-dione was carried out in the melt at 200°C for 3 min using stannous octoate as an initiator, and continued at lower reaction temperatures (100-160°C) for 2-48 h. The highest yields (60%) and intrinsic viscosities ([] = 0.50 dL/g; DMSO, 25°C) were obtained after 3 min reaction at 200°C and 17 h at 130°C using a molar ratio of monomer and initiator of 1000. The polymer prepared by homopolymerization of morpholine-2,5-dione was composed of alternating glycine and glycolic acid residues, and had a glass transition temperature of 67°C and a melting temperature of 199°C. Random copolymers of glycine and glycolic acid were synthesized by copolymerization of morpholine-2,5-dione and glycolide in the melt at 200°C, followed by 17 h reaction at 130°C using stannous octoate as an initiator. The morphology of the copolymers varied from semi-crystalline to amorphous, depending on the mole fraction of glycolic acid residues incorporated
Total destruction of invariant tori for the generalized Frenkel-Kontorova model
We consider generalized Frenkel-Kontorova models on higher dimensional
lattices. We show that the invariant tori which are parameterized by continuous
hull functions can be destroyed by small perturbations in the topology
with
Chaotic versus stochastic behavior in active-dissipative nonlinear systems
We study the dynamical state of the one-dimensional noisy generalized Kuramoto-Sivashinsky (gKS) equation by making use of time-series techniques based on symbolic dynamics and complex networks. We focus on analyzing temporal signals of global measure in the spatiotemporal patterns as the dispersion parameter of the gKS equation and the strength of the noise are varied, observing that a rich variety of different regimes, from high-dimensional chaos to pure stochastic behavior, emerge. Permutation entropy, permutation spectrum, and network entropy allow us to fully classify the dynamical state exposed to additive noise
On the importance of nonlinear modeling in computer performance prediction
Computers are nonlinear dynamical systems that exhibit complex and sometimes
even chaotic behavior. The models used in the computer systems community,
however, are linear. This paper is an exploration of that disconnect: when
linear models are adequate for predicting computer performance and when they
are not. Specifically, we build linear and nonlinear models of the processor
load of an Intel i7-based computer as it executes a range of different
programs. We then use those models to predict the processor loads forward in
time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on
Intelligent Data Analysis
Topological properties and fractal analysis of recurrence network constructed from fractional Brownian motions
Many studies have shown that we can gain additional information on time
series by investigating their accompanying complex networks. In this work, we
investigate the fundamental topological and fractal properties of recurrence
networks constructed from fractional Brownian motions (FBMs). First, our
results indicate that the constructed recurrence networks have exponential
degree distributions; the relationship between and of recurrence networks decreases with the Hurst
index of the associated FBMs, and their dependence approximately satisfies
the linear formula . Moreover, our numerical results of
multifractal analysis show that the multifractality exists in these recurrence
networks, and the multifractality of these networks becomes stronger at first
and then weaker when the Hurst index of the associated time series becomes
larger from 0.4 to 0.95. In particular, the recurrence network with the Hurst
index possess the strongest multifractality. In addition, the
dependence relationships of the average information dimension on the Hurst index can also be
fitted well with linear functions. Our results strongly suggest that the
recurrence network inherits the basic characteristic and the fractal nature of
the associated FBM series.Comment: 25 pages, 1 table, 15 figures. accepted by Phys. Rev.
Hopf Bifurcations in a Watt Governor With a Spring
This paper pursues the study carried out by the authors in "Stability and
Hopf bifurcation in a hexagonal governor system", focusing on the codimension
one Hopf bifurcations in the hexagonal Watt governor differential system. Here
are studied the codimension two, three and four Hopf bifurcations and the
pertinent Lyapunov stability coefficients and bifurcation diagrams, ilustrating
the number, types and positions of bifurcating small amplitude periodic orbits,
are determined. As a consequence it is found an open region in the parameter
space where two attracting periodic orbits coexist with an attracting
equilibrium point.Comment: 30 pages and 7 figure
Classical field theory. Advanced mathematical formulation
In contrast with QFT, classical field theory can be formulated in strict
mathematical terms of fibre bundles, graded manifolds and jet manifolds. Second
Noether theorems provide BRST extension of this classical field theory by means
of ghosts and antifields for the purpose of its quantization.Comment: 30 p
A Tool to Recover Scalar Time-Delay Systems from Experimental Time Series
We propose a method that is able to analyze chaotic time series, gained from
exp erimental data. The method allows to identify scalar time-delay systems. If
the dynamics of the system under investigation is governed by a scalar
time-delay differential equation of the form ,
the delay time and the functi on can be recovered. There are no
restrictions to the dimensionality of the chaotic attractor. The method turns
out to be insensitive to noise. We successfully apply the method to various
time series taken from a computer experiment and two different electronic
oscillators
Additive Nonparametric Reconstruction of Dynamical Systems from Time Series
We present a nonparametric way to retrieve a system of differential equations
in embedding space from a single time series. These equations can be treated
with dynamical systems theory and allow for long term predictions. We
demonstrate the potential of our approach for a modified chaotic Chua
oscillator.Comment: accepted for Phys. Rev. E, Rapid Com
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