239 research outputs found
Long range correlation in cosmic microwave background radiation
We investigate the statistical anisotropy and Gaussianity of temperature
fluctuations of Cosmic Microwave Background radiation (CMB) data from {\it
Wilkinson Microwave Anisotropy Probe} survey, using the multifractal detrended
fluctuation analysis, rescaled range and scaled windowed variance methods. The
multifractal detrended fluctuation analysis shows that CMB fluctuations has a
long range correlation function with a multifractal behavior. By comparing the
shuffled and surrogate series of CMB data, we conclude that the multifractality
nature of temperature fluctuation of CMB is mainly due to the long-range
correlations and the map is consistent with a Gaussian distribution.Comment: 10 pages, 7 figures, V2: Added comments, references and major
correction
Binary Tree Approach to Scaling in Unimodal Maps
Ge, Rusjan, and Zweifel (J. Stat. Phys. 59, 1265 (1990)) introduced a binary
tree which represents all the periodic windows in the chaotic regime of
iterated one-dimensional unimodal maps. We consider the scaling behavior in a
modified tree which takes into account the self-similarity of the window
structure. A non-universal geometric convergence of the associated superstable
parameter values towards a Misiurewicz point is observed for almost all binary
sequences with periodic tails. There are an infinite number of exceptional
sequences, however, which lead to superexponential scaling. The origin of such
sequences is explained.Comment: 25 pages, plain Te
Long-range correlation and multifractality in Bach's Inventions pitches
We show that it can be considered some of Bach pitches series as a stochastic
process with scaling behavior. Using multifractal deterend fluctuation analysis
(MF-DFA) method, frequency series of Bach pitches have been analyzed. In this
view we find same second moment exponents (after double profiling) in ranges
(1.7-1.8) in his works. Comparing MF-DFA results of original series to those
for shuffled and surrogate series we can distinguish multifractality due to
long-range correlations and a broad probability density function. Finally we
determine the scaling exponents and singularity spectrum. We conclude fat tail
has more effect in its multifractality nature than long-range correlations.Comment: 18 page, 6 figures, to appear in JSTA
Пожарная и промышленная безопасность на предприятиях нефтегазодобывающей отрасли
Работа посвящена анализу теоретических основ обеспечения пожарной безопасности, улучшению практических приемов и методах противопожарной защиты, при повседневной эксплуатации установки подготовки нефти на опасном производственном объекте в области пожарной безопасности. В результате исследования изучены методы эксплуатации установки подготовки нефти на опасном производственном объекте, позволяющие не только предотвратить возникновение аварии или пожара, но и быстро ликвидировать последствия. Противопожарные мероприятия существенно повышают уровень пожарной безопасности своих объектов и снижают потери от пожаров.The work is devoted to the analysis of the theoretical foundations of fire safety, improvement of practical techniques and methods of fire protection, in the daily operation of the oil treatment plant at a hazardous production facility in the field of fire safety. As a result of the study, the methods of operation of an oil treatment plant at a dangerous production facility were studied, allowing not only to prevent an accident or fire, but also to quickly eliminate the consequences. Fire-fighting measures significantly increase the level of fire safety of their facilities and reduce losses from fires
Texture classification of proteins using support vector machines and bio-inspired metaheuristics
6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process
Modulated Martensite: Why it forms and why it deforms easily
Diffusionless phase transitions are at the core of the multifunctionality of
(magnetic) shape memory alloys, ferroelectrics and multiferroics. Giant strain
effects under external fields are obtained in low symmetric modulated
martensitic phases. We outline the origin of modulated phases, their connection
with tetragonal martensite and consequences for their functional properties by
analysing the martensitic microstructure of epitaxial Ni-Mn-Ga films from the
atomic to macroscale. Geometrical constraints at an austenite-martensite phase
boundary act down to the atomic scale. Hence a martensitic microstructure of
nanotwinned tetragonal martensite can form. Coarsening of twin variants can
reduce twin boundary energy, a process we could follow from the atomic to the
millimetre scale. Coarsening is a fractal process, proceeding in discrete steps
by doubling twin periodicity. The collective defect energy results in a
substantial hysteresis, which allows retaining modulated martensite as a
metastable phase at room temperature. In this metastable state elastic energy
is released by the formation of a 'twins within twins' microstructure which can
be observed from the nanometre to millimetre scale. This hierarchical twinning
results in mesoscopic twin boundaries which are diffuse, in contrast to the
common atomically sharp twin boundaries of tetragonal martensite. We suggest
that observed extraordinarily high mobility of such mesoscopic twin boundaries
originates from their diffuse nature which renders pinning by atomistic point
defects ineffective.Comment: 34 pages, 8 figure
Less is Different: Emergence and Reduction Reconciled
This is a companion to another paper. Together they rebut two widespread
philosophical doctrines about emergence. The first, and main, doctrine is that
emergence is incompatible with reduction. The second is that emergence is
supervenience; or more exactly, supervenience without reduction. In the other
paper, I develop these rebuttals in general terms, emphasising the second
rebuttal. Here I discuss the situation in physics, emphasising the first
rebuttal. I focus on limiting relations between theories and illustrate my
claims with four examples, each of them a model or a framework for modelling,
from well-established mathematics or physics. I take emergence as behaviour
that is novel and robust relative to some comparison class. I take reduction
as, essentially, deduction. The main idea of my first rebuttal will be to
perform the deduction after taking a limit of some parameter. Thus my first
main claim will be that in my four examples (and many others), we can deduce a
novel and robust behaviour, by taking the limit, N goes to infinity, of a
parameter N. But on the other hand, this does not show that that the infinite
limit is "physically real", as some authors have alleged. For my second main
claim is that in these same examples, there is a weaker, yet still vivid, novel
and robust behaviour that occurs before we get to the limit, i.e. for finite N.
And it is this weaker behaviour which is physically real. My examples are: the
method of arbitrary functions (in probability theory); fractals (in geometry);
superselection for infinite systems (in quantum theory); and phase transitions
for infinite systems (in statistical mechanics).Comment: 75 p
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