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