Comment on "Scaling of atmosphere and ocean temperature correlations in observations and climate models"

In a recent letter [K. Fraedrich and R. Blender, Phys. Rev. Lett. 90, 108501 (2003)], Fraedrich and Blender studied the scaling of atmosphere and ocean temperature. They analyzed the fluctuation functions F(s) ~ s^alpha of monthly temperature records (mostly from grid data) by using the detrended fluctuation analysis (DFA2) and claim that the scaling exponent alpha over the inner continents is equal to 0.5, being characteristic of uncorrelated random sequences. Here we show that this statement is (i) not supported by their own analysis and (ii) disagrees with the analysis of the daily observational data from which the grid monthly data have been derived. We conclude that also for the inner continents, the exponent is between 0.6 and 0.7, similar as for the coastline-stations.Comment: 1 page with 2 figure

Detecting Long-range Correlations with Detrended Fluctuation Analysis

We examine the Detrended Fluctuation Analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling that appear at small time scales become stronger in higher orders of DFA, and suggest a modified DFA method to remove them. The improvement is necessary especially for short records that are affected by non-stationarities. Furthermore, we describe how crossovers in the correlation behavior can be detected reliably and determined quantitatively and show how several types of trends in the data affect the different orders of DFA.Comment: 10 pages, including 8 figure

Towards a unified approach to detection of faults and cyber-attacks in industrial installations

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper investigates enhancing the ability to detect cyber-attacks by using information and methods related to fault detection. An experimental stand, and an associated simulator have been constructed to enable tests of combined cyber attacks and faults in industrial processes, and, possibly, to distinguish between them. Some scenarios of cyber attacks have been presented, analysed theoretically and then tested on the simulator, demonstrating that detection of cyber attacks by this method is possible.Peer ReviewedPostprint (author's final draft

Multifractal detrended fluctuation analysis of nonstationary time series

We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series to those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima (WTMM) method, and show that the results are equivalent.Comment: 14 pages (RevTex) with 10 figures (eps

Characterization of Sleep Stages by Correlations of Heartbeat Increments

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and REM sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the signs series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages.Comment: 7 pages, 4 figures, revte

Ensembl’s 10th year

Ensembl (http://www.ensembl.org) integrates genomic information for a comprehensive set of chordate genomes with a particular focus on resources for human, mouse, rat, zebrafish and other high-value sequenced genomes. We provide complete gene annotations for all supported species in addition to specific resources that target genome variation, function and evolution. Ensembl data is accessible in a variety of formats including via our genome browser, API and BioMart. This year marks the tenth anniversary of Ensembl and in that time the project has grown with advances in genome technology. As of release 56 (September 2009), Ensembl supports 51 species including marmoset, pig, zebra finch, lizard, gorilla and wallaby, which were added in the past year. Major additions and improvements to Ensembl since our previous report include the incorporation of the human GRCh37 assembly, enhanced visualisation and data-mining options for the Ensembl regulatory features and continued development of our software infrastructure