XMM-Newton View of PKS 2155-304: Characterizing the X-ray Variability Properties with EPIC-PN

Starting from XMM-Newton EPIC-PN data, we present the X-ray variability characteristics of PKS 2155-304 using a simple analysis of the excess variance, \xs, and of the fractional rms variability amplitude, fvar. The scatter in \xs\ and \fvar, calculated using 500 s long segments of the light curves, is smaller than the scatter expected for red noise variability. This alone does not imply that the underlying process responsible for the variability of the source is stationary, since the real changes of the individual variance estimates are possibly smaller than the large scatters expected for a red noise process. In fact the averaged \xs and \fvar, reducing the fluctuations of the individual variances, chang e with time, indicating non-stationary variability. Moreover, both the averaged \sqxs (absolute rms variability amplitude) and \fvar show linear correlation with source flux but in an opposite sense: \sqxs correlates with flux, but \fvar anti-correlates with flux. These correlations suggest that the variability process of the source is strongly non-stationary as random scatters of variances should not yield any correlation. \fvar spectra were constructed to compare variability amplitudes in different energy bands. We found that the fractional rms variability amplitude of the source, when significant variability is observed, increases logarithmically with the photon energy, indicating significant spectral variability. The point-to-point variability amplitude may also track this trend, suggesting that the slopes of the power spectral density of the source are energy-independent. Using the normalized excess variance the black hole mass of \pks was estimated to be about $1.45 \times 10^8 M_{\bigodot}$. This is compared and contrasted with the estimates derived from measurements of the host galaxies.Comment: Accepted for publication in The Astrophysical Journa

Modeling of Qiandao Lake submerged floating tunnel subject to multi-support seismic input

Abstract The modeling and seismic analysis of Qiandao lake submerged floating tunnel (SFT) is addressed with particular attention to the mooring system, to dissipation issues and to the spatial variability of the excitation, within a numerical procedure developed by the research group to perform the step-by-step dynamic analysis of discretized non-linear structural systems. The procedure, which can handle arbitrary external loading allowing for multiple-support seismic excitation, is enhanced by enriching the mooring cables model adding non-linear hydrodynamic loads. Different dissipation models account for hydrodynamic damping, structural damping and radiation damping which are included, respectively, as non-linear forces, as linear viscous damping equivalent to linear hysteretic by means of an iterative procedure, and as linear viscous damping. A possible solution is here studied to define an adequate cable discretization in order to correctly model nonlinear geometric effects and to avoid fictitious compressions. A uniformly modulated random process, whose spatial variability is governed by a single coherency function, is deemed adequate to model multi-support seismic input for the given structure. A novel method to obtain response spectrum compatible accelerograms is here proposed

Dynamic patterns of flow in the workplace: characterizing within-individual variability using a complexity science approach

As a result of the growing interest in studying employee well-being as a complex process that portrays high levels of within-individual variability and evolves over time, this present study considers the experience of flow in the workplace from a nonlinear dynamical systems approach. Our goal is to offer new ways to move the study of employee well-being beyond linear approaches. With nonlinear dynamical systems theory as the backdrop, we conducted a longitudinal study using the experience sampling method and qualitative semi-structured interviews for data collection; 6981 registers of data were collected from a sample of 60 employees. The obtained time series were analyzed using various techniques derived from the nonlinear dynamical systems theory (i.e., recurrence analysis and surrogate data) and multiple correspondence analyses. The results revealed the following: 1) flow in the workplace presents a high degree of within-individual variability; this variability is characterized as chaotic for most of the cases (75%); 2) high levels of flow are associated with chaos; and 3) different dimensions of the flow experience (e.g., merging of action and awareness) as well as individual (e.g., age) and job characteristics (e.g., job tenure) are associated with the emergence of different dynamic patterns (chaotic, linear and random)

A new method for complexity determination by using fractals and its applications in material surface characteristics

In this article, a new method for complexity determination by using fractals in combination with an artificial intelligent approach is proposed and its application in laser hardening technology is detailed. In particular, nanoindentation tests were applied as a way to investigate the hardness properties of tool steel alloys with respect to both marginal and relevant changes in laser hardening parameters. Specifically, process duration and temperature were considered, together with nanoindentation, later related to surface characteristics by image analysis and Hurst exponent determination. Three different Machine Learning algorithms (Random Forest, Support Vector Machine and k-Nearest Neighbors) were used and predictions compared with measures in terms of mean, variability and linear correlation. Evidences confirmed the general applicability of this method, based on integrating fractals for microstructure analysis and machine learning for their deep understanding, in material science and process engineering

Uncertainty in the manufacturing of fibrous thermosetting composites: A review

Composites manufacturing involves many sources of uncertainty associated with material properties variation and boundary conditions variability. In this study, experimental and numerical results concerning the statistical characterization and the influence of inputs variability on the main steps of composites manufacturing including process-induced defects are presented and analysed. Each of the steps of composite manufacturing introduces variability to the subsequent processes, creating strong interdependencies between the process parameters and properties of the final part. The development and implementation of stochastic simulation tools is imperative to quantify process output variabilities and develop optimal process designs in composites manufacturing

How random is your heart beat?

We measure the content of random uncorrelated noise in heart rate variability using a general method of noise level estimation using a coarse grained entropy. We show that usually - except for atrial fibrillation - the level of such noise is within 5 - 15% of the variance of the data and that the variability due to the linearly correlated processes is dominant in all cases analysed but atrial fibrillation. The nonlinear deterministic content of heart rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p

Stochastic simulation of the influence of fibre path variability on the formation of residual stress and shape distortion

A stochastic cure simulation approach is developed and implemented to investigate the influence of fibre misalignment on cure. Image analysis is used to characterize fiber misalignment in a carbon non-crimp fabric. It is found that variability in tow orientation is significant with a standard deviation of 1.2°. The autocorrelation structure is modeled using the Ornstein-Uhlenbeck sheet and the stochastic problem is addressed by coupling a finite element model of cure with a Monte Carlo scheme. Simulation of the cure of an angle shaped carbon fiber-epoxy component shows that fiber misalignment can cause considerable variability in the process outcome with a coefficient of variation in maximum residual stress up to approximately 2% (standard deviation of 1 MPa) and qualitative and quantitative variations in final distortion of the cured part with the standard deviation in twist and corner angle reaching values of 0.4° and 0.05° respectively. POLYM. COMPOS., 2015. © 2015 The Authors Polymer Composites published by Wiley Periodicals, Inc. on behalf of Society of Plastics Engineer