In this work, I present the results of a thorough nonlinear time series analysis study concerning the long-term variability behaviour of Mrk 421. I use all the archival data obtained by the proportional counter array (PCA) and the all-sky monitor (ASM) aboard the Rossi X-ray timing explorer (RXTE), since 1996. Linearity and stationarity tests reveal that the long-term timing properties of the s...

8 [1] The Madden-Julian Oscillation (MJO), the primary 9 mode of large-scale intraseasonal variability in the tropics, 10 is known to relate to the mid-latitude atmospheric 11 variability. Using neural network techniques, a nonlinear 12 projection of the MJO onto the precipitation and 200-hPa 13 wind anomalies in the northeast Pacific during January– 14 March shows asymmetric atmospheric patter...

[1] A regional hydroeconomic model is developed to include demand shifts from nonprice water conservation programs as input parameters and decision variables. Stochastic nonlinear programming then jointly identifies the benefit-maximizing portfolio of conservation and leak reduction programs, infrastructure expansions, and operational allocations under variable water availability. We present a ...

Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high-dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph-theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical ...

Objective To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus. Methods The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rat...

The goal of this paper is to assess various combinations of heart rate variability (HRV) features in successful classification of four different cardiac rhythms. The rhythms include: normal, congestive heart failure, supraventricular arrhythmia, and any arrhythmia. We approach the problem of automatic cardiac rhythm classification from HRV by employing several features’ schemes. The schemes are...