Overwash is a physical process of coastal sediment transport driven by storm events and is essential to landscape resilience in low-lying barrier environments. This work establishes a comprehensive set of scaling laws for overwash morphology: unifying quantitative descriptions with which to compare overwash features by their morphological attributes across case examples. Such scaling laws also ...

The eigenvalues of a matrix polynomial can be determined classically by solving a generalized eigenproblem for a linearized matrix pencil, for instance by writing the matrix polynomial in companion form. We introduce a general scaling technique, based on tropical algebra, which applies in particular to this companion form. This scaling, which is inspired by an earlier work of Akian, Bapat, and ...

Experimental and numerical results are presented for a fracture experiment carried out on a fiber-reinforced element under flexural loading, and a statistical analysis is performed for acoustic emission waiting-time distributions. By an optimization procedure, a recently proposed scaling law describing these distributions for different event magnitude scales is confirmed by both experimental an...

The labeled magnitude scale (LMS) is a verbally anchored quasi-logarithmically spaced response scale with properties similar to magnitude estimation. Three experiments examined whether the LMS showed context effects similar to those found with magnitude estimation and category scales. Two versions of the LMS were used, one anchored at the high end to the strongest imaginable sweetness and the o...

We investigate the stability of strange quark matter and the properties of the corresponding strange stars, within a wide range of quark mass scaling. The calculation shows that the resulting maximum mass always lies between 1.5M⊙ and 1.8M⊙ for all the scalings chosen here. Strange star sequences with a linear scaling would support less gravitational mass, and a change (increase or decrease) of...

Deep neural networks (DNN) have been successfully applied for music classification tasks including music tagging. In this paper, we investigate the effect of audio preprocessing on music tagging with neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting and scalin...

Fluctuation Analysis (FA) and specially Detrended Fluctuation Analysis (DFA) are techniques commonly used to quantify correlations and scaling properties of complex time series such as the observable outputs of great variety of dynamical systems, from Economics to Physiology. Often, such correlated time series are analyzed using the magnitude and sign decomposition, i.e., by using FA or DFA to ...

We propose an approach for analyzing signals with long-range correlations by decomposing the signal increment series into magnitude and sign series and analyzing their scaling properties. We show that signals with identical long-range correlations can exhibit different time organization for the magnitude and sign. We find that the magnitude series relates to the nonlinear properties of the orig...

The scaling behaviour of self-potential data (in the frequency range between 0 and 0.125Hz) observed in 1995 at Acapulco station in Mexico were investigated using the detrended fluctuation analysis (DFA), which is a powerful method to detect scaling in nonstationary time series. Approximately two scaling regions with two different scaling exponents α1 (on low timescales) and α2 (on high timesca...

To solve nonlinear equations by an optimization method, scaling is very important. Two types of poor scaling where: (a) the variables differ greatly in magnitude; (b) the merit function of system is highly sensitive to small changes in certain variables and relatively insensitive to changes in other variables. If poor scaling is ignored, the algorithm may produce solutions with poor quality. To...