Variable mass pendulum behaviour processed by wavelet analysis

Mavilia

et al. 2020

Materials

Experimental findings obtained by FTIR and Raman spectroscopies on montmorillonite-water mixtures at three concentration values are presented. To get some insight into the hydrogen bond network of water within the montmorillonite network, FTIR and Raman spectra have been collected as a function of time and then analyzed following two complementary approaches: An analysis of the intramolecular OH stretching mode in the spectral range of 2700–3900 cm−1 in terms of two Gaussian components, and an analysis of the same OH stretching mode by wavelet cross-correlation. The FTIR and Raman investigations have been carried as a function of time for a montmorillonite-water weight composition (wt%) of 20–80%, 25–75%, and 35–65%, until the dehydrated state where the samples appear as a homogeneous rigid layer of clay. In particular, for both the FTIR and Raman spectra, the decomposition of the OH stretching band into a “closed” and an “open” contribution and the spectral wavelet analysis allow us to extract quantitative information on the time behavior of the system water content. It emerges that, the total water contribution inside the montmorillonite structure decreases as a function of time. However, the relative weight of the ordered water contribution diminishes more rapidly while the relative weight of the disordered water contribution increases, indicating that a residual water content, characterized by a highly structural disorder, rests entrapped in the montmorillonite layer structure for a longer time. From the present study, it can be inferred that the montmorillonite dehydration process promotes the layer self-assembly.

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

Self-Assembly Processes in Hydrated Montmorillonite by FTIR Investigations

Mavilia

et al. 2020

Materials

“…It is well known that wavelet tranform (WT) allows the evaluation of signal frequency and time localization. From a general viewpoint, WT assigns to function y(x) another function WT(a, τ), where a, with a > 0 denotes the scale and τ a time shift [53][54][55][56][57]. Thus, WT decomposes y(x) into wavelets components 1 √ a ψ x−τ a , as follows:…”

Section: Wavelet Analysismentioning

confidence: 99%

A Physical–Mathematical Approach to Climate Change Effects through Stochastic Resonance

Magazù

2019

Climate

The aim of this work is to study the effects induced by climate changes in the framework of the stochastic resonance approach. First, a wavelet cross-correlation analysis on Earth temperature data concerning the last 5,500,000 years is performed; this analysis confirms a correlation between the planet's temperature and the 100,000, 41,000, and 23,000-year periods of the Milankovitch orbital cycles. Then, the stochastic resonance model is invoked. Specific attention is given to the study of the impact of the registered global temperature increase within the stochastic model. Further, a numerical simulation has been performed, based on: (1) A double-well potential, (2) an external periodic modulation, corresponding to the orbit eccentricity cycle, and (3) an increased value of the global Earth temperature. The effect of temperature increase represents one of the novelties introduced in the present study and is determined by downshifting the interaction potential used within the stochastic resonance model. The numeric simulation results show that, for simulated increasing values of the global temperature, the double-well system triggers changes, while at higher temperatures (as in the case of the absence of a global temperature increase although with a different threshold) the system goes into a chaotic regime. The wavelet analysis allows characterization of the stochastic resonance condition through the evaluation of the signal-to-noise ratio. On the basis of the obtained findings, we hypothesize that the global temperature increase can suppress, on a large time scale corresponding to glacial cycles, the external periodic modulation effects and, hence, the glacial cycles.Climate 2019, 7, 21 2 of 16 the oceans [6][7][8][9]. Equilibrium temperature at these times represents an upper stationary regime. On the other hand, climate variations can give rise to harsh conditions that are of interest for the study of extremophile organisms [10][11][12][13] for biotechnological applications [14][15][16][17][18][19][20][21][22].Temperature determinations performed at the Vostok scientific station (Figure 1) revealed a strong correlation with the Milankovitch cycles [23][24][25][26][27][28][29][30][31][32], and are shown in Figure 2.

“…Variance increases are present in the temperature sequence with a period of 41 K years; these maxima correspond to the period of the Earth's axis inclination (second Milankovitch cycle); In order to extract more information on the frequency components of the temperature variation as a function of time during the last 5.5 M years, in the following, we applied the wavelet analysis. It is well known that, when it is important to single out the signal frequency localizations, one can take a huge advantage from WT analysis [31][32][33][34][35][36]. In fact, differently from FT, which shows all the signal frequencies without a time localization, WT shows the component frequencies and at what time they occur [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

On the Breaking of the Milankovitch Cycles Triggered by Temperature Increase: The Stochastic Resonance Response

Magazù

2021

Climate

Recent decades have registered the hottest temperature variation in instrumentally recorded data history. The registered temperature rise is particularly significant in the so-called hot spot or sentinel regions, characterized by higher temperature increases in respect to the planet average value and by more marked connected effects. In this framework, in the present work, following the climate stochastic resonance model, the effects, due to a temperature increase independently from a specific trend, connected to the 105 year Milankovitch cycle were tested. As a result, a breaking scenario induced by global warming is forecasted. More specifically, a wavelet analysis, innovatively performed with different sampling times, allowed us, besides to fully characterize the cycles periodicities, to quantitatively determine the stochastic resonance conditions by optimizing the noise level. Starting from these system resonance conditions, numerical simulations for increasing planet temperatures have been performed. The obtained results show that an increase of the Earth temperature boosts a transition towards a chaotic regime where the Milankovitch cycle effects disappear. These results put into evidence the so-called threshold effect, namely the fact that also a small temperature increase can give rise to great effects above a given threshold, furnish a perspective point of view of a possible future climate scenario, and provide an account of the ongoing registered intensity increase of extreme meteorological events.