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Abstract: The present work employs time series analysis, a proven powerful mathematical tool, for investigating the complex molecular dynamics of the thermal lens (TL) system induced by the duty cycle (C) variation. For intensity modulation, TL spectroscopy commonly uses optical choppers. The TL formation involves complex molecular dynamics that vary with the input photothermal energy, which is implemented by varying the duty cycle of the chopper. The molecular dynamics is studied from the fractal dimension (D), phase portrait, sample entropy (S), and Hurst exponent (H) for different duty cycles. The increasing value of C is found to increase D and S, indicating that the system is becoming complex and less deterministic, as evidenced by the phase portrait analysis. The value of H less than 0.5 conforms the evolution of the TL system to more anti-persistent nature with C. The increasing value of C increases the enthalpy of the system that appears as an increase in full width at half maximum of the refractive index profile. Thus the study establishes that the sample entropy and thermodynamic entropy are directly related.
Keywords: Time series analysis Fractal analysis Photothermal lens spectroscopy Fractal dimension Hurst exponent Sample entropy
Published in RUNG: 05.07.2022; Views: 2707; Downloads: 0
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Abstract: Objectives: The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Findings: The spectral analyses reveal fewer, low-intensity frequency components along with its overtones in the intermittent and rapidly damping RB signal. The complexity in the dynamics of airflow in BB and RB is investigated through the fractal dimension, Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the RB signal provides an insight into the internal morphology of the airways containing mucous and other obstructions. The Principal Component Analysis (PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along with Pattern Recognition Neural Network (PRNN) uses non-linear measures for predicting BB and RB. Signal classification based on phase portrait features evaluates the multidimensional aspects of signal intensities, whereas that based on PSD features considers mere signal intensities. The principal components in PCA cover about 86.5% of the overall variance of the data class, successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100% accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the potential of non-linear measures and PSD features in classifying these signals enabling its application to be extended for low-cost, non-invasive COVID-19 detection and real-time health monitoring.
Keywords: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Published in RUNG: 30.06.2022; Views: 2653; Downloads: 0
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Abstract: The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
Keywords: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Published in RUNG: 28.06.2022; Views: 3066; Downloads: 0
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Abstract: The aim of the present study is to characterize and improve the still insufficient knowledge of the recharge processes that have an important influence on the flow and solute transport in karst aquifers and thus also on the quantity and quality of karst water sources. A binary karst aquifer in the recharge area of the Malenščica and Unica springs, which covers an area of about 820 km2 in SW Slovenia, was selected as the study area. A dense monitoring network was established at 20 observation points (six springs, four ponors, seven water-active caves and three surface streams) for simultaneous monitoring of the hydrological characteristics and the physicochemical properties of the water, the so-called natural tracers. Data-loggers were installed to measure water pressure, temperature and conductivity. During selected storm events, samples were taken for chemical and microbiological analyses and discharge measurements were made. The meteorological and hydrological data of the Slovenian Environment Agency complemented the extensive dataset. Collected data allowed the analysis and comparison of the spatial and temporal variations of the natural tracers under different hydrological conditions. Frequent discharge measurements allowed the generation of rating curves and proved to be a crucial element for understanding the hydraulic processes that determine the functioning of this system. The calculation of the water budget allowed an assessment of the proportion of autogenic and allogenic recharge of the springs and a quantitative estimate of autogenic recharge under different hydrological conditions. The hydrological analysis, i.e. the flow duration curve, the hydrograph separation techniques and the recession analysis, revealed that the Malenščica spring has a higher storage capacity, a greater proportion of autogenic recharge, especially at low-flow, and a slower recession than the Unica spring. This was also confirmed by correlation and spectral analyses, which were also used to investigate the relationships between discharges at ponors and springs. However, the results of the cross-correlation analysis showed hardly any difference between the two springs and in this case proved to be unsuitable for studying the influence of allogenic recharge. Instead, partial cross-correlation analysis was used to control the input parameters of effective precipitation and discharge of one of the sinking streams to determine the contribution of the other sinking stream to the observed spring. The results confirmed differences in allogenic recharge of the Unica and Malenščica springs. Hysteresis analysis has been applied as a complementary method to time series analysis and represents an improved approach to the characterization of the karst hydrological system. The hydraulic approach to the construction of hysteresis enabled a detailed analysis of allogenic and autogenic water interaction and its influence on the Malenščica and Unica springs under different hydrological conditions. Narrow shapes of the hysteresis indicate a direct hydraulic connection between the ponor and the spring and thus a well-developed drainage system. Any deviation towards a convex or concave shape indicates a less developed, more matrix-related drainage system or the influence of other recharge sources. Analysis of physicochemical hysteretic function of individual locations confirmed the differences in the recharge characteristics of the two springs. Compared to the Unica spring, the Malenščica spring has specific recharge characteristics that result in lower vulnerability to the effects of the sinking streams. A greater proportion of autogenic recharge in the initial phase of the storm event is important, as it allows for a time delay of the possible negative effects of the sinking stream. However, possible pollution from the area of autogenic recharge can have strong negative effects, as in this initial phase with low discharges the dilution effect is negligible.
Keywords: karst aquifer, dynamics of natural tracers, storm events, discharge measurements, time series analysis, hysteresis, Unica spring, Malenščica spring
Published in RUNG: 01.03.2022; Views: 3848; Downloads: 133
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