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1.
Distance-based configurational entropy of proteins from molecular dynamics simulations
Federico Fogolari, Alessandra Corazza, Sara Fortuna, Miguel Angel Soler, Bryan VanSchouwen, Giorgia Brancolini, Stefano Corni, Giuseppe Melacini, Gennaro Esposito, 2015, original scientific article

Abstract: Estimation of configurational entropy from molecular dynamics trajectories is a difficult task which is often performed using quasi-harmonic or histogram analysis. An entirely different approach, proposed recently, estimates local density distribution around each conformational sample by measuring the distance from its nearest neighbors. In this work we show this theoretically well grounded the method can be easily applied to estimate the entropy from conformational sampling. We consider a set of systems that are representative of important biomolecular processes. In particular: reference entropies for amino acids in unfolded proteins are obtained from a database of residues not participating in secondary structure elements; the conformational entropy of folding of β2-microglobulin is computed from molecular dynamics simulations using reference entropies for the unfolded state; backbone conformational entropy is computed from molecular dynamics simulations of four different states of the EPAC protein and compared with order parameters (often used as a measure of entropy); the conformational and rototranslational entropy of binding is computed from simulations of 20 tripeptides bound to the peptide binding protein OppA and of β2-microglobulin bound to a citrate coated gold surface. This work shows the potential of the method in the most representative biological processes involving proteins, and provides a valuable alternative, principally in the shown cases, where other approaches are problematic.
Found in: ključnih besedah
Keywords: entropy, protein, molecular dynamics, simulations, MD
Published: 12.10.2016; Views: 3166; Downloads: 192
.pdf Fulltext (2,40 MB)

2.
Accurate estimation of the entropy of rotation-translation probability distributions
Federico Fogolari, Cedrix Jurgal Dongmo Foumthuim, Sara Fortuna, Miguel Angel Soler, Alessandra Corazza, Gennaro Esposito, 2016, original scientific article

Abstract: The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational–translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational–translational entropy.
Found in: ključnih besedah
Keywords: entropy, probability distribution, molecular dynamics, nearest-neighbor
Published: 11.10.2016; Views: 3205; Downloads: 0
.pdf Fulltext (1,47 MB)

3.
Thermal Lensing of Multi-Walled Carbon Nanotube Solutions as Heat-Transfer Nanofluids
SANKARARAMAN SANKARANARAYANA IYER, CABRERA HUMBERTO, RAJ VIMAL, SWAPNA MOHANACHANDRAN NAIR SINDHU, 2021, original scientific article

Abstract: This paper unwraps nanofluids’ particle dynamics with multi-walled carbon nanotubes (MWCNTs) in base fluids such as acetone, water, and ethylene glycol. Having confirmed the morphology and structure of the MWCNTs by field emission scanning electron microscopy, X-ray diffraction, and Raman spectroscopic analyses, the nanofluids are prepared in three different concentrations. The nonzero absorbance at the laser wavelength, revealed through the UV−visible spectrum, makes the thermal diffusivity study of the sample by the sensitive nondestructive single beam thermal lens (TL) technique possible. The TL signal analysis by time series and fractal techniques divulges the complex particle dynamics, through phase portrait, sample entropy, fractal dimension, and Hurst exponent. The study unveils the effect of the amount of nanoparticles and the viscosity of the medium on thermal diffusivity and particle dynamics. The observed inverse relation between thermal diffusivity and viscosity is in good agreement with the Sankar−Swapna model. The complexity of particle dynamics in MWCNT nanofluids reflected through sample entropy, and fractal dimension shows an inverse relation to the base fluid’s viscosity. This paper investigates the role of viscosity of the base fluid on particle dynamics and thermal diffusivity of the nanofluid to explore its applicability in various thermal systems, thereby suggesting a method to tune the sample entropy through proper selection of base fluid.
Found in: ključnih besedah
Summary of found: ...complex particle dynamics, through phase portrait, sample entropy, fractal dimension, and Hurst exponent. The study...
Keywords: MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy
Published: 28.06.2022; Views: 157; Downloads: 0
.pdf Fulltext (3,59 MB)

4.
Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
SANKARARMAN S, SREEJYOTHI S, RENJINI A, RAJ VIMAL, SWAPNA MOHANACHANDRAN NAIR SINDHU, 2020, original scientific article

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.
Found in: ključnih besedah
Summary of found: ...portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative...
Keywords: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Published: 28.06.2022; Views: 137; Downloads: 0
.pdf Fulltext (2,73 MB)

5.
Soot effected sample entropy minimization in nanofluid for thermal system design
Sankaranarayana Iyer Sankararaman, K. Satheesh Kumar, Vimal Raj, Mohanachandran Nair Sindhu Swapna, 2020, original scientific article

Abstract: The present work suggests a method of improving the thermal system efficiency, through entropy minimisation, and unveils the mechanism involved by analysing the molecular/particle dynamics in soot nanofluids (SNFs) using the time series, power spectrum, and wavelet analyses of the thermal lens signal (TLS). The photothermal energy deposition in the SNF lowers the refractive index due to the temperature rise. It triggers the particle dynamics that are investigated by segmenting the TLS and analysing the refractive index, phase portrait, fractal dimension (D), Hurst exponent (H), and sample entropy (SampEn). The wavelet analysis gives information about the relation between the entropy and the frequency components. When the phase portrait analysis reflects the complex dynamics from region 1 to 2 for all the samples, the SampEn analysis supports it. The decreasing value of D (from 1.59 of the base fluid to 1.55 and 1.52) and the SampEn (from 1.11 of the base fluid to 0.385 and 0.699) with the incorporation of diesel and camphor soot, indicate its ability to lower the complexity, randomness, and entropy. The increase of SampEn with photothermal energy deposition suggests its relation to the thermodynamic entropy (S). The lowering of thermal diffusivity value of the base fluid from 1.4 × 10−7 m2/s to 1.1 × 10−7 and 0.5 × 10−7 m2 /s upon diesel and camphor soot incorporation suggests the heat-trapping and reduced molecular dynamics in heat dissipation.
Found in: ključnih besedah
Keywords: soot, entropy, thermal system, photothermal, time series, nanofluid, fractal
Published: 30.06.2022; Views: 112; Downloads: 0
.pdf Fulltext (2,27 MB)

6.
Downscaling of sample entropy of nanofluids by carbon allotropes
Sankaranarayana Iyer Sankararaman, K. Satheesh Kumar, S. Sreejyothi, Vimal Raj, Mohanachandran Nair Sindhu Swapna, 2020, original scientific article

Abstract: The work reported in this paper is the first attempt to delineate the molecular or particle dynamics from the thermal lens signal of carbon allotropic nanofluids (CANs), employing time series and fractal analyses. The nanofluids of multi-walled carbon nanotubes and graphene are prepared in base fluid, coconut oil, at low volume fraction and are subjected to thermal lens study. We have studied the thermal diffusivity and refractive index variations of the medium by analyzing the thermal lens (TL) signal. By segmenting the TL signal, the complex dynamics involved during its evolution is investigated through the phase portrait, fractal dimension, Hurst exponent, and sample entropy using time series and fractal analyses. The study also explains how the increase of the photothermal energy turns a system into stochastic and antipersistent. The sample entropy (S) and refractive index analyses of the TL signal by segmenting into five regions reveal the evolution of S with the increase of enthalpy. The lowering of S in CAN along with its thermal diffusivity (50%–57% below) as a result of heat-trapping suggests the technique of downscaling sample entropy of the base fluid using carbon allotropes and thereby opening a novel method of improving the efficiency of thermal systems.
Found in: ključnih besedah
Keywords: carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Published: 30.06.2022; Views: 117; Downloads: 0
.pdf Fulltext (4,22 MB)

7.
Time series and fractal analyses of wheezing
Sankaranarayana Iyer Sankararaman, S. Sreejyothi, Vimal Raj, A. Renjini, Mohanachandran Nair Sindhu Swapna, 2020, original scientific article

Abstract: Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms refected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-fve signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.
Found in: ključnih besedah
Summary of found: ...respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in...
Keywords: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Published: 30.06.2022; Views: 113; Downloads: 0
.pdf Fulltext (2,46 MB)

8.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
SWAPNA MOHANACHANDRAN NAIR SINDHU SWAPNA,, 2022, original scientific article

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.
Found in: ključnih besedah
Summary of found: ...exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the...
Keywords: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Published: 30.06.2022; Views: 121; Downloads: 0
.pdf Fulltext (1,50 MB)

9.
Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features
SWAPNA MOHANACHANDRAN NAIR SINDHU, 2021, original scientific article

Abstract: Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.
Found in: ključnih besedah
Summary of found: ...through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst...
Keywords: Croup cough Pertussis Fractal dimension Phase portrait Sample entropy Machine learning techniques
Published: 04.07.2022; Views: 82; Downloads: 0
.pdf Fulltext (5,42 MB)

10.
Unwrapping the laser beam quality through nonlinear time series and fractal analyses: A surrogate approach
SWAPNA MOHANACHANDRAN NAIR SINDHU, 2021, original scientific article

Found in: ključnih besedah
Summary of found: ...Laser beam quality Sample entropy Fractal dimension Nonlinear time series analysis Hurst...
Keywords: Laser beam quality Sample entropy Fractal dimension Nonlinear time series analysis Hurst exponent Fractal analysis
Published: 04.07.2022; Views: 116; Downloads: 0
.pdf Fulltext (4,23 MB)

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