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Abstract: The fractal analysis has now been recognized as a potential mathematical tool in analyzing complex structures. The present work reports not only the fractal nature of Vigna radiata seed analyzed with the help of Field Emission Scanning Electron Microscopic images but also the variation of fractal dimension (FD) in a germinating seed. The variation of FD during germination in different media—water, salt, and diesel soot with carbon nanoparticles (CNPs)—is studied using the box-counting technique. The study is the first report of the fractality of seed. Irrespective of the media, the FD attains a maximum value on the day of germination and decreases after that. The time (T) for achieving maximum FD varies with the nature of stress. In the study, when the CNPs of diesel soot lower the T value, the salt raises the T value with respect to the control set. The Fourier Transform Infrared analysis of the seeds germinating in different media shows an increased rate of protein formation during the initial stage of germination and a steady state after that. In conjunction with the literature, the variation in the amino nitrogen, soluble nucleotide—RNA, and protein content of the seed during the initial days of germination gets reflected in its FD.
Keywords: fractal analysis, seed germination, Vigna radiata
Published in RUNG: 04.07.2022; Views: 1178; Downloads: 0
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Abstract: Porous materials have gained significant attention in recent years as a class of material exhibiting interesting chemical and physical properties. The existing methods of porosity analysis have limitations that prevent absolute porosity measurement. Hence, a technique independent of surface physical properties alone can give the absolute porosity of the material. The porosity greatly influences the thermal diffusivity of a material. The manuscript is the first report of employing the Sankar–Swapna model for analyzing the porosity variations in carbon allotropic nanofluids. The model helps not only in getting information about the absolute porosity variations among samples, but also suggests morphological modifications through the thermal diffusivity study using the sensitive single-beam thermal lens technique. The variations in thermal diffusivity and absolute porosity values are also correlated to morphological modifications based on the theoretical model and thereby proposing this as a surrogate method for absolute porosity analysis.
Keywords: absolute porosity, Sankar–Swapna model, thermal diffusivity, thermal lens, thermal conductivity
Published in RUNG: 04.07.2022; Views: 1181; Downloads: 0
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Abstract: The work reported in the paper elucidates morphological modification induced nanoart and surface area enhancement of graphite, graphene, and soot containing carbon allotropes through ultrasonication and alkali-treatment. The field emission scanning electron microscopic (FESEM) analysis of the samples before and after exfoliation reveals the formation of brilliant flower-like structures from spindle-like basic units due to Ostwald ripening. The x-ray diffraction analysis of the samples gives information about structural composition. The fractal analysis of the FESEM images indicates a multifractal structure with the dimensions—box-counting dimension D0 (1.72), information dimension D1 (1.66), and correlation dimension D2 (1.63)—preserved upon exfoliation. The process of ultra-sonication assisted liquid phase exfoliation resembles blooming as if the carbon allotropes are efflorescent.
Keywords: carbon allotropes, fractal dimension, soot, fractality, alkali treatment, exfoliation
Published in RUNG: 04.07.2022; Views: 1191; Downloads: 0
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Abstract: The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.
Keywords: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Published in RUNG: 30.06.2022; Views: 1091; Downloads: 0
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Abstract: This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.
Keywords: wet cough, dry cough, complex network, quadratic SVM, neural net
Published in RUNG: 30.06.2022; Views: 1048; Downloads: 0
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