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1.
Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Saibal K. Pal, Rajan Gupta, Gaurav Pandey, 2021, izvirni znanstveni članek

Opis: Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
Najdeno v: ključnih besedah
Povzetek najdenega: ...modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting...
Ključne besede: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Objavljeno: 15.07.2021; Ogledov: 1169; Prenosov: 19
.pdf Polno besedilo (3,16 MB)
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2.
Assessment of riverbed clogging in reservoirs by analysis of periodic oscillation of reservoir level and groundwater level
Eva Koren, Miran Veselič, Goran Vižintin, 2021, izvirni znanstveni članek

Najdeno v: ključnih besedah
Povzetek najdenega: ...riverbed clogging, alluvial aquifer recharge, time series, analytical modelling...
Ključne besede: riverbed clogging, alluvial aquifer recharge, time series, analytical modelling
Objavljeno: 14.10.2021; Ogledov: 909; Prenosov: 30
URL Polno besedilo (0,00 KB)
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3.
Characterization of a karst aquifer in the recharge area of Malenščica and Unica springs based on spatial and temporal variations of natural tracers
Blaž Kogovšek, 2022, doktorska disertacija

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...been applied as a complementary method to time series analysis and represents an improved approach...
Ključne besede: karst aquifer, dynamics of natural tracers, storm events, discharge measurements, time series analysis, hysteresis, Unica spring, Malenščica spring
Objavljeno: 01.03.2022; Ogledov: 822; Prenosov: 47
.pdf Polno besedilo (18,38 MB)

4.
Thermal Lensing of Multi-Walled Carbon Nanotube Solutions as Heat-Transfer Nanofluids
SANKARARAMAN SANKARANARAYANA IYER, CABRERA HUMBERTO, RAJ VIMAL, Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...technique possible. The TL signal analysis by time series and fractal techniques divulges the complex...
Ključne besede: MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy
Objavljeno: 28.06.2022; Ogledov: 312; Prenosov: 0
.pdf Polno besedilo (3,59 MB)

5.
Unravelling the potential of phase portrait in the auscultation of mitral valve dysfunction
SANKARARAMAN SANKARANARAYANA IYER, RAJ VIMAL, RENJINI A, SREEJYOTHI S, Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: The manuscript elucidates the potential of phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of normal (healthy) and mitral regurgitation (MR) in the diagnosis of valve-related cardiovascular diseases. The temporal evolution study of phase portrait and the entropy analyses of HM unveil the valve dysfunctioninduced haemodynamics. A tenfold increase in sample entropy in MR from that of normal indicates the valve dysfunction. The occurrence of a large number of frequency components between lub and dub in MR, compared to the normal, is substantiated through the spectral analyses. The machine learning techniques, K-nearest neighbour, support vector machine, and principal component analyses give 100% predictive accuracy. Thus, the study suggests a surrogate method of auscultation of HM that can be employed cost-effectively in rural health centres.
Najdeno v: ključnih besedah
Povzetek najdenega: ...phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of...
Ključne besede: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Objavljeno: 28.06.2022; Ogledov: 242; Prenosov: 0
.pdf Polno besedilo (2,02 MB)

6.
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, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Ključne besede: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Objavljeno: 28.06.2022; Ogledov: 266; Prenosov: 0
.pdf Polno besedilo (2,73 MB)

7.
Soot effected sample entropy minimization in nanofluid for thermal system design
Sankaranarayana Iyer Sankararaman, K. Satheesh Kumar, Vimal Raj, Mohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...molecular/particle dynamics in soot nanofluids (SNFs) using the time series, power spectrum, and wavelet analyses of...
Ključne besede: soot, entropy, thermal system, photothermal, time series, nanofluid, fractal
Objavljeno: 30.06.2022; Ogledov: 228; Prenosov: 0
.pdf Polno besedilo (2,27 MB)

8.
Downscaling of sample entropy of nanofluids by carbon allotropes
Sankaranarayana Iyer Sankararaman, K. Satheesh Kumar, S. Sreejyothi, Vimal Raj, Mohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...signal of carbon allotropic nanofluids (CANs), employing time series and fractal analyses. The nanofluids of...
Ključne besede: carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Objavljeno: 30.06.2022; Ogledov: 233; Prenosov: 0
.pdf Polno besedilo (4,22 MB)

9.
Time series and fractal analyses of wheezing
Sankaranarayana Iyer Sankararaman, S. Sreejyothi, Vimal Raj, Ammini Renjini, Mohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Ključne besede: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Objavljeno: 30.06.2022; Ogledov: 234; Prenosov: 0
.pdf Polno besedilo (2,46 MB)

10.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
SWAPNA MOHANACHANDRAN NAIR SINDHU SWAPNA,, 2022, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Ključne besede: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Objavljeno: 30.06.2022; Ogledov: 307; Prenosov: 0
.pdf Polno besedilo (1,50 MB)

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