Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30General SciencesStatic and Quasi-Static Deformation of a Uniform Half-Space Due to a Center of Rotation
English0111Nishu VermaEnglish Kuldip SinghEnglish Renu MuwalEnglishObjective: The objective is to obtain static and quasi-static deformation of a uniform half-space due to a center of rotation.
Methodology and Results: The Galerkin vector approach has been used to calculate deformation field at an arbitrary point of an elastic half-space. Closed form analytical expressions for the displacements and stresses caused by a center of rotation buried in a homogenous, isotropic, perfectly elastic half-space are derived. The quasi-static deformation field for a viscoelastic medium has been obtained by applying the correspondence principle of linear viscoelasticity to the associated elastic solution. Explicit expressions giving the quasi-static deformation of a uniform half-space caused by a center of rotation are obtained when the medium is elastic in dilatation and Kelvin, Maxwell or SLS type viscoelastic in distortion.
Conclusion: The explicit expressions for the displacements and stresses in an elastic and viscoelastic medium due to a center of rotation source have been obtained. Numerical results are shown graphically for displacements and stresses.
EnglishCenter of rotation, Static and quasi-static deformation, Correspondence principle, Viscoelastic, MaxwellINTRODUCTION
Nuclei of strain are the concentrated sources of a displacement field and are built up from the simple superposition of single forces which are acting at a point in the elastic medium (Love). Therefore, the displacement field owing to nuclei of strain is necessary for applications to crustal deformation. Analytical expressions for three-dimensional static displacement fields owing to a step-type single force in an elastic half-space, have been obtained by Mindlinby using Galerkin’s method. Mindlin and Cheng
derived the solutions in the form of the Galerkin vector for various nuclei of strain in a uniform half-space by the process of superposition, differentiation and integration. Many theoretical formulations have been developed (Okada), which were describing the deformation of an isotropic, homogeneous, and semi-infinite medium. Analytical expressions for the quasi-static surface displacements due to a vertical strike-slip fault in a Kelvin or Maxwell viscoelastic half-space were determined by Singh and Rosenmanby applying the correspondence principle of linear viscoelasticity. The correspondence principle has been broadly used to calculate the quasi-static deformation of a viscoelastic half-space by a point or extended sources (see. e.g. [6-10]). Singh and Singhhave identified the combinations in which moduli occur in the expressions for the displacements, strains and stresses in a uniform elastic half-space due to buried sources. Using analytical integration, the displacement field in two welded elastic half-spaces due to a finite rectangular fault has been obtained by Singh et al and they have also compared it with the corresponding field in an elastic half-space and in an infinite medium. To model the ground deformation in volcanic areas, Singh et al used four axially-symmetric source models in an elastic half-space and also compared it with the corresponding field due to a center of dilatation.
Cochard et alshowed that the observations of seismic rotational motions will give important new information referring to the Earth’s surface and are complementary to those found from the observations of the translational motions of the Earth’s surface using conventional seismometers. Cowsik et al resolved that to detect rotational motions, the basic design concept of using a torsion balance as a filter is validated and can be implemented for the construction of rotational seismometers.
The static displacements from the seismic recordings and identifying translation signals caused by rotation can be estimated by using rotational motions (Trifunac and Todorovska). Rotational seismology is of great interest to a wide range of geophysical disciplines, including strong-motion seismology, seismic tectonics, earthquake engineering, and geodesy as well as to physicists using Earth-based observatories for detecting gravitational waves generated by astronomical sources (Lee et al). The solutions for the displacement field produced by a center of rotation is also useful for many purposes and in bio-mechanical research.
RESEARCH METHODLOGY
In this paper, we study the 3-D deformation of a uniform half–space caused by a center of rotation by using the Galerkin vector approach. Explicit expressions for the static strains can be easily obtained with the help of strain -displacement relations and the stresses follow immediately by using stress-strain relations. The correspondence principle of linear viscoelasticity has been used to obtain the quasi-static displacements, strains and stresses.
The paper has been divided into two parts. Part-A deals with the static deformation field while the quasi-static deformation field is considered in Part-B.
Englishhttp://ijcrr.com/abstract.php?article_id=2337http://ijcrr.com/article_html.php?did=23371. Love, AEH. A Treatise on the Mathematical Theory of Elasticity, Dover, New York, 1927.
2. Mindlin, RD. Force at A Point in The Interior of a Semi-Infinite Solid. J App Phys 1936; 195-202.
3. Mindlin, RD and Cheng, DH. Nuclei of Strain in The Semi-Infinite Solid. J App Phys 1950; 21: 926-930.
4. Okada, Y. Internal Deformation Due to Shear and Tensile Faults in a Half-Space. Bull Seism Soc Am 1992; 82: 1018-1040.
5. Singh, SJ and Rosenman, M. Quasi-static deformation of a viscoelastic half-space by a displacement dislocation. J Phys Earth Planet Int 1974; 8: 87-101.
6. Rundle, JB. Viscoelastic Crustal Deformation by Finite Quasi-Static Sources. J Geophys Res 1978; 83: 5937-5945.
7. Iwasaki, T and Matsu’ura, M. Quasi-Static Strains and Tilts Due to Faulting in a Layered Half-Space with an Intervenient Viscoelastic Layer. J Phys Earth 1981; 29: 499-518.
8. Iwasaki, T. Quasi-Static Deformation Due to a Dislocation Source in a Maxwellian Viscoelastic Earth Model. J Phys Earth 1985; 33: 21–43.
9. Bonafede, M, Dragoni, M and Quareni, F. Displacement and Stress Fields Produced by A Center of Dilatation and by A Pressure Source in A Viscoelastic Half-Space: Application to The Study of Ground Deformation and Seismic Activity at Campi Flegeri, Italy. Geophys J R AstrSoc 1986; 87: 455-485.
10. Singh, K and Singh, SJ.Static and Quasi-Static Deformation of a Uniform Half Space by Buried Sources. Geophy Res Bull 1989;27: 1-30.
11.Singh, K and Singh, SJ. Simple Procedure for Obtaining the Quasi-Static Displacement, Strains, And Stresses in a Viscoelastic Half-Space. Bull Seism Soc Am 1990;80: 488-492.
12. Singh, SJ, Kumari, G. and Singh, K. Static Deformation of Two Welded Elastic Half Spaces Caused by A Finite Rectangular Fault. Phys Earth Planet In 1993; 79: 313-333.
13. Singh, SJ, Kumari, G and Singh, K.Modelling of Crustal Deformation in Volcanic Areas. Nat Acad Sci Letters 1998; 21:165-176.
14. Cochard, A, Igel, H, Schuberth, B, et al. Rotational Motions in Seismology: Theory, Observations, Simulation, Earthquake Source Asymmetry, Sturctural Media and Rotation Effects, R. Teisseyre, M. Takeo, and E. Majewski(Editors), Springer, Berlin 2006; 391-412.
15.Cowsik, R, Nussinov, TM, Wagoner, K, et al. Performance Characteristics of a Rotational Seismometer for Near-Field and Engineering Applications. Bull Seism Soc Am 2009; 99: 1181-1189.
16. Trifunac, MD and Todorovska, MI. A note on the useable dynamic range of accelerographs recording translation. Soil Dyn and Earth Eng2001;21: 275-286.
17. Lee, WHK, Celebi, M, Todorovska, ML, et al. Introduction to the Special Issue on Rotational Seismology and Engineering Applications, Bull Seism Soc Am2009; 99:945-957.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30General SciencesNatural Periodic Oscillations Extracted in the Precipitation using Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition methods
English1218S. JyothiEnglish S. V. B. RaoEnglish P. KishoreEnglishObjective: The monthly mean precipitation over India is used to investigate natural periodic wave characteristics using a novel technique of Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD).
Methods: These two methods empirical mode decomposition and ensemble empirical mode decomposition are interesting approach to decompose the signals into locally periodic oscillations.
Result: The Intrinsic mode functions (IMFs), will easily Identify the Embedded structures, even with those smaller amplitudes. Ensemble Empirical Mode Decomposition is better performed than the Empirical Mode Decomposition technique. The Empirical Mode Decomposition method was observe the mode mixing of two signals in Intrinsic Mode Function, but Ensemble Empirical Mode Decomposition found the distinct and clear peak of each periodic signal in each Intrinsic Mode Function.
Conclusion: In Empirical Mode Decomposition method has 11 Intrinsic Mode Functions and Ensemble Empirical Mode Decomposition has 10 Intrinsic Mode Functions, it is due to the Noise-assisted method to reduce the Intrinsic Mode Function numbers. The decomposed oscillations in Ensemble Empirical Mode Decomposition are above confidence interval and significant, it has 515 iterations than Empirical Mode Decomposition method has 740 iterations. We observe the computational time is lesser in Ensemble Empirical Mode Decomposition than Empirical Mode Decomposition method.
Key Words: Precipitation, Empirical mode decomposition (EMD), Ensemble empirical mode decomposition (EEMD), Periodic oscillations, Lomb-Scargle (LS) spectral analysis
EnglishPrecipitation, Empirical mode decomposition (EMD), Ensemble empirical mode decomposition (EEMD), Periodic oscillations, Lomb-Scargle (LS) spectral analysisIntroduction
Precipitation is probably the most important of the essential climate condition and its crucial role to sustain any form of life on earth as a major source of fresh water, its major impact on weather, climate change, and related issues of society's adaptation. The occurrence of precipitation is highly variable in space and time. Finally, high-quality monthly precipitation data sets across a long-term period are key information for an improved understanding of the global water cycle (Becker et al., 2012).
The spatial and temporal variations of rainfall are important in understanding the hydrological balance on regional and global scales. The distribution of precipitation is also critical for water control in agriculture, power generation and drought-monitoring. Nishant Malik et al., (2011) evaluates the Indian summer monsoon (ISM) rainfall over South Asia is the result of the interaction of several complex atmospheric processes evolving at many different spatial and temporal scales (e.g., Webster 1987). By the influences of the interplay of synoptic scale weather phenomena, the Indian summer monsoon rainfall patterns are also modulated by the steep topography of the Himalayas (e.g., Bookhagen and Burbank 2010). Hence, monsoonal rainfall has highly complex spatiotemporal patterns.
The Indian summer monsoon (June to September) rainfall is essential for the economic development of population, disaster management, hydrological planning for the country by Guhathakurta and Rajeevan (2008). Earlier, Parthasarathy (1993 and 1994) used 306 uniformly distributed rain-gauge stations for construct the precipitation series. Attempts have been to study the annual, seasonal and long-term trends for the Indian region as well as for smaller sub-divisions using rainfall data (Parthasarathy et al., 1993 and P. Kishore et al. 2015).
It is well known that, the rainfall during monsoon season over Indian region exhibits, large spatial, temporal, intra-seasonal and inter-annual variability. It is evident that Indian Summer Monsoon (ISM) exhibits different variations with different periodicities starting from active and breaks period too, intra-seasonal, inter-annual, quasi-biennial oscillation (Rao and Lakhole, 1978), El~Nino Southern oscillation (Shukla and Paolino, 1983), solar cycle (Bhalme and Jadhav, 1984). Their analysis reveals that the annually sampled seasonal data is characterized by near periodic oscillations of 3, 5.8, 11.6, 20.8 and 37 year periods. In general, this variability is extracted from long-term data sets by using different methods like Fourier and wavelet analysis. From the above analysis, the authors have concentrated the only one oscillation but not all.
In this study, we make use of two different methods like empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) to extract different oscillations present in the long-term rainfall data over Indian region.
Empirical mode decomposition is a form of adaptive time series decomposition method by Huang et al., (1998). Some standard forms of spectral analysis methods like Fourier analysis assume that a time series (either linear or nonlinear) can be decomposed into a set of linear components.
In contrast, the Empirical mode decomposition method does not assume a time series is linear or stationary before analysis, it lets the data speak for themselves. Empirical mode decomposition adaptively decomposes a signal into a set of intrinsic mode functions and a residual component. When the intrinsic mode functions and residual are summed together, they form the original time series (Srikanthan et al., 2011). An inconvenient feature of Empirical mode decomposition is mode mixing, where a fluctuation of given frequency may split across two intrinsic mode functions (Peel et al., 2011a). The adaptive iterative nature of the Empirical mode decomposition algorithm means mode mixing is difficult to avoid without subjectively deciding on the likely nature of any signal to be extracted before analysis. Mode mixing between intrinsic mode functions is problematic, to investigate the significance of intrinsic mode functions, as an expected physical signal may be present but split across intrinsic mode functions. Wu and Huang (2009) proposed as Ensemble Empirical mode decomposition, it is a noise-assisted data analysis method, to overcoming the mode mixing problem in intrinsic mode functions.
In Ensemble Empirical mode decomposition, an ensemble of Empirical mode decomposition trials is obtained by adding white noise to the time series of before the each Empirical mode decomposition run. The intrinsic mode functions and residual from each trial are grouped by intrinsic mode functions order into ensembles, and the intrinsic mode function and residual ensemble averages to form the Ensemble Empirical mode decomposition. Since the white noise is different for each trial of Empirical mode decomposition and its noise cancels out during averaging as the ensemble size increases. However, the noise serves the useful purpose of changing the order of local maxima and minima within the time series, thus different Empirical mode decomposition outcome in each trial is formed. Wu and Huang (2009) believe Ensemble Empirical mode decomposition method provides more physically meaningful intrinsic mode functions and residue than the traditional Empirical mode decomposition method.
We present here different period of oscillations using the Indian Meteorological Department precipitation data. The data from 1901-2010 has been used in the present study. The analysis is expected to provide more information in the periodic oscillations using different spectral analysis techniques. In the next section, a brief description of our data collection of analysis procedure is given below section2. Results and discussion are given in the subsequent section3. Finally, our results are summarised in section 4.
Data and Methodology
IMD Precipitation Datasets
The Indian Meteorological Department (IMD) 1ox1ogridded precipitation datasets of the periods from 1901-2010 over the Indian region (Rajeevan et al., 2006, 2008) is used for the present study. This data analysis and results are organized from 3700 rain-gauge stations over India. Each grid consists of several stations of data and linear interpolation technique (Shepard, 1968) is used to provide the missing data points.
Empirical Mode Decomposition (EMD) technique
The Empirical mode decomposition method was firstly introduced by Huang et al., (1998). The essence of the approach is to empirically perceive the intrinsic oscillatory modes by way of their functional time scales within the statistics to decompose them consequently. Empirical mode decomposition method makes use of local features time scale of the signal, extracting some intrinsic mode functions and residual from the original signal and intrinsic mode function show the local features of the data while the residual component shows the slow change of the signal. The key idea of this method is empirical mode decomposition, and it can make any complex data sets be decomposed for a limited, usually a few numbers of intrinsic mode functions. An intrinsic mode function meet two conditions: (1) In the whole data set, the number of extrema and the number of zero crossings must either equal or differ at most by one. (2) At any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero. It is versatile in a broad range of applications for extracting signals from data generated from non-stationary processes (see an example, Huang et al., 2008; Kishore et al., 2012) the decomposition procedure is as follows.
Let the original time series of monthly precipitation data be Po(t). First the upper (E+) and lower (E-) envelopes of local maxima and minima respectively are estimated using a cubic spline interpolation. Next, at each time instance, the mean value of the two envelopes is computed using mo(t)=((E+) + (E-))/2. This mean is subtracted from the original signal to get P1(t)=Po(t)-mo(t). The procedure is repeated until the mean of the envelopes is close enough to zero. If the procedure was repeated n times until to reach the zero-criterion, then Pm(t) would be first intrinsic mode function (IMF1). After the initial intrinsic mode function is found, it is subtracted from the original time series, Po(t), and the procedure is repeated to locate the second intrinsic mode function. The above process is repeated until satisfies above two conditions. The first intrinsic mode function corresponds to the highest frequency component of the signal and lower frequency components are extracted in the subsequent intrinsic mode functions. The last intrinsic mode function always represents the climate average, which remains almost constant.
Ensemble Empirical Mode Decomposition (EEMD) technique
A noise-assisted data analysis method is Ensemble Empirical mode Decomposition (EEMD), represents a major improvement of the Empirical mode Decomposition method, eliminating largely the mode mixing problem and preserving the physical uniqueness of decomposition (Wu et al., 2009). The principle of Ensemble Empirical mode Decomposition is to add white noise, which populates the whole time-frequency space uniformly with the constituent components of different scales separated by filter bank (Flandrin et al., 2004; Wu and Huang, 2004). The Ensemble Empirical mode Decomposition process is explained as follows:
1. Add a white noise series to the targeted data set.
2. Decompose the data with added white noise into intrinsic mode functions using Empirical mode Decomposition.
3. Repeat step 1 and step 2 again and again, but with different white noise series at each time.
4. Obtain the (ensemble) means of corresponding intrinsic mode functions of the decompositions as the final result.
The number of trials in the ensemble N, has to be large. In this study, alpha was set to 0.21 and N was set to 210.
Results
Figure.1 shows that the mean precipitation over India into thirteen intrinsic mode functions can be extracted using empirical mode decomposition (EMD) technique, but only eleven intrinsic mode functions are to the most important components. It can that all intrinsic mode functions exhibit slow varying amplitudes and frequencies. Each intrinsic mode function component denotes the variation of different timescales. investigate the gross characteristics of oscillations with dominant periods, we applied Lomb Scargle (L-S) periodogram analysis to the precipitation data; each intrinsic mode function and resultant amplitude spectral plots at the side of 1. Compared to the other methods, the Lomb Scargle (L-S) method weights the data on a per point basis instead of a per time interval basis (Press et al., 1992). This technique is equivalent to a pure harmonic least-square analysis. The advantage of this method is that the input data do not have to be evenly spaced in (Scargle et al., 1982; Press et al., 1992). Semi-annual and annual oscillations in the mode of intrinsic mode function (IMF2), and mode of intrinsic mode function (IMF3), respectively. These two oscillations with maximum amplitudes of periodicities about 6 and 12 months are observed. The fifth mode intrinsic mode function (IMF5) corresponds to quasi-biennial oscillation (QBO), its periodicity between 20-34 months and the maximum amplitude at around 24 months. The seventh intrinsic mode function (IMF7) has periods (3.8 to 6 and it corresponds to the El-Nino southern oscillation (ENSO) cycle. The IMF7 dominates with maximum amplitudes at ~ and ~6 year periods. The tenth mode of intrinsic mode function (IMF10), a clear peak between 9 and 11 years, and the maximum peak at around 10-year and it corresponds to the solar cycle. The eleventh mode intrinsic mode function (IMF11) corresponds to the oscillations (IDO) and the period oscillates between 18-22 years periods. All these oscillations with 90% confidence level. Agnihotri et al. (2011) also have reported this inter-decadal (16-30 years) variability Total Solar Irradiance (TSI) and Indian rainfall datasets. 6-month and 12-month oscillations, the remaining oscillations quasi-biennial oscillation (QBO), El-Nino southern oscillation (ENSO), solar cycle, and Inter-decadal oscillation in the original time series data set.
From Figure 1, frequent occurrence of mode mixing, which is defined as a single intrinsic mode function either consist of widely scales, or a signal of a similar scale residing in different intrinsic mode function components. To overcome the scale separation issue without introducing a subjective test, a new noise-assisted data analysis (NADA) method, known as ensemble empirical mode decomposition (EEMD), which defines the true IMF components as the mean value of an ensemble number of trials (Wu and Huang et al., 2009). The ensemble empirical mode decomposition (EEMD) method detailed procedure given in Section 3.
This method the input precipitation data to 13 intrinsic mode functions, but we show only the first 10 intrinsic mode functions here in the figure 2 for the clarity of the most components. The semi-annual and annual oscillations in the modes of IMF2 and IMF4. The amplitude of the semi-annual oscillations is smaller than annual oscillations. IMF5 is a mode with a dominant period of 24-28 months, but the peak value is at about 26 , and this mode indicates quasi-biennial oscillation (QBO). IMF6 has with the El-Nino southern oscillation (ENSO) cycle about a year period. The average period of IMF7 and IMF8 corresponds to 132 and 216 months, and these to solar and oscillations. These modes all fall above the confidence interval and therefore are significant. It is worth mentioning here that the decomposed oscillations are fixed intervals than the empirical mode decomposition (EMD). is likely due to the adding the white noise to the Indian meteorological department (IMD) of precipitation data.
Figure 3 and 4 provide a more detailed look at the iterations in empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods using precipitation over India. Box plots describe the statistical distribution of iterations for each intrinsic mode function. The box plots identify the five important statistics on two . Moreover, while in the empirical mode decomposition (EMD) case the total number of iterations is 740, in the case of ensemble empirical mode decomposition (EEMD) is 515 iterations. It is clear that the ensemble empirical mode decomposition (EEMD) method provides less shifting iterations, less computational time for the given time series dataset.
We further applied Morlet wavelet analysis for six dominant periods of intrinsic mode functions in figure 2 are shown in figure 5. In the second intrinsic mode function the presence of wave, and the maximum amplitude at about ~5 mm. In the fourth intrinsic mode functionperiod covers annual periods ~12 months. The annual amplitude (IMF4) is greater than semi annual oscillations (IMF2), fifth intrinsic mode function period covers 23-33 months, and the maximum peak at around 28 months is quasi-biennial oscillation (QBO), the amplitude of 28 months during the periods at 1915-1920, 1960-1972, and 2003-2009.The sixth intrinsic mode function period covers 4-6 year periods is El-Nino Southern Oscillation (ENSO) and extends nearly months period during the observation period, where the maximum amplitudes around 1935-1960, 1995-2005. The dominant oscillation of 9-14 years . The maximum amplitude is at about 10.8 years during the period of 1940-1960. This intrinsic mode function represents a solar cycle. The period of oscillations 16-23 years in IMF8 is Inter-decadal oscillation. The amplitudes in quasi-biennial oscillation (QBO), El-Nino southern oscillation (ENSO), and Annual oscillations in the wavelet analysis.
Discussion
The empirical mode decomposition and Ensemble empirical mode decomposition using to investigate the natural periodic oscillations into two sets over India, first one is semi annual, annual oscillations is small amplitudes and other oscillations are Quasi bi-ennial oscillation and El-Nino southern oscillation is coincided with the IMF1 with an average period of 2.7 years and second mode IMF2 is a dominant period of 5-6 years and the long periods are solar cycle, Inter decadal oscillation are also observed in IMF3 is associated with sunspot cycle of about 11 yearsand fourth IMF is about 20-24 years in all India rainfall using EMD method by Iyenger et al., (2005). The importance of these oscillations are changed the variability of rainfall.
Conclusions
In the present Study, we have investigated the natural periodic oscillations using India Meteorological Department (IMD) precipitation data during the period from 1901-2010. We examine the semi annual oscillation (SAO), annual oscillation (AO), quasi bi-ennial oscillation (QBO), El~Nino southern oscillation (ENSO), inter-decadal oscillation (IDO) periods using Empirical mode decomposition (EMD) and Ensemble Empirical mode decomposition (EEMD) technique. We found mixed mode oscillations in Empirical mode decomposition. The Empirical mode decomposition method a strong mixture of modes, where Intrinsic mode function dramatically changes with no valid reason. This instability can have a dramatic effect on the study of any signal. According to the obtained results, the Ensemble Empirical mode decomposition improves the precipitation intrinsic mode function and offers a simple approach for the stable prediction of non-stationary data. As a future work, it would the possibility of employing different aggregation methods as well as performing an addition and more significant test that exposes more reliable results, may be considering for other datasets.
Acknowledgements
The Authors like to thank all the members of India Meteorological Department (IMD) for providing the high resolution of rainfall datasets. extends her sincere thanks to the UGC, New Delhi for providing the Fellowship during course of work. Thanks to the authorities of Sri Venkateswara University for providing the necessary facilities to carry out this work. Authors acknowledge the immense help received from the scholars whose articles are citied and included in references of this manuscript. The authors are also grateful to the authors / editors / publishers of all those articles, Journals and books from where the literature for this article has been reviewed and discussed. Authors are grateful to IJCRR editorial board members and IJCRR team of reviewers who have helped to bring quality to this manuscript.
Figure captions
Figure1: Time series of monthly mean India Meteorological Department (IMD) precipitation during 1901 to 2010. Intrinsic mode function (IMF) components extracted from Empirical mode decomposition (EMD) method, the first to eleven intrinsic mode functions are shown in left column. Corresponding Lomb-Scargle periodograms are shown in right column. Dashed horizontal line indicates 95% confidence level.
Figure 2: Time series of monthly mean India Meteorological Department (IMD) precipitation during 1901 to 2010. Intrinsic mode function (IMF) components extracted from Ensemble Empirical mode decomposition (EEMD) method, the first to ten intrinsic mode functions are shown in left column. Corresponding Lomb-Scargle periodograms are shown in right column. Dashed horizontal line indicates 95% confidence level.
Figure 3: Box plot of Empirical mode decomposition (EMD) performance measures for the monthly precipitation dataset. The vertical axis is number of iterations.
Figure 4: Box plot of Ensemble Empirical mode decomposition (EEMD) performance measures for the monthly precipitation dataset. The vertical axis is number of iterations.
Figure 5: Contours of wavelet intensities of frequency with time for the precipitation datasets and its six intrinsic mode functions (IMFs) are (2, 4, 5, 6, 7, and 8) as shown in Figure.2.
Figures with captions
Figure1: Time series of monthly mean India Meteorological Department (IMD) precipitation during 1901 to 2010. Intrinsic mode function (IMF) components extracted from Empirical mode decomposition (EMD) method, the first to eleven intrinsic mode functions are shown in left column. Corresponding Lomb-Scargle periodograms are shown in right column. Dashed horizontal line indicates 95% confidence level.
Figure 2: Time series of monthly mean India Meteorological Department (IMD) precipitation during 1901 to 2010. Intrinsic mode function (IMF) components extracted from Ensemble Empirical mode decomposition (EEMD) method, the first to ten intrinsic mode functions are shown in left column. Corresponding Lomb-Scargle periodograms are shown in right column. Dashed horizontal line indicates 95% confidence level.
Figure 3: Box plot of Empirical mode decomposition (EMD) performance measures for the monthly precipitation dataset. The vertical axis is number of iterations.
Englishhttp://ijcrr.com/abstract.php?article_id=2338http://ijcrr.com/article_html.php?did=2338
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30General SciencesIdentifying and Analyzing Interactive Media Contact Points in Context of Fashion Brands
English1925Ridhima Bhanot SharmaEnglish Marshal Mukesh SahniEnglish Vibha Dua SatijaEnglishThe transformation that fashion market has achieved in this period is phenomenal. Traditional marketing exists but is gradually losing the market share. Marketers and fashion brand managers are thus reconsidering the communication strategies from Digital Media perspective. The aim of this research is to develop and validate a measurement scale for understanding consumer’s perception of interactive media contact points in context of fashion brands. This research involves survey responses from 378 fashion consumers. A three dimensional 40-item scale was developed based on literature review and expert opinions. The study presents the complete process of scale development and validation suggested by DeVellis, 1991 as well as implications of the main findings.
EnglishInteractive Media, Contact Points, Fashion, Brand Communication, Digital Media, IMC, Touchpoints, Scale DevelopmentAbbreviations
IMCP - Interactive Media Contact Point
IMC - Integrated Marketing Communication
AVE - Average variance extracted
CR - Composite Reliability
Introduction
Fast moving technologies are offering pool of opportunities for marketers to look at. It is no more a traditional path that a customer will adopt to buy a product. There is no predetermined channel or way that a marketer can expect a customer to follow. It is a 360 degrees contact point revolution that has occurred as a result of digitization. Marketers are surrounded by a range of multiplying contact points where customers are directly or indirectly interacting with the brand and coherent understanding of which contact point is delivering what is debatable. The complexity of coordinating across swarm of Interactive Media contact points (IMCP) is a challenging quest.
Ron Shevlin, author of 'Everything They've Told You About Marketing Is Wrong' and an analyst at Aite Group, LLC defines contact points as repeated interactions of the consumer that strengthen the emotional, psychological and physical connect of a brand. The broader definition of marketing communication rightly covers the various aspect of ongoing, interactive; cross functional (Duncan and Mulhern 2004) contact points (or touchpoints). Seeing the enormous popularity of the digital interactive media and its applications, it would not be wrong in saying that the contact point analysis in the marketing communication in the near future should happen through the Interactive Media Contact Points (IMCP) study. This study not being in place, new client acquisitions, brand positioning, imagery and visibility, possessing a strong hand over the competitors would be at stake. Thus, IMCP can be defined as any points or marketing activity on a digital interactive platform where you can communicate with your audience, customer, prospect or web user or "All consumer interaction platforms which cater to 2-way effective communication act as a part of Interactive Media Contact Point".
Fashion trends and styles are ever changing and "fashion brands and retailers are struggling to find the right business model answers" (Rinnebach and Richter, 2014). A successful fashion brand must inspire its audience. New media is "drastically changing the dialogue about fashion whereby we can upon our on-demand desire for trends and gain access to them anytime, anywhere, and on any platform" (How Fashion Trends are Being Dictated by Social Media Trends. Retrieved from https://blog.ketchum.com/how-fashion-trends-are-being-dictated-by- social-media-trends/ (2012, Oct 26)Interactive media is changing the way fashion is presented. "The way that we shoot it, the way that we showcase it and the way that we make the clothes and design them has changed," Wang said (Schneier, 2014). Thus the research paper focusses upon the identification and analysis of IMCP for fashion brands that have come in picture as a result of the popularity gained by the Interactive Media.
Literature Review
Evolution of Brand Communication
The concept of Interactive Media is derived from the Integrated Marketing Communication (IMC). Steenkamp and Geyskens (2006) found that interactivity and brand learning is directly related to each other, greater interactivity promotes greater brand learning through better information assimilation and could help companies forge cognitive and emotional bonds with their brand users. Belch and Belch (2009) found that IMC plays a major role in developing sustainable brand identity and equity. Merriam-Webster's dictionary redefined the classical definition of marketing as "the process or technique of promoting, selling, and distributing a product or service” by constituting communication as an interchange of thoughts, opinions and information by means of medium. The idea of Integrated Marketing Communication has been in discussions since last two decades (Schultz and Barnes 1999; Schultz 2003; Shimp 2007). The concept initiated from Integrated Marketing Communication and is now transforming towards interactive and engagement models (Swain, 2004).
Businesses cannot solely depend on one single medium when it comes to communication with its customers. Synchronous use of various forms of mediums is required due to alliance existing between them (Naik, Mantrala, and Sawyer, 1998; Naik and Raman, 2003). As rightly said, we cannot choose one of the two - Traditional or Digital. It has to be a mix of both depending upon the nature of the business. The explorative study on the simultaneous use of mediums is now the prime focus of many researchers (Nowak, Cameron, and Krugman, 1993) (Stammerjohan, Wood, Chang, and Thorson, 2005).
Integrated marketing communication in the following years of research got retransformed by the increasing involvement of customers on digital platforms. Prior theories and literature identifies several studies related to digital contact point(Agichtein et. al, 2008; Ahlqvistet.al., 2008; Kaplan and Haenlein, 2010; Perdue, 2010; Rumman and Alhadid, 2014).(Schivinski and D?browski, 2015) studied the effect of firm created social media communication and user generated social media communication on consumer perception and brand equity. (Christodoulides and Chernatony, 2004) concentrated his study on one of the Interactive Media Contact Point apart from Social Media i.e. Website. (Bushelow, 2012) expanded the vision and concentrated the study on Facebook fan pages which were termed as Online Brand Communities. Bushelow's research envisaged the relationship between Facebook Fan Page Interactions and Brand Loyalty. (Berger and Milkman, 2012) investigate the relationship between article characteristics and blogging. The studies only focus on few IMCP, whereas in the present scenario 'integrated marketing paradigm focuses on the full set of contacts that affect the consumer's brand experience' (Calder and Malthouse, 2005) which has led to theory related gap.
Contact Points Categorization
Shopping behavior, be it physical or over the Internet is always affected by the responsiveness and the encounters. Positive word of mouth is the result of the positive perusal of offline/online buying. Contact points (or touch points) have thus been area of interest for researchers and practitioners. Several categorizations for contact points have been proposed in the past - based upon the customer experience - Pre-Purchase, During-Purchase and Post-Purchase (Dunn and Davis, 2004), based on control - controllable, influenceable and uncontrollable (Martenson, 2008), based on purchase point of view - One to One, Point of Sale, Indirect, and Mass Media (Spengler et al., 2010), based on origin - company created, intrinsic, unexpected, customer-initiated and based on operation - Functional, Social, Community, Corporate and based on media channels - paid/bought (e.g. Banners, Search Engine Advertising, Advertorials, Interactive television), owned (e.g. b2c website, b2b website, web shop, mobile web, mobile app, tablet app, e-mail, interactive point of sale, narrowcasting, desktop widgets, embedded software, campaign site, affiliates, sales content, social media) and earned (e.g. Social Media, Viral Campaigns, Blogs and News.)(Frampton, 2014).
With emerging communication channels such as online and mobile phones, marketers must constantly examine functionality in relation to consumer need, to discover new and exciting ways to engage them (Wyner, 2006). However, with fragmentation of markets and traditional communication channels, such as mass media, it is progressively more difficult for marketers to execute meaningful and measurable communications to target groups and individuals. There are some practical difficulties in agreeing with the theory that necessarily all brand contact points must be used. For instance, the cost or the budget will clearly escalate. And as Ries and Ries (2005) explain that the brand communications arena is highly competitive and cluttered. The result is proliferation of communication messages directed at the consumers, creating communication overload. We live in an over communicated society. (Shultz and Barnes, 2002) observe too many messages, too many advertisers, too much noise, and too much stimulation to the consumer. Therefore, selection of the most appropriate contact points would be a more pragmatic approach.
Methodology
An initial qualitative study based on a review of literature and interviews of experts from fashion industry was conducted to generate list of IMCP for fashion brands. Next, the scale was refined and administered to fashion consumers.
Data Gathering
The aim of the study is to identify Interactive Media contact points for fashion brands and develop a scale to measure consumer's perception of IMCP in context of fashion industry. Validity of the content relates to how illustrative the items of the latent construct are in defining the purposive statements. Pilot testing of the scale was carried out by Digital Media experts from fashion industry and keeping their recommendations in mind 40-item scale was formed. The scale was then tested on sample of 378 respondents to further carry out the reliability and validity tests.
Scale Description
Table 1 - Description of the Measurement Scale
Component
Items
Labels
Website/Blogs
IMCP1
"Fashion Brand's website is the prime source of information for the brand.”
IMCP2
"Interactive fashion websites are more appealing than plain text.”
IMCP3
"You consider content on blogs as important source of information for fashion brands.”
IMCP4
"Content on blogs is relatable, real and consistent.”
IMCP5
"Discussions on blog posts are worthy of a look.”
Advertisements
IMCP6
"Advertisements appearing on search engine's (Google/Yahoo/Bing) result page for your typed fashion query are appropriate.”
IMCP7
"Display advertisement of fashion products attracts your attention.”
IMCP8
"Advertisement being sponsored or non-sponsored does not affect your buying process.”
Social Media
IMCP9
"Browsing through Facebook official fashion brand's pages gives you information about the brand.”
IMCP10
"Twitter tweets and mentions keep you updated with news from the fashion industry.”
IMCP11
"Instagram post relates you to fashion brand's updates and trends.”
IMCP12
"Snapchat short stories by fashion brands are engaging.”
IMCP13
"Websites like Pinterest and Roposso provides you valuable info related to fashion brands.”
IMCP14
"Video post by fashion brands (Youtube/Vimeo/Vine) enriches your experience with the brand.”
IMCP15
"Videos by fashion brands give a realistic product view of the brand's product.”
Forums and Groups
IMCP16
"Positive word of mouth from peers elevates your likelihood towards a fashion brand.”
IMCP17
"Online feedbacks and reviews for fashion brand are influence able.”
IMCP18
"Celebrity endorsement by fashion brand makes it more relatable to the audience.”
Mobile
IMCP19
"Fashion Brand's mobile apps keep you in continuous touch with the brand.”
IMCP20
"Buying process through a mobile app is more convenient and simpler.”
IMCP21
"Advertisements on play store or ios store catch your eyes.”
IMCP22
"Browsing fashion through mobile phones or tablets is convenient and comfortable.”
Customer Service
IMCP23
"Contact us section/tab should be easily navigable.”
IMCP24
"Live Chat option on fashion brand's website makes the buying process convenient.”
Product Information
IMCP25
"You look at detailed description of the product and quality/fabric of the material while considering a fashion brand.”
IMCP26
"Presence of appropriate size chart impacts your purchasing process.”
IMCP27
"360 degree video of the product makes your selection stronger.”
IMCP28
"Appropriate resolution of images and 4X Zoom features are useful in understanding the details.”
Virtual Mirror
IMCP29
"Virtual mirror on fashion website provides you the exact fit and look to great extent.”
IMCP30
"3D Imaging and Virtual Mirror reduces the chances of returns/refunds.”
IMCP31
"Virtual mirror reduces the evaluation time during your buying process.”
Payments
IMCP32
"You consider secured payment gateways and options for using all types of cards/e-wallets while purchasing fashion products.”
IMCP33
"Option of cash on delivery effects your purchase decision.”
IMCP34
"Convenient Return/Refund policies of fashion brands ease out the decision process.”
Communication
IMCP35
"Informative emails by fashion brands (Tips/Styles/Trends) create a positive brand image.”
IMCP36
"Discounts and offers in emails attract your attention.”
IMCP37
"Asking for feedback on the product/ rating your experience make you considerate of the fashion brand.”
Loyalty
IMCP38
"Post purchase loyalty benefits - memberships/points are welcomed by you.”
IMCP39
"Emails create top of mind recall for the fashion brand.”
IMCP40
"SMS/Whatsapp notification by fashion brands drives your loyalty towards the brand.”
Sample and Data Collection -
The survey was posted on the site - surveymonkey.com. The link to the survey was then constantly shared on social networking sites - Facebook, Instagram, Twitter, Whatsapp Groups during the span of 3 months (September'2016 to November'2016). Emails were sent to people to respond to the survey link. Respondents were regular online shoppers selected through convenience sampling. Facebook Fan Pages were approached to share the survey. Those who filled the survey were then encouraged to circulate it further with their friends, family and colleagues. A total of 437 surveys were collected, out of which 378 were completely filled usable surveys.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30General SciencesPrediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review
English2631Ashok Kumar T.English Rajagopal B.EnglishPrediction of a novel or potential lead molecules for a therapeutic drug target without adverse effects is a challenging task in the drug designing, discovery, and development process. The systematic integration of multi-omics data from various data/knowledge bases through computational techniques enables to identify potential lead molecules and study the therapeutic properties. Over the last decades, several drug discoveries using multi-omics and huge dataset integration methods proven with successive results. In this paper, we present different types of computational approaches for prediction of potential lead molecules through the systems-level integration of multi-omics datasets.
EnglishSystematic Integration, Multi-omics Datasets, Drug Discovery, Lead Identification, Big Data Analysis
INTRODUCTION
In drug discovery, lead is a chemical compound that binds to active site regions of the biological target molecule and hence minimizes the binding free energy. Leads may be a natural product, synthetic, or semi-synthetic compound which has therapeutic effects[1].Natural product (or natural drug) consists of bioactive compounds which were produced by the living organisms that are present in nature. Plants, minerals, and animals (including microorganisms) are the common sources of natural products[2,3,69,74,75]. Natural products can also be developed by chemical synthesis (both semi-synthesis and total synthesis) and have been placed a major role in the development of potential synthetic targets. But synthetic and semi-synthetic compounds are chemically synthesized by the humans in the laboratory using in silico and/or experimental approaches[4,5].
Developing a potential lead molecule by using the experimental method is tedious, complicated, expensive, time-consuming, and trial-and-error process[6]. Recently, many advanced computational techniques analogous to wet-lab techniques were introduced to reduce the problem. Modern computer-aided drug design and discovery (CADDD) involve virtual screening, testing, and validation of lead molecules in a short time span using large datasets and software [73].The resulting lead molecule further undergoes a series of preclinical and clinical studies to test the toxicity and adverse effects. The successful drug candidate is released in different dosage forms in the market after passing the food and drug administration (FDA) verification process[7,8].
MULTI-OMICS AND BIG DATA INTEGRATION
Multi-omics is a new approach for analyzing biological problems in various aspects through combining multipleomics datasets[3,9]. The common types of omics include genomics, proteomics, metabolomics, epigenomics, phytochemomics, interactomics, and microbiomics[10-12]. Integration of multiple omics data in a systematic way enables to study the functional relationship or identify the key problem in an efficient manner. An association of large datasets or complex datasets of multi-omics data is a difficult task and must have sound knowledge in all areas of omics.The pattern matching (or regular expression) is a general and most popular technique for extraction of knowledge from the datasets. Analyzing the large multi-omics datasets involves big data handling.
Due to rapid growth in data size, diversity, and complexity of datasets in the biological databases, big data were introduced to analyze, manage, and derive knowledge from the datasets. Big data (aka huge data or massive data) refer to a very large volume of data or data storage, which cannot be processed using traditional computing devices and applications. Size of big data ranges from petabytes(1 PB = 1015 bytes) to exabytes (1 EB = 1018 bytes), or even more[13-15].Even though the big data analysis is a hot topic today, the concept has evolved over many years ago in IT and R and D sector. Next-generation sequencing (NGS) and drug discovery are the two most popular areas of biological sciences which currently implement big data analysis in knowledge discovery[16-18].
Comprehensive Data Integration Methods
Integrating comprehensive and related datasets from various biological databases or other external sources increases the accuracy in lead prediction, and also reveals hidden functions and interrelationship within the molecules[19].There are three types of approaches adopted to combine comprehensive data and reduce data size (Table 1): (i) semantic web approach - searching, retrieving, or annotating data from other external data sources through metadata or a RESTful APIweb services [20,21]; (ii) data warehousing approach - extracting data from other external sources and combining into a global dataset[19,22]; and (iii) data mining approach - extracting data or knowledge from different types of large datasets through suitable patterns[23,24].
Most of the popular three-dimensional (3D) molecular structure databases such as RCSB Protein Data Bank[25], NCBI PubChem[26], EMBL-EBI ChEBI [27], Drug Bank[28], etc. have implemented REST ful API web services or SOAP to share or integrate data in the form of FTP, HTML, XML, JSON, plain text, or AWK commands[29].Moreover, cloud computing services were offered to handle, analyze, or interpret big datasets through various remote applications/servers. There are many cloud servers such as Cloud BLAST[30], Myrna[31], Cloud Burst[32], Hadoop-BAM[33], GPU-BLAST[34], Hydra[35], Peak Ranger[36],Crossbow[37], etc. were available over cloud for analyzing different types of big datasets [38-41].
Unsupervised Data Analysis and Analytics
Handling big dataset or multi-omics data is a difficult task, because it is often very comprehensive and available in real time. In Bioinformatics, sequence (alphabets) and structure (XYZ coordinates) are the major data used for big data analysis and analytics. An effective lead identification and functional interrelationship prediction require integration of very large datasets of3D chemical libraries and disease-target-ligand interaction network. Usually unsupervised multi-omics/big datasets are integrated using clustering and grouping technique. The different types of dataset integrations are target-ligand interactions, intermolecular interactions, disease-target interactions, disease-disease relationships, protein-protein interactions, target-disease-metabolic pathways, drug-side effect relationships, gene interactions, structure-function relationships, etc. [42-44].
The network model graphical representation of biological data interrelations and various types of unsupervised dataset integration methods are [44,56]:(i) network-based methods - graphical representation of interrelations using the network (distance) datasets [45,46],(ii) Bayesian methods - probabilistic graphical representation of interrelations using the probability distribution datasets [47-51], (iii) correlation-based methods - multivariate graphical representation of interrelations using the partial least squares datasets [52,53], (iv) matrix factorization methods - graphical representation of interrelations using the product and rank of the two matrix datasets [54], and (v) kernel-based methods - graphical representation of interrelations using the pattern datasets predicted from kernel matrix [55].
Big Data Accessing Methods
Accessing large datasets requires high-performance computing (HPC) infrastructure and a suitable big data framework [14,15]. The common methods for big data handling are cloud computing, graphics processing unit (GPU) computing, Xeon Phi computing, grid computing, and cluster computing [57,58]. Large datasets can be accessed from various data sources using big data framework, which is based on client-server technology [59]. There are many types of big data processing frameworks used for accessing datasets through a pipeline, among which popularly used frameworks and programs are: Apache Hadoop [76], Apache Spark [77], Apache Flink [78], Apache Storm [79], Apache Samza [80], Apache Cassandra [81], NoSQL [82], R [83], and Python[84].
SYSTEMATIC MULTI-OMICS DATA INTEGRATIONA successful drug discovery requires exact compound or most suitable compound which can fit all pocketsin the active site of the target molecule and brought to a stable state [7,8]. The systematic integration of theoretical and experimental datasets of multi-omics, target-ligand interaction network, physicochemical properties, and functional properties leads to design a safe and efficient therapeutics [60].
Integrative Systems Biology Approach
To design an effective drug molecule, it is most essential to understand the nature and causes of the disease [61].Integrative systems biology advances thorough study of biological phenomenon of a system (organism, e.g. human) in a systematic way (Figure 1).The complex interaction networks in a system can be combined through either top-down or bottom-up approaches using multi-omics datasets [62,63].Currently there are many bioinformatics databases and tools were available for collection of various omics data and hence can design a new virtual system.
Computational Methods for Lead Identification
A lead molecule can be identified by integrating or comparing target data with large datasets using computational and statistical approaches. The common computational lead identification techniques using large datasets include:
Multiple sequence alignment -It is a popular method to find local similarity, homology, and phylogenetic relationship between different genes or protein sequences [41]. The sequence similarity through structure-based sequence alignment enables to find the similar target-ligand interacting molecules. Structural superposition is another alternative approach to compare similar protein structures based on the root mean square deviation (RMSD) calculation [64]. Moreover, systematic integration of large datasets of target-ligand molecular interaction network data with multi-omics data enables to predict or design a potential lead molecule [60].
Maximum common substructure - It is a widely used method in CADD for finding similar 3D structures through structured-based or ligand-based virtual screening [60].Maximum common substructure search using SMILES (Simplified Molecular Input Line Entry System)pattern is commonly used to find structural similarity between large chemical datasets [65].The substructure search with compounds in the phenotype linked target-ligand interacting network datasets integrated with multi-omics data enables to predict or design a novel and potential lead molecule [66-68].
Molecular interaction network - It is the modern and most successive approach to find a novel drug by systematic integration of large datasets of multi-omics data [60].Data scientists integrates big data into complex network in the order of phenotype ? target ?target-ligand?ligand?chemical library to predict or design a novel and potential lead molecule (Figure 2). Recently, many big pharmaceutical companies and R and D organizations have renewed their interest in discovering potential lead compounds from the natural products due to the structural diversity and medicinal properties [3,69,70].
CONCLUSION
Biological systems are analogous to the computer system in disease/target identification and drug design. To troubleshoot hardware issues in the computer, we must have the complete circuit diagram and the component to fix the problem [71]. In contrast, through increasing the volume of multi-omics datasets and systematic integration of large datasets, it is possible to design an effective drug molecule [72]. Recent research advances in cloud computing, big data analysis, multi-omics data integration, and virtual screening and testing technology have reduced the cost and time in predicting potential lead molecules.
ACKNOWLEDGEMENT
Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Conflict of Interest
The authors declare that there is no conflict of interest regarding publication of the paper.
Table 1: A comparison between various comprehensive data integration approaches.
Approaches
Advantages
Disadvantages
Semantic web
Occupies less storage space
Provides more information
Provides updated information
High quality of data
Multiple access options
Non-uniform data from external sources
Sometimes links may be broken
The data access format may be changed
Sometimes data may be ambiguous
Interlinking is not possible
Sometimes data process may timeout
Interrelationship study is not possible
Data warehousing
Provides more information
High quality of data
Uniform access options
Interlinked to the target source
Can predict interrelationships
Can add more features
Occupies more storage space
Provides outdated information
Manual data synchronization
Data mining
Provides updated information
Uniform access options
Interlinked to the target source
Can predict interrelationships
Can add more features
Occupies more storage space
Provides less information
Less quality of data
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4. All natural, Nat. Chem. Biol. 3 (2007) 351–351. 5. A.M. Lourenço, L.M. Ferreira, P.S. Branco, Molecules of natural origin, semi-synthesis and synthesis with anti-inflammatory and anticancer utilities, Curr. Pharm. Des. 18 (2012) 3979–4046. 6. F. Ooms, Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry, Curr. Med. Chem. 7 (2000) 141–158. 7. I.M. Kapetanovic, Computer-aided Drug Discovery and Development (CADDD): in silico-chemico-biological approach, Chem. Biol. Interact. 171 (2008) 165–176. 8. G. Sliwoski, S. Kothiwale, J. Meiler, E.W. Lowe, Computational Methods in Drug Discovery, Pharmacol. Rev. 66 (2013) 334–395. 9. A. Ebrahim, E. Brunk, J. Tan, E.J. O’Brien, D. Kim, R. Szubin, J.A. Lerman, A. Lechner, A. Sastry, A. Bordbar, A.M. Feist, B.O. Palsson, Multi-omic data integration enables discovery of hidden biological regularities, Nat. Commun. 7 (2016) 13091. 10. M. Bersanelli, E. Mosca, D. Remondini, E. Giampieri, C. Sala, G. Castellani, L. Milanesi, Methods for the integration of multi-omics data: mathematical aspects, BMC Bioinformatics. 17 (2016) 167–202. 11. C. Bock, M. Farlik, N.C. Sheffield, Multi-Omics of Single Cells: Strategies and Applications, Trends in Biotechnol. 34 (2016) 605–608. 12. C. Vilanova, M. Porcar, Are multi-omics enough?, Nat. Microbiol. 1 (2016) 16101. 13. M. Swan, The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery, Big Data. 1 (2013)
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30HealthcareEffect of Lifestyle on Body Fat Percentage and Visceral Fat in Indian Women with Above Normal Body Mass Index
English3236Nikita SureshEnglish R. P. Lalitha ReddyEnglishObjective: This study aimed to explore the effect of lifestyle on body fat percentage and visceral fat in Indian women with above normal Body Mass Index.
Methods: Body fat percentage and visceral fat was measured using a body composition analyzer that works on the principle of bio-electrical impedance. Information on lifestyle was collected using a questionnaire and a 3 day dietary record. Indian women (n= 100) in the age group of 25 to 35 years and having a Body mass index above 24.9from various fitness centers in Bangalore were enrolled for this study.
Results: The mean body fat percentage of the study group was 41.4% (±4.61) and mean visceral fat was 8.26 (±2.08). This study shows that as calories increased there was a positively significant association with fat percentage and visceral fat. Body fat percentage and visceral fat was lower in those who exercised 3-5 times/week in comparison to those who exercised 0-3 times/week depicting an inverse association. This study also showed that subjects with sleep duration of 5-6 hours per day had a higher fat percentage than those who slept for 8-9hours per day.
Conclusion: The evidence from the present study indicates that increased physical activity, longer sleep duration and decreased calorie consumption can be recommended as a long term treatment for obesity management. Furthermore, longitudinal research could be done for in-depth understanding of the impact of lifestyle on body fat percentage and visceral fat in Indian women.
EnglishBody composition, Diet Intake, Dietary Assessment, Overweight, ObeseIntroduction
Obesity is a major public health issue in India and worldwide and its prevalence is increasing. Obesity is associated with many health disorders such as diabetes mellitus, hypertension, dyslipidemia, and coronary heart disease especially when fat accumulation is in the abdominal area(visceral compartment). Indian populations have a tendency to accumulate fat in the abdominal region(1, 2).
According to the National Family Health Survey, the percentage of ever-married women aged 15-49 years who are overweight or obese increased from 11% to 15% between the two consecutive studies of National Family Health Survey. Overweight and obesity are more than three times higher in urban areas than rural areas. Furthermore, undernutrition and overweight/obesity are both higher among women than men(3). According to National Family Health Survey-4 released on 17 January 2017, obesity in the country has doubled in the past 10 years (4).
Dietary factors are the major modifiable factors through which many of the external forces promoting weight gain act (5).Different methods are developed that are used to study the relationship between food and nutrient intake and health/disease occurrence. The methods developed should be able to measure food consumption relatively easily, with sufficient accuracy and at a reasonable cost (6). Methods such as single or multiple 24 hour dietary recalls, weighed diet records, self-reported diet history and Food Frequency Questionnaires have been used to assess dietary intake in populations (7-9). The Food record method is used in the present study since it does not rely on memory and have revealed relationships not observed in the Food Frequency Questionnaire(10).
Assessment of body composition is important in the prevention and treatment of obesity. Regular body composition analysis is used to monitor the effect of obesity treatment in weight loss interventions. Body mass index is the most commonly used measure which is only an indirect measure of fatness, so reliable methods of assessing body composition are also needed. Methods such as hydrodensitometry, stable isotope methods and X-ray densitometry are impractical and expensive while X-Ray densitometry equipment is non-portable. Hence an inexpensive and portable method such as Bioelectrical Impedance Analysis is valuable. The Bioelectrical Impedance Analysis procedure is simple, quick, safe, non-invasive and painless, making it a suitable method for studying large groups of participants. There are a great variety of Bioelectrical Impedance Analysis machines such as hand-to-foot and foot to foot. Unlike the foot to foot device, the hand to foot, 8-electrode Bioelectrical Impedance Analyzer estimates whole body compositions without by-passing the trunk and arms (11,12).
Obesity is commonly caused by excessive consumption of calories more than energy requirements over a long period (1). Lifestyle behaviors such as physical activity, sleep duration, food habits etc can also impact energy consumption and/or expenditure(13). In this context, this paper studies the effect of lifestyle on body fat percentage and visceral fat in Indian women with BMI(Body mass index) of more than 24.9.
Material and Methods
Participants
A sample of 100 subjects from various fitness centers in Bangalore, Karnataka, India attending a weight management program were selected by random sampling method. The sample was enrolled for the study considering the inclusion (age group of 25 years to 35 years, Body Mass Index of more than 24.9,exercising for a minimum of 60minutes) and exclusion ( normal Body Mass Index, age group below 25 years or above 35 years of age, women who were pregnant or lactating, sedentary lifestyle) criteria.
Information was collected pertaining to age, sex, date of birth, education, occupation, health, lifestyle including dietary intake and habits.
Body Composition
Anthropometric measurements (height and body weight) were collected in the morning in fasting conditions according to the standardized procedures. This was then followed by body composition analysis. Weight was measured using the weight mode on the body composition analyzer (Tanita BC 601) and height was measured to the nearest 0.1cm using an anthropometric rod(14).
Dietary Intake
The data was collected based on the 3 day dietary record of the subjects. Before obtaining the information, subjects were informed on how to record their food intake. They were asked to mention the ingredients used in the preparation of the dish, the portion size and the time of consumption. The portion sizes of food were recorded using standard household measures such as cup, glass and spoon. The 3 day diet record method is considered as one of the most accurate methods to measure dietary intake and hence this method was used in this study(10).
Nutrient intake data from the 3 day dietary record were entered into the validated software 'DietCal' version 3.0 (Profound Tech Solution; http://dietcal.in/), which is based on values from the Nutritive Value of Indian Foods (15). The data collected was then analyzed.
Statistical Analysis
Descriptive statistics was conducted comprising of number, frequency, mean and standard deviation(SD). Inferential statistics computed for the research findings were standard t test and F test. Age, food habits and sleeping duration were subjected to F test and Body Mass Index, smoking, drinking and physical activity pattern were subjected to t test. Relationship analysis was done using Karl pearson coefficient of correlation. The evidence of positive result indicates the existence of positive relationship between the two variables at 5% level of significance.
Results
The study sample included 100 subjects. The mean age group of the study sample was 30.46 years (±+3.4) with a mean weight of 73.48 kg (±10.57)and mean height of 159.77cm (±4.77). The mean BMI of the study group was 28.96(±3.93) with 67% of the subjects in the range of 25 – 29.9 and 33% above 30. Majority (63%) of the subjects were non-vegetarians followed by 27% vegetarians and 10% ovo-vegetarians.65% of the subjects were in the habit of consuming alcohol occasionally and 30% of the subjects were smoking on a regular basis. The duration of exercise being 60 minutes/day, 84% of the subjects exercised between 3-5 times/ week followed by 16%who exercised 0-3 times/week.
Data on body fat percentage and visceral fat was collected using the body composition analyzer Tanita BC 601 and compared with the ranges provided by Tanita. According to Tanita the range of healthy percentage body fat is between 21-33% and healthy level of visceral fat is between 0-12(16). The results of the present study showed that all the subjects had a body fat percentage above 33% (41.4 ±4.61) and 97% of the subjects had a visceral fat reading in the normal range (8.26 ±2.08).
It is evident from the findings (Table 1) that as the age increases the body fat percentage increases positively, however the relationship is non-significant statistically. The visceral fat had a positive significant relationship with increase in age. The table also depicts that with increase in weight and Body Mass Index there was a statistically significant increase in body fat percentage and visceral fat.
Table 2 shows that age group 29-32years had the highest fat and visceral fat. As per the food habits the non-vegetarians had the highest body fat percentage and visceral fat. Short sleep duration is a factor for obesity(17) and the results from this study show that subjects with sleeping duration per day between 8-9hours had the lowest fat and visceral fat compared to subjects with sleep duration between 5-7 hours however the relationship was statistically non-significant.
Table 3 which compared the relationship of Body Mass Index and lifestyle habits such as smoking, drinking and physical activity pattern per week with fat and visceral fat showed that there was a positive significant relationship of Body Mass Index with fat and visceral fat. The table also shows that the fat and visceral fat was higher in the obese group when compared to the overweight group. Obesity treatment should aim at decreasing fat mass and increasing the physical activity is an important factor that promotes the loss of body fat mass (18). It is also evident from the findings in this study that with increased duration of physical activity the fat and visceral fat was lower. Further fat percentage depicts the existence of statistically significant positive relationship with physical activity.
The dietary intake data showed that the mean energy consumption of the study group was 1945Kcal (±255) with a mean carbohydrate consumption of 285.9g (±49.1), mean protein consumption of 61.5g(±14.4) and mean fat consumption of 60.3g(±16.5). Weight gain is usually due to excessive consumption of calories along with lifestyle habits and low physical activity. Along with the daily caloric intake, the composition of the diet may also be an important factor in understanding obesity and its prevention(2). The result in Table 4 shows a positive significant relationship of fat percentage and visceral fat with energy and carbohydrate consumption.
Conclusion
Classification of subjects into overweight and obesity is necessary in the treatment. Body Mass Index is the most commonly used method for such classification. However it takes into consideration only the weight and height of an individual under the assumption that higher weight is associated with increased body fat and consequent morbidity and mortality. When only Body Mass Index is considered there is a high chance of overlooking skinny fat subjects and subjects with higher muscle mass and lower body fat(19,20). In this context the present study analyzed samples with above normal Body Mass Index to find out their percentage body fat and visceral fat and simultaneously study the impact of lifestyle factors on the above. This study is one of the first of its kind to study this impact in an Indian female population of Bangalore.
Weight is a combination of lean mass, fat mass, water, visceral fat etc and an understanding of which factors favour fat mass deposition could help in management of obesity in a scientific manner(2). The results from this study provide scope to various researchers to study in-depth the various other factors impacting body composition. The study also showed the positive impact of exercise on body fat however further research on the type of exercise and its impact on body fat can be studied.
Evidence from the present study thus indicates that increased physical activity, longer sleep duration and decreased calorie consumption can be recommended as a long term treatment for obesity management.
One of the limitations of this study is the sample size. Increasing the sample size could help in obtaining significant associations between lifestyle factors and adiposity. The findings of this study are clinically relevant to public health interventions particularly in treating overweight and obesity. Furthermore, longitudinal research could be done for an in-depth understanding of the impact of lifestyle on body fat percentage and visceral fat in an Indian population.
Acknowledgement
Authors are grateful to Mr. Surendra H.S, Associate professor, GKVK , Bangalore to have helped in the statistical analysis for this study and Stepperz Fitness Studio Centres , Bangalore to have permitted to use their facilities to conduct this study. Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Conflict of interest
None
Source of funding
The study was funded by the corresponding author herself as a part of her PhD work.
Ethical clearance
The study was approved by the Institutional Ethical Committee of Smt. VHD Central Institute of Home Science, Bangalore.
Tables
TABLE-1
Correlation coefficient between Somatic data with Fat and Visceral Fat
n=100
No.
Correlation coefficient (r)
Fat (%)
Visceral fat
1
Age
0.094 NS
0.262 *
2
Height
0.002 NS
-0.008 NS
3
Weight
0.869 *
0.853 *
4
Body mass index
0.922 *
0.909 *
* Significant at 5% Level, NS Non-Significant
TABLE - 2
Relationship of Age, Food habit and Sleeping duration with Fat (%) and Visceral Fat
No
Characteristics
Category
Sample (n)
Fat (%)
Visceral Fat
Mean
SD
'F'
Test
Mean
SD
'F'
Test
1
Age group
(years)
25-28
33
41.03
4.2
0.45 NS
7.82
2.0
1.28NS
29-32
31
42.04
5.2
8.32
1.8
33-35
36
41.14
4.5
8.31
2.2
2
Food habit
Vegetarian
27
40.88
3.6
0.28 NS
8.00
1.9
0.29NS
Non-vegetarian
63
41.49
4.9
8.35
2.2
Ovo vegetarian
10
42.06
5.6
8.40
2.1
3
Sleeping duration
5-6 hrs
29
42.09
4.4
0.50 NS
8.66
2.1
0.82NS
7 hrs
34
41.20
5.0
8.21
2.2
8-9 hrs
37
40.99
4.5
8.00
2.0
NS Non-Significant
TABLE- 3
Relationship of BMI, Occupation and Lifestyle habits with Fat (%) and Visceral Fat
No.
Characteristics
Category
Sample (n)
Fat (%)
Visceral Fat
Mean
SD
't'
Test
Mean
SD
‘t’
Test
1
Body mass index
Overweight
67
39.01
2.6
9.26*
7.19
1.3
9.62*
Obese
33
46.19
4.1
10.42
1.7
2
Smoking
Yes
30
41.82
4.0
0.66 NS
8.47
2.0
0.67NS
No
70
41.19
4.9
8.17
2.1
3
Drinking
Yes
65
41.17
4.5
0.60 NS
8.15
2.1
0.71NS
No
35
41.77
4.8
8.46
2.0
4
Physical activity/week
0-3 times
16
44.53
6.20
2.33*
9.19
3.17
1.35NS
3-5 times
84
40.78
4.01
8.08
1.78
* Significant at 5% Level, NS Non-Significant,
TABLE-4
Correlation coefficient between Nutrients with Fat (%) and Visceral Fatn=100
No.
Nutrients
Correlation coefficient (r)
Fat (%)
Visceral fat
1
Energy
+ 0.422 *
+ 0.451 *
2
CHO
+ 0.527 *
+ 0.568 *
3
Protein
+ 0.114 NS
+ 0.092 NS
4
Fat
+ 0.120 NS
+ 0.092 NS
* Significant at 5% Level, NS Non-Significant
Englishhttp://ijcrr.com/abstract.php?article_id=2341http://ijcrr.com/article_html.php?did=2341References
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Ferreira M, Silva N, Schmidt F, Silva R, Sichieri R, Guimarães L, et al.Development of a Food Frequency Questionnaire for adults in a population-based sample in Cuiabá, Mid-Western Region of Brazil. Revista Brasileira de Epidemiologia. 2010;13(3):1-11.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30HealthcarePleomorphic Adenoma of Breast, Unusual in its Location
English3739Kshitija WajekarEnglish Silky PatelEnglish Priti TrivediEnglish Dhaval JetlyEnglishAim: Pleomorphic adenoma (PA) is a common benign mixed tumor of salivary gland. It rarely involves the breast and due to limited yield of tissue samples on fine needle aspiration and core biopsies, it poses a diagnostic difficulty to the pathologist.
Case Report: Here we report a rare case, with clinical suspicion of malignancy of breast in 60 year old lady, which was diagnosed as pleomorphic adenoma on histopathology.
Discussion: PA is grossly well circumscribed and on microscopy shows both epithelial and myoepithelial cells embedded in chondromyxoid stroma. With adequate sampling, it is not difficult to diagnose this rare entity of breast on histopathology.
Conclusion: As PA mimicks malignancy, it is important to identify this benign entity in breast and prevent radical mastectomy surgery in these patients.
EnglishPleomorphic adenoma, BreastIntroduction:
Pleomorphic adenoma is also known as benign mixed tumor as it has a mixture of both epithelial and myoepithelial cells embedded in chondromyxoid stroma. It most commonly involves the salivary glands (90% parotid gland) and uncommonly palate, lip, nose, paranasal sinuses, larynx, skin (where it is known as chondroidsyringoma). It rarely occurs in breast. The first case was published in 1906 by Lecene1. Till now less than 80 cases of pleomorphic adenoma of breast have been reported in literature2. Pleomorphic adenoma of breast most commonly presents as a retroareolar mass, mimicking cancer3. As radiology is nonspecific, histopathology is essential for making a final diagnosis 4. Three cases of malignant transformation of pleomorphic adenoma (carcinoma ex-pleomorphic adenoma) have been reported by Hayes et al5.
Case Report:
A 65 year old woman presented with chief complaint of lump in left breast since one month. There was no history of pain or nipple discharge. On examination, a lump was palpated in retroareolar region measuring 1.5x1.0x1.0 cm3 . There was no nipple retraction. Contralateral breast was unremarkable on palpation. No axillary lymph nodes were palpable bilaterally. On routine investigation patient was HCV positive and hypothyroid. On mammography, left breast showed ill defined soft tissue opacity with foci of macrocalcification (Figure 1). It was reported as highly suspicious lesion for malignancy with BIRAD category IVc. Ultrasonography showed 21x18 mm sized ill defined hypoechoic lesion with internal specks of macrocalcification and adjacent parenchymal distortion. FNA showed benign ductal epithelial cells in sheets and in clusters along with lymphocytic inflammatory infiltrate on a haemorrhagic background. Final FNAC report was negative for malignancy. Subsequently patient underwent lumpectomy which was sent for frozen. Total specimen measured 6.0x5.0x3.0 cm3. On gross, breast lump with overlying nipple areola was seen. On cutting, a circumscribed tumor was identified in subareolar region measuring 1.5x1.5x1.0 cm3 having chalky white gritty cut surface (Figure 2). Grossly soft tissue resection margins were away and free from tumor. Frozen section was reported as benign breast tumor with possibility of 1) Fibroadenoma with chondroid and osseous metaplasia 2) Benign mixed tumor (Pleomorphic adenoma). The specimen was then submitted for paraffin embedding. On histopathological examination, a well circumscribed tumor comprising of both epithelial cells (arranged in tubules and cords) and myoepithelial cells embedded in chondromyxoid stroma were seen(Figure 3). Tumor showed osseous and chondroid metaplasia, ductal papilloma and collagen spherulosis like areas (Figure 4). Microscopically nipple and areola were unremarkable and all soft tissue resection margins were free of tumor. Immunohistochemically, epithelial cells were positive for CK7 and myoepithelial cells were positive for S-100, p63 and actin confirming presence of both types of cells. Final histopathological diagnosis of Pleomorphic adenoma of breast was made.
Discussion:
Pleomorphic adenoma of breast is an uncommon neoplasm. The hypothesis postulated is that breast is a modified sweat gland and it shares same embryological ectodermal layer with its counterparts of skin and salivary glands.6
As per previous reports, PA of breast commonly occurs in women and presents as a lump in retroareolar region of breast2,3,7. Only 4 cases have been reported in males8. The tumor ranges in size from 0.6 to 17.0 cm, average being 2.0 cm7. PA of breast has non-specific features on imaging so final diagnosis should be made on histopathological examination.
On histology, tumor is generally well circumscribed and consists of both epithelial and myoepithelial cells embedded in stroma. Stroma can be myxoid, chondroid, osseous or combination of any of these. Due to limited tissue yields on fine needle aspiration and core biopsy and presence of chondroid or myxoid matrix, it can be mistaken for fibroadenoma with calcification, metaplastic or mucinous carcinoma.6,9,10 In a study by Reid Nicholson et al, in all the cases of mucinous carcinoma breast, the extracellular mucin stained positively with alcian blue and was not obliterated by hyaluronidase pretreatment whereas, in PA of breast, hyaluronidase pretreatment obliterated alcian blue staining. Alcian blue staining with concomitant hyaluronidase treatment could therefore serve as a simple stain to help differentiate these two entities6. Metaplastic carcinoma can be differentiated from pleomorphic adenoma of breast by absence of myoeithelial cells and presence of frankly malignant mesenchymal component9. PA can be also confused with intraductal papilloma with osseous and chondroid differentiation, but proliferating myoepithelial cells can differentiate it from
Around 30% cases reported earlier made initial diagnosis of carcinoma due to suspicious mammographic findings11 or misdiagnosis on FNAC12and frozen sections13
PA has pseudopod like extension into adjacent tissue and is susceptible to recur. Treatment of choice is surgical excision with adequate clear margin3,14. Usually pleomorphic adenoma has indolent benign behavior but local recurrence has been reported in two cases14,15. Malignant transformation of PA is rare, with only 3 cases of carcinoma ex pleomorphic adenoma been reported till date. Pleomorphic adenoma has low metastatizing potential5.
Conclusion:
Pleomorphic adenoma of breast are rare tumors, more common in females and occur in retroareolar region. Complete surgical resection with wide margins is the treatment of choice. Since it mimicks malignancy, it is important to identify this benign entity in breast and prevent radical mastectomy surgery in these patients. Our patient on three months follow up showed no recurrence and is in good health.
Source of Funding: Nil
Conflict of Interest: No author has any competing interest.
Acknowledgement
Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Address corresponds to: Dr Silky Patel, Department of Pathology, Gujarat Cancer And Research Institute, Ahmedabad(India).
Email: silky.patel92@gmail.com
Figure 1: Retroareolar soft tissue opacity with foci of macrocalcification on mammography
Figure 2: Gross- Circumscribed tumor having chalky white cut surface
Figure 3: Epithelial cells and myoepithelial cells embedded in chondromyxoid stroma (H and E, 10x)
Figure 4: Tumor showing osseous metaplasia (H and E, 40x)
Englishhttp://ijcrr.com/abstract.php?article_id=2342http://ijcrr.com/article_html.php?did=23421. Lecène AL. Observation d'un cas de tumeur "mixte" du sein. Revue de Chirurgie 1906; 33: 434-468.
2. Pleomorphic adenoma of the breast: a report of two cases and a literature review Yunan Han , Qingfu Zhang, Shawn Xiang Li , Liang Feng , Lei Zhan , Zhan Li1 , Xueshan Qiu2, Feng Jin , Bo Chen: Int J Clin Exp Pathol 2016;9(2):2459-2465
3. Pleomorphic Adenoma of Breast-A Case Report and Review of Literature: Nitin Leekha, Madhu Muralee, Anitha Mathews, T. R. Preethi, M. Iqbal Ahamed: Indian Journal of Surgical Oncology, June 2014, Volume 5, issue 2, pp 152-154
4. Pleomorphic adenoma of breast Iulian Radu, Ioana Petcu, Andrian P?nu??, Drago? Scripcariu, Mihaela Buna-Arvinte, Karina Bilavschi, ViorelScripcariu: Archives of clinical cases, December 2016, Volume 3, issue 4, pp 144-148
5. Carcinoma ex-pleomorphic adenoma of the breast. Report of three cases suggesting a relationship to metaplastic carcinoma of matrix-producing type. Malcolm M. Hayes, David Lesack, Christophe Girardet, Marina Del Vecchio, Vincenzo Eusebi: Virchows Archive February 2005, Volume 446, issue 2 , pp 142-149
6. Reid-Nicholson M, Bleiweiss I, Pace B, Azueta V, Jaffer S. Pleomorphic Adenoma of Breast. Archives of Pathology and Laboratory Medicine 2003;127(4):474-7.
7. Diaz NM, McDivitt RW and Wick MR. Pleomorphic adenoma of the breast: a clinicopathologic and immunohistochemical study of 10 cases. Hum Pathol 1991; 22: 1206-1214.
8. Molland JG, Morgan GJ, Walker DM and Lin BP. Pleomorphic adenoma of the parotid and breast in a male patient. Pathology 2005; 37: 263-265
9. Pleomorphic adenoma of breast - a case report and distinction with metaplastic carcinoma D Gupta, S Agrawal, N Trivedi, A Tewari: Journal of Diagnostic Pathology 2014;9(2)33-37
10. Pleomorphic Adenoma of the Breast - Surgical Pathology Criteria [Internet]. California: Stanford University School of Medicine; 2005. Available from: http://surgpathcriteria.stanford.edu/breast/pleoadbr/printable.html.
11. Sheth, M.T. , D. Hathway, and M. Petrelli., Pleomorphic adenoma("mixed tumor") of human female breast mimickin carcinoma clinico-radiologically: Cancer 1978:41:659-665.
12. Parham, D.M. and A.Evans. Pleomorphic adenoma of the breast; a potential for misdiagnosis of malignancy on fine needle aspiration(FNA). Cytopathology 1998,9:343-348
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14. John BJ, Griffiths C and Ebbs SR. Pleomorphic adenoma of the breast should be excised with a cuff of normal tissue. Breast J 2007; 13: 418-420.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241919EnglishN-0001November30TechnologyA Review on Comparison of Computational Fluid Dynamics Analysis of Natural Fluid and Nanofluid in Helical Coil Heat Exchanger
English4046Arvind Kumar PathakEnglish Yogesh Kumar TembhurneEnglish Mohit GangwarEnglishAs associated to straight tubes, helical tubes are more valuable because of its compact construction and it has been experienced as ace of the passive heat transfer improvement techniques and is being most widely practiced in several heat transfer applications. Many researchers are trying to improve the heat transfer rate in a helical coil heat exchanger. This Research paper shows the comparison between natural fluid and Nanofluid with the help of Computational Fluid Dynamics (CFD) on aluminium & copper tubes. The Zinc oxide (ZnO) and Titanium oxide (TiO2) is used as nanofluid and water as its base.
EnglishHelical Coil, Heat Exchanger, Nano Fluid, CFD, Pressure DropHeat exchangers were used in a wide-ranging of applications including power generation plants, nuclear reactors for generation of electricity, Refrigeration and Air Conditioning (RAC) systems, self-propelled industries, food industries, heat retrieval systems, and chemical handling. The upgrading methods can be distributed into two groups: active and passive methods. The active method requires peripheral forces. The passive methods need discrete surface geometries. Both methods have been commonly used to improve performance of heat exchangers. Due to their compact structure and high heat transfer coefficient helical tubes have been declared as one of the passive heat transfer improvement method and they are broadly used in many industrial applications. [1, 4, 17,]
Several studies have specified that helical tubes are greater to straight tubes when working in heat transfer applications. The centrifugal force will be occurring because of twisting in tube and it improves the heat transfer rate in secondary flow. This sight can be useful, especially in the laminar flow system. Heat transfer rate of helical tube is more than straight tube heat exchanger. It required small volume of base area related to other heat exchangers. The major problem of helical tube heat exchanger is the difficulty in calculating the heat transfer coefficients and the surface area available for heat transfer. This problem comes because of deficiency of data in helical tube heat exchangers, and the poor probability of the flow characteristics around the outside of the coil. [1, 4, 23, 25]
Heat transfer fluid is one of the serious factors as it disturbs the size and cost of heat exchanger systems. Conventional fluids like oil and water have partial heat transfer potentialities. There is top priority for developing different group of fluids so as to reduce cost and meet the increasing demand of industry and commerce. By chance, the developments in nanotechnology make it possible to get higher efficiency and cost saving in heat transfer methods. Nanoparticles are taken as the fresh group of materials which having potential applications in the heat transfer area.
Nano Fluid
Nano fluid is nothing but fluid particles which are less than even a micron (9-10 times) smaller in diameter and highly reactive and proficient material which can be used to increase factor like rate of reaction, thermal conductivity of any metal or material, they are that much reactive and strong.[21]
There are different types of Nano fluids basically:
Al2O3 + Water
CuO + Water
ZnO + Water
TiO2 + Water
TiN + Water [3,7]
The following benefits are expected when the nano particles of nano fluids are properly circulated:
Heat conduction is higher:The thermal interaction is directly available if the particles are finer than 20 nm and if they carry 20% of their atoms on their surface. The nanoparticle is of size so there will be the advantage in the movement of particles and it increased the heat transfer because of micro convection of fluid. When the nano particles having large heat surface area then the large heat transfer is allowable. Dispersion of heat is increasing in the fluid at a faster rate because of large heat transfer. When there will be a rise in temperature then the thermal conductivity of nanofluid increases significantly.[18,26]
Stability: The nanoparticle of nano-fluids is smaller in size (9-10 times smaller) or in µ size, so they are weightless, that’s why the chances of sedimentation are reduced.When sedimentation is reducing it will provide the stability in nanofluid by settling the nano-particles.[18,26]
Choking not occurs in Micro passage cooling:For transferring of heat in heat exchanger the nanofluid is a best optionin overall and they can be perfect for micro passage uses where high heat loads are faced. A large area of heat transfer and highly conducting fluids will occur by the mixture of micro passage and nanofluid and it cannot be managed with meso or micro-particles because they clog micro passages. Nano particles are smaller in size it is µ which is very small to micro passage.[18,26]
Probabilities of erosion reduced:The momentum which is conveyed by a solid wall is minor because nanoparticles are very small. The probability of erosion of components is reducing when the momentum reduces and it occursin pipelines, pumps and heat exchangers.[18,26]
Pumping power is reducing:Pumping power is increasing by a factor of ten when the heat transfer of conventional fluid is increased by a factor of two. If there is a severe increase in fluid viscosity then the pumping power will be increased satisfactory. Thus, a large savings in pumping power can be attained. Thermal conductivity can be increased by small volume fraction of particles. [18,26]
The four unique features are listed below.
Improvement of thermal conductivity:In nanofluid the nano particles are there which have high thermal conductivity and by the properties of nano particles the thermal conductivity of nano fluid is also increased.[20]
Stability:Nano fluids can be worked as a stabilized agent.
Small absorption and Newtonian behaviour: The nano particles in nano fluid used in very small absorption by completely maintaining the Newtonian behaviours of the fluid then there is improvement in the thermal conductivity.The development in viscosity was minimal; hence, pressure drop was increased to some extent.
Dependency in particles size:Dissimilar the condition with micro slurries, the improvement of conductivity was found to be governed by not only on particle concentration but also on particle size. There is increased in improvement in conductivity when the particles size is reduced.
All the above possibilities are compulsory for starting the research in nanofluid with this probability that the nanofluid will play a very important role for developing the next generation of technology.Nanofluid is stable and so much is expecting from nanofluid with various application in future. It is necessary to say that this field of research is very important, with inputs from chemistry, mechanical and chemical engineering, physics, and material science. [26]
Table-1 Specification of Helical Coil Heat Exchanger
Sr. No.
Specification
Dimension
1
PCD
35 mm
2
Tube Dia
8 mm
3
Material
Aluminium and Copper
4
Length of the tube
1000 mm
5
Pitch
15 mm
Literature Review
Helical coil is very compact in structure and it possess high heat transfer coefficient that why helical coils heat exchangers are widely used. In literature it has been informed that heat transfer rate of helical coil is larger than straight tube.
Vijaykant Pandey et. al. [1]have done the study on the effect of geometrical constraints on heat transfer in helical coil heat exchanger at three different mass flow rate 0.005, 0.02 and 0.05 kg/s. Helical coil was fabricated by bending 1000 mm length of aluminium tube having 6,8,10 mm tube diameter and each time coil diameter should be 40 mm and at same pitch 15 mm and at same length. The relation between pressure drop and mass flow rate has been obtained for three different curvature ratio 0.15, 0.2, 0.25 at three different mass flow rates. The result shows that by increasing the tube diameter 10 mm and at curvature ratio 0.25 at mass flow rate of 0.05 kg/s there is increase in pressure drop of about 12100 Pa (262.275 %) and Nusselt number also increases about 2.25% in comparison to tube diameter 6 and 8 mm and at mass flow rate 0.005 and 0.02 kg/s. This can increases heat transfer in helical coil heat exchangers. The increase in heat transfer are a consequences of curvature of the coil which induces centrifugal force to act on moving fluid resulting in development of secondary flow.
M. Balchandaran et. al. [2]have done the experimental study and CFD simulation of helical coil heat exchanger using Solid works Flow Simulation using water as fluid. The fluid used for both coil and tube side is water. The flow rate of both fluids is maintained below as laminar and the flow rate of cold fluid is kept constant while that of hot fluid is changed. The readings during experimental study are taken once steady state has reached. The performance parameters pertaining to heat exchanger such as effectiveness, overall heat transfer coefficient, velocity contours, temperature contours etc. have been reported. Based on the results, it is inferred that the heat transfer rates and other thermal properties of the helical coil heat exchanger are comparatively higher than that of a straight tube heat exchanger.
K. Abdul Hamid et. al. [3]have done their work on pressure drop for Ethylene Glycol (EG) based nanofluid. The nanofluid is prepared by dilution technique of TiO2 in based fluid of mixture water and EG in volume ratio of 60:40, at three volume concentrations of 0.5 %, 1.0 % and 1.5 %. The experiment was conducted under a flow loop with a horizontal tube test section at various values of flow rate for the range of Reynolds number less than 30,000. The experimental result of TiO2 nanofluid pressure drop is compared with the Blasius equation for based fluid. It was observed that pressure drop increase with increasing of nanofluid volume concentration and decrease with increasing of nanofluid temperature insignificantly. He found that TiO2 is not significantly increased compare to EG fluid. The working temperature of nanofluid will reduce the pressure drop due to the decreasing in nanofluid viscosity.
Shiva Kumar et. al. [4] have worked on both straight tube and helical tube heat exchanger. He has compared CFD results with the results obtained by the simulation of straight tubular heat exchanger of the same length under identical operating conditions. Results indicated that helical heat exchangers showed 11% increase in the heat transfer rate over the straight tube. Simulation results also showed 10% increase in
nusselt number for the helical coils whereas pressure drop in case of helical coils is higher when compared to the straight tube.
Fakoor et. al. [5]arestudied the pressure drop characteristics of nanofluid flow inside vertical helically coiled tubes are investigated experimentally for the laminar flow regime. The temperature of the tube wall is maintained constant at around 95 C to have isothermal boundary condition Experiments are implemented for fluid flow inside helically coiled tubes and a straight one. A wide range of various variables is taken into account. Pitch to tube-diameter ratio ranges between 1.6 and 6.1 and coil-to-tube diameter ratio varies from 14.1 to 20.5. Heat transfer oil is used as the base fluid, and Multi-Walled Carbon Nano Tubes (MWCNTs) are utilized as the additive to provide the nano fluids. The working fluids are extremely temperature dependent, so rough correlations are proposed to predict their thermo-physical properties. Regarding the experimental data, utilization of helical coiled tubes instead of straight ones increases the pressure drop exponentially. Irrespective of the tube geometry in which the fluid flows, nanofluid flows show higher rate of pressure drop compared to that of the base fluid flow.
Ahskan Alimoradi et. al. [6]are investigated on the effect of operational and geometrical parameters on the thermal effectiveness of shell and helically coiled tube heat exchangers. Analysis was performed for the steady state. The working fluid of both sides is water, that its viscosity and thermal conductivity were assumed to be dependent on temperature. Based on the results, two correlations have been developed to predict the thermal effectiveness, for wide ranges of mass flow rates ratio, dimensionless geometrical parameters and product of Reynolds numbers. The result was found for same values of Number of transfer units (NTU) and Capacity ratio (Cr), the effectiveness is averagely 12.6% less than the effectiveness of parallel flow heat exchangers and this difference is approximately constant.
Hemasunder Banka et. al. [7] have done an analytical investigation on the shell and tube heat exchanger using forced convective heat transfer to determine flow characteristics of nano fluids by varying volume fractions and mixed with water , the nano fluids are titanium carbide (TiC), titanium nitride (TiN) and ZnO nanofluid and different volume concentrations (0.02, 0.04, 0.07 and 0.15%) flowing under turbulent flow conditions. CFD analysis is done on heat exchanger by applying the properties of nano fluid with different volume fractions to obtain temperature distribution, heat transfer coefficient and heat transfer rate. He found that heat transfer coefficient and heat transfer rates are increasing by increasing the volume fractions.
T. Srinivas et. al. [8]have done experimental study on heat transfer Enhancement using Copper Oxide (CuO)/Water Nanofluid in a Shell and Helical coil heat exchanger. Experiments have been carried out in a shell and helical coil heat exchanger at various concentrations of CuO nanoparticles in water (0.3, 0.6, 1, 1.5 and 2%), speed (500, 1000 and 1500rpm) and shell side fluid (heating medium) temperatures (40, 45 and 500C). Water has been used as coil side fluid. He found that the heat transfer rate increases with increase in concentration of CuO/water nanofluid. This can be attributed to increased thermal conductivity of base fluid due to the addition of nano particles.
Tushaar A Sinha et. al. [9] have done experimental investigation into the thermal properties of nano fluid on the effect of sonication time, settling time and temperature on the thermal conductivity, viscosity and specific heat of zinc oxide (ZnO, 14 nm and 25 nm size) and single walled carbon nanotube (SWCNT, 10nm size) based Nano fluid are investigated and the results of ZnO with Deionized (DI) water and EG as base fluids are compared. The experimental results indicate that the studied parameters have a remarkable effect on the thermal properties of Nano fluid. The rate of enhancement in thermal conductivity of EG based Nano fluid is found to be less than that of water based Nano fluid. The SWCNT based DI water Nano fluid found to be very unstable i.e. the nanoparticles settle down very rapidly. The 0.02% volume fraction of SWCNT nanoparticles suspension results in 10% increase in the specific heat of DI water. A decrement of 24% and 13% in the specific heat of 14 nm size ZnO based Nano fluid were obtained at a volume fraction of 0.001% and 0.002% respectively.
J.S. Jayakumar et. al. [10] carried out an experimental study of fluid to fluid heat transfer though a helical coiled tube at different PCD, inside tube diameter and pitch. Heat transfer characteristics were also studied using CFD code fluent. They observed CFD predictions match reasonably with experimental results for all operating conditions.The effect of coil curvature is to supress turbulent fluctuation ascending in the flowing fluid. Thus, it increases the value of Reynolds number required to attain a fully turbulent flow. As the PCD increases the impact of coil curvature on mass flow rate reduces and therefore the centrifugal force plays lesser role. The difference between the Nu at the inner and outer location increases. Same as coil pitch increases the difference of Nu increases. While the pipe diameter is low, the secondary flows are weaker and hence mixing is lesser.
Amar Raj Singh Suri et. al. [11] has done an experimental study on Nusselt number (Nurs) and friction factor (frs) of heat exchanger circular tube fitted with multiple square perforated with square wing twisted tape inserts. The experimental determination encompassed the geometrical parameters namely, wing depth ratio (Wd/Wt) of 0.042–0.167, perforation width ratio (a/Wt) of 0.250, twist ratio (Tl /Wt) of 2.5, and number of twisted tapes (Nt) T of 4.0. The effect of multiple square perforated twisted tape with square wing has been investigated for the range of Reynolds number (Ren) varied from 5000 to 27,000. The maximum enhancement in Nurs and frs is observed to be 6.96 and 8.34 times of that of the plain circular tube, respectively. Correlations of Nurs, frs and ηp are established in term of Ren and geometrical parameters of wings twisted tape which can be used to predict the values of Nurs, frs and ηp with considerably good accuracy.
Wandong Zheng et. al. [12] are studied on a high density polyethylene helical coil heat exchanger is firstly adopted by a seawater source heat pump system and experiments are conducted to study the thermal performance of the heat exchanger in icy condition. The external convective heat transfer coefficient is calculated by the experimental results and used in the development of the mathematical models. To predict the heat transfer process of the Heat Coil Heat Exchanger (HCHE) in icy condition in frigid periods of winter, a mathematical model is developed; simulations with different parameters are conducted to investigate the effects of variable parameters on the thermal performance, such as inlet temperature, intermediate medium's flow rate, heat exchanger's length and diameter, temperature of seawater. The study indicates that the developments of the mathematical model are very helpful in the designing of Heat exchanger used for the SWHP system.
Changnian Chen et. al. [13] studied the characteristics of Pressure Drop and Heat Transfer of coils Used in Solar collectors. He was found that under the experimental conditions of lower mass flow rate, the flow resistance pressure drop of coiled tube is larger than those of the other pipes, especially when the Reynolds number is more than 15000 under the high mass flow rate and the flow resistance pressure drop of bigger plate coil is smaller and the larger the mass flow rate is the grater difference will be. For heat transfer characteristics, the heat transfer coefficient of coiled tubes showed a trend of increase with increasing the curvature of spiral, and the length to diameter ratio on heat transfer coefficient of the influence for plane spiral tube. At the same time the experimental and simulation results found that length to diameter ratio of coiled tube has little effect under the experimental conditions and the effect of helical pitch on heat transfer coefficient is the least among all the factors.
Vinita Sisodiya et. al. [14]study on the use of Helical coil heat exchangers (HCHEs) with (Aluminium Oxide) Al2O3 -Water phase change material to understand if HCHEs can yield greater rates of heat transfer. An analytical study was conducted using a counter flow HCHE consisting of 8 helical coils. Two analysis was conducted, one where water was used as heat transfer fluid (HTF) on the coil and sell sides, respectively; while the second one made use of different Volume fractions of Al2O3 and water on the coil and shell sides, respectively. The NTU effectiveness relationship of the HCHE when Al2O3 fluid is used approaches that of a heat exchanger with a heat capacity ratio of zero. The heat transfer results have shown that when using an Al2O3, an increase in heat transfer rate can be obtained when compared to heat transfer results obtained using straight heat transfer sections. It has been concluded that the increased specific heat of the Al2O3 as well as the fluid dynamics in helical coil pipes are the main contributors to the increased heat transfer.
Jaafar Albadr et. al. [15] has done experimental study on the forced convective heat transfer and flow characteristics of a nanofluid consisting of water and different volume concentrations of Al2O3 nanofluid (0.3–2) % flowing in a horizontal shell and tube heat exchanger counter flow under turbulent flow conditions are investigated. The Al2O3 nanoparticles of about 30 nm diameter are used in the present study. The results show that the convective heat transfer coefficient of nanofluid is slightly higher than that of the base liquid at same mass flow rate and at same inlet temperature. The heat transfer coefficient of the nanofluid increases with an increase in the mass flow rate, also the heat transfer coefficient increases with the increase of the volume concentration of the Al2O3 nanofluid, however increasing the volume concentration cause increase in the viscosity of the nanofluid leading to increase in friction factor.
N. K. Chavdaet. al. [16] has done an experimental investigation to determine the effect of various concentration of Al2O3 nano-dispersion mixed in water as base fluid on heat transfer characteristics of double pipe heat exchanger for parallel flow and counter flow arrangement. The volume concentrations of Al2O3 nanofluid prepared are 0.001 % to 0.01 %. The conclusion derived for the study is that overall heat transfer coefficient increases with increase in volume concentration of Al2O3 nano-dispersion compared to water up to volume concentration of 0.008 % and then decreases.
Problem Formulation
In the literature survey we found that so much work had been done to increase the heat transfer rate in heat exchanger. But there is no work has been done on heat transfer rate of comparing the fluid and nanofluid. In my work I am trying to showing the comparison of nanofluid and water fluid for the given helical coil heat exchanger keeping in mind that it nanofluid should produce maximum heat transfer rate with minimum power consummation. Because some times in the process of improving the heat transfer coefficient we consume more power without knowing the economic cost. Consider the helical coil heat exchanger of PCD 35 mm of length 1000 mm the pitch of the coil is 15 mm, the coil diameter is 8 mm and the material of coil is Copper and Aluminium. In my research I am using Water as a natural fluid, ZnO and TiO2 as a Nanofluid. [3,7]
Table No. – 2 Comparison Table of the work from the literature Survey
Author Name
Fluid Used
Type of Heat Exchanger
Pressure Drop
Vijaykant Pandey et. al
Water
Helical Coil
12100 Pa pressure drop Increased to other coil
M. Balchandaran et. al
Water
Helical and Straight Tube
Pressure Drop of Helical tube is more than Straight Tube
K. Abdul Hamid et. al.
EG and TiO2 Nano Fluid
Helical Coil
Pressure Drop of TiO2 is not Increased compared to EG nanofluid
Shiva Kumar et. al.
Water
Helical Tube and Straight Tube
Pressure Drop of Helical tube is more than Straight Tube
Fakoor et. al.
Water and Nano Fluid
Helical Coil
Pressure drop of Nanofluid is higher as compared to water fluid
Ahskan Alimoradi et. al.
Water
Helical Coil and Shell andTube
-
Hemasunder Banka et. al.
Tic, TiN, ZnO
Shell and Tube
-
T. Srinivas et. al.
CuO Nano fluid
Shell and Tube, Helical Coil
Pressure Drop Increased as Volume Concentration Increased
Tushaar Sinha et. al.
ZnO and EG Nanofluid
SWCNT
-
J. S. Jayakumar et. al.
Water
Helical Coil
-
Amar Raj Singh Suri et. al.
Solar Energy
Tube with multiple square perforated twisted tapes with square wing
-
Wandong Zheng et. al.
Sea water
Helical Coil
-
Changnian Chen et. al.
Solar Collector
Coiled tube
Pressure drop of coiled tube is larger than pipes.
Vinita Sisodiya et. al
Al2O3 Nanofluid
Helical Coil
-
Jaafar Albadar et. al.
Water and Al2O3 Nanofluid
Shell and tube
Volume Concentration Increases then pressure drop increases.
N. K. Chavda et. al.
Al2O3 Nanofluid
Double Pipe
Volume Concentration Increases then pressure drop increases.
Discussion
The different boundary conditions are taken for helical coil heat exchanger for the numerical simulations. The numerical study considers the effect of natural fluid that is water and nanofluid such as Zinc Oxide (ZnO) and Titanium Oxide (TiO2) on the flow and heat transfer characteristics of tube. The thermal properties of fluid are lesser as compared to nanofluid. Nano fluids have Nano particles of solid materials which increase the thermal properties of Nano fluid also because of vortex flow the pressure drop will be increased. In all above literatures we found that normally the water fluid is used for analysis of helical coil only the geometries have been changed for calculation of pressure drop. We made a helical coil of 35 mm PCD and 8 mm tube diameter of length 1000 mm and the water fluid and Nano fluid is flow inside the tube by which the pressure drop is increased in helical coil which flows the Nano fluid and the fluid which has thermal conductivity higher that will give high pressure drop. [24]
Conclusion:
Increase in centrifugal force due to increases in curvature ratio of coil, mass flow rate and tube diameter. This increases more generation of secondary flow inside helical coil. Secondary flow produces additional transport of fluid and strong mixing (advection - diffusion) in the fluid over the cross section of pipe. Addition of nanoparticles in base fluid increased pressure drop, which indicated higher heat transfer into the fluids. Thus nano fluids could be a promising replacement for pure water in heat exchanger where there is need to more efficient heat transfer.When the thermal conductivity in fluid is increases the temperature is also increases.
Englishhttp://ijcrr.com/abstract.php?article_id=2343http://ijcrr.com/article_html.php?did=2343
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