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Germination of seed is a crucial step in a plant’s life cycle. It is essential for emergence of seedling and finally producing viable offspring. Germination encompasses a wide number of events influenced by different factors and sound is not an exception. Sound is a vibration that propagates as an acoustic wave. Sound waves with different frequencies, intensities and amplitudes affect different phases of plant’s life cycle including germination. Seed germination kinetics, particularly germination speed contributes a major role in the variability of germination. This work is aimed to analyse the effect of musical sound with specific spectral characteristics mainly Detrended Fluctuation Analysis scaling exponent on seed germination kinetics in Pisum sativum. Indian Classical music and Natural music was used to stimulate Pisum sativum seeds for 72 hours. Germination kinetics was evaluated in terms of Mean Germination Time, t20 and Germination Percentage. Experimental results showed that musical sound can speed up germination process in Pisum sativum. Extremely high statistically significant differences were observed in germination kinetics after musical sound treatment (p

Color perception is a major guiding factor in the evolutionary process of human civilization, but most of the neurological background of the same are yet unknown. This work attempts to address this area with an EEG based neuro-cognitive study on response of brain to different color stimuli. With respect to a Grey baseline seven colors of the VIBGYOR were shown to 16 participants with normal color vision and corresponding EEG signals from different lobes (Frontal, Occipital & Parietal) were recorded. In an attempt to quantify the brain response while watching these colors, the corresponding EEG signals were analysed using two of the latest state of the art non-linear techniques (MFDFA and MFDXA) of dealing complex time series. MFDFA revealed that for all the participants the spectral width, and hence the complexity of the EEG signals, reaches a maximum while viewing color Blue, followed by colors Red and Green in all the brain lobes. MFDXA, on the other hand, suggests a lower degree of inter and intra lobe correlation while watching the VIBGYOR colors compared to baseline Grey, hinting towards a post processing of visual information. We hope that along with the novelty of methodologies, the unique outcomes of this study may leave a long term impact in the domain of color perception research.

“Gitanjali”, a Nobel Prize winning literature, is a collection of poems of one of the greatest poets of all time, Rabindranath Tagore. The literature was written by him at first in Bengali and then in English. This paper aims to correlate between the evolving behavior of the thoughts over a decade in both versions of the texts using chaos based non-linear Multifractal Detrended Cross-Correlation Analysis (MFDXA), a noble state of the art methodology. The study shows a nonlinear pattern of correlation with high degree of complexity.

The phenomenon of increasing the temperature of the Earth’s atmosphere over the years is known as the Global warming. The Global warming has several adverse effects on our climate, our health and our communities. The resulting climate changes due to global warming produce significant changes in the weather patterns. It is also argued that the climate changes occurring due to global warming is responsible for the various geological hazards occurring in the world, like earthquakes, volcanic eruptions, tsunamis (Gutenberg and Richter, 1954; Turcotte and Schubert, 2002; Wu and Johnston, 2000; Wu and Hasegawa, 1996a; Wu and Hasegawa, 1996b; Wu, 1997). McGuire in his book (McGuire, 2012) has explained in detail the interlink between the climate changes occurring due to global warming and the occurrence of geological disasters like earthquakes, volcanoes and tsunamis. Looking back to the past, when the climate changed naturally and our planet emerged from the ice age, the huge pressure exerted on the Earth’s crust suddenly disappeared causing the Earth’s crust to bounce back, the increased stress on the Earth’s crust activated earthquakes, tremors and volcanic eruptions along the pre-existing fault lines. Currently, our planet, the Earth is still responding to the end of the last ice age some 20,000 or more years ago when the temperatures began to rise, huge masses of ice-sheets disappeared resulting from the melting of large masses of ice. Thus the rising temperature may be related to the anomalous emanation of soil radon-222 gas prior to an earthquake. The prime focus of this paper is to search for any cross correlation existing between the daily land-surface temperature anomalies and the soil radon concentration using a robust state of art method, Multifractal detrended cross correlation analysis (MF-DXA). The data sets for both the series used for the analysis are for a period from 2005 to 2014. The analysis shows that these two time series are not only cross correlated but having the cross correlation coefficient γ X = 0 . 012 ± 0 . 01 , which clearly indicates a strong correlation between the soil radon-222 anomalies and the daily land-surface temperature anomalies.

Cardiovascular responses to changes in posture have been the focus of numerous investigations in the past. Analysis of ECG and arterial blood pressure (ABP) data for different postures, obtained from ‘Physionet’ was done from a new perspective of analyzing nonlinear time series having multifractal features. Multifractal detrended fluctuation analysis (MFDFA) and also the cross-correlation between ECG and BP with another technique Multifractal detrended cross-correlation analysis (MFDXA) reveals ECG and ABP time series of different subjects to be multifractal in nature with different degree of complexity. Different degree of auto and cross-correlation for both ECG and ABP signals is also observed for all subjects. Further significant information obtained from the cross-correlation parameters obtained from MFDXA analysis is very important and relevant particularly for understanding the dynamics of orthostatic stress.

In Hindustani music, a small segment is the precomposed but the remains involve with a gradual systematic exploration of the raga by various forms of improvisation keeping the grammar of the raga intact. Performers are free to explore their improvisational patterns based on their experience and expertise. According to the artist’s cognitive and imaginative abilities, the quality and nature of improvisation differ among artists. One of the most important tools used in improvisation is musical ornamentation (alankar/alankaran). Extempore variations that a musical performer creates while performing within the limit of melodic pattern and the rhythmic cycle could be termed as alankar or alankaran (ornamentation). These variations in performance embellished and enhanced the beauty of the raga. Here, in this study, we are interested in finding the hidden musical patterns and structures in Indian classical music which are fundamental in unfolding the musical ornamentation of a raga. Here, we have discussed only two ornamentations, viz. meend and andolan. The study leads to identifying the style of vocal performers.

Can the sound of a vocalist be qualified by timbre? Can we identify the style of a vocalist from timbre? In this work, our objective is to identify timbre characteristics of vocal sound signals of eminent vocalists in Hindustani music, and we tried to identify the style of the artist from those timbre structures. The descriptive timbre parameters that help with identifying the style of vocalists are brightness, odd and even harmonics, irregularity among partials and spectral centroids. Beside these, shimmer, jitter and frequency per partial number were also descriptive. The study concludes that timbre is an important feature to distinguish or identify the style of a vocalist.

Music across all genres evokes a variety of emotions, irrespective of its timbre and tempo. Indian classical music (ICM) is no exception. Although being biased towards vocal musical styles, instrumental music forms one broad section of ICM. In this study, we have tried to compare the neural responses of music practitioners and non-musicians towards different emotions using audio clips from two popular plucked string instruments used in ICM, Sitar and Sarod. From pre-recorded performances of two eminent maestros, 20 clips of approximately 30 s duration were selected from the Alaap sections (initial introductory section without any rhythmic accompaniment) of different Raagas played in the two instruments. From an audience response assessment of 100 participants, a total of eight clips having maximum arousal for happy and sad emotions were identified from the 20 clips, using which EEG (Electroencephalography) recordings were collected from five musicians and five non-musicians. Robust nonlinear Multifractal Detrended Fluctuation Analysis technique (MFDFA) was applied to quantitatively measure the brain-state changes in different lobes for both categories of participants. In essence, this study attempts to encapsulate if and how prior musical training influences the brain responses towards two basic musical emotions in ICM using two instruments of same family.

With the advent of various techniques to assess the bioelectric signals on the surface of the body, it has become possible to develop various Human–Computer interface systems. In this study, for the first time a cross-correlation based data is reported for two different types of bio-signals, viz. Electroencephalography (EEG) and Electromyography (EMG). Whereas EEG refers to the neuro-electric impulses generated in the brain recorded in the form of electric potentials, EMG records the activation potentials of the muscle cells when they contract or relax. The ability of Hindustani Music (HM) to evoke a wide range of emotional experience in its listeners is widely known. For this study, we took simultaneous EEG and EMG data of 5 participants while they listened to two Hindustani ragas of contrast emotions namely Chayanat (corresponding to happy/joy) and Darbari Kanada (corresponding to sad/pathos) emotion. We make use of two latest signal processing algorithms—Wavelet-based power spectra and cross-correlation coefficient to assess the arousal based activities in response to the acoustic clips in the two different bio-signals. For the first time, an attempt is being made to quantify and categorize musical emotions using EMG signals and an attempt to correlate that with the EEG signals obtained from the brain. The alpha, theta, and gamma frequency range in the frontal and parietal electrodes is found to be the most responsive in case of musical emotions. The EMG response has been studied by segregating the entire signal into different frequency ranges as is done in case of EEG frequency bands. Interestingly, the response in case of EMG data is strongest in the same frequency bands as that of EEG signals. Novel pitch detection algorithm has also been applied to EMG signals to ratify the rationale behind the separation of frequency bands. This is the first of its kind study which looks for categorization and quantification of musical emotions using simultaneously two different bio-signals with the help of robust mathematical analysis. The results and implications have been discussed in detail.

Traditionally invariant-mass/transverse-momentum methods were being used to probe various resonance-states in high-energy-collision. Here, we have done scaling analysis by implementing complex-network-based-method (a chaos-based-approach) of visibility-graph to analyze different resonance-states. In the process we use scaling-exponent [power of scale-freeness-of-visibility-graph (PSVG)] calculated from the aforementioned graph. The method is applied to analyze the fractal behavior of pseudo-rapidity-space of the produced dimuons from the events produced in p–p collision at 7 and 8 TeV from CMS collaboration for different ranges of invariant mass of the dimuons. It is found that the fluctuation-pattern of the pseudorapidity-spaces with different ranges of invariant mass has inherent scale-freeness and shows high degree of self-similarity and fractal nature. Moreover, the experiment reveals that the scaling pattern, degree-of-scale-freeness and fractal na ture of the fluctuation differ with different pseodorapidity-space having different ranges of invariant-mass. This change in scaling behavior is reflected in the values of (PSVG) extracted from the corresponding pseudorapidity-space. These different degree of changes in the simple yet rigorous parameter (PSVG) may be indicative of the formation of various resonance-like states(producing di-leptons) and other unusual phenomena. We propose that this method may detect the resonance states in various high energy interactions without using conventional methods of invariant-mass/transverse-momentum techniques and also may detect some unusual phenomena like formation of clusters or structures of cascades etc. This paper reports an application of this method to probe resonance states in p–p collision data at 7 and 8 TeV from CMS by analyzing pseudorapidity-space of the produced dimuons without analyzing their invariant-mass-spectrum.

A deailed knowledge of pp collision is required both as input to comprehensive theoretical models of strong interactions and as baseline to decipher the AA collisions at relativistic and ultrarelativistic energies, which has been of great interest in the area of theoretical and experimental physics. The multiplicity distribution of particles produced in pp collisions and the multiplicity dependence of various global event features serve as rudimentary observables which reflect the features of the inherent dynamics of the process of particle production. Recent availability of dimuon data has triggered spur of interests in revisiting strong interaction process, the study of which in detail is extremely important for enhancement of our understanding on not only the theory of strong interaction but also possible physics scenarios beyond the standard model. Numerous papers have come up where background of production process of dimuon in pp collision has been discussed and analyzed particularly for production of dimuon from {\gamma}{\gamma} interaction. Apart from conventional approaches the present authors proposed a new approach with successful application in context of symmetry scaling in AA collision data from ALICE, pp collisions at 8TeV from CMS and so on. The different approach essentially analyses fluctuation pattern from the perspective of symmetry scaling or degree of self-similarity involved in the process. The proposed methods of analysis using pseudorapidity values of di-muon data taken from the primary dataset of RunA(2011)-7TeV and RunB(2012)-8TeV of the pp collision from CMS, reveal that pseudorapidity spaces corresponding to different ranges of rapidity are highly scale-free and the scaling pattern changes from one rapidity range to another at both energy. Also, the degree of cross-correlation between rapidity and azimuthal space has been found to follow the similar behavior., Comment: 14 pages, 14 figures, primary category-Experiment [hep-ex], PACS 10.-"The Physics of Elementary Particles and Fields"

The most interesting feature of Indian Classical Music is the existence of Raagas. Each Raaga has its own peculiar ascending and descending movement called the Arohana and Avarohana . Even if two (or more) Raagas are made up of the same notes, the combinational varieties of notes evoke different emotions. In this work, we envisage to study how emotion perception in listeners’ changes when there is an alteration of merely a single note in a pentatonic Raaga and also when a particular note(s) is replaced by its flat/sharp counterpart. Approximately 60 sec recordings were done for two pair of Raaga s which were chosen in a manner such that they are having difference in only one note keeping all others same. The fractal dimension of auditory waveform provides a robust nonlinear quantitative parameter with which the two pair of audio clips can be compared. Also, the emotional appraisal from these two pairs were assessed on the basis of psychological listening tests as well from cognitive response in the form of EEG experiments done on 5 participants. Interesting new results are obtained on how trivial changes in the note structure of a particular Raaga influences human emotion to a large extent.

Studies on large density fluctuations in produced particle spectra in high energy interactions provide enough information regarding the dynamics of the process of particle production. Various analyses have revealed self-similarity in particle production process. The concept of self-similarity is indicative of fractal geometry. The present analysis reports an exhaustive study on centrality dependence of event-by-event fluctuation of pions produced in16O-AgBr interactions at 60 A GeV using a non conventional tool based on complex network analysis, viz. visibility graph method. The analysis reveals different fractal behaviour as well as different clustering property for different centrality events and the amount of fractality and average clustering coefficient decrease with the increase of centrality. Estimation of Hurst exponent hints towards two different sources of fluctuation, fractional Brownian motion (fBm) and fractional Gaussian noises (fGn) for two different centrality classes.

The multiplicity-distribution of High-Energy-Interaction, had earlier been analysed w.r.t. clan-model and Negative-Binomial-Distribution (NBD), which described the underlying particle production process by means of cascading mechanism. Clan was defined to contain the particles stemming from the same ancestor [1] and Average-multiplicity within clan was deduced from the best fitted NBD for the multiplicity-distribution of full-phase-space. Since latest data couldn’t be confronted with NBD alone to deduce number of clans and their internal dynamics, we propose a new rigorous method using complex-network and chaos-based Visibility-Graph-algorithm to extract clusters from different rapidity-regions around the central-rapidity-(cr) of exemplary data of 32S-AgBr(200 A GeV)-interaction. These clusters are found to be scale-free and self-similar. For each cluster Power-of-Scale-freeness-of-Visibility-Graph (PSVG) and two important topological parameters: Average-clustering-co-efficient, Average-degree are extracted based on visibility of the nodes from each other in the Visibility Graph constructed from the cluster. The clan-model is revisited by correlating clusters with clans and Average-degree of the clusters with the number of particles in the clan. For each rapidity-region around (cr) the number of clusters/clans having higher values of both Average-clustering-co-efficient and PSVG, is extracted. For those clusters (Average-degree)/(number-of-particles-in-the-clan) has been calculated. It has been found that there are fewer number of clusters/clans having higher Average- clustering-co-efficient and PSVG, in the rapidity-region nearest to the (cr) and this count increases monotonically across increasing overlapping rapidity-regions around (cr)

This work revisits our previous study on human gait diseases, (Dutta et al., 2013) where we have studied the autocorrelation of human gait pattern in normal and diseased set. Significant difference in results was observed for normal and diseased set. However we were not able to distinguish between sets of Parkinson’s and Huntington’s disease. In this paper we attempt to study whether cross correlations between two feet of human gait pattern can help to distinguish between different diseased set. The results reveal that study of cross correlations can help to distinguish between Parkinson’s and Huntington’s disease.

History is replete with incidents of financial crisis, which ex-post become a wakeup call for policy makers and the people. But there were no tests which could identify and date financial bubbles in real time, till now. Phillips, Shi and Yu [2015] provides the first and only model to recursively examine for multiple bubbles. Their “flexible window” methodology provides consistent results and has successfully identified the well-known historical episodes of exuberance and collapse. This accuracy provides very useful “warning alerts” to central bankers, fiscal regulators and policy makers to pre-emptively act and possibly eliminate an impending implosion. We extensively examine for the presence and recurrence of multiple bubbles, over four major financial indexes. We find evidence of bubbles and explosive sub-periods over the longterm data for all of the indices, including deciphering the technology bubbles of the 1990s and early 2000s, and the financial crises of 2008.

Accurate prognostic tool to identify severity of Arrhythmia is yet to be investigated, owing to the complexity of the ECG signal. In this paper, we have shown that quantitative assessment of Arrhythmia is possible using non-linear technique based on “Hurst Rescaled Range Analysis”. Although the concept of applying “non-linearity” for studying various cardiac dysfunctions is not entirely new, the novel objective of this paper is to identify the severity of the disease, monitoring of different medicine and their dose, and also to assess the efficiency of different medicine. The approach presented in this work is simple which in turn will help doctors in efficient disease management. In this work, Arrhythmia ECG time series are collected from MIT-BIH database. Normal ECG time series are acquired using POLYPARA system. Both time series are analyzed in thelight of non-linear approach following the method “Rescaled Range Analysis”. The quantitative parameter, “Fractal Dimension” (D) is obtained from both types of time series. The major finding is that Arrhythmia ECG poses lower values of D as compared to normal. Further, this information can be used to access the severity of Arrhythmia quantitatively, which is a new direction of prognosis as well as adequate software may be developed for the use of medical practice.

Study of RR interval time series for Congestive Heart Failure had been an area of study with different methods including non-linear methods. In this article the cardiac dynamics of heart beat are explored in the light of complex network analysis, viz. visibility graph method. Heart beat (RR Interval) time series data taken from Physionet database [46, 47] belonging to two groups of subjects, diseased (congestive heart failure) ( 29 in number) and normal ( 54 in number) are analyzed with the technique. The overall results show that a quantitative parameter can significantly differentiate between the diseased subjects and the normal subjects as well as different stages of the disease. Further, the data when split into periods of around 1 hour each and analyzed separately, also shows the same consistent differences. This quantitative parameter obtained using the visibility graph analysis thereby can be used as a potential bio-marker as well as a subsequent alarm generation mechanism for predicting the onset of Congestive Heart Failure.

In the present work, phase transition and its dependence on target excitation in terms of Levy index and Ginzburg–Landau formalism using fractal properties of pionization process have been studied in 16O–AgBr and 32S–AgBr interactions at incident energies of 60 and 200 AGeV, respectively. For this purpose, the pion data are divided into three sets depending upon the number of grey tracks (ng). Two multifractal moments, namely, G-moment and Takagi moment, have been used as analyzing tools. The study reveals that the pion density distribution possesses multifractal structure for the two types of interactions and at all degrees of target excitation. Different types of phase transitions (thermal and non-thermal) at different energies are indicated by the data but no signal of quark–gluon plasma (QGP) phase transition is obtained.

Various works on multiplicity fluctuation have investigated the dynamics of particle production process and eventually have tried to reveal a signature of phase transition in ultra-relativistic nuclear collisions. Analysis of fluctuations of spatial patterns has been conducted in terms of conventional approach. However, analysis with fractal dynamics on the scaling behavior of the void has not been explored yet. In this work we have attempted to analyze pion fluctuation in terms of the scaling behavior of the void probability distribution in azimuthal space in ultra-relativistic nuclear collisions in the light of complex networks. A radically different and rigorous method viz. Visibility Graph was applied on the data of 32S-Ag/Br interaction at an incident energy of 200 GeV per nucleon. The analysis reveals strong scaling behavior of void probability distributions in azimuthal space and a strong centrality dependence.

Indian classical music is entirely based on the “Raga” structures. In Indian classical music, a “Gharana” or school refers to the adherence of a group of musicians to a particular musical style of performing a raga. The objective of this work was to find out if any characteristic acoustic cues exist which discriminates a particular gharana from the other. Another intriguing fact is if the artists of the same gharana keep their singing style unchanged over generations or evolution of music takes place like everything else in nature. In this work, we chose to study the similarities and differences in singing style of some artists from at least four consecutive generations representing four different gharanas using robust non-linear methods. For this, alap parts of a particular raga sung by all the artists were analyzed with the help of non linear multifractal analysis (MFDFA and MFDXA) technique. The spectral width obtained from the MFDFA method gives an estimate of the complexity of the signal whereas the cross correlation coefficient obtained from the MFDXA technique gives the degree of correlation between two nonlinear time series. The observations give a cue in the direction to the scientific recognition of “Guru-Shisya Parampara” (teacher-student tradition)—a hitherto much-heard philosophical term. Moreover the variation in the complexity patterns among various gharanas will give a hint of the characteristic feature of that particular gharana as well as the effect of globalization in the field of classical music happening through past few decades.

Context: Synchronous Bilateral Wilms tumor (sBWT). Aims: This study aimed to assess the outcome of patients with sBWT treated on SIOP protocol. Settings and Design: Retrospective and prospective randomized study. Subjects and Methods: SIOP 93-01 protocol was used to study nine patients of sBWT in a single center and followed up over a period from 2 to 5 years. Statistical Analysis Used: Unpaired t-test and Mann–Whitney U-test were used for analysis. Results: Of nine patients, six were included in the study as three patients lost to follow-up. Among the six patients, there were four girls and two boys with a median age of 2 years. Mean regression in the size of tumor was 87% in four out of six patients. Tumor with unfavorable histology showed 32% response (ratio of favorable: unfavorable histology 2:1). Event-free survival rate was 81.3% and overall survival was 90% over 2–5 years. Recurrence was seen in two patients of whom one had Denys–Drash syndrome. Mean DTPA glomerular filtration rate was 91.4/ml/min/1.73 m2 preoperatively and that of 3 months after completion of treatment was 84/ml/min/1.73 m2. Health-related quality of life (HRQOL) using Pediatric Quality of Life Inventory and Lansky Play Performance Scale revealed significant improvement results of all functioning domains such as physical, social, emotional, and school subscales with P < 0.05 and performance scale (P < 0.04). Conclusions: We suggest SIOP protocol for sBWT and bilateral nephron-sparing surgery in two stages. However, long-term follow-up is required to assess the ultimate renal function outcome. HRQOL is an essential guide in improving the conditions of pediatric cancer survivors.

Conventionally invariant mass or transverse momentum techniques have been used to probe for any formation of some exotic or unusual resonance states in high energy collision. In this work, we have applied symmetry scaling based complex network approach to study exotic resonance/hadronic states utilizing the clustering coefficients and associated scaling parameter extracted with the complex network based technique of Visibility Graph. We have analyzed the data of Pb–Pb collision data sample at 2.76 TeV from ALICE Collaboration and analyzed different patterns of symmetry scaling, scale-freeness, correlation and clustering among the produced particles. This is a chaos-based complex network technique where simple parameters like Average Clustering Coefficient and Power of Scale-freeness of Visibility Graph (PSVG) may hint at formation of some exotic or unusual resonance states without using conventional methods. From this experiment we may infer that highest range of Average clustering coefficient, might be the resonance states/clusters from where the hadronic decay might have occurred and few clusters with highest value of this parameter may indicate that those clusters may be the ancestors of some strange particles. There have also been extensive study of dilepton production since the study of lepton pair generation in Drell–Yan processes is immensely important as these processes enable us to validate the Standard Model-SM predictions for the fundamental particles interaction at the new energy region and also to probe for new physics beyond SM. Hence we have applied the same methodology and extracted the same parameters for p–p collision data at 8 TeV from CMS, to detect possible resonance states eventually generating lepton pairs.

Recently posture-induced cardiovascular changes have become a subject of study for its obvious implications. This chapter presents analysis of non-linear time series of ECG and arterial blood pressure (ABP) data from a new perspective using state-of-the-art non-linear technique for assessment of cardiac disorder induced by different human posture. Precisely two methods multifractal detrended fluctuation analysis and multifractal detrended cross-correlation analysis have been applied to ECG and ABP data to quantify degree of impact of posture on cardiovascular functions. Moreover the knowledge of cross-correlation parameters is significant as it provides information for understanding the dynamics of orthostatic stress.

Epilepsy has been identified as a common disorder of central nervous system affecting a huge size of population. This chapter presents a new approach for studying EEG patterns of the human brain in different physiological and pathological states in epileptic patients and normal people with the help of multifractal detrended fluctuation analysis. The chapter also includes a brief discussion about Alzheimer’s diseases and its diagnosis techniques. Further multifractal cross-correlation study was also applied on EEG data taken from patients in both stages – during seizure and in seizure-free interval. The chapter ends with a discussion of how this method can be used as a possible biomarker of epilepsy.

Since the autonomic function affecting the sympathetic, parasympathetic nervous system changes due to epileptic seizure, the corresponding changes in cardiac signals may be effectively used as potential biomarker providing an extracerebral indicator of ictal onset in some cases. Patients suffering from epilepsy may experience serious cardiac malfunctions leading to sudden unexpected death (SUDEP). Thus the heart rate fluctuations during seizure are non-linear and extremely complex. This chapter presents a report on the analysis of ECG signals of postictal patients using a modern and rigorous chaos-based non-linear technique.