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Advanced Computational Method to Extract Heart Artery Region
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal. 2022 Seventh Sense Research Group. -
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022. -
Influence of electrochemical co-deposition of bimetallic Pt-Pd nanoclusters on polypyrrole modified ITO for enhanced oxidation of 4-(hydroxymethyl) pyridine
Bimetallic Pt-Pd nanoparticles were dispersed on polypyrrole coated indium-tin oxide coated polyethylene terephthalate sheets (ITO-PET sheets). The excellent filming property of pyrrole gives a high porous uniform active area for the proper adsorption of bimetallic transition metal nanoparticles. Electrochemical behavior of the modified electrodes was determined using cyclic voltammetry and impedance studies. The physicochemical properties of the modified electrodes were analyzed by scanning electron microscopy, X-ray diffraction spectroscopy, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. To study the electrochemical oxidation of 4-(hydroxymethyl) pyridine in the presence of sodium nitrate in aqueous acidic medium, the modified electrode was used. It is evident from the study that the modified electrode shows better electrochemical activity towards the oxidation of 4-(hydroxymethyl) pyridine. 2022 The Royal Society of Chemistry. -
How Can Small and Medium Enterprises Effectively Implement Corporate Social Responsibility?: An Indian Perspective
The current study is a strategic approach to corporate social responsibility (CSR); the aim is to put forward the factors of CSR activities that enhance its effectiveness for small and medium enterprises (SMEs). To achieve this objective, the factors were extracted from the literature and described along with trusteeship theory of Mahatma Gandhi, and an exploratory study was conducted and data were collected using structured questionnaire based on pretested scale from 158 SMEs and tested using partial least square regression (PLSR). The statistics shows the overall model fit, and the findings indicate a significant relationship with effective CSR. The results of the study are in accordance with the previous research work, and we also find that environment-related CSR and partnership are crucial for the effectiveness of CSR in SMEs, stakeholders role are important and SMEs CSR practice is still informal. The variables identified from study will help SMEs in establishing a formal approach towards CSR and meeting the needs of business and society in the twenty-first century. 2019 International Management Institute, New Delhi. -
A survey on the intersection graphs of ideals of rings
Let L(R) denote the set of all non-trivial left ideals of a ring R. The intersection graph of ideals of a ring R is an undirected simple graph denoted by G(R) whose vertices are in a one-to-one correspondence with L(R) and two distinct vertices are joined by an edge if and only if the corresponding left ideals of R have a non-zero intersection. The ideal structure of a ring reects many ring theoretical properties. There is so much research that has been conducted during the last decade to explore the properties of G(R). This is a survey of the developments in the study on the intersection graphs of ideals of rings since its introduction in 2009. 2022 by the authors. -
Controlling RayleighBard Magnetoconvection in Newtonian Nanoliquids by Rotational, Gravitational and Temperature Modulations: A Comparative Study
The effect of three different types of time periodic modulations on the RayleighBard magnetic system involving Newtonian nanoliquids is studied. Multiple-scale analysis (homogenization method) is used to arrive at the GinzburgLandau equation. The curiosity in the work is to know the individual effects of (1) rotation, (2) gravity and (3) temperature modulations on RayleighBard magnetoconvection in weakly electrically conducting Newtonian nanoliquids. A significant effort in this research is devoted toward linear and nonlinear stability analyses as well as the homogenization method which leads to the GinzburgLandau evolution equation. Although several studies have concluded similar results for nanoliquids compared with those of pure base fluids, many fundamental issues like the choice of phenomenological models for the thermo-physical properties and the best type of nanoparticles are not well understood. This research focuses on several important issues involving mathematical and computational problems arising in heat transfer analysis in the presence of nanoliquids. Effects of various nanoliquid parameters, frequency and amplitude of modulation on heat transport are analyzed. This investigation focuses on five nanoliquids, with water as a carrier liquid and five nanoparticles, viz. copper, copper oxide, silver, alumina and titania. Enhanced heat transport was observed for rotation, gravity and temperature modulations. In the case of rotation modulation, it is found that increase in the amplitude of modulation results in a decrease in the critical Rayleigh number and thereby to an increase in the mean Nusselt number. The increase in the amplitude of the gravity modulation is shown to enhance the heat transport, whereas increase in frequency is to inhibit the heat transport. Two types of temperature modulations are considered, viz. in-phase (synchronous) and out-of-phase (asynchronous) temperature modulations with the assumption that the boundary temperatures vary sinusoidally with time. The amplitudes of modulation are considered to be very small. In the case of in-phase modulation, there is no significant difference between the heat transports in the presence and in the absence of temperature modulation. On this reason, out-of-phase temperature modulation is used to either enhance or diminish heat transport by suitably adjusting the frequency and phase difference of the modulated temperature. The effect of magnetic field, in all three cases of modulations, is to inhibit the onset of convection and thereby diminish the heat transport. 2022, King Fahd University of Petroleum & Minerals. -
Synchronous learning and asynchronous learning during COVID-19 pandemic: a case study in India
Purpose: This research aims to study the students' perspectives on synchronous and asynchronous learning during the COVID-19 Pandemic. Both synchronous and asynchronous learning approaches used in online education have positive and negative outcomes. Hence, the aim is to study online education's positive and negative consequences, reflecting sync and async approaches. This research followed a mixed research approach. The key stakeholders of this research are the Indian educational institutions and students. Design/methodology/approach: This research collected data from the students undergoing synchronous and asynchronous learning amidst the COVID-19 Pandemic. The data were collected (N=655) from various students taking online classes during the pandemic. A questionnaire survey was distributed to the students through online platforms to collect the data. In this research, the authors have collected data using simple random sampling, and the same has been used for data analysis using SPSS version 26. The collected data were exposed to a factor analysis using a principal component analysis technique to reduce the vast dimensions. Findings: The study findings show that synchronous learning is sometimes stressful, placing more responsibility on students mainly because of the increased screen time. At the same time, asynchronous learning allows the students to self-explore and research the topics assigned to them. Students also felt that asynchronous activities create a burden because of many written assignments to be submitted within a short period. Overall, the COVID-19 pandemic has been challenging for the students and the teachers. However, teachers have helped students to learn through digital platforms. The majority of the respondents opined that technological disruptions and death in the family circle had been significant reasons for not concentrating during online classes. However, the combination of synchronous and asynchronous learning has led to a balanced education. Practical implications: Higher education has undergone multiple transformations in a short period (from March 2020, 2021 and beyond). Educational institutions underwent a rapid transition in remote teaching and learning in the initial stages. As time progressed, educational institutions did course navigation where they relooked into their course plans, syllabus and brought a structural change to match the pandemic requirements. Meanwhile, educational institutions slowly equipped themselves with infrastructure facilities to bring academic integrity. At present, educational institutions are ready to face the new normality without disrupting services to society. Social implications: Educational institutions create intellectual capital, which is important for the development of the economy. In the light of COVID-19, there are new methods and approaches newly introduced or old methods and approaches, which are reimplemented, and these approaches always work for the benefit of the student community. Originality/value: The authors collected data during the COVID-19 pandemic; it helped capture the students' experience about synchronous and asynchronous learning. Students and faculty members are newly exposed to synchronous and asynchronous learning, and hence, it is essential to determine the outcome that will help many stakeholders. 2022, Cassandra Jane Fernandez, Rachana Ramesh and Anand Shankar Raja Manivannan. -
Phyllanthus Emblica Extract Protects the Rat Liver Cells Against the Toxicity of Monosodium Glutamate: Experimental Evidence
Background: Monosodium glutamate (MSG), used widely in the food industry, is a threat to the public health. We investigated whether the MSG administration depletes non-enzymatic antioxidants, i.e., vitamins C and E in the liver of Wistar albino rats. We also examined the restorative effect of the ethanolic extract of Phyllanthus emblica (P. emblica). Methods: Wistar albino rats (n=42) were adapted and then randomly divided into seven groups of: 1) control, 2, 3, 4) MSG treatment, and 5, 6, 7) combined MSG and P. emblica extract treatment. All rat groups were treated daily for 120 days. They were orally administered either MSG alone or MSG plus the extract combined. The rats were then sacrificed and the liver was harvested from each group, and homogenized to examine the levels of vitamins C and E in the liver, using RP-HPLC method. Results: The vitamins C and E levels significantly declined (P<0.05) in the liver of MSG treated groups compared to those of the control rats. The combined treatment (extract + MSG) at low and moderate doses restored the vitamin C levels but it restored vitamin E only at the low dose (P<0.05). Conclusions: This study clearly demonstrated the deterioration of non-enzymatic antioxidants, i.e., vitamins C and E in the rats' liver after chronic exposure to MSG. The findings support the toxic effect and oxidative stress due to MSG exposure to the liver and the beneficial effect of the extract of P. emblica that inhibits the MSG's harmful effect on the liver. The Author(s), 2022. -
Message framing and COVID-19 vaccine acceptance among millennials in South India
Vaccine hesitancy and refusal remain a major concern for healthcare professionals and policymakers. Hence, it is necessary to ascertain the underlying factors that promote or hinder the uptake of vaccines. Authorities and policy makers are experimenting with vaccine promotion messages to communities using loss and gain-framed messages. However, the effectiveness of message framing in influencing the intention to be vaccinated is unclear. Based on the Theory of Planned Behaviour (TPB), this study analysed the impact of individual attitude towards COVID-19 vaccination, direct and indirect social norms, perceived behavioural control and perceived threat towards South Indian millennials intention to get vaccinated. The study also assessed the effect of framing vaccine communication messages with gain and loss framing. Data was collected from 228 Millennials from South India during the COVID-19 pandemic from September to October 2021 and analysed using PLS path modelling and Necessary Condition Analysis (NCA). The findings reveal that attitudes towards vaccination, perceived threat and indirect social norms positively impact millennials intention to take up vaccines in both message frames. Further, independent sample t-test between the framing groups indicate that negative (loss framed message) leads to higher vaccination intention compared to positive (gain framed message). A loss-framed message is thus recommended for message framing to promote vaccine uptake among millennials. These findings provide useful information in understanding the impact of message framing on behavioural intentions, especially in the context of vaccine uptake intentions of Millennials in South India. Copyright: 2022 Prakash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Bromelain improves the growth, biochemical, and hematological profiles of the fingerlings of Nile Tilapia, Oreochromis niloticus
A 6-week-long feeding trial experiment was conducted to study the efficacy of Bromelain, a blend of proteolytic enzymes present in pineapple wastes on growth performance, biochemical, and hematological profiles of the fingerlings of Nile tilapia, Oreochromis niloticus. For this, 240 Nile tilapia fingerlings (9 0.11 cm) were fed a commercial diet, supplemented with different levels of pineapple peel extract (PPE) at 1:0, 1:1, 1:2, and 1:3 ratios. After 45 days of the feeding trial, growth parameters, biochemical constituents, and the level of blood cells were assessed. It was found that the growth parameters such as weight gain, feed efficiency ratio, and specific growth rate were increased (p < 0.05) along with the total protein and amino acid content and few hematological parameters; whereas the feed conversion ratio was found to be reduced significantly (p > 0.05) without changing the white blood cell count with PPE supplementation. Thus, the PPE can be a potential feed supplement in Nile tilapia aquaculture. 2022 Gopal Raaj et al. -
Nanoparticle aggregation kinematics on the quadratic convective magnetohydrodynamic flow of nanomaterial past an inclined flat plate with sensitivity analysis
The study focuses on the aggregation kinematics in the quadratic convective magneto-hydrodynamics of ethylene glycol-titania ((Formula presented.)) nanofluid flowing through an inclined flat plate. The modified Krieger-Dougherty and Maxwell-Bruggeman models are used for the effective viscosity and thermal conductivity to account for the aggregation aspect. The effects of an exponential space-dependent heat source and thermal radiation are incorporated. The impact of pertinent parameters on the heat transfer coefficient is explored by using the Response Surface Methodology and Sensitivity Analysis. The effects of several parameters on the skin friction and heat transfer coefficient at the plate are displayed via surface graphs. The velocity and thermal profiles are compared for two physical scenarios: flow over a vertical plate and flow over an inclined plate. The nonlinear problem is solved using the RungeKutta-based shooting technique. It was found that the velocity profile significantly decreased as the inclination of the plate increased on the other hand the temperature profile improved. The heat transfer coefficient decreased due to the increase in the Hartmann number. The exponential heat source has a decreasing effect on the heat flux and the angle of inclination is more sensitive to the heat transfer coefficient than other variables. Further, when radiation is incremented, the sensitivity of the heat flux toward the inclination angle augments at the rate 0.5094% and the sensitivity toward the exponential heat source augments at the rate 0.0925%. In addition, 41.1388% decrement in wall shear stress is observed when the plate inclination is incremented from (Formula presented.) to (Formula presented.). IMechE 2021. -
Impact of bioconvection on the free stream flow of a pseudoplastic nanofluid past a rotating cone
In the current work, the repercussions of Brownian motion and thermophoresis on the three-dimensional free stream flow of tangent hyperbolic (pseudoplastic) nanofluid past a rotating cone are explored. The tangent hyperbolic model expresses the characteristics of a shear-thinning nanofluid. Furthermore, oxytactic microorganisms were used as mixers to actively stabilize the nanoparticles. The movement of these microorganisms within the nanofluid gives rise to a major phenomenon termed bioconvection. The flow of nanofluid past a rotating cone finds applications in the field of nuclear reactors, biomedical applications, solar power collectors, steam generators, and so on. The mathematical model is designed using Buongiorno's model that describes the two major slip mechanisms experienced by the nanoparticles moving within a fluid namely thermophoretic force and Brownian motion. The model thus formed is nondimensionalized using the apt similarity transformation. The resulting system is solved by the (Formula presented.) technique by adapting the shooting method. The velocity, temperature, concentration, and motile density profiles are graphically interpreted for different flow parameters involved in the study. It was observed that thermophoresis reduces concentration and enhances the temperature whereas Brownian motion enhanced both temperature and concentration profiles. Also, the increase in the mixed convection parameter effectively decreased the temperature of the nanofluid. 2022 Wiley Periodicals LLC. -
Polycystic ovary syndrome: An exploration of unmarried women's knowledge and attitudes
Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age and a chief cause of subfertility attributed to ovulation. Besides, lack of knowledge about PCOS, its treatment, and lifestyle changes influence the prognosis. The present qualitative inquiry investigates the knowledge and attitudes of unmarried women towards the syndrome, associated treatment, and necessary lifestyle changes in the fight against the same. A total of 15 participants with PCOS were selected using purposive sampling (n from southern parts of India viz. Kerala and Tamil Nadu states. The telephonic interviews were conducted in late November and early December 2020. He conventional content analysis emerged with six major themes. The themes capsulated women's knowledge, causes, complications and risk factors, treatment of PCOS their perceived importance of health promotive behaviours such as physical activity, sleep patterns, and perceived support from society. The importance of diet, exercise and a healthy lifestyle were additional relevant factors stressed by the respondents. Although the medicines helped participants attain regular menstrual cycles, they also had side effects reported in the discussion. Few respondents reported that they lacked the necessary awareness of PCOS when diagnosed at a younger age. The study enhances the understanding of PCOS from a qualitative approach that has cultural relevance apart from pertinent clinical and lifestyle implications. 2022 The Author(s) -
Reading Patterns, Engagement Style and Theory of Mind
Theory of mind (TOM) refers to a set of abilities which enables understanding of mental states including beliefs, emotions and intentions of self and others. The purpose of this paper is to study the effect of different reading patterns including frequency of reading fiction and genre preference on TOM performance. It also aims to compare the accuracy of TOM performance under explicit goal directed and non-directed reading conditions. To achieve this objective, a sample of 72 Indian college students were randomly allocated to two groups and were evaluated on the Reading the Mind in the Eyes Test (RMET) and the Short Story Task (SST). The two groups differed with respect to task instructions aimed at mobilizing different manner of engagement (goal directed and nondirected) with the prose in the SST. The individual reading habits and preferences of all the participants were recorded by a self report questionnaire. Scores on the novel SST showed significant positive correlation with RMET scores. No significant difference in TOM performance with respect to the different engagement styles was found, indicating that TOM abilities function continuously and equally effectively when being used in goal directed and nondirected conditions. Notably, participants who reported to prefer literary fiction performed significantly better on the SST task than the participants who prefer popular fiction. This positive link between literary fiction and TOM has important implications in clinical and developmental fields and necessitates further research. 2021, National Academy of Psychology (NAOP) India. -
The catalytic reduction of 4-nitrophenol using MoS2/ZnO nanocomposite
Nanocomposite MoS2/ZnO was prepared by an exfoliation process and characterized. A flower-like morphology was obtained for the hybrid where uniformly spread ZnO is stacked over thin layers of MoS2. A tight interface between the two components coupled with the energy band bending at the junction has resulted in a high activity of the composite towards the reduction of 4-nitrophenol. A complete reduction to 4-aminophenol from 4-nitrophenol took place within 15 min under the optimized conditions. The catalyst has a recyclability of six times without any perceptible decrease in the catalytic activity. 2022 The Author(s) -
HTLML: Hybrid AI Based Model for Detection of Alzheimers Disease
Alzheimers disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brains ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Nae base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. 2022 by the authors. -
Generalized Ricci solitons on Riemannian manifolds admitting concurrent-recurrent vector field
Let (M,g) be a Riemannian manifold admitting a concurrent-recurrent vector field ?. We prove that if the metric g is a generalized Ricci soliton such that the potential field V is a conformal vector field, then M is Einstein. Next we show that if the metric of M is a gradient generalized Ricci soliton, then either of these three occurs: (i) ?? is invariant along gradient of potential function; (ii) M is Einstein; (iii) the potential vector field is pointwise collinear to concurrent-recurrent vector field ?. Finally, we investigate gradient generalized Ricci soliton on a Riemannian manifold (M,g) admitting a unit parallel vector field, and in this case we show that if g is a non-steady gradient generalized Ricci soliton, then the Ricci tensor satisfies Ric=-??{g-?????}, where ?? is the canonical 1-form associated to ?. 2022, The Author(s), under exclusive licence to The Forum DAnalystes. -
Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification
Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset. 2022 Elsevier Ltd -
Cross-language contributions of rapid automatized naming to reading accuracy and fluency in young adults: evidence from eight languages representing different writing systems
Rapid automatized naming (RAN) is a strong predictor of reading across languages. However, it remains unclear if the effects of RAN in first language (L1) transfer to reading in second language (L2) and if the results vary as a function of the orthographic proximity of L1L2. To fill this gap in the literature, we examined the role of RAN in reading accuracy and fluency in eight languages representing different writing systems. Seven hundred and thirty-five university students (85 Chinese-, 84 Japanese-, 100 Kannada-, 40 Oriya-, 115 English-, 115 Arabic-, 105 Portuguese-, and 91 Spanish-speaking) participated in our study. They were assessed on RAN (Digits and Objects) and reading (accuracy and fluency) in both L1 and L2 (English). Results of hierarchical regression analyses showed significant effects of L1 RAN on L2 reading accuracy in the Chinese-, Portuguese-, and Spanish-speaking groups. In addition, L2 RAN was a significant predictor of reading fluency in L1 in the same language groups. No cross-language transfer was observed in the other languages. These findings suggest first that L1 and L2 RAN capture similar processes and controlling for one does not leave unique variance for the other to explain. Second, to the extent there is cross-language transfer of RAN skills, this appears to be independent of the orthographic proximity of the languages. 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis
This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period. 2022 by the authors.