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The Paradox of Messiah States
[No abstract available] -
MONG: An extension to galaxy clusters
The presence of dark matter (DM), though well established by indirect evidence, is yet to be observed directly. Various DM detection experiments running for several years have yielded no positive results. In view of these negative results, we had earlier proposed alternate models by postulating a minimum gravitational field strength (minimum curvature) and a minimum acceleration. These postulates led to the modified Newtonian dynamics and modified Newtonian gravity (MONG). The observed flat rotation curves of galaxies were also accounted for through these postulates. Here, we extend these postulates to galaxy clusters and model the dynamical velocity-distance curve for a typical cluster such as the Virgo cluster. The radial velocities of galaxies in the Virgo cluster are also obtained through this model. Observations show an inconsistency in the Hubble flow at a mean cluster distance of 17 Mpc, which is expected in regions of high matter density. This decrease in velocity is predicted by our model of modified gravity (MONG). The radial velocity versus distance relation for galaxies in the Virgo cluster obtained using MONG is in agreement with observations. 2022 World Scientific Publishing Company. -
Energy Efficient Evolutionary Algorithm based Clustering with Route Selection Protocol for IoT Assisted Wireless Sensor Networks
Internet of Things (IoT) assisted wireless sensor network (WSN) finds its applicability in several real-time tracking and surveillance applications. However, it suffers from various issues such as restricted battery capacity, repeated interruptions owing to multi-hop data transmission, and limited communication range. Gathering and multihop directing are considered effective solutions to complete enhanced energy competence and a generation of IoT-assisted WSN. An NP-hard problematic that can be handled with an evolutionary algorithm is the collection of the cluster head (CH) and the best potential paths to the goal. Both of these problems involve finding the optimum route to the target (EA). In this context, this study presents the design of the Energy Efficient Evolutionary Algorithm-based Clustering with Route Selection (EEEA-CRS) Protocol for Internet of Things-Assisted Wireless Sensor Networks (IoT-Assisted WSN). The EEEA-CRS technique that has been proposed has the primary intention of enhancing the energy efficiency as well as the lifetime of the IoT-assisted WSN. The EEEA-CRS approach that has been presented is broken down into its basic parts, which are the Fuzzy Chicken Swarm Optimization based Clustering (FCSO-C) phase and the Biogeography Optimization-based Multihop Routing phase (BBO-MHR). The FCSO-C technique that has been suggested chooses CHs with the use of a fitness function that takes into account residual energy, inter-cluster distance, and intra-cluster detachment. In adding, the BBO-MHR strategy identifies the optimum pathways to BS by taking into account the costs of communicating with other clusters, both within and between them. A number of different simulations were carried out in order to demonstrate that the EEEA-CRS methodology yields superior results. The EEEA-CRS method was shown to be superior to other methods in use today, according to the findings of an exhaustive comparison and study. EverScience Publications. -
The Politics of Cultural Homogenization and Territorialization: Representation of Northeast in Tinkle's WingStar Series
Tinkle, the children's magazine in English in India has been instrumental in shaping the imagination of the young urban Indian child ever since its inception in 1980. No other magazine has the readership and reach that Tinkle enjoys with a circulation of more than 3 lakh. The fact that Tinkle has survived unlike many other magazines in India for 40 odd years is testimony (marketing strategies aside) of its reach and popularity. Tinkle, ever since the days of its founder-editor Anant Pai, has been instrumental in constructing imagined communities of national identities for children in India over the decades since the 1970s ever since the Amar Chitra Kathas. One such attempt in constructing children's imaginaries is the addition of a series Wing Star in 2015, scripted by Sean D'mello and inked by Vineet Nair that features Mapui Kawlim, a 13-year-old superhero from Aizwal, Mizoram. While it is empowering that a national mainstream popular magazine for children would feature a female superhero from among the less represented Northeastern states, what is problematic, according to this study, is the manner in which there has been a conscious erasure of all markers of her ethnicity by appropriating her into the larger mainstream homogenised pan-Indian identity of a young female superhero with no specific markers to represent the culture she belongs to. This study will attempt to read this 'sanitised' representation of a Northeastern superhero in the light of the idea of cultural appropriation and deterritorialization and reterritorialization posited by Gilles Deleuze and Felix Guattari that looks at the erasure of specific ethnic and other identities markers. This study will also engage with the implications of how 'sanitised' representations like this in popular narratives would construct and homogenise the imaginaries of the children of a country as they would grow up with erroneous notions of cultural ethnicities and diversity within the country adding to the problematics of marginalisation and hegemonic nationalities. 2022 Aesthetics Media Services. All rights reserved. -
Phytochemical analysis and antioxidant activities of Artemisia stelleriana Besser leaf extracts
The present study aims to report the proximate and mineral composition, phenolic contents, and antioxidant potential of Artemisia stelleriana leaves. The leaf extracts were prepared using various solvents like distilled water, methanol, ethanol and acetone and analyzed for their phenolic and flavo-noid contents and antioxidant activity. The methanolic extracts showed the highest total phenolic and flavonoid contents (10.09 0.24 mg GAE/g and 225.04 0.38 mg QE/g respectively). The methanolic extracts showed signifi-cantly higher 1,1-Diphenyl-2-picrylhydrazyl radical scavenging assay (DPPH-RSA), Reducing power assay and total antioxidant capacity compared to distilled water, ethanol and acetone extracts. Gas Chromatography-Mass Spectroscopy revealed that the methanolic extracts of leaves to be a good source of bioactive compounds like 2,4-di-tert-butylphenol (2,4-DTBP), neo-phytadiene, octacosane and eucalyptol. 2022 Horizon e-Publishing Group. All rights reserved. -
Gym-Goers Self-Identification with Physically Attractive Fitness Trainers and Intention to Exercise
Gym-goers often socially compare themselves with their trainers as they strive to look as attractive as their fitness trainers. The aim of this study was to better understand this phenomenon in the fitness industry. Relying on social comparison theory and social identity theory, self-identification with a physically attractive fitness trainer was posited to have a strong mediating effect on the relationship between appearance motive, weight management motive and gym-goers intention to exercise. The moderation effects of gym-goers age and gender in the direct relationships between appearance motive, weight management motive and exercise intention were also examined. The primary outcome of this study revealed that gym-goers who were influenced by appearance and weight management motives are more likely to identify with physically attractive fitness trainers. Additionally, gender significantly moderates the relationships between appearance motive, weight management motive and exercise intention. Appearance and weight management motives are the primary factors that influence the exercise intention of female gym-goers as compared to their male counterparts. This study sheds new insights into understanding the influence of the physical attractiveness of fitness trainers and its impact on gym-goers exercise intentions via self and social identification process. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
A comparison of in vitro cytotoxicity of undoped and doped surface modified CaS nanoparticles
In the present study we compare the cytotoxicity of undoped and doped surface modified CaS nanoparticles synthesized by wet chemical co-precipitation technique using L929 human fibroblasts cell lines. The toxicity was determined by evaluating the cell viability and changes in cell morphology. In addition, the half-maximal inhibitory concentration (IC50) values for all the samples were also compared. This analysis shows that undoped and terbium doped TEOA capped CaS nanoparticles are more biocompatible and will be better candidates for various applications in the biomedical field. 2021 Elsevier B.V. -
Quantifying the role of nanocarbon fillers on dielectric properties of poly(vinylidene fluoride) matrix
Development of polymers with excellent dielectric properties is a challenge for advanced electronic devices. Impregnating conducting fillers like carbon nanoparticles can enhance the dielectric constant, retaining low loss due to its compatibility and favorable polarization within the polymer matrix. The multifunctional characteristics of coal-derived nanocarbon can improve permittivity and facilitate large-scale production at a lower cost. The incorporation of coal-based nanocarbon in the polymer matrix and its dielectric response is seldom investigated. In this work, different ratios (10:90, 50:50, 90:10 by weight) of nanocarbon/PVDF composite are prepared via a simple solution casting technique. The dielectric measurements show that nanofillers addition significantly augments the dielectric constant value, which is ?3 times (50:50 composite) higher than pure PVDF. The uniform distribution of 50% filler within the polymer matrix impeded the seepage of charge at the interface and enhanced the permittivity via polarization of accumulated charges. The composite also exhibited balanced dielectric loss that is essential for energy storage applications. The Author(s) 2022. -
Usefulness of Augmented Reality on Product Selection: An Experimental Study
Augmented Reality (AR) has brought a revolution in the business world. Most literature in augmented reality is concentrated on the acceptance, responses, and user-friendliness of AR applications. However, it fails to evaluate the ability of AR applications to aid the customer in product selection. Therefore, the primary aim of this study was to fill this gap in the literature by conducting an experimental study to evaluate the furniture selection enabled by AR application. The respondents for the study were grouped into two (experimental and control groups) and were asked to design a room. The respondents in the experimental group were asked to design a room by providing an AR application, and the control group was asked to design a room without an AR application. These designs were evaluated by 15 professionals on five parameters- harmony, volume, design, colour scheme and positioning. The ratings given by these professionals were analysed using a t-test. From the analysis, it was concluded that according to the interior designers' opinion, the AR application proves to be helpful to the customers in creating better room designs. These findings indicate that AR application increases customer ability to select appropriate furniture for designing their homes. Based on these findings, it can be suggested that the AR applications can be used in the furniture selection process for a better choice of furniture. 2022 SCMS Group of Educational Institutions. All rights reserved. -
AN EFFICIENT ACCESS POLICY WITH MULTI-LINEAR SECRET-SHARING SCHEME IN CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION
Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a system in which attribute are used for user's identity and data owner determine the access policy to the data to be encrypt. Here access policy are attached with the ciphertext. In the form of a monotone Boolean formula monotone access structure, an access policy can be interpreted and a linear secret-sharing scheme (LSSS) can be implemented. In recent CP-ABE schemes, LSSS is a matrix whose row represent attributes and there exist a general algorithm which is proposed by Lewko and Waters it transforms a Boolean formula into corresponding LSSS matrix. But we may want to transform the monotone Boolean formula to an analogous but compressed formula first before applying the algorithm. This is a very complex procedure and require efficient optimization algorithm for obtaining equivalent but smaller size Boolean formula. So in this paper we are introducing an extended LSSS called multi-linear secret-sharing scheme where we can eliminate above optimization algorithm and directly convert any Boolean formula to multi-linear secret-sharing scheme. 2022 Little Lion Scientific. All rights reserved. -
An efficient technique to analyze the fractional model of vector-borne diseases
In the present work, we find and analyze the approximated analytical solution for the vector-borne diseases model of fractional order with the help of q -homotopy analysis transform method ( q -HATM). Many novel definitions of fractional derivatives have been suggested and utilized in recent years to build mathematical models for a wide range of complex problems with nonlocal effects, memory, or history. The primary goal of this work is to create and assess a Caputo-Fabrizio fractional derivative model for Vector-borne diseases. In this investigation, we looked at a system of six equations that explain how vector-borne diseases evolve in a population and how they affect community public health. With the influence of the fixed-point theorem, we establish the existence and uniqueness of the models system of solutions. Conditions for the presence of the equilibrium point and its local asymptotic stability are derived. We discover novel approximate solutions that swiftly converge. Furthermore, the future technique includes auxiliary parameters that are both trustworthy and practical for managing the convergence of the solution found. The current study reveals that the investigated model is notably dependent on the time chronology and also the time instant, which can be effectively studied with the help of the arbitrary order calculus idea. 2022 IOP Publishing Ltd. -
Comparative study of sinusoidal and non-sinusoidal two-frequency internal heat modulation in a Rayleigh-Bard system
A linear stability analysis is assented to investigate the effect of two-frequency internal heat modulation at the onset of convection in a Newtonian liquid. The correction Rayleigh number and wave number for small amplitudes is calculated using the Venezian approach. Under two-frequency internal heat modulation, the motion is found to be subcritical. To quantify heat transfer in the system, the three-mode Lorenz model is solved numerically. Various combinations of sinusoidal and non-sinusoidal waveforms influence the onset of convection and heat transfer in the system due to two-frequency internal heat modulation. The parameters' influence on heat transfer is seen to be dependent on the presence of a heat source or sink. 2021 Wiley Periodicals LLC. -
Static perfect fluid space-Time and paracontact metric geometry
The main purpose of this paper is to study and explore some characteristics of static perfect fluid space-Time on paracontact metric manifolds. First, we show that if a K-paracontact manifold M2n+1 is the spatial factor of a static perfect fluid space-Time, then M2n+1 is of constant scalar curvature-2n(2n + 1) and squared norm of the Ricci operator is given by 4n2(2n + 1). Next, we prove that if a (?,?)-paracontact metric manifold M2n+1 with ? >-1 is a spatial factor of static perfect space-Time, then for n = 1, M2n+1 is flat, and for n > 1, M2n+1 is locally isometric to the product of a flat (n + 1)-dimensional manifold and an n-dimensional manifold of constant negative curvature-4. Further, we prove that if a paracontact metric 3-manifold M3 with Q? = ?Q is a spatial factor of static perfect space-Time, then M3 is an Einstein manifold. Finally, a suitable example has been constructed to show the existence of static perfect fluid space-Time on paracontact metric manifold. 2022 World Scientific Publishing Company. -
Perception of Climate Finance: An Empirical Approach
Climate finance is an alternative financing source in which private and public at domestic and global levels invest their funds to support mitigation of and adapt to present and upcoming climate change. It is an enormous challenge since it is incredibly susceptible to climate impact. The main challenge lies in identifying risks of climate change, appropriate response measures, and prioritizing them to control climate change. The paper aims to determine the perception of climate finance among the public while assessing India's current situation concerning climate change. A well-structured questionnaire was prepared, and data were collected from 253 respondents in Chennai city from December 2020 to February 2021 using a convenience sampling method. A chi-square tool was used to examine the association between the demographic profiles of the respondents and the respondents' perception of climate change-related activities. Type of family, age, and number of family members are significantly associated with most statements connected to the perception of climate finance. The majority of the respondents had insufficient knowledge about climate change policies. Forty-two per cent of the respondents believed that the investment made in climate finance is used effectively for sustainable development. It explores the present scenario of climate finance in India during the Covid 19 pandemic period. The study results will be helpful to the social investment companies, and the regulators frame suitable strategic policies. 2022 by authors, all rights reserved. -
Optimal Load Control for Economic Energy Equilibrium in Smart Grid Using Adaptive Inertia Weight Teaching-Learning-Based Optimization
Due to numerous operational restrictions and economic purposes, optimal load management for energy balance in the smart grid (SG) is one of the compensating responsibilities. This research provides a novel multiobjective optimization technique for attaining energy balance in SG, with the goal of avoiding fines due to excessive upstream network power extraction beyond contractual demand. Due to a lack of capacity to create the whole optimization towards the global optimum after each run, optimal load control (OLC) is a prevalent challenge. Adaptive-TLBO, the most recent variation of Teaching Learning Based Optimization (TLBO), comprises both alterations during the exploitation and exploration phases (ATLBO). Because the ATLBO is used on a modified IEEE 33-bus system, the results obtained in this mode are extraordinary. The energy balance has improved in addition to the enhancement of the voltage profile and the reduction of distribution losses. As evidenced by comparisons with PSO, basic TLBO, backtracking search algorithm (BSA), and cuckoo search algorithms, the suggested ATLBO algorithm has precedence over any other proposed algorithm (CSA) 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved. -
Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine
Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%. 2020 The British Computer Society 2020. All rights reserved. -
Search and analysis of giant radio galaxies with associated nuclei (SAGAN): III. New insights into giant radio quasars
Giant radio quasars (GRQs) are radio-loud active galactic nuclei (AGN) that propel megaparsec-scale jets. In order to understand GRQs and their properties, we have compiled all known GRQs (the GRQ catalogue) and a subset of small (size < 700 kpc) radio quasars (SRQs) from the literature. In the process, we have found ten new Fanaroff-Riley type-II GRQs in the redshift range of 0.66 < z < 1.72, which we include in the GRQ catalogue. Using the above samples, we have carried out a systematic comparative study of GRQs and SRQs using optical and radio data. Our results show that the GRQs and SRQs statistically have similar spectral index and black hole mass distributions. However, SRQs have a higher radio core power, core dominance factor, total radio power, jet kinetic power, and Eddington ratio compared to GRQs. On the other hand, when compared to giant radio galaxies (GRGs), GRQs have a higher black hole mass and Eddington ratio. The high core dominance factor of SRQs is an indicator of them lying closer to the line of sight than GRQs. We also find a correlation between the accretion disc luminosity and the radio core and jet power of GRQs, which provides evidence for disc-jet coupling. Lastly, we find the distributions of Eddington ratios of GRGs and GRQs to be bi-modal, similar to that found in small radio galaxies (SRGs) and SRQs, which indicates that size is not strongly dependent on the accretion state. Using all of this, we provide a basic model for the growth of SRQs to GRQs. ESO 2022. -
Effects of variable viscosity and rotation modulation on ferroconvection
We theoretically explore the dynamics of a ferrofluid with temperature and magnetic field-dependent viscosity, which is in a RayleighBard situation and is subjected to rotation. The problem considers both sinusoidal and non-sinusoidal time-periodic variations of rotation to study the onset and post-onset regimes of RayleighBard ferroconvection. We perform a weakly nonlinear stability analysis using a truncated Fourier series representation and arrive at the third-order Lorenz system for ferrofluid convection with variable viscosity. By using the linearized form of the Lorenz system for ferrofluid convection with variable viscosity, we arrive at the critical Rayleigh number to study the onset of rotating ferroconvection. The heat transport is quantified in terms of the time-averaged Nusselt number and the effects of various parameters on it are studied. The effect of modulated rotation is found to have a stabilizing effect on the onset of ferroconvection while that of variable viscosity has a destabilizing effect. The effects of magnetorheological and thermorheological effects are antagonistic in nature. It is found that the square waveform modulation facilitates maximum heat transport in the system due to advanced onset of ferroconvection. 2021, Akadiai Kiad Budapest, Hungary. -
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Convolutional neural network for stock trading using technical indicators
Stock market prediction is a very hot topic in financial world. Successful prediction of stock market movement may promise high profits. However, an accurate prediction of stock movement is a highly complicated and very difficult task because there are many factors that may affect the stock price such as global economy, politics, investor expectation and others. Several non-linear models such as Artificial Neural Network, fuzzy systems and hybrid models are being used for forecasting stock market. These models have limitations like slow convergence and overfitting problem. To solve the aforementioned issues, this paper intends to develop a robust stock trading model using deep learning network. In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and taken as feature vectors. Subsequently, feature vectors are converted into an image using Gramian Angular Field and fed as an input to the CNN. Closing price of stock data are manually labelled as sell, buy, and hold points by determining the top and bottom points in a sliding window. The duration considered over a period from January 2009 to December 2018. Prediction ability of the developed TI-CNN model is tested on NASDAQ and NYSE data. Performance indicators such as accuracy and F1 score are calculated and compared to prove effectiveness of the proposed stock trading model. Experimental results demonstrate that the proposed TI-CNN achieves high prediction accuracy than that of the earlier models considered for comparison. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.