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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. -
Theory of planned behavior in predicting the construction of eco-friendly houses
Purpose: The present study aimed to explore the applicability of theory of planned behavior in construction of eco-friendly houses. Design/methodology/approach: Study utilized cross-sectional correlational research design, collected data from 269 adult house owners of Kerala, India, with the help of a self-report measures namely, attitude towards eco-friendly house construction, subjective norm, perceived behavioral control, behavioral intention to build eco-friendly houses, check list of eco-friendly house and socio-demographic data sheet. Descriptive statistics, Karl Pearson product moment correlation, confirmatory factor analysis and mediation analysis with the help of AMOS were used to describe the distribution of study variables and to test the research hypotheses and proposed model. Findings: Study revealed that behavioral intention to build eco-friendly house was the immediate and strongest predictor of actual behavior of constructing an eco-friendly house. Behavioral intention mediated the relationship of attitudinal variables, normative variables and control variables with the behavior of constructing eco-friendly houses. Research limitations/implications: The results vouched the applicability of theory of planned behavior as a comprehensive model in explaining the behavior of eco-friendly house construction. Practical implications: Results of the study iterates the utility of attitudinal, normative and control factors in enhancing the choice of constructing eco-friendly houses. The results can be applied to develop a marketing tool to enhance the behavior of choosing or constructing eco-friendly houses in the population. Originality/value: Role of conventional concrete construction in climate crisis is unquestioned, and adopting eco-friendly architecture is a potential solution to the impending doom of climate crisis. Behavioral changes play a significant role in the success of global actions to curb the climate crisis. Present study discusses the role of psychological variables in constructing eco-friendly houses. 2022, Emerald Publishing Limited. -
Cloud security based attack detection using transductive learning integrated with Hidden Markov Model
In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. 2022 -
Towards a theory of well-being in digital sports viewing behavior
Purpose: Social television (Social TV) viewing of live sports events is an emerging trend. The realm of transformative service research (TSR) envisions that every service consumption experience must lead to consumer well-being. Currently, a full appreciation of the well-being factors obtained through Social TV viewing is lacking. This study aims to gain a holistic understanding of the concept of digital sports well-being obtained through live Social TV viewing of sports events. Design/methodology/approach: Focus group interviews were used to collect data from the 40 regular sports viewers, and the qualitative data obtained is analyzed thematically using NVivo 12. A post hoc verification of the identified themes is done to narrow down the most critical themes. Findings: The exploration helped understand the concept of digital sports well-being (DSW) obtained through live Social TV sports spectating and identified five critical themes that constitute its formation. The themes that emerged were virtual connectedness, vividness, uncertainty reduction, online disinhibition and perceived autonomy. This study defines the concept and develops a conceptual model for DSW. Research limitations/implications: This study adds to the body of knowledge in TSR, transformative sport service research, digital customer engagement, value co-creation in digital platforms, self-determination theory and flow theory. The qualitative study is exploratory, with participants views based on a single match in one particular sport, and as such, its findings are restrained by the small sample size and the specific sport. To extend this studys implications, empirical research involving a larger and more diversified sample involving multiple sports Social TV viewing experiences would help better understand the DSW concept. Practical implications: The research provides insights to Social TV live streamers of sporting events and digital media marketers about the DSW construct and identifies the valued DSW dimensions that could provide a competitive advantage. Originality/value: To the best of the authors knowledge, the exploration is the first attempt to describe the concept of DSW and identify associated themes. 2021, Emerald Publishing Limited. -
Earthquake and flood resilience through spatial Planning in the complex urban system
Urban Communities are exposed to different disaster risks. The paper aims at understanding the interrelation of spatial planning and the resilience of the urban communities for earthquakes and floods. Various spatial planning components were used to evaluate the community resilience to earthquake and flood in the city of Pune of Maharashtra state in India. It has been identified that spatial planning contributes to a greater extent in determining community resilience. Spatial planning results in differential resilience among communities. In the study area, economically weaker households are found to be more vulnerable to disaster risk due to their spatial locations and limited accessibility to share the resources. These factors are found to be contributing to reduced resilience in the city. 2022 The Authors -
Positive side effects of the Covid-19 pandemic on environmental sustainability: evidence from the quadrilateral security dialogue countries
Purpose: The eruption of coronavirus disease 2019 (COVID-19) has pointedly subdued global economic growth and producing significant impact on environment. As a medicine or a treatment is yet available at mass level, social distancing and lockdown is expected the key way to avert it. Some outcome advocates that lockdown strategies considered to reduce air pollution by curtailing the carbon emission. Current investigation strives to affirm the impact of lockdown and social distancing policy due to covid-19 outbreak on environmental pollution in the QUAD nations. Design/methodology/approach: To calibrate the social movement of public, six indicators such residential mobility, transit mobility, workplace mobility, grocery and pharmacy mobility, retail and recreation mobility and park mobility have been deliberated. The data of human mobility have been gathered from the Google mobility database. To achieve the relevant objectives, current pragmatic analysis exerts a panel autoregressive distributed lag model (ARDL)-based framework using the pooled mean-group (PMG) estimator, proposed by Pesaran and Shin (1999), Pesaran and Smith (1995). Findings: The outcome reveals that in the long-run public mobility change significantly impact the pollutants such as PM2.5 and nitrogen dioxide; however, it does not lead to any changes on ozone level. As per as short run outcome is concerned, the consequence unearths country wise heterogeneous impact of different indicators of public mobility on the air pollution. Research limitations/implications: The ultimate inferences of the above findings have been made merely on the basis of examination of QUAD economies; however, comprehensive studies can be performed by considering modern economies simultaneously. Additionally, finding could be constraint in terms of data; for instance, Google data used may not suitably signify real public mobility changes. Originality/value: A considerable amount of investigation explores the impact of covid-19 on environmental consequences by taking carbon emission as a relevant indicator of environmental pollution. Hence, the present pragmatic investigation attempts to advance the present discernment of the above subject in two inventive ways. Primarily, by investigating other components of environmental pollution such as nitrogen dioxide, PM2.5 and ozone, to reveal the impact of covid-19 outbreak on environmental pollution, as disregarded by the all preceding studies. Additionally, it makes a methodological contribution before integrating supplementary variables accompanying with ecological air pollution. Finally, the current research article provides an alternative and creative approach of modeling the impact of public mobility on environmental sustainability. 2021, Emerald Publishing Limited.