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Article
Publication date: 21 January 2019

Shashank Vadlamani and Arun C.O.

The purpose of this paper is to discuss about evaluating the integrals involving B-spline wavelet on the interval (BSWI), in wavelet finite element formulations, using Gauss…

124

Abstract

Purpose

The purpose of this paper is to discuss about evaluating the integrals involving B-spline wavelet on the interval (BSWI), in wavelet finite element formulations, using Gauss Quadrature.

Design/methodology/approach

In the proposed scheme, background cells are placed over each BSWI element and Gauss quadrature rule is defined for each of these cells. The nodal discretization used for BSWI WFEM element is independent to the selection of number of background cells used for the integration process. During the analysis, background cells of various lengths are used for evaluating the integrals for various combination of order and resolution of BSWI scaling functions. Numerical examples based on one-dimensional (1D) and two-dimensional (2D) plane elasto-statics are solved. Problems on beams based on Euler Bernoulli and Timoshenko beam theory under different boundary conditions are also examined. The condition number and sparseness of the formulated stiffness matrices are analyzed.

Findings

It is found that to form a well-conditioned stiffness matrix, the support domain of every wavelet scaling function should possess sufficient number of integration points. The results are analyzed and validated against the existing analytical solutions. Numerical examples demonstrate that the accuracy of displacements and stresses is dependent on the size of the background cell and number of Gauss points considered per background cell during the analysis.

Originality/value

The current paper gives the details on implementation of Gauss Quadrature scheme, using a background cell-based approach, for evaluating the integrals involved in BSWI-based wavelet finite element method, which is missing in the existing literature.

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Article
Publication date: 14 February 2022

Manish Kumar, Arun Arora, Raghwendra Banchhor and Harishankar Chandra

This paper aims to analyze energy and exergy analysis of solar-based intercooled and reheated gas turbine (GT) trigeneration cycle using parabolic trough solar collectors (PTC…

100

Abstract

Purpose

This paper aims to analyze energy and exergy analysis of solar-based intercooled and reheated gas turbine (GT) trigeneration cycle using parabolic trough solar collectors (PTC) with the use of MATLAB 2018.

Design/methodology/approach

In the first section of this paper, the solar-based GT is validated with the reference paper. According to the reference paper, the solar field is comprising 30 modules in series and 35 modules in parallel series, where a total of 1,050 modules of PTC are taken into consideration. In the second part of this paper, the hybridization of the solar, GT trigeneration cycle is analyzed and optimized. In the last section of this paper, the hybridization of solar, intercooled and reheated GT trigeneration systems is examined and compared.

Findings

The results examined the first section, the power produced by the cycle will be 37.34 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and the efficiencies of energy and exergy will be 38.34% and 39.76%, respectively. The results examined in the second section, the power produced by the cycle will be 38.4 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and accordingly the efficiency of energy and exergy is found to be 40.011% and 41.763%. Where in the last section, the power produced by the cycle will be 41.43 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and the energy and exergy efficiencies will be 39.76% and 40.924%, respectively.

Originality/value

The author confirms that this study is original and has neither been published elsewhere nor it is currently under consideration for publication elsewhere.

Details

World Journal of Engineering, vol. 20 no. 4
Type: Research Article
ISSN: 1708-5284

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Article
Publication date: 17 January 2025

Arun Kumar Bairwa and Irfan Ahmad Sofi

This study investigates the caste-based disparities in employment probabilities and wage earnings within India’s rapidly growing IT industry, using insights from the labour market…

35

Abstract

Purpose

This study investigates the caste-based disparities in employment probabilities and wage earnings within India’s rapidly growing IT industry, using insights from the labour market segmentation theory. Our theoretical conceptualization attempts to pin down the inaccessibility of marginalised sections of the population to the high productivity job market.

Design/methodology/approach

We rely on the National Sample Survey rounds of 2011–2012 and 2020–2021 to estimate employment probabilities and wage differentials using linear and logic regression models, controlling for educational attainments and other important determinants of individual’s job market outcomes.

Findings

The results indicate a significant −1.24 odds differential, even after considering education and other control variables. Notably, this disparity has increased since 2011–2012, with lower caste graduate pass-outs facing a mere 13% probability of IT sector employment compared to their upper caste counterparts at 41%. Further, our findings expose gender and rural-urban differentials, highlighting the vulnerability faced by females and individuals from rural areas. The wage analysis shows a 24% and a 22% earning gap for SCs and OBCs, respectively, which remain statistically significant even after controlling for educational attainments and employment arrangements.

Originality/value

This is first micro-level study that counters Indian IT sector’s claim of “castelessness” and “pro-merit”, identifying significant presence of labour market segmentation in the sector. The caste-based labour market segmentation has far-reaching consequences as it can perpetuate income inequalities and hurt industrial efficiency, stifling economic growth in the long-run. Concerted policy responses are imperative to eliminate structural barriers, ensuring equitable access to quality education and employment opportunities for marginalized sections of the society.

Details

International Journal of Social Economics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0306-8293

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Article
Publication date: 26 June 2024

Molla Ramizur Rahman, Arun Kumar Misra and Aviral Kumar Tiwari

Interconnections among banks are an essential feature of the banking system as it helps in an effective payment system and liquidity management. However, it can be a nightmare…

150

Abstract

Purpose

Interconnections among banks are an essential feature of the banking system as it helps in an effective payment system and liquidity management. However, it can be a nightmare during a crisis when these interconnections can act as contagion channels. Therefore, it becomes essentially important to identify good links (non-contagious channels) and bad links (contagious channels).

Design/methodology/approach

The article estimated systemic risk using quantile regression through the ΔCoVaR approach. The interconnected phenomenon among banks has been analyzed through Granger causality, and the systemic network properties are evaluated. The authors have developed a fixed effect panel regression model to predict interconnectedness. Profitability-adjusted systemic index is framed to identify good (non-contagious) or bad (contagious) channels. The authors further developed a logit model to find the probability of a link being non-contagious. The study sample includes 36 listed Indian banks for the period 2012 to 2018.

Findings

The study indicated interconnections increased drastically during the Indian non-performing asset crisis. The study highlighted that contagion channels are higher than non-contagious channels for the studied periods. Interbank bad distance dominates good distance, highlighting the systemic importance of banking network. It is also found that network characteristics can act as an indicator of a crisis.

Originality/value

The study is the first to differentiate the systemic contagious and non-contagious channels in the interbank network. The uniqueness also lies in developing the normalized systemic index, where systemic risk is adjusted to profitability.

Details

Review of Accounting and Finance, vol. 23 no. 5
Type: Research Article
ISSN: 1475-7702

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Article
Publication date: 10 February 2021

Sathies Kumar Thangarajan and Arun Chokkalingam

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI…

165

Abstract

Purpose

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

Design/methodology/approach

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

Findings

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

Originality/value

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

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Article
Publication date: 24 January 2023

Arun Kumar Misra, Molla Ramizur Rahman and Aviral Kumar Tiwari

This paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan…

309

Abstract

Purpose

This paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.

Design/methodology/approach

It derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.

Findings

It has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.

Research limitations/implications

The RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.

Practical implications

The article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.

Social implications

It has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.

Originality/value

The article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.

Details

The Journal of Risk Finance, vol. 24 no. 2
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 30 May 2023

R.V. ShabbirHusain, Atul Arun Pathak, Shabana Chandrasekaran and Balamurugan Annamalai

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

1386

Abstract

Purpose

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

Design/methodology/approach

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

Findings

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

Research limitations/implications

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

Practical implications

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

Originality/value

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

Details

International Journal of Bank Marketing, vol. 42 no. 2
Type: Research Article
ISSN: 0265-2323

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Book part
Publication date: 1 March 2021

Siti Khomsatun, Hilda Rossieta, Fitriany Fitriany and Mustafa Edwin Nasution

The unique characteristic of Islamic bank leads in governance and disclosure. Using stakeholder, signaling, and market discipline theory, governance and adequate disclosure may…

Abstract

The unique characteristic of Islamic bank leads in governance and disclosure. Using stakeholder, signaling, and market discipline theory, governance and adequate disclosure may increase bank soundness. This study aims to investigate the relationship of sharia disclosure and Sharia Supervisory Board in influencing Islamic bank soundness in the different regulatory framework of the country. Using purposive sampling, the research covered 84 Islamic banks in 16 countries during the period 2013–2015 with lag data of Islamic bank soundness. The result shows sharia disclosure influences on Islamic bank soundness for management efficiency, capital adequacy ratio, asset quality, and liquidity. The results also show that sharia disclosure mediates the indirect effect of SSB on Islamic bank soundness. The regulatory framework (sharia accounting standard and SSB regulation) shows moderating effect of regulation framework proved on the association of sharia disclosure with management efficiency, capital, and liquidity. The effect is indirectly depending on the regulatory framework for proxy management efficiency, capital, and liquidity. The implication of the research suggests that sharia disclosure could increase the market discipline mechanism of Islamic bank stream. The Islamic bank can increase the transparency using sharia disclosure as a branding for increasing public trust, even though in the deficient Islamic bank regulation countries.

Details

Recent Developments in Asian Economics International Symposia in Economic Theory and Econometrics
Type: Book
ISBN: 978-1-83867-359-8

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Article
Publication date: 9 March 2020

Bharat Arun Tidke, Rupa Mehta, Dipti Rana, Divyani Mittal and Pooja Suthar

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and…

268

Abstract

Purpose

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

Design/methodology/approach

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

Findings

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Originality/value

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

Details

Kybernetes, vol. 50 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 19 April 2023

Abhishek Poddar, Sangita Choudhary, Aviral Kumar Tiwari and Arun Kumar Misra

The current study aims to analyze the linkage among bank competition, liquidity and loan price in an interconnected bank network system.

297

Abstract

Purpose

The current study aims to analyze the linkage among bank competition, liquidity and loan price in an interconnected bank network system.

Design/methodology/approach

The study employs the Lerner index to estimate bank power; Granger non-causality for estimating competition, liquidity and loan price network structure; principal component for developing competition network index, liquidity network index and price network index; and panel VAR and LASSO-VAR for analyzing the dynamics of interactive network effect. Current work considers 33 Indian banks, and the duration of the study is from 2010 to 2020.

Findings

Network structures are concentrated during the economic upcycle and dispersed during the economic downcycle. A significant interaction among bank competition, liquidity and loan price networks exists in the Indian banking system.

Practical implications

The study meaningfully contributes to the existing literature by adding new insights concerning the interrelationship between bank competition, loan price and bank liquidity networks. While enhancing competition in the banking system, the regulator should also pay attention toward making liquidity provisions. The interactive network framework provides direction to the regulator to formulate appropriate policies for managing competition and liquidity while ensuring the solvency and stability of the banking system.

Originality/value

The study contributes to the limited literature concerning interactive relationship among bank competition, liquidity and loan price in the Indian banks.

Details

The Journal of Risk Finance, vol. 24 no. 3
Type: Research Article
ISSN: 1526-5943

Keywords

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