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Classification of countries based on development indices by using K-means and grey relational analysis
Clustering countries based on their development profile is important, as it helps in the efficient allocation and use of resources for institutions like the World Bank, IMF and many others. However, measuring the status of development in each country is challenging, as development encompasses several facets such as economic, social, environmental and institutional aspects. These dimensions should be captured and aggregated appropriately before attempting to classify countries based on development. In this context, this paper attempts to measure various dimensions of development through four indices namely, Economic Index (EI), Social Index (SI), Sustainability Index (SUI) and Institutional Index (II) for the period between 1996 through 2015 for 102 countries. And then we categorize the countries based on these development indices using the grey relational analysis and K-means clustering method. Our study classifies countries into four clusters with twelve countries in the first cluster, fifty in second, twenty-seven and thirteen countries in third and fourth clusters respectively. Having taken each of the dimensions of development independently, our results show that no cluster has performed poorly in all four aspects. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Multimodal emotional analysis through hierarchical video summarization and face tracking
The era of video data has fascinated users into creating, processing, and manipulating videos for various applications. Voluminous video data requires higher computation power and processing time. In this work, a model is developed that can precisely acquire keyframes through hierarchical summarization and use the keyframes to detect faces and assess the emotional intent of the user. The key-frames are used to detect faces using recursive Viola-Jones algorithm and an emotional analysis for the faces extracted is conducted using an underlying architecture developed based on Deep Neural Networks (DNN). This work has significantly contributed in improving the accuracy of face detection and emotional analysis in non-redundant frames. The number of frames selected after summarization was less than 30% using the local minima extraction. The recursive routine introduced for face detection reduced false positives in all the video frames to lesser than 2%. The accuracy of emotional prediction on the faces acquired through the summarized frames, on Indian faces achieved a 90%. The computational requirement scaled down to 40% due to the hierarchical summarization that removed redundant frames and recursive face detection removed false localization of faces. The proposed model intends to emphasize the importance of keyframe detection and use them for facial emotional recognition. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Investigating Factors in Quality of Work-life in Indian Garment Industry at Bangalore
The Indian manufacturing sector has a long way to go in enhancing work-life standards for employees. Low standards of work-life hamper the productivity of an organization. Most employees of garment manufacturing units in Bengaluru are from outer rural areas. They come in search of employment in garment units. Though there are labour acts and labour laws, most of the manufacturing units provide poor job environments for employees. This leads to fluctuations in the performance of employees and would have detrimental effects on their health and performance, resulting in attrition. Quality of work life is the solution. This paper aims at unravelling factors leading to recognition of work-life standards so those garment units can work in that dimension to solve their productivity issues and also improve the happiness of their employees. A descriptive approach was made to attain objectives with survey-based data collection. The collected data were subjected to exploratory factor analysis and multiple regression analysis. The study found welfare and safety lead to a quality of work-life in garment units. More cross-sectoral studies are suggested to understand the blend of factors defining the quality of work life and arrive at a generalized model nation-wide. This generalization in the long term should be a key decision-making point for safety and welfare policy development in the world. 2022 The authors. -
Label free electrochemical detection of stress hormone cortisol using sulphur doped graphitic carbon nitride on carbon fiber paper electrode
Sulphur doped graphitic carbon nitride (SGCN) was effectively prepared and comprehensively described. A straightforward synthetic process based on thermal condensation was reported for the synthesis of SGCN using thiourea as a precursor. Cyclic voltammetry (CV) and differential pulse voltammetry were used to evaluate the electrochemical performance of cortisol. SGCN sheets were used to boost the electro-catalytic activity, exhibiting very exceptional electrochemical behaviour towards cortisol. As a result, we obtained a broad linear response range of 0.1-100 ?M, as well as a comparatively low detection limit (15.8 10?8 M). Furthermore, the fabricated SGCN/CFP electrode shows excellent selectivity in the presence of a large number of possible interfering species. SGCN comprises (C with N and S) heteroatoms, which most likely would have led to a better current response towards cortisol detection. Furthermore, the structural defects that generate additional active sites on the surface of SGCN may enhance the quick electron transfer that occurs during the oxidation of cortisol. 2022 The Royal Society of Chemistry. -
Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts
The Big Five Inventory-10 (BFI-10; Rammstedt & John, 2007) is one of many short versions of personality inventories that measure the Big Five trait dimensions. Short versions of scales often present methodological challenges as a trade-off for their convenience. Based on samples from 28 countries (N = 10,560), the current study investigated inter-item correlations estimated using Omega coefficients within each of the five personality characteristics measured by the BFI-10. Results showed that inter-item correlations were significantly lower, in the sample data from non-Western countries compared with the Western countries, for three of the five personality traits, specifically Conscientiousness, Extraversion, and Emotional Stability. Our findings indicate that the psychometric challenges exist across different cultures and traits. We offer recommendations when using short-item scales such as BFI-10 in survey research. 2022 Elsevier Ltd -
Demographic characteristics influencing financial wellbeing: amultigroup analysis
Purpose: The study attempts to understand the factors impacting the financial wellbeing of IT employees in India using confirmatory factor analysis (CFA). It utilizes well-established survey instruments to assess the impact of financial literacy, financial behaviour and financial stress on financial wellbeing. The study also attempts to understand the role of demographic factors (age, gender, monthly income, job category and work experience) in determining financial wellbeing through multigroup analysis. Design/methodology/approach: Structured equation modelling (SEM) is used to study the link between the determinants. The study also attempts to understand the role of demographic factors (age, gender, monthly income, job category and work experience) in determining financial wellbeing through multigroup analysis. Data used for the analysis covers 237 employees working in the IT sector. Findings: While financial literacy and financial behaviour have a significant positive impact on financial wellbeing, financial stress has a significant negative impact. Financial behaviour and financial stress were found to have a mediating role in the relationship between financial literacy and financial wellbeing. The demographic variables significantly moderate the relationship between the factors leading to financial wellbeing. Originality/value: The results show the need for financial wellbeing programs to focus on enhancing financial knowledge and improving financial planning. Further, it suggests offering customized financial wellbeing programs based on the employee's demographic characteristics rather than following a one program, fits all approach. 2021, Emerald Publishing Limited. -
Cross-Border Acquisitions and Shareholders Wealth: The Case of the Indian Pharmaceutical Sector
Cross-border acquisitions by Indian companies have increased tremendously, especially during the last two decades, and the pharmaceutical industry is one of the top acquiring industries. This study verifies the relationship between cross-border acquisitions and shareholders wealth in the Indian pharmaceutical sector. For this purpose, the data related to acquisitions were acquired from 2005 to 2019 and the event study methodology was applied along with two parametric tests. The findings of the current research prescribe that cross-border acquisitions have a positive and significant impact on shareholders wealth. Furthermore, the outcomes also indicate higher positive abnormal returns in the short run when the targets are based in the US and the UK as compared to the positive but insignificant abnormal returns when the targets are based in locations other than the US and the UK. 2022 by the authors. -
The mathematical model for heat transfer optimization of Carreau fluid conveying magnetized nanoparticles over a permeable surface with activation energy using response surface methodology
The sensitivity analysis and response surface methodology (RSM) is performed for the key parameters governed by the magneto-flow and heat transport of the Carreau nanofluids model toward a stretching/shrinking surface in the presences Arrhenius activation energy and chemical reaction. Nanofluid that displayed Brownian motion and thermophoresis was considered with the permeable condition. The effects of different physical parameters were analyzed by employing appropriate similarity transformations in nonlinear partial differential equations and converted to the dimensionless system of ordinary differential equations. The finite difference method in bvp4c code solves the equations numerically. Associated parameters are presented graphically and interpreted against local Nusselt number, Sherwood number, and skin friction coefficient. An increase in the activation energy factor leads to increased concentration in permeable flow. The higher the activation energy lower the temperature and causes the reaction rate constant to decrease. In addition, it slows down the chemical reaction and increases the concentration characteristics. The increase of radiation and Prandtl number leads to an increase in heat transfer for the permeable surface. Furthermore, the Schmidt number and the binary reaction rate parameter increase the mass transfer for suction/injection flow. As a result, the Nusselt number's highest sensitivity is the Eckert number and the lowest to the thermophoresis parameter. The Sherwood number's positive sensitivity is observed for the Eckert number and Brownian motion parameter, whereas negatively sensitive to thermophoresis. 2022 Wiley-VCH GmbH. -
Polymer Nanocomposite Graphene Quantum Dots for High-Efficiency Ultraviolet Photodetector
Influence on photocurrent sensitivity of hydrothermally synthesized electrochemically active graphene quantum dots on conjugated polymer utilized for a novel single-layer device has been performed. Fabrications of high-performance ultraviolet photodetector by depositing the polypyrrole-graphene quantum dots (PPy-GQDs) active layer of the ITO electrode were exposed to an Ultraviolet (UV) source with 265 and 355 nm wavelengths for about 200 s, and we examined the time-dependent photoresponse. The excellent performance of GQDs was exploited as a light absorber, acting as an electron donor to improve the carrier concentration. PGC4 exhibits high photoresponsivity up to the 2.33 A/W at 6 V bias and the photocurrent changes from 2.9 to 18 A. The electrochemical measurement was studied using an electrochemical workstation. The cyclic voltammetry (CV) results show that the hysteresis loop is optically tunable with a UV light source with 265 and 355 nm at 0.1 to 0.5 V/s. The photocurrent response in PPy-GQDs devices may be applicable to optoelectronics devices. 2022 by the authors. -
Exploration of Thermophoresis and Brownian motion effect on the bio-convective flow of Newtonian fluid conveying tiny particles: Aspects of multi-layer model
This research deals with the analysis of bioconvection caused by the movement of gyrotactic microorganisms. The multi-layer immiscible Newtonian fluid flowing through the vertical channel conveying tiny particles is accounted. The immiscible fluids are arranged in the form of a sandwich where the middle layer has a different base fluid that does not mix with the base fluid of the adjacent fluid layer. This separation of the fluid layers gives rise to the interface boundary conditions. Such flows have found applications in electronic cooling and solar reactors processes. Buongiornos model has been incorporated to design the mathematical model that describes the three-layer flows of Newtonian fluid conveying tiny (metal/oxide) particles under thermophoretic force and Brownian motion. The model thus formed is in the form of the ordinary differential system of equations that are solved using the DTM-Pade approximant after non-dimensionalization. The limited results have an excellent comparison with the existing literature results. The results are discussed through graphs and tables. It is seen that thermophoresis enhances the temperature and particle concentration of the fluid whereas, the Brownian motion is found to enhance the temperature and decrease the concentration. The presence of bioconvection helps in achieving enhanced energy and mass transportation. Moreover, the heat transfer occurring between the different base fluids helps to maintain the optimum temperature in the systems. IMechE 2022. -
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Colorimetric and theoretical investigation of coumarin based chemosensor for selective detection of fluoride
Coumarin based Sensor 1 has been designed and synthesized to recognize fluoride ion visually with high selectivity and sensitivity over other anionic analytes through color change from very faint yellow to pink in acetonitrile. The probable binding phenomenon in solution phase has been explained by 1H NMR study of sensor 1 with different concentration of fluoride ions. The binding constant of the sensor 1 with fluoride has been determined as 3.9 104 M?1 and the lower detection limit 6.5 M of the sensor 1 towards fluoride, which has made the sensor 1 as a promising backbone for selective detection of fluoride. For the practical application, test strips based on sensor 1 were fabricated, which could act as a convenient and efficient naked eye F?test kits. The experimentally observed absorption maxima along with its binding nature with fluoride ions also have been supported through theoretical calculations using density functional theory (DFT) calculations. 2022 -
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model. 2012 IEEE. -
Is the Electronic Market the Way Forward to Overcome Market Failures in Agriculture?
This paper examines the performance of agricultural markets through analysing the primary data from 856 farm households in six states along with secondary data. It argues that adequate physical and storage infrastructure is crucial even for the functioning of the electronic market, and other related policy measures are needed to have a significant improvement in agricultural marketing. The results indicate that farmers obtained 3.75% higher prices in these markets vis-vis the prices received before selling to these markets. This is significant as the prices plummeted by 8.34% in the manual transactions. 2022 Economic and Political Weekly. All rights reserved. -
Examining University Students Attitude towards e-Learning and Their Academic Achievement during COVID-19
Higher education around the world has moved to online teaching due to COVID-19 pandemic. Students in higher education are compelled to attend online classes and adopt e-learning mode. There is not much evidence on training the students to adopt e-learning and remote learning. However, do they have a positive attitude towards e-learning and has it affected their academic performance? Present study administered an e-learning attitude scale to 840 students of a reputed University to examine whether they have positive or avoidance attitude to e-learning and also analysed e-learning attitude across students demographic characteristics. Study revealed a slight positive correlation between e-learning attitude and academic achievement of postgraduate students and not for undergraduate students. Girls excelled in their achievement and have a more positive e-learning attitude than boys have. Boys showed avoidance e-learning attitude. However, Socio Economic Status (SES) did not affect students e-learning attitude but influenced their academic achievement. Study concludes that stakeholders must create more opportunities to develop a positive attitude towards e-learning as future education is likely to be technology integrated. 2022 by the authors. -
On the Non-Inverse Graph of a Group
Let (G, ?) be a finite group and S = {u G|u u-1}, then the inverse graph is defined as a graph whose vertices coincide with G such that two distinct vertices u and v are adjacent if and only if either u ? v S or v ? u S. In this paper, we introduce a modified version of the inverse graph, called i?-graph associated with a group G. The i?-graph is a simple graph with vertex set consisting of elements of G and two vertices x, y ? are adjacent if x and y are not inverses of each other. We study certain properties and characteristics of this graph. Some parameters of the i?-graph are also determined. 2022 Javeria Amreen et al., published by Sciendo. -
On (?)-Lorentzian para-Sasakian Manifolds
The object of this paper is to study (?)-Lorentzian para-Sasakian manifolds. Some typical identities for the curvature tensor and the Ricci tensor of (?)-Lorentzian para-Sasakian manifold are investi-gated. Further, we study globally ?-Ricci symmetric and weakly ?-Ricci symmetric (?)-Lorentzian para-Sasakian manifolds and obtain interesting results. 2022 Academic Center for Education, Culture and Research TMU. -
A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer's disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber's law which determines the luminance factor of the image. In the region extraction process, Chan-Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively. 2022 World Scientific Publishing Europe Ltd. -
A review of innovative bond instruments for sustainable development in Asia
Purpose: Advancing the economies in Asia toward meeting sustainable development goals (SDGs) needs an unprecedented investment in people, processes and the planet. The participation of the private sector is necessary to bridge the financing gap to attain this objective. Engaging the private sector can contribute significantly to attaining the 2030 agenda for SD. However, the financial markets in Asian economies are yet to realize this potential. In this context, this paper aims to discuss the state of finance for SD in Asia and identifies innovative financial instruments for attracting private investments for SDs in these economies. Design/methodology/approach: This study relies on published articles, reports and policy documents on financing mechanisms for SD. The literature review covered journal data sources, reports from global institutions such as the UN, World Bank, International Monetary Fund and think-tanks operating in the field of climate change policies. Though the topic was specific to financial market instruments, a broader search was conducted to understand the different sources of sustainable finance available, particularly in Asia. Findings: The investments that are required for meeting the SDGs remain underfunded. Though interest in sustainability is growing in the Asian economies, the financial markets are yet to transition to tap the growing interest in sustainable investing among global investors. This paper concludes that to raise capital from private investors the Asian economies should ensure information availability, reduce distortions and unblock regulatory obstacles. It would also need designing policies and introducing blended financing instruments combining private and public funds. Research limitations/implications: Though the study has grouped Asian economies, the financing strategy for SDGs should be developed at the country-level considering the domestic financial markets, local developmental stage, fiscal capacity and nationally determined contributions. Further research can focus on developing country-specific strategies for using innovative financial instruments. Originality/value: Mobilizing funds for implementing the 2030 Agenda for SD is a major challenge for Asian economies. The paper is addressed to national policymakers in Asian economies for developing strategies to raise capital for SD through private participation. It provides opportunities for revisiting national approaches to sustainable finance in these economies. 2021, Emerald Publishing Limited. -
Nexus Between Tax Buoyancy and Economic Growth Evidence from India1
This paper examines the trends in fiscal revenue collections and the relationship between tax buoyancy and economic growth. In recent years, the responsiveness of revenue growth in relation to economic growth is a growing concern as we witness staggered tax revenue growth with the convergence of percentage contribution of direct taxes to the aggregate revenue with indirect taxes. Using the Log OLS model, for the period between 2000-2018 the variability in tax revenue due to the change in economic growth as measured by GDP was verified. We confirm the test results using M-estimation Robust Log-OLS. We critically appraise the policy interventions and administrative initiatives taken up to improve the buoyancy rate and suggest ways to enhance the voluntary tax compliance rate in the country. The studys findings show a significant relationship between tax revenue and economic growth, but it does not necessarily improve the tax buoyancy rate. Indian Institute of Finance.