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Authentic leadership in a pandemic world: an exploratory study in the Indian context
Purpose: The purpose of this paper is to explore the strategies that helps leaders be authentic in order to be able to respond proactively and become effective in helping their organisations they lead in the context of the COVID-19 pandemic. Design/methodology/approach: Using a qualitative approach, 25 business leaders from diverse sectors were interviewed to understand what sustained them in an adverse context. Findings: Results reveal various dimensions of authentic leadership in a disruptive environment. Authentic leaders have to exhibit distinct behaviours that stems from re-examining oneself to reaffirming organisational purpose. Reimagining the work is emerged as the newer dimension to the authentic leadership considering the context of COVID-19. Practical implications: The results of the study provides insights for anyone leading organisations in today's disruptive business environment. The findings of this study can be used further to undertake quantitative studies to test professional relationships and understand the leadership strategies at different time frames. Originality/value: This paper addresses the strategies that leaders successfully follow to withstand the COVID crisis and highlights the different roles and behaviours that helped leaders to address the crisis confidently. 2022, Emerald Publishing Limited. -
Exploring the Role of Multi-Catalytic Sites in an Amorphous Co-W-B Electrocatalyst for Hydrogen and Oxygen Evolution Reactions
Amorphous materials are used in multitude of catalytic applications, including electrocatalytic water-splitting. Identification and investigation of active sites in amorphous catalysts are rarely reported, mainly owing to the complexity of the systems. Herein, we report an amorphous bifunctional Co-W-B electrocatalyst for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The optimized Co-W-B catalyst showed promising overpotential values of 97 mV (HER) and 292 mV (OER), respectively, to achieve 10 mA cm-2 in 1 M KOH, with good stability. The promoting effect of W in Co-B was investigated experimentally, while computational tools were used to identify all the possible catalytic sites in an amorphous Co-W-B model and classify the most preferred sites for HER and OER. The presence of multi-catalytic sites with specific selectivity toward HER and OER was observed, which explained the bifunctional activity of Co-W-B. This study will foster better understanding of the origin of catalytic activity in similar amorphous systems. 2023 The Authors. Published by American Chemical Society. -
New constituents triterpene ester and sugar derivatives from Panax ginseng Meyer and their evaluation of antioxidant activities
Panax ginseng C. A. Meyer (Araliaceae), is a well-known herb and used in the old established system of Oriental remedy, especially in Japan, China and Korea. Four new compounds characterized as (cis)- 7?,11?,19,21-tetra-O-decanoyl-18, 22?-dihydroxy-dammar-1-en-3-one (1), 3?,4?,12?-trihydroxystigmast-5-en-21-yl octadecan-9?,12?-dienoate (2), dammar-12, 24-dien-3?, 6?, 15?-triol-3?-D-arabinopyranosyl-6?-L-arabinopyranoside (3) and dammar-24-en-3?, 6?, 16?, 20?-tetraol-3?-D-arabinopyranosyl-6?-D-arabinopyranoside (4) were isolated and established from the ethyl acetate and butanol extracts of the roots of P. ginseng. Their structures were established on the basis of spectral data and chemical reactions. Natural compounds indicative a great reservoir of materials and compounds with evolved biological activity, including antioxidant. Compounds 14 were investigated in vitro for antioxidant potential using ferric reducing antioxidant power (FRAP), the Nitric oxide (NO) scavenging activity, reducing power, phosphomolybdenum and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging actions, and the decision showed the compounds 3and 4 have probablyessential antioxidant properties than the compounds 1and 2 presented weak activity. 2016 The Authors -
Ecclesiology, piety, and presbyterian and independent polemics during the early years of the english revolution
Religious controversy swept across England during the revolutionary decades of the 1640s and 1650s. Historians have studied the attendant ecclesiological debates meticulously. The piety as practiced by the puritans has also been carefully examined. Yet generally, these two subjects of ecclesiology and piety have been kept as separate compartments of analysis. The plethora of tracts that rolled off the press during the initial years of the 1640s, nevertheless, shows that many contemporary polemicists were keen to tie the two themes together. The Presbyterian and Independent polemicists were no exception. As this article seeks to demonstrate, a common feature of their publications was the belief that their preferred ecclesiastical polity best served the purpose of promoting individual piety and creating a godly society. Thus the Presbyterian and Independent conflict waged not only over issues of ecclesiology proper such as categories of church offices and of governing councils or composition of church membership to which historians have directed their attention hitherto, but also over questions of how ecclesiology affected piety. Such conflict was a reflection of the commitment of Presbyterians and Independents to their respective vision of reformation for the country. More broadly, this article shows a facet of religious controversy that ultimately led to the disintegration of the godly community and weakened the base of support for the Commonwealth and the Protectorate. American Society of Church History 2015. -
The effect of servant leadership on ad hoc schoolteachers affective commitment and psychological well-being: The mediating role of psychological capital
Progress on the fourth United Nations Sustainable Development Goal (SDG4), which strives to ensure inclusive and equitable quality education, can only be made with teachers whose levels of job satisfaction and dedication to teaching are high. The authors of this article conducted a survey among ad hoc teachers (also referred to as para teachers) in rural India. The purpose of their study was to find out the extent to which being led by principals who practised a management style termed servant leadership positively impacted respondents affective commitment and psychological well-being. A servant leader seeks to serve by developing the followers selfhood in various relational, ethical, emotional and spiritual contexts. This has the effect of encouraging the followers to become the best version of themselves. Data collection involved the completion of a questionnaire by a sample of 1,120 (840 female, 280 male) para teachers from 17 non-formal community learning centres and 10 schools in the Indian state of Jharkhand. The results of the survey revealed that there is an indirect effect of servant leadership on affective commitment and psychological well-being through a set of three elements, hope, efficacy and resilience, which together amount to a para teachers personal resource of psychological capital. Relying on the findings of their research, the authors suggest that it will be beneficial for Jharkhands Department of Education to implement interventional teacher training programmes which nurture servant leadership among school principals and educational officers and thereby foster psychological capital among para teachers. 2020, UNESCO Institute for Lifelong Learning and Springer Nature B.V. -
Predictors of Positive Psychological Capital: An Attempt Among the Teacher Communities in Rural Jharkhand, India
In the recent times, researchers have shown an increased interest in positive psychological capital (PsyCap). However, it is acknowledged that due to the limited number of studies conducted on the antecedents of psychological capital, there is a lack of sufficient data for conclusively proving the antecedents of PsyCap. Consequently, this article aims to explore the potential antecedents of PsyCap as a reliable source of data in the context of rural school teachers. The focus is to investigate both the individual differences and the contextual factors as desirable variables that constitute PsyCap among the school teachers of rural Jharkhand, India. Samples of 1,120 respondents from different rural schools were collected and analysed with Structural Equation Modeling (AMOS 20.0). The findings of the study explained that both the individual differences (proactive personality and emotional intelligence) and the contextual factors (perceived organizational support, servant leadership and meaningful work) have a positive relationship with PsyCap. The impact of PsyCap on teacher performance can form the basis for further research on the subject. JEL Codes: M12, M53 2021 XLRI Jamshedpur, School of Business Management & Human Resources. -
The Jesuit educational mission in rural Chotanagpur, India: historical achievements and contemporary challenges
Jesuit schools, in particular, have been known for a long time to be centres of learning in Chotanagpur area, India, where tremendous efforts were made to achieve a high level of academic excellence; yet it appears that this legacy is not being sustained among rural, tribal, vernacular schools of Chotanagpur these days because of varied reasons. In addition, it is observed that the agents of education, namely teachers, working in these schools have ceased to represent desirable attitudes (psychological wellbeing and affective commitment) which are essential for the teaching-learning process. If Jesuits want to maintain relevant and inspirational education in Chotanagpur, then they need to discern, decide, and dedicate themselves to guarantee teachers psychological well-being and affective commitment so that teachers can become creators of the Jesuit education ethos. Against this backdrop, the present research paper discusses the scenario of Jesuit run rural schools in Chotanagpur its historical achievements and contemporary challenges. In addition, practical implications and recommendations for a better future are discussed. 2019 Informa UK Limited, trading as Taylor & Francis Group. -
Effects of dark matter in red giants
Dark matter (DM) which constitutes five-sixths of all matter is hypothesized to be a weakly interacting non-baryonic particle, created in the early stages of cosmic evolution. It can affect various cosmic structures in the Universe via gravitational interactions. The effect of DM in main sequence stars and stellar remnants like neutron stars and white dwarfs has already been studied. Red giant phase is a late stage of the evolution of stars. In this work, we study, low-mass red giants stars with admixture of DM and how this can effectively change the intrinsic properties of red giants such as their luminosities, temperatures and lifetimes. 2020 Elsevier B.V. -
A review on feature selection algorithms
A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection. Springer Nature Singapore Pte Ltd 2019. -
Diabe Maigre and Diabe Gras Revisited
[No abstract available] -
Psychosocial correlates of resilience among older adults in Mexico
There is a tremendousglobal increase in the older adultspopulation. Mental health in older age is as important in as it is for other age categories. Majority of older adults show healthy states, vitality, good humor and enthusiasm in performing various activities, interest in continuing to contribute to their family and society despite the difficulties of this stage of life due to large part to resilience they have. The aim of the study was to establish social and psychosocial factors associated with resilience.A cross-sectional and correlation study was conducted on older adults who were hospitalized in a public General Hospital of Mexico in 2013. Resilience, gender, occupation, family environment, self-esteem, presence of critical life events, and the presence of significant persons were assessed. 186 older adults participated. Higher levels of resilience were found in males and employed people. Participants with a functional family and high self-esteem had the highest levels of resilience. Besides, 15% of the variance of the total resilience score was explained by family environment, and 27% was explained by self-esteem (p<0.05).Although all participants were older adults, individual characteristics such as gender, occupation and self-esteem; besides family environment, were found to be associated to the levels of resilience in this population. Specific programs- -enhancing these factorsare needed to improve resilience. 2019 Oriental Scientific Publishing Company. All rights reserved. -
Translation and Validation of the Malayalam Version of the Subjective Happiness Scale
The subjective happiness scale (SHS) is a brief instrument used to measure global subjective happiness that has been translated from its original English to many other languages. To date, there is no reported translation of this scale into Malayalam, a language spoken by over 32 million people especially in the southern state of Kerala, India. In the present study, 656 community-dwelling older adults participating in the Kerala Einstein study (KES) completed the Malayalam version of the SHS. The Malayalam version demonstrated high internal consistency and good convergent validity, as assessed by comparison to measures of depression and anxiety. We also used factor analysis to determine that the Malayalam version of the SHS has a unidimensional structure, akin to the original English as well as other language adaptations. Our study adds to the repertoire of tools to measure happiness in non-English-speaking populations, enabling future research to explore the foundations of well-being across diverse cultures. The Author(s) 2024. -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Interaction of Generational Differences with Gender and Residential Nature in Attitudes Toward Interfaith Marriages
The present study examined the interaction effects of generations, gender, and residential nature on attitudes toward interfaith marriage in a sample of 1190 Indian participants from iGen, Xennials and Millennials, and Baby Boomers generations. Data were collected using a socio-demographic response sheet and the Attitude Scale, with lower ratings indicating positive attitudes and higher ratings indicating negative attitudes. The results of this study demonstrated that generational differences are significantly associated with gender and residential nature. There was a significant interaction between generation and gender and generation and residential nature on attitudes toward interfaith marriages. 2024 Taylor & Francis Group, LLC. -
Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification
The deep learning algorithms on a small dataset are often not efficient for image classification problems. Make use of the features learned by a model trained on large similar dataset and saved for future reference is a method to solve this problem. In this work, we present a comparison of full training and transfer learning for image classification using Deep Learning. Three different deep learning architectures namely MobileNetV2, InceptionV3 and VGG16 were used for this experiment. Transfer learning showed higher accuracy and less loss than full-training. According to transfer learning results, MobileNetV2 model achieved 98.96%, InceptionV3 model achieved 98.44% and VGG16 model achieved 97.405 as highest test accuracies. The full-trained models did not achieve as much accuracy as that of transfer learning models on the same dataset. The accuracies achieved by full-training for MobileNetV2, InceptionV3 and VGG16 are 79.08%, 73.44% and 75.62% respectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning
Heart disease is the leading cause of mortality globally according to the World Health Organization. Every year, it results in millions of mortalities and thus billions of dollars in economic damage throughout the world. Many lives can be saved if the disease is detected early and accurately. The typical methods to predict or diagnosis heart diseases require medical expertise. Such facilities and experts are relatively expensive and not very commonly available in under developed and developing countries. Recent times, much research is done on leveraging technology for the prediction as well as diagnosis of heart diseases. Machine Learning techniques have been extensively deployed as quick, inexpensive, and noninvasive ways for heart disease identification. In this work, we present a machine learning approach in detecting heart disease using a dataset that contains vital body parameters. We used seven different models and combined them with Soft-Voting and Hard-Voting ensemble approaches to improve accuracy in 7-model and various 5-model combinations. The ensemble combinations of 5 models achieved the highest test accuracy score of 94.2%. 2022 IEEE.