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In this article, the authors consider the following nonlinear singular boundary value problem (SBVP) known as Lane–Emden equations, −u″(t)-(α/t) u′(t) = g(t, u), 0 < t < 1 where α ≥ 1 subject to two-point and three-point boundary conditions. The authors propose to develop a novel method to solve the class of Lane–Emden equations.

The authors improve the modified variation iteration method (VIM) proposed in [JAAC, 9(4) 1242–1260 (2019)], which greatly accelerates the convergence and reduces the computational task.

The findings revealed that either exact or highly accurate approximate solutions of Lane–Emden equations can be computed with the proposed method.

Novel modification is made in the VIM that provides either exact or highly accurate approximate solutions of Lane-Emden equations, which does not exist in the literature.

The purpose of this paper is to propose an efficient computational technique, which uses Haar wavelets collocation approach coupled with the Newton-Raphson method and solves the following class of system of Lane–Emden equations:

To deal with singularity, Haar wavelets are used, and to deal with the nonlinear system of equations that arise during computation, the Newton-Raphson method is used. The convergence of these methods is also established and the results are compared with existing techniques.

The authors propose three methods based on uniform Haar wavelets approximation coupled with the Newton-Raphson method. The authors obtain quadratic convergence for the Haar wavelets collocation method. Test problems are solved to validate various computational aspects of the Haar wavelets approach. The authors observe that with only a few spatial divisions the authors can obtain highly accurate solutions for both initial value problems and boundary value problems.

The results presented in this paper do not exist in the literature. The system of nonlinear singular differential equations is not easy to handle as they are singular, as well as nonlinear. To the best of the knowledge, these are the first results for a system of nonlinear singular differential equations, by using the Haar wavelets collocation approach coupled with the Newton-Raphson method. The results developed in this paper can be used to solve problems arising in different branches of science and engineering.

The numerical solution of third-order boundary value problems (BVPs) has a great importance because of their applications in fluid dynamics, aerodynamics, astrophysics, nuclear reactions, rocket science etc. The purpose of this paper is to develop two computational methods based on Hermite wavelet and Bernoulli wavelet for the solution of third-order initial/BVPs.

Because of the presence of singularity and the strong nonlinear nature, most of third-order BVPs do not occupy exact solution. Therefore, numerical techniques play an important role for the solution of such type of third-order BVPs. The proposed methods convert third-order BVPs into a system of algebraic equations, and on solving them, approximate solution is obtained. Finally, the numerical simulation has been done to validate the reliability and accuracy of developed methods.

This paper discussed the solution of linear, nonlinear, nonlinear singular (Emden–Fowler type) and self-adjoint singularly perturbed singular (generalized Emden–Fowler type) third-order BVPs using wavelets. A comparison of the results of proposed methods with the results of existing methods has been given. The proposed methods give the accuracy up to 19 decimal places as the resolution level is increased.

This paper is one of the first in the literature that investigates the solution of third-order Emden–Fowler-type equations using Bernoulli and Hermite wavelets. This paper also discusses the error bounds of the proposed methods for the stability of approximate solutions.

The purpose of this paper is to develop a machine learning model that predicts the component self-alignment offsets along the length and width of the component and in the angular direction. To find the best performing model, various algorithms like random forest regressor (RFR), support vector regressor (SVR), neural networks (NN), gradient boost (GB) and K-nearest neighbors (KNN) were performed and analyzed. The models were implemented using input features, which can be categorized as solder paste volume, paste-pad offset, component-pad offset, angular offset and orientation.

Surface-mount technology (SMT) is the technology behind the production of printed circuit boards, which is used in several types of commercial equipment such as communication devices, home appliances, medical imaging systems and sensors. In SMT, components undergo movement known as self-alignment during the reflow process. Although self-alignment is used to decrease the misalignment, it may not work for smaller size chipsets. If the solder paste depositions are not well-aligned, the self-alignment might deteriorate the final alignment of the component.

It were trained on their targets. Results obtained by each method for each target variable were compared to find the algorithm that gives the best performance. It was found that RFR gives the best performance in case of predicting offsets along the length and width of the component, whereas SVR does so in case of predicting offsets in the angular direction. The scope of this study can be extended to developing this model further to predict defects that can occur during the reflow process. It could also be developed to be used for optimizing the placement process in SMT.

This paper proposes a predictive model that predicts the component self-alignment offsets along the length and width of component and in the angular direction. To find the best performing model, various algorithms like RFR, SVR, NN, GB and KNN were performed and analyzed for predicting the component self-alignment offsets. This helps to achieve the following research objectives: best machine learning model for prediction of component self-alignment offsets. This model can be used to optimize the mounting process in SMT, which reduces occurrences of defects and making the process more efficient.

The coronavirus disease 2019 (COVID-19) pandemic has become a global humanitarian challenge. This scourge has impacted people from all walks of life as well as every economic sector and activity, from travel to automotives, hotels to banking, and supply chain to retail. The pandemic has affected not only physical and mental health but also financial health. Studies have examined the pandemic's economic impact, but very few have examined its impact on personal finances. Efforts to contain the pandemic's spread, such as lockdowns, have resulted in suspended business operations throughout the world that have intensified joblessness. To prepare and protect people from such unforeseen situations, financial education and planning are necessary. We attempt to expand the evidence on this issue by applying a structural equation modelling approach to identify the mediating role of financial literacy programs in preparing and protecting household wealth against sudden worldwide setbacks. The research design is descriptive and exploratory using snowball sampling technique. The data was collected through an internet survey. In total, 400 survey responses were obtained. After testing the measurement model for key validity dimensions, the hypothesised causal relationships are examined in several path models. The results indicated that coronavirus awareness exerts a direct or indirect influence on the financial health of individuals through financial literacy. We conclude that financial literacy has a full mediating effect on the personal finance of individuals during the COVID-19 pandemic. The findings not only contributed to the need and understanding of financial literacy but also have managerial implications. Financial literacy programs provide investment advice and suggestions which are actionable and also work to help individuals to come out stronger in terms of knowledge and skill set when the COVID-19 crisis passes.

The purpose of the study was to find out the effect of authentic leadership on innovative work behavior among employees in the context of Indian startups that have gone global in the past one decade. The study also aimed to investigate the mediating effect of one organizational-level variable, i.e. organizational climate and one individual-level variable, i.e. readiness for change on the direct relationship between authentic leadership and innovative work behavior.

The study used survey method with a sample of 261 employees working in Indian startups that have gone global in the past one decade. Authentic leadership, organizational climate, readiness for change and innovative work behavior were measured using standardized questionnaires.

Structural equation modeling revealed that authentic leadership had significant direct effect on innovative work behavior and this effect was mediated by readiness for change. However, organizational climate had no significant effect.

The findings of the study have important implications for startups that employ millennials as employees and have plans to go global. Millennials have very different characteristics in the workplace and they regard authenticity in the leader very highly. Since they are assertive and they question everything, it becomes imperative for founder-leaders to exhibit authenticity in their actions, words and thoughts.

This is the first study in the context of Indian startups that have gone global by taking important variables of authentic leadership, readiness for change and innovative work behavior.

This paper aims to discuss innovations in the training and development practices of companies and delineate a new approach to training and development in the context of the hybrid workplace using the ADDIE and Kirkpatrick training models.

This paper discusses innovations in training and development in modern times and builds on the instructional training design approach or the ADDIE Model and the Kirkpatrick Model of training evaluation.

The paper presents new approaches to training and development in the context of the hybrid work model applying the ADDIE Model and the Kirkpatrick Model. These new approaches are both necessitated and also made possible due to the technological advancements of modern times.

With the rapid transition of companies to the hybrid model of work in recent times, several human resource management practices need to be transformed to suit the requirements of the new work model. Training and development is one function that needs to change in the hybrid work model to ensure its effectiveness. This paper analyses innovations in the training and development practices of companies and discusses new approaches while applying existing training models, the ADDIE and Kirkpatrick Models, to adapt to the changes associated with the hybrid work model.

Work-from-home (WFH) gained ground with COVID and will now continue to be a part of India’s future of work. Under WFH information and communication technologies (ICT) media become the primary/sole mode of communication for employees, which holds several implications for employers and employees. Therefore, this study aims to investigate the impact of ICT media characteristics and usage frequency on multiple WFH outcomes. Specifically studied was ICT media’s ability to support synchronicity or coordinated behaviours of individuals working together.

This work examined the effect of ICT media’s synchronicity-supporting ability and usage frequency on WFH employees’ need for competence and relatedness satisfaction, thereby wellbeing and preference to WFH. Data from 301 white-collar employees of varied manufacturing and services organizations of India was analysed via partial least squares structural equation modelling.

Achieving more synchronicity by frequently using ICT media that can better facilitate coordinated behaviours did not directly influence WFH employees’ feeling of belongingness (need for relatedness) or wellbeing. It did, however, positively affect their feeling of effectance (need for competence) and thereby wellbeing. However, unexpectedly, it negatively influenced preference to WFH more often.

This study has uniquely combined media synchronicity and self-determination theories to investigate the implications of a work practice on employee wellbeing and preferences. Also, an extensible media evaluation parameter was created that encompasses the characteristics and usage frequency of a set of ICT media.

This study aims to examine the relationship between work-family conflict (WFC) and personal self-efficacy among career persistent women in India. Further, this relationship was explained with the help of the mediating role of perceived managerial support.

Survey data were collected from 510 career persistent women working in the Information Technology (IT) sector in India. The data were analyzed using structural equation modeling and mediation was tested using Process- Macro.

Findings depicted a positive relationship between WFC and professional self-efficacy demand and self-efficacy challenges. Perceived managerial support was also found to be positively related professional self-efficacy. Furthermore, perceived managerial support was found to have a significant mediating effect in WFC and professional self-efficacy relationships.

Findings of the study may enhance the understanding of WFC in emerging economies, as most of the research has been done in the western context. Findings of the study are crucial, as it highlights the relationship between WFC and professional self-efficacy in the presence of perceived managerial support. Moreover, the paper uniquely discusses the role of WFC in professional self-efficacy among career persistent women in IT sector.

This comprehensive study aims to take a punctilious approach intended to present qualitative and quantitative knowledge on the emerging concept of noise trading and identify the emerging themes associated with noise trading.

This study combines bibliometric and content analysis to review 350 publications from top-ranked journals published from 1986 to 2020.

The bibliometric and content analysis identified three major themes: the impact of noise traders on the functioning of the stock market, traits of noise traders and different proxies used to measure the impact of noise trading.

This study undertakes research papers related to the field of finance, published in peer-reviewed journals and that too in the English language.

This study shall accommodate rational traders, portfolio consultants and other investors to gain deeper insights into the functioning of noise traders. This will further help them to formulate their trading/investment strategies accordingly.

The successful combination of the bibliometric and content analysis revealed major gaps in the literature and provided future research directions.