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The study principally aims to investigate the relationship among work–life balance (WLB), job satisfaction (JS) and employee commitment (EC) among the railway staff by integrating the social exchange theory (SET). The study also explores JS as a mediator in the context of the new normal.

A mixed-method sequential explanatory design was utilized for this study. The research instrument was administered to 533 railway employees, using purposive sampling to ensure reliability and validity. Structural equation modeling (SEM) was employed to analyze the quantitative data and measure mediation effects. Additionally, 15 interviews were conducted with employees from three job positioning groups (A, B and C) to gain further insights into commitment-related concerns.

The study found that WLB and JS positively influenced EC, with JS acting as a mediator between WLB and EC. Furthermore, factors such as fostering friendships among colleagues, effectively managing work–life integration and recognizing the importance of job roles were identified as crucial in enhancing the relationship between WLB and EC.

The study includes SET to examine the social exchange process while considering WLB benefits as a reward from the employer and EC as the outcome of this reward. This study contributes by examining the effects of COVID-19 on the railway industry and EC. The mixed-methods sequential explanatory design gave a comprehensive understanding of the relationships between WLB, JS and EC. The study’s implications highlight the importance of implementing supportive policies, such as flexible work schedules and a supportive organizational culture, to enhance employee commitment and reduce attrition rates. The study emphasizes the significance of prioritizing employee well-being to achieve organizational goals and enhance organizational commitment.

The purpose of this paper is to present a multi criterion failure mode effect and criticality analysis for coal-fired thermal power plants using uncertain data as well as substituting the traditional risk priority number estimation method.

Grey-complex proportional assessment (COPRAS-G) method, a multi criteria decision making tool is applied to evaluate the criticalities of the failure modes (alternatives). In this model the criteria (criticality factor) against each alternative are expressed in grey number instead of crisp values.

Rupture failure of the straight tube of economizer (ECO) due to erosion is the highest critical failure mode whereas rupture failure of the stub of ECO due to welding defect is the lowest critical failure mode.

This paper incorporates human and environmental factors as additional factors which also influence the failure modes significantly. The COPRAS-G method is modified according this problem. Uncertainty in the scoring of criticality factors against each failure mode by various maintenance personnel is expressed in grey numbers.

The purpose of this paper is to analyse of structures made in rock mass with multiple intersecting discrete discontinuities such as joint, fault, shear plane.

In this study, a numerical method is proposed for analyzing multiple intersecting joints with varying dip angles, spacing and roughness in eXtended Finite Element Method platform. A procedure is also outlined to treat excavated enhanced (jointed) elements for analysing the effect of excavation sequences.

The proposed method is compared with the existing interface element methods (Phase-2 model) by considering the stress and displacement distributions of a multiple intersecting jointed rock sample under uniaxial loading conditions. A circular tunnel in rock mass having intersecting joints is also analyzed for the distribution of mobilised friction angle of joints and results are compared with a derived analytical solution.

Nucleation and propagation of cracks should be incorporated into the proposed framework in future studies.

The proposed method is a useful tool for rock mechanics and geotechnical engineering problems to analyse strength and deformability of jointed rock masses.

The paper enumerates concepts and detail implementation procedures of the proposed method in three-noded triangular elements. The intersection of joints is formulated in such a way that no additional (junction) enrichment is required in model. The method has been improved for inclusion of Dirichlet and Neumann boundary conditions to be applied in the enhanced part of a problem domain.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly.

By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts.

Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment.

Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP.

To the best of the authors’ knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS.

The purpose of this paper is to examine the impact of public expenditure on economic growth and poverty alleviation in developing countries like India. If poverty and inequality are high, the government may resort to distributive policies at the cost of long-term growth. The distributive policies and poverty alleviation measures fail to achieve success due to lack of good governance, lack of proper targeting and problems in the implementation of such schemes. On the other hand, if the nature of public expenditure is such that it enhances per capita income, it will help reduce poverty.

After analytical digression and construction of hypotheses panel regression has been done using state-level data in the Indian context to empirically verify the above propositions. Both Fixed effects and Random effects models have been used for this purpose.

The results show that in states where ratio of public expenditure on the development of infrastructure such as road, irrigation, power, transport and communication is higher, per capita income is also higher and incidence of poverty is lower indicating that economic growth is important for poverty alleviation and development of infrastructure is necessary for growth.

This study demonstrates how public policy and public finance can be used as instruments for removal of poverty.