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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.

To resolve an inherent dilemma in extant research on geographically dispersed research and development (R&D), this study explores interdependencies between formal and informal network structures. Firms that seek to benefit from the decentralization associated with disperse R&D must align it with an informal structure that enhances organizational members’ motivation to share and assimilate their unique knowledge and skills. On the basis of an investigation among 424 US biotechnology firms between 1973 and 2003, this study reveals the moderating effect of the firm’s informal social structure on the effect that geographically dispersed R&D personnel have on the exploration of new technological opportunities. Specifically, the higher the social network density among R&D members, the more likely geographic disparity is to affect exploration; however, this likelihood decreases with an increase in power asymmetries. These results offer insights into the conditions in which the appropriate management of geographically dispersed R&D varies.

The notion of deeper experiences of Hindu devotees inspired by divine images and sacred places has roots in the historical past, going back to the Vedic period (ca. 2500 BCE), where we find rich literature on performances, rituals and merits of pilgrimages. Considered the bridge between human beings and divinities, the experiences received are the resultant ‘blissful fruit’ (phala) that helps the spiritual healing of pilgrims through awakening conscience and understanding the manifested meanings, symbolism, purposes and gains. This system can be viewed concerning the ‘texts’ (the mythology, ancient text and related narratives) and the ‘context’ (contemporality and living tradition). These rules and performances have regional perspectives of distinctions, but they also carry the sense of universality, i.e. locality (sthānic) and universality (sarvavyāpika) interfaces. The devout Hindus reflect their experiences in conception, perception, reception and co-sharedness – altogether making the wholistic network of belief systems, i.e. the religious wholes in Hindu society. This chapter deals with four aspects: the historical and cultural contexts, the meanings and merits received, the motives and the journey and interfacing experiences. The study is based on the experiential and questionnaire-based exposition and interviews of pilgrims at nine holy places during 2015–2019 on various festive occasions and is illustrated with ancient texts and treatises. The sacred cities included are Prayagraj, Varanasi, Gaya, Ayodhya, Vindhyachal, Ganga Sagar, Chitrakut, Mathura Vrindavana and Bodh Gaya.

The work presents a novel implementation of the generalized minimum residual (GMRES) solver in conjunction with the interpolating meshless local Petrov–Galerkin (MLPG) method to solve steady-state heat conduction in 2-D as well as in 3-D domains.

The restarted version of the GMRES solver (with and without preconditioner) is applied to solve an asymmetric system of equations, arising due to the interpolating MLPG formulation. Its performance is compared with the biconjugate gradient stabilized (BiCGSTAB) solver on the basis of computation time and convergence behaviour. Jacobi and successive over-relaxation (SOR) methods are used as the preconditioners in both the solvers.

The results show that the GMRES solver outperforms the BiCGSTAB solver in terms of smoothness of convergence behaviour, while performs slightly better than the BiCGSTAB method in terms of Central processing Unit (CPU) time.

MLPG formulation leads to a non-symmetric system of algebraic equations. Iterative methods such as GMRES and BiCGSTAB methods are required for its solution for large-scale problems. This work presents the use of GMRES solver with the MLPG method for the very first time.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

The purpose of this study is to identify factors that are important for logistics organizations from the perspective of manpower readiness for digitization of logistics operations. The study also prioritizes the identified factors and also evaluates the readiness index of manpower for the digitalization of logistics processes.

The factors for manpower readiness are identified through literature review and analysis of a case study. Three major categories of factors are identified. These are organizational, behavioural and technological factors. Under these three major categories of factors, 18 sub-factors are identified. Thereafter, with experts' inputs, the factors are prioritized using Fuzzy analytic hierarchy process (AHP). Further, a case illustration of an Indian logistics company has been taken to understand the current processes, technical capabilities, manpower skills and organization culture. After the case analysis and expert inputs, the manpower readiness index has been evaluated by using graph theory matrix approach (GTMA).

The prioritization of manpower readiness factors has been done using Fuzzy AHP. Organizational factors are found to be the most important factors which require quick attention. Sub-factors that are most important for building competencies in the logistics sector are providing the right training on functional skill development (0.129), top management support and commitment for digitalization (0.117), and organizational culture for process digitalization (0.114), etc. Finally, framework for evaluation of manpower readiness index for logistics operations in the digital age has been illustrated for a case company.

Indian logistics companies can benchmark their readiness index with respect to the best in the industry. Based on the readiness index, logistics companies can analyse their position, gaps from best and worst and can also identify potential areas for improvement.

The novelty of the study lies in the development of a framework for manpower readiness for digitalization in the logistics sector. In literature, this field is very less researched and provides the scope for developing strategies for improving manpower competencies for Industry 4.0. Logistics companies can improve their performance by making their manpower ready based on results obtained for readiness index.

The purpose of this study is to investigate dimensional accuracy (Δd), surface roughness (Ra) and micro hardness (HV) of partial dentures (PD) prepared with synergic combination of fused deposition modelling (FDM) assisted chemical vapour smoothing (CVS) patterns and conventional dental casting (DC) from multi-factor optimization view point.

The master pattern for PD was prepared with acrylonitrile butadiene styrene (ABS) thermoplastic on FDM set-up (one of the low cost additive manufacturing process) followed by CVS process. The final PD as functional prototypes was casted with nickel–chromium-based (Ni-Cr) alloy by varying Ni% (Z). The other input parameters were powder to water ratio P/W (X) and pH value (Y) of water used.

The results of this study suggest that for controlling the Δd and R_a of the PD, most important factor is X, followed by Z. For hardness of PD, the most important factor is Z. But from overall optimization viewpoint, the best settings are X-100/12, Y-10 and Z-61% (in Ni-Cr alloy). Further, based upon X-bar chart (for HV), the FDM-assisted DC process used for preparation of PD is statistically controlled.

This study highlights that PD prepared with X-100/12, Y-10 and Z-61% gives overall better results from multi-factor optimization view point. Finally, X-bar chart has been plotted to understand the statistical nature of the synergic combination of FDM, CVS and DC.

Manufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).

This research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.

A dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.

The study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.

To the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.