PurposeIndustry 4.0 and circular economy are the two major areas in the current manufacturing industry. However, the adoption and implementation of Industry 4.0 and circular economy worldwide are still in the nascent stage of development. To address this gap, the purpose of this article is to conduct a systematic literature review on integrating Industry 4.0 and circular economy. Further, identify the research gaps and provide the future scope of work in this area.Design/methodology/approachContent-based analysis was adopted for reviewing the research articles and proposed a transition framework that comprises of four categories, namely, (1) Transition from Industry 3.0 to Industry 4.0 and integration with circular economy; (2) Adoption of combined factors and different issues; (3) Implementation possibilities such as front-end technologies, integration capabilities and redesigning strategies; (4) Current challenges. The proposed study reviewed a total of 204 articles published from 2000 to 2020 based on these categories.FindingsThe article presents a systematic literature review of the last two decades that integrates Industry 4.0 and circular economy concepts. Findings revealed that very few studies considered the adoption and implementation issues of Industry 4.0 and circular economy. Moreover, it was found that Industry 4.0 technologies including digitalization, real-time monitoring and decision-making capabilities played a significant role in circular economy implementation. The major elements are discussed through the analysis of the transition and integration framework. The study further revealed that a limited number of developing countries like India have taken preliminary initiatives toward Industry 4.0 and circular economy implementation.Research limitations/implicationsThe study proposes a transition and integration framework that identifies adoption and implementation issues and challenges. This framework will help researchers and practitioners in implementation of Industry 4.0 and circular economy.Originality/valueReviews of articles indicated that there are very few studies on integrating Industry 4.0 and circular economy. Moreover, there are very few articles addressing adoption and implementation issues such as legal, ethical, operational and demographic issues, which may be used to monitor the organization's performance and productivity.

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

In this work, we study the parameterized complexity of various classical graph-theoretic problems in the dynamic framework where the input graph is being updated by a sequence of edge additions and deletions. Vertex subset problems on graphs typically deal with finding a subset of vertices having certain properties that are of interest to us. In real-world applications, the graph under consideration often changes over time and due to this dynamics, the solution at hand might lose the desired properties. The goal in the area of dynamic graph algorithms is to efficiently maintain a solution under these changes. Recomputing a new solution on the new graph is an expensive task especially when the number of modifications made to the graph is significantly smaller than the size of the graph. In the context of parameterized algorithms, two natural parameters are the size k of the symmetric difference of the edge sets of the two graphs (on n vertices) and the size r of the symmetric difference of the two solutions. We study the Dynamic Π-Deletion problem which is the dynamic variant of the Π-Deletion problem and show NP-hardness, fixed-parameter tractability and kernelization results. For specific cases of Dynamic Π-Deletion such as Dynamic Vertex Cover and Dynamic Feedback Vertex Set, we describe improved FPT algorithms and give linear kernels. Specifically, we show that Dynamic Vertex Cover admits algorithms with running times 1.1740 k n O(1) (polynomial space) and 1.1277 k n O(1) (exponential space). Then, we show that Dynamic Feedback Vertex Set admits a randomized algorithm with 1.6667 k n O(1) running time. Finally, we consider Dynamic Connected Vertex Cover, Dynamic Dominating Set and Dynamic Connected Dominating Set and describe algorithms with 2 k n O(1) running time improving over the known running time bounds for these problems. Additionally, for Dynamic Dominating Set and Dynamic Connected Dominating Set, we show that this is the optimal running time (up to polynomial factors) assuming the Set Cover Conjecture.

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