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This paper aims to identify and analyze the challenges for the Varanasi handloom industry after the COVID pandemic by considering their impact on different sections of the weavers and subsequently suggest the best possible solution for the same.

A combined approach of expert opinion and in-depth literature reviews are used to identify the challenges, and a multicriteria decision-making tool is used to rank the challenges for the type of weaver.

This research provides an elaborated view of the problems faced by the handloom industry after the COVID pandemic and suggests that the success of the handloom business is subjected to the eradication of a wide number of challenges according to the type of weaver.

The findings of this research will help the policymakers to make and align their policies and strategies for the upliftment of the Varanasi handloom industry efficiently and effectively.

To the best of the authors’ knowledge, this is the first kind of study that focuses on identifying and prioritizing the barriers affecting the success of the Varanasi handloom industry after the COVID pandemic. Furthermore, the uniqueness of this research lies in its ability to study all three independent sections of the handloom industry, having different capabilities and limitations.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

In the present study, a steel lifting lug is replaced with a composite (carbon fiber-reinforced epoxy [CFRP]) lifting lug made of a carbon/epoxy composite. The purpose of this paper was to obtain a composite lifting lug with a higher level of strength that is capable of carrying loads without failure.

The vibration and static behaviors of steel and composite lifting lugs have been investigated using finite element analysis (FEA), ANSYS software. The main consideration in the design of the composite (CFRP) lifting lug was that the displacement of both steel and composite lugs was the same under the same load. Hence, by using the FEA displacement result of the steel lifting lug, the thickness of the composite lifting lug is determined using FEA.

Compared to the steel lifting lug, the composite (CFRP) lifting lug has much lower stresses and much higher natural frequencies. Static behavior was experienced by the composite lifting lug, showing a reduction in von Mises stress, third principal stress and XZ shear stress, respectively, by 48.4%, 34.6% and 89.8%, respectively, when compared with the steel lifting lug. A higher natural frequency of mode shape swaying in X (258.976√1,000 Hz) was experienced by the composite lifting lug when compared to the steel lifting lug (195.935√1,000 Hz). The safe strength of the design composite lifting lug has been proven by FEA results, which showed that the composite (CFRP) lifting lug has a higher factor of safety in all developed stresses than the steel lifting lug. According to von Mises stress, the factor of safety of the composite lifting lug is increased by 76% when compared to the steel lifting lug. The von Mises stress at the edge of the hole in the composite lifting lug is reduced from 23.763 MPa to 20.775 MPa when compared to the steel lifting lug.

This work presents the designed composite (CFRP) lifting lug, which will be able to carry loads with more safety than a steel one.

This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC).

A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution.

Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended.

The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts.

This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.

The metaverse marks the beginning in a new era of digital interaction and innovation, having a significant impact on a variety of established sectors, including banking. This chapter exposes readers to the principles of data wrangling, laying the groundwork for comprehending its significance. It also looks at the specific issues presented by Metaverse Banking data, which includes a wide range of data kinds. To ensure consistency and practical relevance, these data must be processed in real time, whether for individuals or organizations. This chapter then transitions to unleash the power of data (which forms the lifeblood of Metaverse Banking), followed by a detailed explanation of advanced data-wrangling techniques and integration with Artificial Intelligence and machine learning. It showcases case studies illustrating how effective data wrangling has helped drive Metaverse Banking platforms by utilising real-world use cases that show the best practices of metaverse entities for a more customer-centric experience. This chapter also explores future trends, expecting the evolution of technologies for data wrangling and their possible repercussions. It further delves into regulatory considerations the nascent industry faces. This chapter underscores the need for a planned strategy in data management and provides suggestions along with best practices to guide stakeholders towards placing metaverse banks based on data. As the metaverse continues to expand and change, wrangling data will remain how banks can win on this digital frontier by keeping themselves nimble, safe and consumer-friendly in an ever more virtual world.

Industry 4.0 or I4.0 has transformed the manufacturing landscape by integrating social and technical factors by means of the sociotechnical framework. However, the sociotechnical aspects of digitalization of total quality management (TQM 4.0), especially in small and medium enterprises (SMEs) remain largely unexplored. This groundbreaking research endeavors to delve into the pivotal role played by social (soft) and technical (hard) TQM 4.0 in driving I4.0 readiness among SMEs.

A research framework has been developed by harnessing the principles of Socio-technical systems (STS) theory. Data collection from a sample of 310 randomly selected SMEs manufacturing in Malaysia through an online survey approach. The collected data is then subjected to analysis using Partial Least Square-Structural Equation Modeling (PLS-SEM) through SmartPLS.

The study findings indicate that both hard and soft TQM 4.0 factors are vital to promoting I4.0 readiness (R² = 0.677) and actual implementation (R² = 0.216). Surprisingly, the findings highlight that customer-related construct has no impact on hard TQM 4.0 attributes. Furthermore, hard TQM 4.0 factors have played a partial mediating role on the relationship of soft TQM 4.0 and I4.0 attributes (20% = VAF = 80%).

This is a novel research as it explores the underexplored domain of sociotechnical aspects of TQM 4.0 within SMEs amid I4.0 transformation. The study distinctive contributes include revealing the pivotal role of both soft and hard TQM 4.0 factors in driving I4.0 readiness, emphasizing the primacy of people-related dimensions for successful implementation in manufacturing SMEs.

This study aims to focus on optimization of process parameters for porosity and strength of polyamide porous bone scaffolds fabricated via selective laser sintering (SLS) process.

Taguchi’s design of experiment approach with L18 orthogonal array (OA) has been used to optimize the process parameters. Five process and four response parameters have been considered for this study. Initially, minimum size of the pores that can be depowdered was identified. Then, porous CAD models of test specimen to measure porosity and strength were designed in Solidworks® software and fabricated using EOSINT P395 m/c. Signal-to-noise ratio and analysis of variance were used to identify the optimal levels of parameters and statistical significance of the parameters.

Among five parameters, powder refresh rate, build chamber temperature and layer thickness were found to have significant influence on all the response parameters, whereas build orientation and build position were found insignificant for all the responses. The Taguchi’s confirmation test validated the results of the study with maximum deviation of 5.8% for compressive strength. Comparison of predicted and experimental values revealed a satisfactory predictability of all the developed linear regression models.

This study reveals optimal set of parameters for SLS of the polyamide porous bone scaffolds. The optimal set of parameters may be used by other researchers to get enhanced combination of strength and porosity while fabricating porous scaffolds.

Recent years have witnessed a spike in Industry 4.0 initiatives among manufacturing organizations, particularly in the automotive sector. This acceleration aims to enhance competitiveness by addressing various aspects, from efficiency and workforce productivity to safety and insightful decision-making. However, merely adopting technological solutions in isolation may not suffice. Automotive companies need a holistic approach that integrates the antecedents of Industry 4.0 into their overall strategy. This study aims to identify and analyse key antecedents for Industry 4.0 adoption in the Indian automotive sector.

The study follows a structured six-stage methodology, which includes a systematic literature review, expert consultations and best–worst method (BWM) analysis. The research identifies, validates and systematically ranks 16 antecedents that are pivotal for Industry 4.0 adoption.

The study categorizes 16 antecedents into four dimensions: regulatory framework (RF), technology infrastructure (TI), operational optimization (OO) and performance dynamics (PD). The findings emphasize the significance of “Government policies to support smart factories”, “Support from top management”, “Financial performance” and “Technology readiness” as crucial antecedents for Industry 4.0 implementation in the Indian automotive sector.

These findings provide valuable guidance for industry practitioners and policymakers in strategically planning the Industry 4.0 deployment in the automotive sector.

This study contributes to the limited body of research on the identification and analysis of key antecedents for Industry 4.0 adoption in the automotive sector, particularly in emerging economies such as India. By using the BWM, it offers a structured and efficient approach to determining the priority order of these antecedents.