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The purpose of this study is to solve two unique but difficult partial differential equations: the foam drainage equation and the nonlinear time-fractional fisher’s equation. Through our methods, we aim to provide accurate solutions and gain a deeper understanding of the intricate behaviors exhibited by these systems.

In this study, we use a dual technique that combines the Aboodh residual power series method and the Aboodh transform iteration method, both of which are combined with the Caputo operator.

We develop exact and efficient solutions by merging these unique methodologies. Our results, presented through illustrative figures and data, demonstrate the efficacy and versatility of the Aboodh methods in tackling such complex mathematical models.

Owing to their fractional derivatives and nonlinear behavior, these equations are crucial in modeling complex processes and confront analytical complications in various scientific and engineering contexts.

The purpose of this paper is to compile a comprehensive status report on pipes/piping networks across different industrial sectors, along with specifications of materials and sizes, and showcase welding avenues. It further extends to highlight the promising friction stir welding as a single solid-state pipe welding procedure. This paper will enable all piping, welding and friction stir welding stakeholders to identify scope for their engagement in a single window.

The paper is a review paper, and it is mainly structured around sections on materials, sizes and standards for pipes in different sectors and the current welding practice for joining pipe and pipe connections; on the process and principle of friction stir welding (FSW) for pipes; identification of main welding process parameters for the FSW of pipes; effects of process parameters; and a well-carved-out concluding summary.

A well-carved-out concluding summary of extracts from thoroughly studied research is presented in a structured way in which the avenues for the engagement of FSW are identified.

The implications of the research are far-reaching. The FSW is currently expanding very fast in the welding of flat surfaces and has evolved into a vast number of variants because of its advantages and versatility. The application of FSW is coming up late but catching up fast, and as a late starter, the outcomes of such a review paper may support stake holders to expand the application of this process from pipe welding to pipe manufacturing, cladding and other high-end applications. Because the process is inherently inclined towards automation, its throughput rate is high and it does not need any consumables, the ultimate benefit can be passed on to the industry in terms of financial gains.

To the best of the authors’ knowledge, this is the only review exclusively for the friction stir welding of pipes with a well-organized piping specification detailed about industrial sectors. The current pipe welding practice in each sector has been presented, and the avenues for engaging FSW have been highlighted. The FSW pipe process parameters are characteristically distinguished from the conventional FSW, and the effects of the process parameters have been presented. The summary is concise yet comprehensive and organized in a structured manner.

Heritage tourism is an increasingly popular form of tourism that allows individuals to connect with the past and immerse themselves in cultural and historical narratives. Hence, the purpose of this study is to explore the intricate relationships among vicarious nostalgia (VNOS), memorable tourism experiences (MTEXs) and their collective influence on tourists’ behavioral intentions (BINTs). Additionally, this study examines the moderating effect of social return (SN) in the context of heritage tourism.

Data were gathered using a self-administered questionnaire from 259 tourists visiting heritage sites in Jaipur. The proposed model was tested using structural equation modeling.

The results confirmed that VNOS had a significant positive impact on BINT in the context of heritage tourism. The causal relationship between VNOS and BINT was fully mediated by MTEX. The results further verified that the presence of SN strengthens the association between MTEXs and BINT.

This research will guide the firms associated with heritage tourism to target specific cohorts interested in heritage tourism. Policymakers may find it easier to create unique offerings and packages that appeal to visitors interested in historical sites and produce memorable travel experiences. One key implication is to create “social media friendly spaces” at different locations of the sites. To increase tourism, managers may use the findings from this research to create plans for the ethical promotion and protection of cultural and natural heritage sites.

Overall, this research advances the understanding of the role of VNOS in heritage tourism by elucidating its cognitive and emotional aspects and their subsequent influence on the memorability of tourist experiences and BINT s. Additionally, by considering the moderating effect of SN, this study provides a comprehensive view of how these factors collectively shape tourists’ decisions and actions in the context of heritage destinations. This research has been conducted in the heritage city of Jaipur (North-Western India), which, surprisingly – despite its popularity as a heritage tourism site – has not been sufficiently explored in the scholarly research.

HR analytics is a process for systematic computational analysis of data or statistics. It discovers, interprets and communicates significant patterns in data to enable evidence-based HR research and uses analytical insights to help organizations achieve their strategic objectives. However, its adoption and utilization among HR professionals remain a subject of concern. This study aims to determine the reasons that facilitate or inhibit the acceptance of HR analytics among HR professionals in the banking, financial services and insurance (BFSI) sector.

A sample of 387 HR professionals in BFSI firms across India was collected through non-probabilistic purposive sampling. Structural equation modeling was applied to analyze the association between predetermined variables. In addition, the predictive relevance of “Data Availability” was analyzed using hierarchical regression.

The results revealed that data availability, hedonic motivation and performance expectancy positively influenced behavioral intention (BI). In contrast, effort expectancy, social influence and habit had an insignificant effect on BI. Also, facilitating conditions (FCs), habit, BI achieved a variance of 60% in HR analytics use. The use behavior of HR analytics was significantly influenced by FCs and BIs.

This study focuses on insights into the elements that influence HR analytics adoption, revealing additional light on success drivers and grey areas for failed adoption.

This research adds to the body of knowledge by identifying factors that hinder the adoption of HR analytics in Indian organizations and signifies the relevance of easy accessibility and availability of data for technology adoption.

The study investigates the mediated moderation impact of Memorable Religious Experience (MRE) and Religiosity (REL) on the relationship between Memorable Tourism Experience (MTE) and Attitude towards Pilgrimage (ATT) finally driving Recommend Intention (RCI) of visitors to a religious site. It suggests visitors' incentive variable religiosity can influence their decision to recommend visiting a religious destination.

The research uses a quantitative cross-sectional approach wherein a self-administered survey was used for data collection from 223 pilgrims who visited a popular pilgrimage site. Partial least squares-structural equation modeling (PLS-SEM) was employed for analysis.

The results showed that MTE has a significant influence on ATT which further influences RCI (a dimension of behavioral intention-BI) of visitors towards a religious destination. Further, MRE mediates the relationship between MTE and ATT. Nevertheless, REL illustrated a significant moderation influence on the relationship between MRE and ATT, further verifying the mediated moderation impact of MRE and REL in the model.

Recommendation of existing customers is one of the most powerful indicators of customer loyalty and usually leads to revisit. The research provides destination managers/tourism planners of pilgrimage sites to formulate appropriate marketing strategies to develop RCI and sustainable branding.

This study adds to the empirical studies conducted on REL by constructing a composite picture of the memorable tourism experience within a pilgrimage tourism context. The uniqueness lies in the attempt to investigate the mediated moderation impact of MRE and REL using a symmetric (PLS-SEM) approach.

Purpose: This chapter examines the impact of artificial intelligence (AI) on employability and dispels the misconception that AI negatively affects job opportunities. The study aims to shed light on the ways in which AI can enhance employability by complementing natural intelligence and enabling employees to demonstrate creativity in various aspects of their work.

Need for the study: In the 21st century, AI has become ubiquitous, and governments worldwide are actively promoting its integration into various industries and systems. However, concerns about the potential negative consequences of AI have emerged.

Methodology: It is reviewing commentary secondary sources of data viz. books, articles, journals, newspaper articles, reports which have been considered to bring forth the advent of AI being an important premise for the construct of employability

Findings: The findings of this study reveal that the perceived negative impact of AI on employability is a misconception. AI technology, such as Alexa, ChatGPT, and OpenAI, has made significant advancements in the market but is still unable to pass the Turing test. Consequently, it is recommended that AI companies take a pause to fully understand and address the consequences associated with AI implementation.

Practical implications: The practical implications of this study are twofold. First, it debunks the myth that AI jeopardises employability associated with natural intelligence, highlighting the importance of human skills in conjunction with AI technologies. Second, it calls for a strategic approach for organisations and governments to adapt to AI while ensuring the workforce remains adaptable and equipped with the necessary skills. This study provides insights for policymakers, employers, and individuals to embrace AI to augment human potential and improve global market productivity.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

The purposes of this paper are (1) to identify and rank the various enablers for an integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS), and (2) to study their correlations and their impact on organizational performance.

Three tiers methodology is used to analyze the enablers for the successful adoption of ISGLSAMS. First, a total of 32 ISGLSAMS enablers are identified through a comprehensive literature review. Then, data are collected with a structured questionnaire from 108 Indian manufacturing industries. Then, an analytic approach is used to analyze (1) the relevance and significance of enablers and (2) their correlations (1) with each other, and (2) with the organizational performance outcomes, to strengthen the understanding of ISGLSAMS.

The findings suggest that top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, customers' and stakeholders' involvement, corporate social responsibility (CSR), customers and stakeholders-focused strategic alliances, dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, standardized tasks for continuous improvement, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment enablers are the higher level enablers for the adoption of ISGLSAMS. Findings also suggest that there is a scope for research in the incorporation of lot size reduction, Keiretsu-Kraljic supply chain relationship strategy, external collaborations with the stakeholders other than supply chain members, matrix flatter organization structure, employees' career development, justified employees' wages, government support for research fund and subsidies and vendor-managed inventory practices for ISGLSAMS. Top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, corporate social responsibility (CSR), dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment have a significant effect on (1) sustainable product design, (2) sustainable production system, (3) improvement in the sale, (4) improvement in market responsiveness, (5) improvement in the competitive position and (6) improvement in the global market image.

Through this study of ISGLSAMS enablers and their interdependence, and their impact on ISGLSAMS performance outcomes government, organizations, stakeholders, policymakers and supply chain partners may plan the policy, roadmap and strategies for the successful adoption of (1) ISGLSAMS in the organizational value chain, and (2) Industrial ecology and industrial symbiosis in India. The study also contributes to the industrial managers, and value chain partners a better understanding of ISGLSAMS.

This study is the first attempt to understand (1) the ISGLSAMS enablers and their correlations, and (2) the effect of ISGLSAMS enablers on ISGLSAMS performance outcomes to get the competitive and sustainability advantage. The study contributes to the practitioners, policymakers, organizations, government, researchers and academicians a better understanding of ISGLSAMS enablers and its performance outcomes.