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This paper aims to study boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems and to present their efficient numerical resolution and analysis.

This study includes cases in which the diffusion parameters are small, distinct and can differ in order of magnitude. The source term is considered to be discontinuous. The asymptotic behavior of the solution is examined. The layer structure is analyzed, leading to the development of a variant of layer-resolving Shishkin mesh. For efficient numerical resolution, two methods are developed by combining additive schemes on a uniform mesh to discretize in time and an upwind difference scheme away from the line of discontinuity and a specific upwind difference scheme along the line of discontinuity, defined on a variant of layer resolving Shishkin mesh, to discretize in space. The analysis of the numerical resolution is discussed using the barrier function approach. Numerical simulations provide a verification of the theory and efficiency of the approach.

The discontinuity in the source term, along with the inclusion of small and distinct diffusion parameters, results in multiple overlapping and interacting boundary and interior layers. The work demonstrates that the present approach is robust in resolving boundary and interior layers. From a computational cost perspective, the numerical resolution presented in the paper is more efficient than conventional approaches.

Efficient numerical resolution and analysis of boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems are provided. The discretization of the coupled system in the approach incorporates a distinctive feature, wherein the components of the approximate solution are decoupled at each time level, resulting in tridiagonal linear systems to be solved, in contrast to large banded linear systems with conventional approaches.

The present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium Enterprises (MSMEs). Furthermore, the study intended to investigate the influence of ESE on the operating efficiency of Indian MSMEs and its mediating role.

In this study, exploratory research design is used. The study heavily relies on the primary data which has been collected by using the survey research method from a cross-section of 617 women-owned MSMEs, located in urban, rural, suburban and exurban areas of Haryana, Uttarakhand, Himachal Pradesh and NCR-Delhi. The partial least square structural equation modeling method version 3.3.3 has been used to evaluate.

In terms of the selected factors affecting access to finance, it has been established that the Loan Formalities, Banking Process, Loan Process, Staff Responsiveness and Incentive Scheme have a positive and significant influence in enhancing accessibility to finance and improving the self-efficacy and operating performance of firms. The findings also show that ESE mediates the relationship between various factors of loan access and the operating efficiency of MSMEs.

The study’s findings show that entrepreneurial capacity is significantly and favorably impacted by attitudes toward entrepreneurship, ESE, perceived access to findings and business operations. It has also been demonstrated that entrepreneurial intentions are strongly and favorably influenced by entrepreneurial ability to access commercial bank financing for small businesses and the impact of the same on the women-owned MSMEs in India. It also revealed unfavorable loan terms, limited collateral, fear of repaying of loan and intricate loan application were among the many reasons for loan denial.

The study offers a comprehensive approach that simultaneously considers financial accessibility and ESE. This all-encompassing method offers a thorough grasp of the variables affecting MSMEs' operational efficiency (OE). In contrast to earlier research that might have concentrated only on direct relationships, this study explores the mediating mechanisms involved. This study examines how ESE modulates the influence of financing availability on OE, providing a comprehensive understanding of the underlying mechanisms. By taking into account particular MSME sector characteristics like size, industry or regional variations, the study may provide a unique contextual lens. Understanding how these contextual factors interact with entrepreneurial attributes and access to finance adds depth to the analysis.

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

Entrepreneurs belonging to ethnic minority groups have emerged as a significant and more powerful element within the private sector, having considerable economic and social impact. Nevertheless, prior empirical research has indicated that each geographical area has distinct social and cultural obstacles that impact entrepreneurs in varying ways. Hence, the purpose of this study is to examine the difficulties and barriers faced by women entrepreneurs from diverse ethnic origins in the United Kingdom, a developed region, while managing their firms.

In this research, the cross-concepts of intersectional theory were used as the study’s analytical framework. The research methodology involved conducting semi-structured face-to-face interviews with a group of 30 Nepali women entrepreneurs residing in the United Kingdom. A qualitative approach was employed, and thematic analysis was used to extract meaningful findings.

The study’s outcomes underscore the emergence of social stereotypes as a salient factor affecting Nepali female entrepreneurs. Furthermore, the research identifies challenges and barriers, which fall into several cross-concept categories: those related to self-efficacy; family; social and cultural factors; business-related issues; access to financial resources; and ethnicity and work-based categorization. The findings might also have broader implications, benefiting ethnic female entrepreneurs in general, as well as ethnic communities and governmental and non-governmental organizations. Insights gained from the study can inform the development of tailored training and educational programs aimed at supporting and nurturing the entrepreneurial aspirations of ethnic women.

To the best of the researchers’ knowledge, there is a dearth of empirical investigations that probe the challenges and barriers faced by Nepali women who have embarked on entrepreneurial endeavours in the UK. This study contributes to the limited literature knowledge on ethnic women entrepreneurs, by linking ethnicity, class and gender/sexual orientation, as well as business, family, personal and financial constructs. By adopting the cross-concept of intersectional theory, this study further contributes to the knowledge of the discriminatory realities of Nepali women entrepreneurs as they grapple with the complex experiences of running a business. By doing this, our study can contribute further to the knowledge of gender and entrepreneurship from the ethnic background of UK enterprises.

In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph.

To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method.

The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks.

This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

In the era of the circular economy, the economic growth of a country is highly dependent on the sustainable performance of the manufacturing sector. In today’s increasingly competitive world, it is important to constantly improve the manufacturing or service industry. Quality with quantity is a main characteristic, which helps a company stay in the competition. Flexibility and responsiveness to customer demands are very important for success. Generally, additional time is needed for setup caused by poor design of equipment. At this point, the terms continuous process improvement and single-minute exchange of dies (SMED) as an approach of lean manufacturing come into play. Lean manufacturing system has been identified as an approach for improving the performance of the process and product.

In this paper, high setup time is considered as major problem in the industry and a major cause and effect for high setup time was found. On the basis of the literature review and experts’ opinions, four categories of barriers, namely method, manpower, machine and tools are identified. In this study, a hybrid approach comprising of the analytic hierarchy process (AHP) and graph theoretic approach (GTA) has been used. First, prioritization of different categories of barriers by AHP has been done, and second, GTA has been applied for finding the barriers' intensity index. Based on this study, machine and manpower barriers have emerged as major hurdles in the high setup time of machine. The proposed framework will help organizations quantify barriers in high setup time in different manufacturing processes, thereby developing effective strategies for sustainable production.

Findings of this research will contribute to ensuring sustainable competitive advantages, but it has some limitations. Development of the permanent matrix equation for barriers of high setup time is complex and lengthy when barriers are more in number. Absolute and relative values considered while quantifying the intensity of barriers are based on experts’ opinions, which may be inconsistent. In spite of these limitations, organizations can use an illustrated approach to quantify the barriers, thereby developing strategies for successful implementation of high setup time for making them sustainable in the global market. Organizations can also benchmark their sustainability preparedness with the best in the industry. As a future scope of study, high setup time can be reduced by using SMDE technology can be further validated through an empirical and case-based approach to generalize the findings.

Authors remain confined only to setup time. The approach is generalizable and can be extended in other areas. As a future scope of study, high setup time can be reduced by using single minute die exchange technology and can be further validated through an empirical and case-based approach to generalize the findings.

The study guides and facilitates researchers and practitioners in using the most appropriate techniques such as AHP and GTA for empirical studies and in developing, modifying and/or reviewing application frameworks for production. It also guides implementation experience regarding high setup time by using advanced techniques such as single point die exchange (SMDE), which can be beneficiary for both developing and developed country contexts. Industries can accelerate implementation by understanding and using most important AHP, GTA and SMDE techniques.

Lean manufacturing system has been identified as an approach for improving the performance of the process and product. A lean manufacturing system is part of corporate culture, like tools and approaches. High setup time can be classified as waste for the company. Reduction in time is a direct way to increase the productivity and profit. Therefore, there is a need to reduce the time by using some new lean methodology. In global industry, different techniques are used for reduction of time.

The substantial rise in tax evasion raises concerns about its adverse impact on the tax system’s integrity. This article aims to empirically investigate the factors affecting income tax evasion behavior among individuals in India’s National Capital Region (NCR).

A cross-sectional design was applied to gather primary data from (N = 548) taxpayers using a “self-administered survey questionnaire.” The hybrid “partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA)” approach was applied to analyze the data.

This research presents a comprehensive model that explains 53.4% of the variance in behavioral intention and accounts for 51.4% in the actual behavior of individuals who participate in income tax evasion. The outcomes show that high tax burden, corruption and complexity of the tax system significantly influence behavioral intention. On the contrary, digitalization and tax morale insignificantly influence behavioral intention. Notably, behavioral intention is significantly associated with the actual behavior of individuals engaging in income tax evasion.

The outcomes offer valuable implications for practitioners, including policymakers, governments and tax authorities, to effectively curb income tax evasion behavior and help them make informed decisions.

The innovative research model enhances prevailing knowledge by providing empirical insights into the effect of income tax evasion behavior among individuals in India. This study also contributes methodologically by combining PLS (linear) and fsQCA (nonlinear) techniques, demonstrating that both methodologies offer a deeper comprehension of the factors affecting individual behavior to engage in income tax evasion.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.