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The information and knowledge about a product and its assembly are necessary to generate all feasible assembly sequences of that product. Assemblies contain a very large amount of information and complex relationships. Identifying assembled parts as well as their contact surfaces is very important in design and manufacturing since this information is essential. The problem is to not only make the information available but also use the relevant information for making decisions, especially determination of the optimum assembly sequence. This paper aims to address these issues.

This paper describes a system for processing assembly models and extracting assembly related data using application programming interface (API) of the computer‐aided design (CAD) software. These data are used to identify the relationships between different components of an assembly thus encouraging generation of feasible assembly sequences.

Instead of total human interpretation of the assembly design, a direct CAD database interface approach has been proposed to extract the relation with minimal manual involvement. The information extracted is used to generate a list describing the links between the assembled parts, the involved features and the type of link explicitly to facilitate assembly analysis and planning.

The methodology of using the API of the CAD modeling package SolidWorks, is a novel approach in which the assembly mate information is captured. Instead of total human interpretation of the assembly design, a direct CAD database interface approach has been proposed to extract the relation with minimal manual involvement.

In recent years, the topic of women's entrepreneurship has gained increasing attention from researchers and policymakers. Its role in economic growth and development has been widely recognized in several studies. However, the relationship between gender in entrepreneurship and innovation is an underexplored aspect in particular at a country-level perspective. This paper aims to answer the following question: Does female entrepreneurship impact innovation at a national level?

Using a panel dataset of 35 Organization for Economic Co-operation and Development (OECD) member countries over the period 2002–2019, the authors carried out a comprehensive econometric analysis, based on the fixed-effect model, the random-effect model and the feasible generalized least squares estimator, as well as a battery of tests to prevent problems of multicollinearity, heteroscedasticity and autocorrelation of the error terms. In doing so, the authors found consistent and robust results on the linear and nonlinear relationship between women's entrepreneurship and innovation, using selected country indicators from the Global Entrepreneurship Monitor (GEM) consortium, the Worldwide Governance Indicators (WGI) and the World Development Indicators (WDI), including female self-employment, female nascent entrepreneurship and R&D investment and controlling for the same relationships in the case of men's entrepreneurship.

This study shows that the level of R&D investment, which according to the literature can be considered as a proxy of innovation, is higher when the level of women's entrepreneurship is low. However, exploring more in depth this relationship and the relationship between male entrepreneurship and innovation, the authors found two important and new results. The first one involves the different impact on R&D investment of female self-employment and female nascent entrepreneurship. In particular, female self-employment appears to have a linear negative impact on the R&D, while the impact of female nascent entrepreneurship is statistically nonsignificant. The second one affects the nonlinearity of the negative effect, suggesting that very different challenges are possible at different levels of women's entrepreneurship. In addition, analyzing the role of human capital in the relationship between R&D investment and women entrepreneurship, it emerges that higher education (as the main component of human capital) makes early-stage women's entrepreneurship more technologically consuming, which promotes R&D investment. A higher level of education lessens the significance of the negative relationship between the simplest type of women entrepreneurship (female self-employment) and R&D investment.

The originality of the study is that it provides new evidence regarding the link between women's entrepreneurship and innovation at the macro level, with a specific focus on self-employed women entrepreneurs and early-stage women entrepreneurship. In this sense, to the best of the authors' knowledge, this study is among the few showing a nonlinear relationship between women's entrepreneurship and country-level innovation and a negative impact only in the case of female self-employment. Moreover, this study has relevant implications from a policymaking perspective, in terms of promoting more productive women's entrepreneurship.

The purpose of this paper is to explore an employability enhancement initiative, the Additional Skills Acquisition Programme (ASAP) project in the state of Kerala, India, as a case for the inclusive development of employability in college and university students.

ASAP is applied as a case study to examine the employability enhancement initiative in India.

Participation in the ASAP project led to increased measures of employability. These increases were most pronounced in women and individuals living below the poverty line. Partnerships between educational institutions and organisations focusing on students’ technical and skill development can help overcome local and national talent shortages.

The theoretical implications come from addressing the lack of representation of skills based employability initiatives among students from India in the vocational behaviour literature. Practical implications come from knowledge sharing of innovative strategies to enhance the employability outcomes of individuals entering the labour market. New ways to overcome the reported mismatch in business education between curriculum content and the development of employability skills in graduates are presented. Benefits for diversity and inclusion are also provided.

The purpose of this study is to explore how international entrepreneurial orientation of top management team (TMT) of software product firms influence their firms' internationalization activities.

This study uses interpretive analysis techniques and examines data collected from in-depth interviews and secondary sources from 20 software product firms.

The analysis of data reveals the existence of a pathway through which the international entrepreneurial orientation of the TMT influences the firm's strategic learning functions (knowledge creation, dissemination, interpretation and implementation) which in turn influences the firm's internationalization activities.

The authors extend the existing knowledge by demonstrating an existence of path to interpret the individual attribute of international entrepreneurial orientation of TMT with the organization's knowledge management functions. Building on the theories of knowledge-based view and organizational learning, and by leveraging the microfoundations approach, a process model is also derived based on evidence from data analysis to enable examination of the combined effects of the international entrepreneurial orientation of TMT, firm's strategic learning on internationalization.

The authors provide an integrative process model that connects TMTs' international entrepreneurial orientation to the firms' strategic learning processes, which in turn is linked to examine the combined influence of these constructs on the internationalization activities of a firm.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

This study investigates the various themes around the demand-driven approach of vocational education and training (VET). The study focuses on investigating two major aspects of VET. Firstly, the extant literature has been systematically classified based on seven parameters: Geography, Focus on Vocational Education, Research Methods, Economic Sector, Origin of Study, Type of Training and Level of Skill. Secondly, this study has explored various themes around the VET implementation, performance gaps and road ahead.

Thematic analysis with the systematic literature review (SLR) method is applied to 50 research papers on VET, published between 2011 and 2022.

This systematic review has found that experiential compliments VET and VET is the medium of skill development. Further, it was extracted through themes that VET may help ensure alternate employment in rural areas and contribute to micro- and macro-level economic development. Also, there is a requirement to adopt a demand-driven approach to VET by localizing VT content and delivery.

The study's findings are directed towards the need for demand-driven and customized VET. This study also explores many potential areas for further empirical research through various themes.

This study is a novel effort that extracts the themes constituting the effect of VETs along with the descriptive analysis of the extant literature using the SLR approach. The study has rationalized the findings by providing due coding to various parameters in the previous studies under investigation.

The personality of an individual plays a vital role in the way an individual perceives organizational politics and justice in the workplace. However, there is meager research on how an individual's personality affects the perceptions of organizational politics and justice. This study endeavors to fill this gap by analyzing the mediating role of organizational politics perceptions on the relationship between Big Five personality dimensions and organizational justice by controlling various demographic variables. The study also proposes a benchmarking model that the policymakers can use to create positive organizational justice perceptions.

In this cross-sectional research, the data were collected through a multi-stage random sampling technique from 493 faculty members working in four public universities of Punjab, India. Out of 493 employees, 76.9% of the employees were assistant professors, 12.0% were associate professors and 11.2% were assistant professors. 51.5% of the employees were female, and 48.5% of the employees were male. To test the proposed hypothesized relationships, a structural equation modeling technique was used.

Results of the structural equation modeling showed that openness to experience, conscientiousness and extraversion have a negative relationship with perceptions of organizational politics. However, their relationship with perceptions of organizational justice is positive. Neuroticism has a positive relationship with perceptions of organizational politics, whereas it has a negative relationship with perceptions of organizational justice. Results also showed that high perceptions of organizational politics have a negative effect on employee's perceptions regarding organizational justice. The mediation analysis results showed that perceptions of organizational politics mediate the relationship between an individual's personality and perceptions of organizational justice.

There is a scant amount of research available that considers Big Five personality dimensions and organizational politics as the antecedents of organizational justice. Hence, the current study tries to fill this research gap by proposing a research model on antecedents and consequences of perceptions of organizational politics based on the cognitive-affective processing system (CAPS).

This paper begins with a challenge to explore the scope and dimensions of corporate social responsibility (CSR) in Islamic banking and design a CSR disclosure index, which may gauge the level of CSR disclosure in Islamic banking.

It adopts a two-fold approach to develop the CSR disclosure index for Islamic banking, such as “identification” and “prioritization.” In the ambit of identification, it relies on the existing literature related to CSR and Islamic banking. However, it undertakes analytical hierarchy process (AHP) method for prioritization through the sample of 104 experts related to Islamic banking of Pakistan.

It concludes the CSR index for Islamic banking contains five dimensions, including 79 items across 20 sub-dimensions. The results of AHP indicate that the CSR dimensions are important for Shariah governance, employee, community, customer and environment. Moreover, within dimensions, the most important sub-dimensions are Shariah compliance, customer service and quality, green investing/banking, customer relationship, training and development and poverty alleviation.

The CSR disclosure index of this study has important implications for academicians, such as it paves the ways for further investigations and practical usage of index to gauge the level CSR disclosure of Islamic banking. Moreover, it delineates the spectrum of responsibilities for managers of Islamic banking under the domain of CSR.

The proposed CSR disclosure index is comprehensive and stresses on the social responsibility of Islamic banking toward stakeholders. In nutshell, this study offers what is expected from the practitioners of Islamic banking in the domain of social responsibility.

The purpose of this study is two-fold: classifying non-Muslim halal fashion buyers by applying quantitative techniques and identifying the persuading determinants of the non-Muslim women’ halal fashion buying behaviour (HFBB).

By adapting items from prior studies, a structured questionnaire was developed and distributed face-to-face to various Muslim fashion stores in Malaysia. After a one-month effort, 221 responses were obtained from non-Muslim consumers by using convenience sampling. Next, a clustering analysis was used to classify them from a contrasting perspective. Finally, regression and Andrew F. Hayes’s process procedures were applied to examine the three independent variables’ effect and the moderating variables.

The results revealed the characteristic behaviour of the non-Muslim women explicitly, which is related to their halal fashion purchasing decision. Based on the ANOVA results, there were different motives for buying halal fashion by non-Muslim women. Additionally, it was found that the most crucial determinants for non-Muslim’s HFBB are “cultural adaptation”, albeit, there is no substantial proof of a significant moderating effect of age and income on the consumers.

These discoveries are advantageous for halal fashion retailers and provide an appealing domain for further investigations in the context of the global halal study.

This study provided an idea for an untapped segment on the halal fashion sellers’ segmentation and positioning strategy. The study’s results suggested specific managerial and practical recommendation that the sellers can use to attract non-Muslim consumers.

This study was amongst the uncommon investigations within the halal fashion context that will enlighten the managers’ selling strategy on the most neglected market segment. The results of this study provided an empirical understanding of how to sell halal fashion to non-Muslim consumers.