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This study focuses on the structural performance assessment of hybrid polymer composites for pick-and-place robot grippers used in critical infrastructure. This research involved the creation of composite materials with different nanoparticle concentrations, followed by extensive testing to assess the mechanical properties of the materials, such as strength, stiffness and durability.

The composites comprised bidirectional interply inclined carbon fibers (C), S-glass fibers (SG), E-glass (EG), an epoxy matrix and silica nanoparticles (SNiPs). During construction, the composite materials must be carefully layered using quasi-static sequence techniques (45°C1/45°SG2/45°EG2/45°C1/45°EG2/45°SG2/45°C1) to obtain the epoxy matrix reinforcement and bonding using 0, 2, 4 and 6 wt. % of silica nanoparticles.

According to various test findings, the 4 wt. % of SNiPs added to polymer plates exhibits the maximum strength outcomes. The average results of the tensile and flexural tests for the polymer composite plates with 4 wt. % addition SNiPs were 127.103 MPa and 223.145 MPa, respectively. The average results of the tensile and flexural tests for the plates with 0 wt.% SNiPs were 115.457 MPa and 207.316 MPa, respectively.

The authors hereby attest that the research paper they have submitted is the result of their own independent and unique labor. All of the sources from which the thoughts and passages were derived have been properly credited. The work has not been submitted for publication anywhere and is devoid of any instances of plagiarism.

1. The study enhances the engineering materials for innovative applications.

2. The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

3. Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

The study enhances the engineering materials for innovative applications.

The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

Purpose: Although the transnational mobility of youth is prevalent across the globe, the policies and strategies used by developing countries to promote youths’ transnational mobility remains unclear. This chapter aims to discuss a few emerging trends and concerns related to youth transnational mobility and human capital accumulation. It focuses on the specific case of youth transnational migration from India. As such, this chapter considers the factors that have led to the construction of aspirations for transnational migration among Indian youth.

Study approach: In this chapter, we examine youth transnational mobility by synthesising the major literature available in the area of globalisation and transnational migration in the developing countries context. The study also incorporates the policy debates and secondary data evidence in these domains to substantiate the arguments.

Findings: Mainly focussing on the internal state of affairs of India, this work highlights three critical issues (a) new norms and practices of human capital accumulation within India’s knowledge economy; (b) understanding state and formal-informal ‘commoditised’ market dynamics in the changing aspirations for global human capital formation, specifically through transnational migration; and (c) examining the upshots of youth transnational mobility in terms of the production of relatively new forms of caste and class inequalities within Indian society.

Originality: This conceptual piece of work is an initial foray to unpack the complexities of Indian youths’ migration for specialised forms of human capital accumulation in a global landscape. The study motivates future policy-oriented interdisciplinary research on education and transnational mobility, specifically in empirical research projects.

Contemporary frameworks in the resource-based view (RBV) of the firm observe that the proprietary firm-specific assets of multinational companies (MNCs) from developed economies give them competitive advantage in international markets. However, the question “how do emerging market firms (EMFs) achieve accelerated internationalisation in knowledge based industries – despite not possessing proprietary assets and lacking critical elements of innovation eco-systems, institutions and infrastructure” has yet to be addressed. This paper aims to adopt a knowledge-based view (KBV) of the firm, identifying knowledge, both inside and outside of the firm, as a critical element for the internationalization of EMFs.

This research entailed deductive econometric analyses using panel data analysis from 925 firm-year observations, which tested our predictions on capabilities that contribute to the internationalization of EMFs from the IT industry in India.

The findings of the authors’ panel data analysis reveal that the capacity to internationalize is predicated by knowledge leverage in three principal domains: absorptive capacity, tacit knowledge and knowledge-codification. This study shows internationalization is driven by higher-order capabilities of EMFs that draw on the absorptive capacity of individuals and collectives as a dynamic capability to serve international clients worldwide.

This study highlights that the process by which EMFs gain competence is different to the Western MNCs insofar as the extent to which EMFs have to stretch their efforts of learning from clients.

For practitioners, the findings of this research are a useful guide to understand that EMFs need to make strategic investments to understand the idiosyncrasies of a variety of clients’ needs and operating environments to dynamically adapt, document the learning and leverage the tacit knowledge.

This study captures the innate ability of entrepreneurs in emerging economies to unlock their potential in human capital for globalizing operations and targeting new market segments in the industry. This can directly benefit in uplifting the income level of millions in relatively underdeveloped countries and bringing about much-needed equity in the level of income in the society.

The value of this study lies in its novel and contemporary insight on how EMFs leapfrog in a fast-changing technology space. What distinguishes the work from the static framework in literature is that EMFs learning is dynamic, and happens in an interactive mode, alongside clients in close proximity. This study captures the innate ability of entrepreneurs to unlock the potential of human capital in emerging economies for globalizing operations.

The purpose of this paper is to understand the customer attitude and perception towards the online brand communities (OBCs) and purchase intention (PI). The paper also analyses the moderating effects of age, income and internet usage on the relationship among the proposed variables.

The study employed a multi-analytical approach through the use of structural equation modelling and neural networks to test the proposed framework. The information was obtained from 236 respondents using a web-based questionnaire survey. Further, moderation analysis, with age, annual family income and weekly internet usage as moderating variables, was conducted on the structural model.

The results of the analysis demonstrate that the multi-group moderating effect for different groups within age, income and internet usage have significant differences for the relationships between the independent variables content perception, perceived usefulness, critical mass, brand attachment and attitude towards brand and the dependent variable, PI.

The study is one of its kind to understand and analyse the role of moderating variables (age, income and internet usage) on OBC-induced PI.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to examine the relationship between the perception of consumers about corporate social responsibility (CSR) and consumers’ purchasing behaviour in the retail sector. Specifically, this study investigates the impact of perceived CSR on consumer attitude and behaviour and the influence of attitude on the relationship between perceived CSR and purchase behaviour.

In this study for collection of the data, an online questionnaire was distributed among the Indian retail consumers. From the collected primary data set, 249 data points were found fit for analysis. Further, the direct, indirect and moderating effects were evaluated using the structural equation modelling technique.

It is identified that while perceived CSR has a significant influence on consumer purchase behaviour, consumer attitude is having an insignificant impact on the relationship between perceived CSR and purchase behaviour. The findings of this study also show that consumer demographics do not have any moderating impact on the relationship between perceived CSR and purchase behaviour.

The findings of this study are useful to retail managers interested in enhancing CSR. The results of this study suggest that retailers should focus on strengthening consumers’ perceptions about retailers’ CSR initiatives and enhancing co-creation activities. As an extension to this research, further study can include more potential mediators like consumer effectiveness and timing of CSR initiatives.

This study applies stakeholder theory as well as extends the classic theory of planned behaviour model and proposes the establishment of links among consumers’ perceptions about CSR, consumer attitude and behaviour around the retail sector. In addition, this study considers not only overall consumer behaviour but also specific dimensions of consumer behaviour, namely, loyalty, intention and satisfaction.

The demand for aluminium alloys has been increasing in almost all the fields. In this study, the friction stir welding (FSW) of similar aluminium alloy AA 8011-h14 has been presented using three levels of tool rotational speed (n), tool tilt angle (ϴ) and tool feed (f). The purpose of this paper is to study the effect of welding parameters on various properties and time-temperature plots.

FSW was carried out using the L-9 orthogonal array of welding parameters generated using the Taguchi approach. Visual inspection and radiography testing were conducted to detect the surface and volume defects, respectively. Taguchi analysis was carried out to get optimised welding parameters for tensile testing. The microstructural analysis was carried out for the specimen possessing maximum tensile strength and the obtained grain structures were compared with the microstructure results of the base material. The peak process temperatures were noted and time-temperature plots were analysed for the varying parameters. The maximum value of hardness was recorded and analysed.

Visual inspection and radiography testing confirmed defect-free joints. The maximum tensile strength achieved was 84.44 MPa with 64.95 per cent efficiency. The optimised parameters obtained using Taguchi analysis for tensile testing were 1,500 rpm, 1° and 50 mm/min. Microstructure analysis for the specimen possessing maximum tensile strength revealed fine and equiaxed grains in the nugget zone. Time-temperature plots suggested the maximum temperature of 389 °C on the advancing side. A maximum hardness value of 36.4 HV was obtained in the nugget zone.

As per the knowledge of the authors, this study is the first attempt for the detailed experimental analysis on the FSW of this particular aluminium alloy AA 8011-h14.