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This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain.

A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups.

The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups.

Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience.

Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work.

The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment.

A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis.

The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks.

Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.

The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA).

A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management.

The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA.

AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition.

This chapter aims to present the idea of leader robots (LRs) and proposes how LRs can step in to fill various manager positions. LRs are the robots that lead robots. Robotics engineers have already produced many robots for various applications such as in manufacturing, marketing, accounting even human resources in business management. We have witnessed many significant advancements in robotics and artificial intelligence research to achieve digital transformation. There are robots for various purposes of providing digital transformation toward Industry 5.0. Some post-modern organizations have already had robot employees and artificial intelligence applications. There are many studies on human–robot collaboration, robot development, and robot employee but limited research on LR. In this study, therefore, it is discussed the possibility of LRs that may be a key concept of Industry 5.0 in perspective digital transformation. Therefore, it is believed that this study will be one of the focuses of many upcoming organizational robotics research studies.

This study aims to evaluate the potential of using the components of the quadruple helix and quintuple helix models, which are extensions of the triple helix university-private sector-public sector cooperation model. Thus, the triple helix model shaped by university-private-public sector cooperation has transformed into a quadruple helix innovation model with the inclusion of the media and culture-oriented public helix. In this context, while the triple helix emphasizes tripartite networks and hybrid organizations, the quadruple helix system focuses on intertwined collaborations, coevolution, and specialization within the framework of firms, institutions, and stakeholders. In the quadruple helix innovation system, the coevolution of art and innovation has assumed a central role in knowledge generation and innovation. In the quintuple helix innovation model, the natural environment of society is added to the quadruple helix. This study consists of three parts. In the first part, the literature on triple helix, quadruple helix, and quintuple helix models is reviewed. In the second part, digital transformation and technological innovations from Industrial Revolution 1.0 to Industry 5.0 are analyzed. In the third section, the contribution of the quintuple helix model to Industry 5.0 and Society 5.0 is explained.

Unmanned aerial vehicles are commonly known as UAVs and drones. Nowadays, industries have begun to realise the operational and economic benefits of drone-enabled tasks. The Internet of Things (IoT), Big Data, drones, etc., represent implementable advanced technologies intended to accomplish Industry 4.0. The purpose of this study is to discuss the significant contributions of drones for Industry 4.0.

Nowadays, drones are used for inspections, mapping and surveying in difficult or hazardous locations. For writing this paper, relevant research papers on drone for Industry 4.0 are identified from various research platforms such as Scopus, Google Scholar, ResearchGate and ScienceDirect. Given the enormous extent of the topic, this work analyses many papers, reports and news stories in an attempt to comprehend and clarify Industry 4.0.

Drones are being implemented in manufacturing, entertainment industries (cinematography, etc.) and machinery across the world. Thermal-imaging devices attached to drones can detect variable heat levels emanating from a facility, trigger the sprinkler system and inform emergency authorities. Due partly to their utility and adaptability in industrial areas such as energy, transportation, engineering and more, autonomous drones significantly impact Industry 4.0. This paper discusses drones and their types. Several technological advances and primary extents of drones for Industry 4.0 are diagrammatically elaborated. Further, the authors identified and discussed 19 major applications of drones for Industry 4.0.

This paper’s originality lies in its discussion and exploration of the capabilities of drones for Industry 4.0, especially in manufacturing organisations. In addition to improving efficiency and site productivity, drones can easily undertake routine inspections and check streamlines operations and maintenance procedures. This work contributes to creating a common foundation for comprehending Industry 4.0 outcomes from many disciplinary viewpoints, allowing for more research and development for industrial innovation and technological progress.

This study explores the immobilisation of enzymes within porous catalysts of various geometries, including spheres, cylinders and flat pellets. The objective is to understand the irreversible Michaelis-Menten kinetic process within immobilised enzymes through advanced mathematical modelling.

Mathematical models were developed based on reaction-diffusion equations incorporating nonlinear variables associated with Michaelis-Menten kinetics. This research introduces fractional derivatives to investigate enzyme reaction kinetics, addressing a significant gap in the existing literature. A novel approximation method, based on the independent polynomials of the complete bipartite graph, is employed to explore solutions for substrate concentration and effectiveness factor across a spectrum of parameter values. The analytical solutions generated through the bipartite polynomial approximation method (BPAM) are rigorously tested against established methods, including the Bernoulli wavelet method (BWM), Taylor series method (TSM), Adomian decomposition method (ADM) and fourth-order Runge-Kutta method (RKM).

The study identifies two main findings. Firstly, the behaviour of dimensionless substrate concentration with distance is analysed for planar, cylindrical and spherical catalysts using both integer and fractional order Michaelis-Menten modelling. Secondly, the research investigates the variability of the dimensionless effectiveness factor with the Thiele modulus.

The study primarily focuses on mathematical modelling and theoretical analysis, with limited experimental validation. Future research should involve more extensive experimental verification to corroborate the findings. Additionally, the study assumes ideal conditions and uniform catalyst properties, which may not fully reflect real-world complexities. Incorporating factors such as mass transfer limitations, non-uniform catalyst structures and enzyme deactivation kinetics could enhance the model’s accuracy and broaden its applicability. Furthermore, extending the analysis to include multi-enzyme systems and complex reaction networks would provide a more comprehensive understanding of biocatalytic processes.

The validated bipartite polynomial approximation method presents a practical tool for optimizing enzyme reactor design and operation in industrial settings. By accurately predicting substrate concentration and effectiveness factor, this approach enables efficient utilization of immobilised enzymes within porous catalysts. Implementation of these findings can lead to enhanced process efficiency, reduced operating costs and improved product yields in various biocatalytic applications such as pharmaceuticals, food processing and biofuel production. Additionally, this research fosters innovation in enzyme immobilisation techniques, offering practical insights for engineers and researchers striving to develop sustainable and economically viable bioprocesses.

The advancement of enzyme immobilisation techniques holds promise for addressing societal challenges such as sustainable production, environmental protection and healthcare. By enabling more efficient biocatalytic processes, this research contributes to reducing industrial waste, minimizing energy consumption and enhancing access to pharmaceuticals and bio-based products. Moreover, the development of eco-friendly manufacturing practices through biocatalysis aligns with global efforts towards sustainability and mitigating climate change. The widespread adoption of these technologies can foster a more environmentally conscious society while stimulating economic growth and innovation in biotechnology and related industries.

This study offers a pioneering approximation method using the independent polynomials of the complete bipartite graph to investigate enzyme reaction kinetics. The comprehensive validation of this method through comparison with established solution techniques ensures its reliability and accuracy. The findings hold promise for advancing the field of biocatalysts and provide valuable insights for designing efficient enzyme reactors.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

Embedded passives technologies can provide benefits of size, performance, cost, and reliability to high density, high‐speed designs. A number of embedded passive technology solutions are available to the designer. Based on our experience with Rohm and Haas's thin‐film, high‐ohmic, InSite^TM embedded resistor materials (500 and 1000 Ω/sq), this paper provides some guidelines for selecting the appropriate embedded resistor technology and implementing it at a board fabricator. The design of embedded resistors, and the trade‐offs between resistor size, tolerance, and capability of board fabrication processes, are analyzed in detail. This paper also discusses selection of the appropriate embedded capacitor technology and introduces some initial results on Rohm and Haas's thin‐film, high‐D_k, InSite embedded capacitor material (200 nF/cm²).

Industry 5.0 (I5.0) is eventually set to supersede Industry 4.0 (I4.0), despite the fact that I4.0 continues to gain ground in emerging nations like India. Now India is aspiring to be a global manufacturing hub, and I5.0 offers enormous potential to position India as a forerunner in intelligent and collaborative manufacturing systems. Therefore, this research article aims to understand the relationship between I5.0 and sustainable manufacturing (SM) thoroughly; pinpoint its impact and implementation challenges; analyze its impact on Triple-Bottom-Line (TBL) sustainability; and present an inclusive framework for I5.0 implementation for Indian manufacturing enterprises.

The coexistence of two industrial revolutions raises questions, which necessitates debates and explanations. Thus, the systematic literature review (SLR) approach is used to address this issue and this study used Web of Science, Scopus, Science Direct and Google Scholar databases. Following a critical SLR, 82 research papers have been cited in this article, and the majority of cited articles were published from 2010 to 2022, to ensure a focused analysis of pertinent and recent scholarly contributions.

I4.0 is considered to be technology-driven, however, I5.0 is perceived to be value-driven. I5.0 is not a replacement or a chronological continuation of the I4.0 paradigm. The notion of I5.0 offers a distinct perspective and emphasizes the necessity of research on SM within the TBL sustainability boundaries. I5.0 introduces a new TBL: resilience in value creation, human well-being and sustainable society. Indeed, I5.0 seems to be economically, socially, and environmentally sustainable while manufacturing products with high productivity.

Theoretical implications pertain to restructuring business models and workforce transformation, whereas practical implications underscore the significance for manufacturing enterprises to embrace I5.0 for their sustainable development. By understanding the nuanced relationship between I5.0 and SM, enterprises can navigate implementation challenges, maximize TBL sustainability and embrace an inclusive I5.0 framework for high productivity and resilience.

The existing literature presents the general notion of I5.0 but lacks in-depth TBL sustainability analysis. This research used a systematic and rigorous SLR approach that evaluates the existing literature, enables an in-depth understanding, identifies research gaps and provides evidence-based recommendations for the decision-making process. Furthermore, this research aims to stand on an unbiased assessment, exploring theoretical and practical implications of I5.0 implementation for manufacturing enterprises and suggesting future research avenues.