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The purpose of this paper is to develop a simulation tool which permits reducing the cost of long time‐range simulation of complex converters and running at high frequency.

A different method is used to represent a simplified converter but the adopted technique uses the average representation of the cell converter.

The paper shows that the use of averaged representation of the pulse width modulation switch in multilevel converters is staying applied. The main advantage of the proposed averaged model is its simplified representation when only electrical behaviour is considered.

The analytical algorithm of the averaged model can be introduced in different simulator as it has a description language, enabling study of the Compatibilité Electromagnétique and electrothermal phenomena.

This paper presents an averaged model of the multilevel converter which can be implemented in any simulator as it has a description language.

This paper aims to focus on the trade-off between losses and converter cost.

The continual development of power electronic converters, for a wide range of applications such as renewable energy systems (interfacing photovoltaic panels via power converters), is characterized by the requirements for higher efficiency and lower production costs. To achieve such challenging objectives, a computer-aided design optimization based on genetic algorithms is developed in Matlab environment. The elitist non-dominated sorting genetic algorithm is used to perform search and optimization, whereas averaged models are used to estimate power losses in different semiconductors devices. The design problem requires minimizing the losses and cost of the boost converter under electrical constraints. The optimization variables are, as for them, the switching frequency, the boost inductor, the DC capacitor and the types of semiconductor devices (IGBT and MOSFET). It should be pointed out that boost topology is considered in this paper but the proposed methodology is easily applicable to other topologies.

The results show that such design methodology for DC-DC converters presents several advantages. In particular, it proposes to the designer a set of solutions – as an alternative of a single one – so that the authors can choose a posteriori the adequate solution for the application under consideration. This then allows the possibility of finding the best design among all the available choices. Furthermore, the design values for the selected solution were obtainable components.

The authors focus on the general aspect of the discrete optimization approach proposed here. It can also be used by power electronics designers with the help of additional constraints in accordance with their specific applications. Furthermore, the use of such non-ideal average models with the multi-objective optimization is the original contribution of the paper and it has not been suggested so far.

The paper aims to focus on the semiconductor temperature prediction in the multichip modules by using a simplified 1D model, easy to implement in the electronic simulation tools.

Accurate prediction of temperature variation of power semiconductor devices in power electronic circuits is important for obtaining optimum designs and estimating reliability levels. Temperature estimation of power electronic devices has generally been performed using transient thermal equivalent circuits. This paper has studied the thermal behaviour of the power modules. The study leads to correcting the junction temperature values estimated from the transient thermal impedance of each component operating alone. The corrections depend on multidimensional thermal phenomena in the structure.

The classic analysis of thermal phenomena in the multichip structures, independently of powers’ dissipated magnitude and boundary conditions, is not correct. An advanced 1D thermal model based on the finite element method is proposed. It takes into account the effect of the heat‐spreading angle of the different devices in the module.

The paper focuses on mathematical model of the thermal behaviour in the power module. The study leads to a correction of the junction temperature values estimated from the transient thermal impedance of each component given by manufacturers. The proposed model gives a good trade‐off between accuracy, efficiency and simulation cost.

Over the past few decades, lean manufacturing has focussed on being customer-centred and now Lean 4.0 technologies have made it possible for manufacturers to have a deeper view of waste reduction. Technologies such as the internet of things, artificial intelligence, three-dimensional printing, robotics, real-time data, cloud computing, predictive analytics and augmented reality, are helpful to achieve Lean 4.0. This study aims to develop the conceptual understanding of Lean 4.0, related tools and linkage with Industry 4.0. Further, it provides the strategies for implementing Lean 4.0, developing lean culture and highlights the Lean 4.0 application in the manufacturing context.

This study relates to Lean 4.0 and its technologies. Prominent research is identified through Scopus, Web of Science, ScienceDirect and Google Scholar and studied as per the objective of this study. This lean revolution provides customers desire for personalisation, connectedness, high-quality and valuable products. Lean 4.0 provides valuable information on the value chain and production process. This revolution has significantly impacted refining production processes for a greater level of adaptability and cost reduction.

This paper is brief about Lean 4.0 and its capabilities for the reduction of waste. The authors discussed different tools used in Lean 4.0 and its relationship with Industry 4.0. The classical strategies and progressive features of Lean 4.0 for overall enhancing the manufacturing sphere are discussed diagrammatically. Finally, it identified and discussed 14 significant applications of Lean 4.0 for manufacturing industries.

This study provides a comprehensive understanding of Lean 4.0 and related tools and strategies that help the upcoming manufacturing industries.

Acceptance and use of mobile shopping as the preferred shopping medium is becoming the new normal today. The proliferation of mobile shopping practices follows unsteady growth patterns in different countries. Despite challenges of price, quality and privacy matters, young consumers still lead the race to engage in mobile shopping activity in developing countries. This research investigates the determinants that either support or hinder the mobile shopping intentions of aspirant young consumers in Pakistan.

The present research applies a consumer-centric approach to the technology adoption framework to unearth the behavioral patterns of these young consumers in Pakistan. Employing the structural equation modeling technique, this research examines the significant effect of structural assurance, perceived risk, trust and various unified theory of acceptance and use of technology (UTAUT) model constructs on behavioral intention to engage in mobile shopping.

This research examines the significant effect of structural assurance, perceived risk, trust and various UTAUT model constructs on behavioral intention to engage in mobile shopping. Results also demonstrate a significant moderating effect of structural assurance and prior shopping experience on the relationship between perceived risk, trust and mobile shopping intentions. Further, the mobile shopping patterns for the male and female segment indicate a significant difference for perceived risk, trust structural assurance and social influence. The research contributes to the growing body of knowledge which advocates the application of consumer-centric customized model approach to explore various factors that either facilitate or impede the adoption of mobile shopping in a developing economy.

The study validates the need to enforce structural assurance mechanism for facilitating mobile shopping in a developing country. It also offers practical implications for online businesses and marketers striving to attract and retain more mobile customers.

The study offers practical implications for online businesses and marketers striving to attract and retain more mobile customers.

This study offers fresh insights about driving elements and impediments of mobile shopping behavioral intentions. Structural assurance and prior shopping experience appear to influence the mobile shopping behavior through direct and indirect effect.