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This paper aims to propose a numerical methodology to reduce the number of computations required to optimally design the rotors of synchronous reluctance machines (SynRMs) with multiple barriers.

Two objectives, average torque and torque ripple, have been simulated for thousands of SynRM models using 2D finite element analysis. Different rotor topologies (i.e. number of flux barriers) were statistically analyzed to find their respective design correlation for high average torque solutions. From this information, optimal geometrical constraints were then found to restrict the design space of multiple-barrier rotors.

Statistical analysis of two considered SynRM case studies demonstrated a design similarity between the different number of flux barriers. Upon setting the optimal geometrical constraints, it was observed that the design space of multiple-barrier rotors reduced by more than 56 per cent for both models.

Using the proposed methodology, optimal geometrical constraints of a multiple-barrier SynRM rotor can be found to restrict its corresponding design space. This approach can handle the curse of dimensionality when the number of geometric parameters increases. Also, it can potentially reduce the number of initial samples required prior to a multi-objective optimization.

Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Two different devices, a core-type single-phase transformer and a motor-drive system, are used to show the usefulness and generalizability of the proposed approach. Using a finite element solver, a large database of design possibilities is created by varying design parameters, i.e. the geometrical and control parameters of the systems. Design rules are then extracted by performing a statistical analysis and exploring optimal and sub-optimal designs considering various targets such as efficiency, torque ripple and power factor.

It is demonstrated that the correlation of the design parameters influences the way the data-driven approach must be made. Also, guidelines for defining new design constraints, which can lead to a more efficient optimization routine, are introduced for both case studies.

Using the proposed approach, new design guidelines, which are generally not obtainable by the classical design methods, are introduced. Also, the proposed approach can potentially deal with different parameter–objective correlations, as well as different number of connected systems. This approach is applicable regardless of the device type.

Acoustic noise is a crucial performance index in the design of electrical machines. Due to the challenges associated with modelling a complete motor, the stator is often used to estimate the sound power in the prototyping stage. While this approach greatly reduces lengthy simulations, the actual sound power of the motor may not be known. But, from the acoustic noise standpoint, not much is known about the correlation between the stator and complete motor. This paper, therefore, aims to use the sound pressure levels of the stator and the full motor to investigate the existence of correlations in the interior permanent magnet synchronous motor.

A multiphysics simulation framework is proposed to evaluate the sound pressure levels of multiple motor geometries in a given design space. Then, a statistical analysis is performed on the calculated sound pressure levels of each geometry over a selected speed range to compare the correlation strength between the stator and the full model.

It was established that the stator and the complete motor model are moderately correlated. As such, a reliance on the stator sound power for design and optimization routines could yield inaccurate results.

The main contribution involves the use of statistical tools to study the relationship between sound pressure levels associated with the stator geometry and the complete electric motor by increasing the motor sample size to capture subtle acoustic correlation trends in the design space of the interior permanent magnet synchronous motor.

The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device.

An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors.

The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device.

The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

The mobile payment system has changed payment patterns and has the potential to improve people’s quality of life and increase the bank’s efficiency. In return, the risks and trust factors inevitably led to increased challenges and become a major concern in the adoption of mobile payment service. Yet, little is known about how risk and trust factors can affect the adoption of mobile payment. Hence, this paper aims to come into contact to solve these issues in the context.

A comprehensive research model that reflects the customer satisfaction and loyalty to the adoption of mobile payment services is developed and empirically tested using exploratory and confirmatory factor analysis and structural equation modeling.

Findings reveal that the perceived risk has a significant negative impact on perceived trust and customer satisfaction. Perceived trust is the most important variable in building customer satisfaction, and customer satisfaction is the reasonable predictor of customer loyalty. In addition, gender differences moderate the adoption of the mobile payment service.

The results of the study hold several implications for scholars in the field of technology adoption on financial transactions and offer valuable managerial insights to design their mobile payment adoption strategies to pursue greater acceptance and diffusion of this new payment system.

Grounded in dynamic capability view, this research develops a decision support model, which enables determining consistent and sufficient configurations of resilience strategies to mitigate vaccine operations and distributions (O&D) challenges and thus improve O&D performance (i.e. O&DP).

Through qualitative in-depth interviews, the authors first identified challenges and resilience strategies related to vaccine O&D. Next, using the quality function deployment technique, three quantitative case studies were performed to determine the most important challenges and resilience strategies. Finally, utilising fuzzy set qualitative comparative analysis, the authors determine sufficient conditions of challenges and strategies leading to improve vaccine O&DP.

The findings reveal that strategies alone are not effective instead a combination of strategies and nullification of challenges is needed to enhance vaccine O&DP. Further, the findings revealed that not only the presence of challenges, but also the lack of strategies reduces the vaccine O&DP.

The authors' findings will assist the health service decision-makers for strategizing an effective and efficient vaccination program by selecting the right combination of challenges and resilience strategies.

The authors' study develops a novel decision support model and offers significant learning for the future vaccine O&DP.

The purpose of this study is to investigate the factors that affect students’ behavioral intentions to use virtual classrooms at Princess Sumaya University for Technology (PSUT) in Jordan.

A quantitative research approach was adopted, an online survey method was used and the data were collected among students at PSUT in Jordan. A total of 511 responses were usable for analysis. A structural equation modeling partial least squares technique was used to examine the hypothesized model.

The findings reveal that the proposed factors have direct and indirect relationships with behavioral intentions to use virtual classrooms. They show that students’ satisfaction has a direct influence on behavioral intention, while other variables such as instructor characteristics, virtual classroom quality, perceived self-efficacy, perceived organizational support, perceived ease of use and perceived usefulness have an indirect effect on behavioral intentions to use virtual classrooms.

The study was conducted at PSUT in Jordan, which could limit the generalizability of the findings. Furthermore, the present study measured students’ behavioral intentions to use virtual classrooms and future research should consider the actual use of virtual classrooms.

The findings of this study offer significant and useful information to policymakers, instructors, developers and students regarding the use of virtual classrooms in universities. Based on students’ needs and readiness, the findings identify which factors to consider when developing an e-learning system to enhance learning and teaching performance.

This study extends existing knowledge by developing a conceptual model to identify the key factors of virtual classroom adoption in higher education institutions in Arab countries. This study contributes to the literature in the context of e-learning by validating an extended technology acceptance model from an Arab countries perspective and considering the differences in culture, learning style and physical environment compared to developed countries.

The fundamental purpose of this study is to analyse the determinants of higher education quality in Afghanistan based on insights from student perceptions. Understanding this part holds paramount importance in enunciating sound policies for the smooth functioning of the higher education sector of Afghanistan.

This study aims to classify students' background and demographic data, distinguishing their perception of higher education quality using a deductive research approach. A sample of 418 students from five top private universities in Afghanistan was chosen to assess their perceptions of higher education dimensions by employing a multinomial regression analysis.

The findings indicate that extracurricular activities, students' scholarship status, parents' education, age, previous academic results and the university they attend significantly impact their perception of the quality of higher education.

This research is essential for education policymakers and university administrators. These findings can be replicated to develop regulations and target specific groups of students to ensure a favourable academic environment and boost the brand image of their universities. This would ensure long-term quality improvement and assurance outcomes, allowing higher education institutions to compete with regional and international institutions.

This study contributes to identifying the determinants of higher education quality based on the perceptions of the students in Afghanistan.

The study aims to ascertain whether educational and social support for entrepreneurs significantly affects university students’ intentions to become successful entrepreneurs. This study examines the mediating role of the Theory of Planned Behavior (TPB) variables (e.g. entrepreneurial personal attitude, subjective norms and entrepreneurial perceived behavioral control) and entrepreneurial self-efficacy in encouraging young entrepreneurs.

An online survey with a structured questionnaire collected data from different university students in Bangladesh; subsequently, it was analyzed through the structural equation model.

The results suggested that educational support has a direct positive relationship with the three variables of TPB. Moreover, the findings indicated that social support positively influences the variables of TPB, entrepreneurial self-efficacy and entrepreneurial intentions. The variables of TPB and entrepreneurial self-efficacy were found to have a significant direct impact on entrepreneurial intentions and also exhibited favorable mediating effects of educational and social support on entrepreneurial intentions.

First, the study is only generalized to some sectors of entrepreneurship activities because the researchers used samples from university students across Bangladesh. Second, the implicit limitation of survey-based research is that respondents need to know more ways of understanding the questionnaires accurately, and some participants need to be taught how to answer the question items.

The main practical implication for the relationship between entrepreneurial intentions and educational support involves different entrepreneurial educational programs, which give rise to attitude, behavior, self-efficacy and intentions and enhance the student's awareness of advancing a successful entrepreneurial career.

This study demonstrated that universities and social communities should promote the improvement of innovative thoughts for entrepreneurs and offer essential information about entrepreneurship.

Because entrepreneurial educational support is a crucial factor in entrepreneurial intentions, universities need to develop a practical education system that can help improve the skills required to start new ventures. The results will improve a new route to developing students’ entrepreneurial intentions using the variables of TPB and entrepreneurial self-efficacy. Subsequently, these research findings will help to achieve governmental goals and increase the number of startups in the future.