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With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

This paper aims to present a systematic literature review of 176 studies relating to change management in the context of process optimization and to investigate how companies effectively use change management to optimize processes across different industrial sectors.

Descriptive statistics are used to represent patterns, trends and correlations between change management strategies, research methods applied for processes optimization and industry field. A comprehensive analysis of the papers’ keywords, crossed with research methods and industrial sectors, allowed us to substantiate the results in analytic terms. For some selected studies, chosen on the basis of their significance to the research field, the contents were mapped and discussed in detail.

This study provides numerous insights into the various applications of change management across different industry fields. In general, change management appears to be no longer a theoretical discipline, showing instead practical relevance, which is reflected in testing theories through case studies and real implementations. The review emphasizes the need for careful and systemic planning by companies, effective communication, employee involvement and supportive organizational culture. These factors are crucial for enhancing process efficiency and employee acceptance of change. Digital technologies also prove to be valuable support for change management during process optimization.

The innovative contribution of this paper consists of the joint perspective taken when looking at process optimization and the application of change management strategies. Such a perspective favors an in-depth examination of the interactions between the two aspects and provides more comprehensive results compared to the existing literature.

This research is the first to use bibliometric analysis to provide insight into the landscape and forecast the future of customer experience research in the banking sector.

We used bibliographic coupling and co-word analysis to delineate the existing knowledge structure after reviewing 338 articles from the Web of Science database.

The bibliographic coupling analysis revealed five key clusters: customer engagement and experience in digital banking; customer experience and service management; customer experience and market resilience; digital transformation and customer experience; and digital technology and customer experience—each representing a significant strand of current research. In addition, the co-word analysis revealed four emerging themes: customer experience through AI and blockchain, digital evolution in banking, experience-driven ecosystems for customer satisfaction, and trust-based holistic banking experience.

These findings not only sketch an overview of the current research domain but also hint at emerging areas ideal for scholarly investigation. While highlighting the industry’s rapid adaptation to technological advances, this study calls for more integrative research to unravel the complexities of customer experience in the evolving digital banking ecosystem.

This review presents a novel state-of-the-art analysis of customer banking experience research by employing a science mapping via bibliometric analysis to unveil the knowledge and temporal structure.

This study aims to provide a comprehensive overview of pro-environmental behavior (PEB) research within higher education institutions (HEIs), highlighting current trends and future challenges.

Using 198 journal articles from the Web of Science, the study conducts co-citation, bibliographic coupling and co-word analyses to map influential publications and forecast trends.

The co-citation analysis revealed three distinct clusters: value-driven environmental behavior, intention-based environmental behavior and green organizational practices and employee PEB. The bibliographic coupling and the co-word analysis revealed more nuanced clusters, holistically identifying academic activities towards PEB. The authors conclude that more strategic and PEB-oriented HEI’s actions are crucial due to the social responsibility of the universities for sustainable development.

This paper provides valuable insights into the expanding area of PEB research and climate leadership empowerment within HEIs. The practical implications of this research are significant for HEIs. It guides the creation of effective policies and interventions to foster sustainable behavior and reduce environmental harm. The study shows the development of educational programs and campaigns promoting sustainable practices among individuals and communities, emphasizing the role of HEIs in cultivating a sustainability-conscious generation.

This study aims to measure impact of several firm-specific factors on alternative measures of leverage. The authors also aim to study impact of the subprime crisis on such associations.

The authors utilized an unbalanced panel data of 973 firm-year observations on 47 UK listed non-financial firms for the years 1990–2019. Book-based and market-based long-term and total leverage measures have been used as explained variables. The explanatory variables are profitability, size, two measures of growth, asset tangibility, non-debt tax shields, firm age and product uniqueness. Fixed effect and random effect models with clustered robust standard errors have been utilized for data analysis. To find the effect of subprime crisis, original dataset was split to create pre-crisis and post-crisis datasets.

The authors find that profitability significantly reduces leverage while firms having more tangible assets use significantly more debt in capital structure. Firm size and non-debt tax shield have statistically insignificant positive impact on leverage. Having more unique products reduces use of external debt, albeit insignificantly. Growth, when measured as market-to-book ratio, has inconsistent impact, whereas capital expenditure insignificantly reduces leverage. Age is found to be an insignificant predictor of leverage. After the subprime crisis, firms started relying more on internal fund instead of external debt, more particularly short-term debt. Having more collateral is gradually becoming more important for availing external debt.

Data limitations restrict generalization of the findings.

This is one of the pioneering attempts to show how subprime crisis altered the theoretical domain of capital structure research in the UK.