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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

This study aims to obtain the minimum torque ripple at the maximum average torque for Flux-switching permanent magnet (FSPM) machines.

This paper is about torque performance optimization of the FSPM machines. To achieve that, finite element analysis and genetic algorithm (GA) are used. Five different designs are simulated, optimized and compared on their air gap flux density, back electromotive force, cogging torque, average torque, torque density and torque ripple.

After the thousands of iterations, its proved that all proposed shaping techniques have potential for reducing torque ripple and cogging torque, with slightly reduced average torque. The best design is the joint stator and rotor shaping, Design V, which results in the lowest torque ripple and cogging torque. The techniques should be applicable to FSPMs with other stator slot/rotor pole number combinations.

In this paper, rotor pole shaping by notching, chamfering and generic shaping, stator tooth shaping and joint shaping techniques are investigated for 12 s/10p FSPM machines. Rotor and stator flanks are optimized separately and jointly, by using finite element analysis and GA for optimization to achieve maximum average torque and minimum torque ripple. Five different design is implemented and compared, respectively.

The purpose of this paper is to propose a novel type of switched flux PM machines with two separate stators.

2D-FEA is employed to analyze the electromagnetic performance of the proposed machines. Moreover, the results are validated by experiments.

The proposed machine has higher torque density, less unbalanced magnetic force on the modulating steel piece and uses less PM volume.

The proposed machine is a low-cost novel topology with different rotor pole combinations.

The purpose of this paper is to investigate the influence of end‐effect and cross‐coupling on the torque‐speed characteristics of switched flux permanent magnet (SFPM) machines.

The torque‐speed characteristics are predicted using two different methods. These are direct and indirect finite element methods, at different cross‐coupling levels, namely, full cross‐coupling on both PM flux linkage and dq‐axis inductances, partial cross‐coupling on the PM flux linkage only and without cross‐coupling.

The influence of the cross‐coupling on dq‐axis inductances of the studied machine is relatively small. However, it is more significant on the PM flux linkage. Therefore, the partial cross‐coupling model, which is much easier and faster, exhibits almost the same accuracy as the full cross‐coupling model. Furthermore, the end‐effect causes a large reduction in torque‐speed characteristics. However, such a reduction is more significant in the flux weakening operation region.

This is the first time that the influence of end‐effect of SFPM machines on the torque‐speed characteristics, especially in flux weakening region, and on the dq‐axis inductances has been investigated.

Fractional slot permanent magnet (PM) brushless machines having concentrated non‐overlapping windings have been the subject of research over last few years. They have already been employed in the commercial hybrid electric vehicles (HEVs) due to high‐torque density, high efficiency, low‐torque ripple, good flux‐weakening and fault‐tolerance performance. The purpose of this paper is to overview recent development and research challenges in such machines in terms of various structural and design features for electric vehicle (EV)/HEV applications.

In the paper, fractional slot PM brushless machines are overviewed according to the following main and sub‐topics: first, machine topologies: slot and pole number combinations, all and alternate teeth wound (double‐ and single‐layer windings), unequal tooth structure, modular stator, interior magnet rotor; second, machine parameters and control performance: winding inductances, flux‐weakening capability, fault‐tolerant performance; and third, parasitic effects: cogging torque, iron loss, rotor eddy current loss, unbalanced magnetic force, acoustic noise and vibration.

Many fractional slot PM machine topologies exist. Owing to rich mmf harmonics, fractional slot PM brushless machines exhibit relatively high rotor eddy current loss, potentially high unbalanced magnetic force and acoustic noise and vibration, while the reluctance torque component is relatively low or even negligible when an interior PM rotor is employed.

This is the first overview paper which systematically reviews the recent development and research challenges in fractional‐slot PM machines. It summarizes their various structural and design features for EV/HEV applications.

This paper examines the impact of corporate digital transformation on employee satisfaction. Therefore, this study extends our understanding of the economic consequences of corporate digital transformation from employees’ perspectives.

The data used to construct our main proxy of employee satisfaction are collected from Kanzhun.com, which provides reviews by rank-and-file employees on their employers. This study uses a large sample of Chinese firms and adopts various empirical methods to examine the impact of digital transformation on employee satisfaction.

We find a significant positive relationship between corporate digital transformation and employee satisfaction. Moreover, we document that the relationship between corporate digital transformation and employee satisfaction is more pronounced in firms with higher labor intensity and in state-owned enterprises (SOE).

One significant limitation is that corporate digital transformation is constructed based on word frequency analysis. This approach may be influenced by variations in corporate disclosure practices and might not accurately capture the true extent of corporate digital transformation. This limitation is not only present in our research but is also pervasive in many other studies that utilize similar methodologies. Therefore, our results should be interpreted with this caveat in mind.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study contributes to the literature on the economic consequences of corporate digital transformation and extends existing research on the determinants of employee satisfaction. Additionally, it provides a novel measurement of employee satisfaction for a large sample of Chinese firms.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.