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The purpose of this paper is to determine the structure of nilpotent (n+6)-dimensional n-Lie algebras of class 2 when n≥4.

By dividing a nilpotent (n+6)-dimensional n-Lie algebra of class 2 by a central element, the authors arrive to a nilpotent (n+5) dimensional n-Lie algebra of class 2. Given that the authors have the structure of nilpotent (n+5)-dimensional n-Lie algebras of class 2, the authors have access to the structure of the desired algebras.

In this paper, for each n≥4, the authors have found 24 nilpotent (n+6) dimensional n-Lie algebras of class 2. Of these, 15 are non-split algebras and the nine remaining algebras are written as direct additions of n-Lie algebras of low-dimension and abelian n-Lie algebras.

This classification of n-Lie algebras provides a complete understanding of these algebras that are used in algebraic studies.

The purpose of this study is to determine the impact of job security on doctors’ retention, with job satisfaction and job embeddedness as the mediators. In doing so, the authors seek to contribute to the existing literature by providing additional empirical evidence on the links between job security, job satisfaction, job embeddedness and employee retention by using social exchange theory.

An empirical study was conducted on doctors working in public hospitals in Pakistan. Data from selected public hospitals were collected using semi-structured questionnaires. The simple random sampling method was applied for participant selection and partial least squares-structural equation modelling was used for data analysis purposes.

The findings confirmed the direct and mediation relationships. Thus, all of this study’s hypotheses are supported. The results indicate that job security can improve doctors’ retention. Further, job satisfaction and job embeddedness play crucial roles in mediating the direct relationship.

This study elaborates job security in health-care sector of Pakistan and also provides empirical evidence of the antecedents and mediators of doctors’ intention to continue working in the health-care industry.

Modeling electric machines is one of the powerful approaches for analyzing their performance. A dynamic model and a steady-state model are introduced for each electric machine. Permanent magnet induction machine (PMIM) is a dual-rotor electric machine, which has various advantages such as high-power factor and low magnetizing current. Studying PMIM and its modeling might be valuable. The purpose of this paper is to introduce a simple and accurate method for dynamic and steady-state modeling of PMIM.

In this paper, arbitrary dqo reference frame is used to model PMIM. First, three-phase dynamic equations of stator and rotors are introduced. Then, they are transferred to an arbitrary reference frame. The voltage and magnetic flux equations aligned at dqo axes are obtained. These equations give the dynamic model. To investigate the results, PMIM simulation is performed according to obtained dynamic equations. Simulation results verify the analytic calculations.

In this paper, dynamic equations of PMIM are obtained. These equations are used to determine dynamic equivalent circuits of PMIM. Steady-state equations and one phase equivalent circuit of the PMIM using phasor relations are also extracted.

PMIM equations along dqo axes and their dynamic and steady-state equivalent circuits are determined. These equations and the equivalent circuits can be transformed to different reference frames and analyzed easily.

This study aims to develop a measurement model of the halal supply chain resilience (HSCRES) index, which represents the capability of the supply chain (SC) to handle disruption caused by halal risks. A case study is conducted to apply the HSCRES index in the halal chicken SC in Yogyakarta, Indonesia, to test the proposed methodology.

A literature synthesis was conducted to establish the main capability and vulnerability factors and their relevant indicators. The indicators were validated using the confirmatory factor analysis approach. Then, applying an analytical hierarchy process involving ten experts – practitioners and academicians – the weight of each indicator was obtained. A survey of 20 employees of slaughterhouses, 35 sellers and 100 consumers was conducted to obtain the value of each indicator. Finally, the HSCRES index was calculated by comparing the total weighted capability value to vulnerability.

The results revealed that the resilience of halal chicken SC in Yogyakarta is at a good level, with an index of 3.459, and “halal team” is the most significant indicator. The findings also revealed several capabilities that need improvement, including dedicated halal facilities, employees’ halal competence and halal regulation. However, the lack of a halal certification board, lack of management commitment and packaging contamination were found as vulnerability indicators that need to be reduced.

The case of this study is limited to the halal chicken SC in Yogyakarta, Indonesia. As a consequence, the obtained results are limited to a specific context. The application of this method to different areas and objects enables the establishment of different capability and vulnerability indicators.

The halal resilience measurement model offers a comprehensive understanding of the strengths and weaknesses of the HSC. The findings can help stakeholders improve preparedness for halal risks, deal with halal risks better and recover more quickly. Measuring the HSCRES index can be particularly useful for policymakers in developing evidence-based strategies to increase HSCRES.

The current study is the first to define and classify the contributing halal resilience attributes and also to calculate the halal resilience index.

The study presented healthcare service quality, lean thinking and Six Sigma to enhance patient satisfaction. Moreover, the notion of machine learning is combined with lean service quality to bring about the fundamental benefits of predicting patient waiting time and non-value-added activities to enhance patient satisfaction.

The study applied the define, measure, analyze, improve and control (DMAIC) method. In the define phase, patient expectation and perception were collected to measure service quality gaps, whereas in the measure phase, quality function deployment (QFD) was employed to measure the high-weighted score from the patient's voice. The root causes of the high weighted score were identified using a cause-and-effect diagram in the analysis phase.

The study employed a random forest, neural network and support vector machine to predict the healthcare patient waiting time to enhance patient satisfaction. Performance comparison metrics such as root-mean-square error (RMSE), mean absolute error (MAE) and R2 were accessed to identify the predictive model accuracy. From the three models, the prediction performance accuracy of the support vector machine model is better than that of the neural network and random forest models to predict the actual data.

Lean service quality improvement using DMAIC, QFD and machine learning techniques can be generalized to predict patient waiting times. This study provides better realistic insights into patient expectations by announcing waiting times to enable data-driven service quality deliveries.

Prior studies lack lean service quality, Six Sigma and waiting time prediction to reduce healthcare waste. This study proposes lean service quality improvement through lean Six Sigma (LSS), i.e. DMAIC and machine learning techniques, along with QFD and cause-and-effect diagram.