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This paper aims to propose a new configuration of a shunt active power filter (SAPF) connected with a photovoltaic (PV) system through a Z-source inverter (ZSI) topology. This topology ensures a single-stage operation and overcomes the limitations of the conventional two-stage operation topologies based on the DC–DC boost converter. The proposed system is designed for the purpose of reducing the total harmonic distortion of the source current by eliminating the current harmonics and exploiting the solar irradiation.

First, all the main parts of the proposed shunt active power filter are fully described in this paper, and then a PV system based on a Z-source inverter with a maximum power point tracking controller is used to exploit the solar irradiance and solve the problem of discharging of the direct current (DC) capacitor during the filtering process.

From the extensive simulation tests carried out using MATLAB/Simulink, the obtained results prove that the proposed shunt active power filter performs well despite several operation scenarios, including different load types and under abrupt irradiance.

A new shunt active power filter configuration has been proposed. This configuration benefits from the solar irradiation and overcomes the drawbacks of the conventional configurations by using the Z-source inverter instead of the voltage source inverter and DC–DC boost converter.

The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.

First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs.

The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach.

The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon.

To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms.

The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark.

This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

This study aims to determine how Basel III capital requirements affect the stability of Islamic banks globally during the global financial crisis and the COVID-19 pandemic.

The secondary data for all Islamic banks worldwide from 2004 to 2021 is obtained from the FitchConnect database. The main technique was a two-step generalized method of moment (GMM) system, and the data were tested using pooled ordinary least squares, fixed effects and difference GMM models for robustness checks.

Regression results support the moral hazard hypothesis based on evidence that both the total capital ratio and the Tier 1 capital ratio have a statistically significant positive impact on the stability of Islamic banks globally. Furthermore, neither the global financial crisis of 2008–2009 nor COVID-19 (2020–2021) significantly impacted the stability of Islamic banks worldwide. The results are robust across alternative measures of stability, capital buffers, dummy variables and estimation techniques. According to the descriptive statistics, the number of Islamic banks that disclose their regulatory capital ratios to the public has increased over the study period, and the mean of total capital and Tier 1 ratios are considerably greater than what is required by Basel II and Basel III.

Bankers, regulators and policymakers should benefit from the evidence on capital and risk management in Islamic banking according to Basel Committee on Banking Supervision (BCBS) and Islamic financial services board (IFSB) international standards in various jurisdictions.

This research builds on earlier studies that were both beneficial and instructive by exploring the relationship between BCBS and IFSB capital guidelines and the trustworthiness of Islamic banks in greater depth. This study uses numerous capital ratios, buffers and stability measures to provide an international context for research on Islamic banking. In addition, the database is up-to-date to include information about the COVID-19 pandemic aftereffects in the year 2021. This study also introduces the Basel membership of Islamic banks to provide context for countries still at the Basel II stage or are yet to begin implementing the Basel III international standard.

This paper aims to explore the drivers of banking stability in the case of QISMUT+3 countries (Qatar, Indonesia, Saudi Arabia, Malaysia, United Arab Emirates, Turkey, Pakistan, Kuwait and Bahrain) focusing on social and governmental responsibility (SGR) determinants. Both main indicators of banking stability, namely, profitability and nonperforming loans, were treated as dependent variables. The model is examined with the whole sample and separately by examining commercial banks and Islamic banks.

Cross-country bank-level panel data spanning from 2011 to 2018 is used. Two-step system generalized methods of moments alongside both panel-corrected standard error and feasible generalized least squares models were applied to ensure the robustness of the results.

Findings reveal that capital adequacy and corruption control are the most dominant determinants of banking profitability in the studied sample regardless of the type of the bank. In addition, profitability, efficient management, inflation and government effectiveness were found to be the main drivers of financial vulnerability risk.

Findings of this study offer many insights and policy implications to help stakeholders gain a comprehensive understanding of banking stability. Suggested policy implications targeting bank management, governmental policymakers and investors are offered to better the banking stability of QISMUT+3 countries.

This paper has multiple contributions to the existing literature. The determinants of banking stability are examined in QISMUT+3 group of countries which is the focus of a limited number of studies. In addition, the use of a comprehensive variable set alongside the addition of SGR determinants in the case of banking system stability is one of the main contributions of this paper.

This study aims to investigate the determinants of banking stability in the case of QISMUT + 3 countries (Qatar, Indonesia, Malaysia, United Arab Emirates, Turkey, Pakistan, Kuwait and Bahrain). Both profitability of banks and non-performing loans were treated as dependent variables. Three variations are examined, the sample as a whole and separated to conventional banks (CBs) and Islamic banks (IBs).

Data from 208 banks, both IBs and CBs, were used from 2011 to 2018, after global financial crisis period. Two-step system generalized methods of moments and both feasible least squares and panel-corrected standard error models were used to ensure test the data.

Results suggest that both financial vulnerability and profitability affect each other in both banking systems. In addition, capital adequacy has a positive link with both dependent variables. Corruption varied and followed expectations but for the case of CBs alone with an unexpected negative relationship with profitability.

The findings are expected to help bankers, investors, academics and policymakers gain a better understanding of Islamic banking. The findings would be useful in developing policy for the development of the banking industries in these countries.

This study contributes to existing literature in three ways. First, this study investigates the factors influencing banking non-performing loans for a new class of countries – QISMUT + 3 within 2011–2018 period. Second, only a few studies use such a period, which is after the global financial crisis period. Finally, new indicators are used to determine the non-performing loans and profitability of both types of banks, such as Muslim Share and Share of IBs.