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This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults.

The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step.

The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance.

The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

The purpose of this paper is to focus on the impact of corporate social responsibility (CSR) on dividend policy in the French context. In addition, the authors seek to determine if the individual components of CSR influence dividend policy.

This study uses panel data methodology for a sample of French non-financial firms between 2008 and 2018. Generalized least squares method is used to estimate the models.

Using panel data methodology for a sample of 825 observations for the period 2008–2018, this study finds a positive impact of CSR practices on dividend policy. The authors also find that individual components of CSR positively influence dividend policy. To check the robustness of the results, this study further runs a sensitivity tests, including an alternative measure of dividend policy, all of which confirm the findings.

This study has examined the impact of CSR on dividend policy in France and may have implications for regulatory, investors, analysts and academics. First, the involvement in CSR best practices encourages companies to pay more dividends to investors. Therefore, investors are more motivated to invest in socially responsible firms than socially irresponsible firms. Second, given the association of CSR with the quality of accounting information and financial markets, regulators should step up recommendations relating to the different societal dimensions of CSR.

While little previous work has focused on the causal link between CSR and dividend policy, this research is the first, to the authors’ knowledge, to have looked at the impact of CSR on dividend policy in France.