Does Women Risk Averse in Ownership Behavior: Myth or Reality?

The study aims to explore the impact of female ownership (FO) of financial behavior measured by three dimensions: financial performance (FPER), risk taking behavior (RTB) and stock performance (SPER). Secondary data of sixty Pakistani companies for the period of 2011-17 are collected. Multiple regressions are applied to analyze the data. The findings reveal that Female ownership (FO) has negative (positive) linkage with FPER (RTB) respectively. However, the findings are vice versa in case of female primary ownership (FPO) with same independent variables. The finding of the study is very significant regarding the behavior of women in the context of ownership. FO (FPO) shows indirect (direct) significant effect on financial behavior vice versa to RTB

Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement

This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation

Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement

This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation