Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines’ TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when limited drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models on several ion channels for various drug pairs. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as biomarker features. The drug combination maps were also used to illustrate combined medicines' TdP risk. We found that the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.

Integration of photovoltaic (PV) based power plant have been increased significantly over the years. The integration of this devices is ranging on the small-scale capacity such as in distribution system and large-scale capacity such as in transmission system. Although, PV generation could provide sustainable and clean energy to the grid, they could also bring new challenge on the system stability. One of the stabilities of power system that can be affected by integration of this devices is small signal stability. Hence, it is important to capture the dynamic model of PV generation. This paper is on investigation of dynamic model of PV generation for small signal stability analysis. The simulation is carried out in MATLAB/SIMULINK environment. From the simulation results, it is found that by capturing the dynamic model of PV generation, the modes of the PV generation can be captured.

Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines’ TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when a limited number of drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models for various two-drug combinations. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as features. The drug combination maps were also utilized to illustrate the impact of DDIs on the TdP risk of combined medicines. We found that the DDIs of the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.

The use of engines that motorized the world based on fossil fuel sources has led to many problems, such as air pollution, energy security, global warming, and climate change. To prevent further damage reducing the application of fossil fuel as a source of the motor is crucial. Hence, utilizing an electric motor could be the solution to reduce the application of motors based on fossil fuel. Among the number of electric motors, permanent magnet synchronous motor (PSMS) is becoming more popular due to their efficiency. However, the challenge here is how to design the controller of PSMS, especially the speed controller. Hence, this paper proposed a design of a speed controller of PSMS using a PI controller. The hybrid differential evolution algorithm-particle swarm optimization (DEA-PSO) is used to optimize the PI controller for better performance. From the simulation result, it is found that the proposed method can enhance the performance of PSMS.