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In this study, a lumped parameter model which includes systemic circulation, cerebral blood vessels, systemic arteriolar resistance control, heart rate control, cerebral autoregulation mechanisms and cerebral CO2 reactivity was developed to simulate healthy and heart failure conditions. In the healthy cardiovascular system model, the results were obtained with all control mechanisms connected to the model. Whilst heart failure cases were simulated, all control mechanisms were removed from the model. Then, cerebral autoregulation and cerebral CO2 reactivity mechanisms were connected to the model. Lastly, systemic arteriolar resistance and heart rate control mechanisms were connected to the model. Also, Monte Carlo Analysis was performed to determine the range of parameters controlled for simulations of healthy and heart failure conditions. The results showed that blood flow rate in cerebral circulation can be simulated more accurately by modeling interaction among autoregulatory mechanisms rather than studying separately. [ABSTRACT FROM AUTHOR]

With respect to the ever‐increasing performance needs in communication technologies, the need for accurate and computational efficient design optimization methods for high‐end microwave designs are also increased. Many studies had been proposed for the last decades for creating numerical modeling methods for having high accurate, stable, and computation efficient solutions suitable to be used in the design optimization process. Ensemble learning is a technique that the models are strategically created and combined to solve a specific computer intelligence challenge and primarily employed to boost the efficiency of a model or to lower the risk of a weak learner collection. Herein, XGBoosting‐based ensemble learning had been used for having surrogate models for three different microwave designs. In the first and second study cases, two microwave designs from the literature are taken into consideration for testing the performance of the proposed model with existing methods. Furthermore, a novel antenna design had been studied as a third study case with sparse training samples, to test the performance of the proposed modeling technique. As a result, the proposed method had achieved a remarkable performance for all the mentioned study cases both based on its own performance measures and its comparison with the counterpart algorithms. [ABSTRACT FROM AUTHOR]