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In the performance assessment on the designed PID sliding surface, the controller parameter is first obtained through conventional tuning method known as Ziegler-Nichols (ZN), which is then compared with the particle swarm optimization (PSO) computational tuning algorithm.
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A) Future combinatorial drug discovery approaches will benefit from tighter integration of gene signatures and phenotypic screen data with computational models, tuning the models to specific cancer cell-lines.
Computational models tuned to specific cancer cell lines using bioinformatics and experimental data could be perturbed to make predictions of effective drug combinations to validate in preclinical models (Figure 3A).
We aimed to explore whether these results would be replicated when implementing similar ventilation strategies in a high-fidelity computational simulator, tuned to match data on the responses of a number of human ARDS patients to different ventilator inputs.
In this study, we explore whether the application of a similar approach using a high-fidelity computational simulator tuned to a number of human ARDS patient datasets confirms or refutes the results of this previous animal study.
SMURFLite enables researchers to trade modeling power for computational cost by tuning an interleave threshold.
Besides, it has better control performance than PI controller and saves the lengthy computational work of parameters tuning for PI controller with the aid of pole placement technique.
The results show that the distributed controller achieves the same performance as the centralized one, while leading to increased modularity, reduced complexity, lower computational costs, and easier tuning.
Here, we propose a computational model which involves tuning motivational arousal to the appraised stressing condition.
Most of them usually suffer from intensive computational burden induced by tuning extra parameters such as bootstrap size, and thus they are not computationally feasible at genome-wide scale.
Predictive multi-scale models; Feedback mechanisms to prompt new data generation (remote observations and measurements); Methods for integrated interaction between data, parameters, models and visualized results ("fine tuning" experiments, computational steering); New approaches for decision support when model outcomes result in various scenarios.
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