By simulating a control-group based on the real data of first measurement month we could visualize a situation where no attention would have been paid to PU-s, a situation in which all conditions are held the same.
Process control of product variability (control error standard deviation) is possible by developing and simulating a feedback control algorithm for dead-time processes.
The effect of age difference between the case and control groups was confirmed by simulating the control group and a random sampling that considered only the male-to-female ratio and selected case subjects under the same conditions.
Power calculations were performed by simulating cases and controls assuming a multiplicative model for disease risk, and varying the genetic risk associated with each copy of the high-risk variant.
Computation results show that for the industrial SSP process for poly ethylene terephthalate) (PET), the overall reaction rate for a single pellet can be appropriately simulated by a model controlled jointly by diffusion and reaction rates.
We performed an OR correction by simulating 1000 matched case-control analyses in which the ascertainment probability for Campylobacter was fixed within clinics, regardless of case or control status, but allowed to vary between clinics.
We simulated a quantitative trait, controlled by 2, 4, or 6 loci with additive gene action.
By using metabolic control analysis and by simulating biomarker levels under a variety of conditions, we found that 5-oxoproline and ophthalmic acid concentrations may not only depend on the glutathione but also on the methionine status of the cell.
In our computations, the steady-state was identified by simulating the system corresponding to the control condition (no stimulus) for a long time (t = 1000 hr).
By simulating the execution, we have complete control over how many instructions are executed at a time, how the address translation works, and how interrupts and exceptions (including system calls) are handled.
