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The establishment of a high productivity microbial fermentation process requires the experimental investigation of many interacting variables.
Fuzzy network modeling can be used as a tool for aiding human reasoning when many interacting variables participate in complex interaction networks on several scales.
The interactions between human health and physical environments are complex, for they involve many interacting variables that are geographical, chronological, and demographical.
The methods discussed and applied here can be used to analyze any kind of short time series of many interacting variables for inferring the regulatory network.
The model is based on the assumption that the slurry erosion characteristics of ductile materials is an imprecise complex function of many interacting variables, and can be described and evaluated by the theory of fuzzy sets.
SDM is based on the following principles (Richardson 2011): Complex systems include many interacting variables that change over time.
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The many different (interacting) variables at play in the simulation required careful data collection and analysis.
However, the tuning of polymer composition and structure as well as polymer formulations to optimize the ?nal performance properties can be challenging, - pecially since in many cases several interacting variables need to be optimized simultaneously.
The control of complex systems involves monitoring many time varying and interacting variables.
Several personal accounts of interviewees suggest that many of these contextual factors act as interacting variables (moderators/mediators).
The contribution margin π s is difficult to assess because it depends on many interacting and fluctuating parameters (e.g., market prices, variable logging costs, transportation costs, variable taxes).
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