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This contribution investigates how different technology modeling methodologies affect the design of decentralized multi-energy systems, especially when fuel cell and energy storage are considered.
Understanding how different collection methodologies affect the stability of CSF Aβ levels over time is of paramount importance for the design of clinical trials, where these biomarkers would be utilized to study pharmacodynamic activity, and ultimately may determine whether Aβ has utility in a diagnostic fashion.
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In general the use of multiple methodologies in conjunction in specific problems, allows us to more clearly understand how different assumptions in the model, including those implicit in the modelling methodology, affect the conclusions and predictions.
It would be useful to quote examples of how a new methodology affects the efficiency of the prediction.
The objective here is to illustrate how selecting a particular allocation methodology affects the interpretation of results, not to present a new methodology.
To test whether the hemocyte collection methodology affected the results, proboscis snips [ 62] and low injection/recovery [ 14] hemocyte collection methods were also conducted.
This variability in the assessment methodologies might affect the final decision of choosing one or another assessment test by scientists, clinicians, and sport practitioners.
In fact, the choice of a given methodology could affect the outcome of the experiments.
If not properly controlled, bias in the design of trial methodology can affect the validity of the study results.
In addition, we examine how a changed methodology might affect the continuity of results with respect not only to the latent proficiency variables themselves but also to their correlations with the context constructs.
Blocking or competitive ELISAs have been constructed using monoclonal antibodies in PEDV serodiagnosis, and the specific methodology can affect the overall specificity and performance of the assay.
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