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We designed a study to characterize time trends in these parameters and their impact on patient risk stratification.
The time dependent coefficients correlate well with the integral flame luminosity, and characterize time evolution of the combustion pattern in the chamber.
These two time parameters characterize time in which appear all mining impacts on the surface it can be assumed that they are the same as time coefficient introduced by Knothe.
Our objective was to characterize time course changes in signaling pathways for GH and insulin in human skeletal muscle in vivo following GH exposure in the presence and absence of an oral glucose load.
Hence, taking more stage samples such as days 10 or 14 may help to better characterize time course of apoptotic factors activation.
To characterize time trends in the primary and secondary outcomes, we will use linear mixed models (or Generalized Estimating Equations in the case of categorical variables) to analyze the repeated measures obtained at all follow-up interviews.
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We propose a Markov swarm mobility model to characterize time-dependent changes in the network topology, based on which a quantized collaboration degree is obtained.
The main goal of the present study was to develop and validate a quantitative method to characterize time-dependent changes mainly in the capillary network of the CAM.
In this section we present a time-frequency procedure to characterize time-varying MIMO-OFDM channels.
Hence, we adapt the fast Hermite projection method to characterize time-frequency regions of different origins, namely, by using a small fixed number of Hermite functions, each region is reconstructed with a certain error.
Besides this discrete-time Gilbert model, a continuous-time 2-state Modulated Markov Poison Processes (MMPP-2) can be used to characterize time-varying packet loss processes that alternate between low and high packet loss periods (see Figure S1b, Additional file 1).
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