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As shown in Fig. 6, only the raw JSON data will go through this transformation, as the pre-transformed Avro file has already been transformed at the data pipeline level and the raw Avro data will be transformed at the analytic time when it is required.
25 26 Eligible models may include any analytic time horizon.
Three analytic time horizons (10, 20 and 30 years) were selected for simulation.
We evaluated each outcome over a 5-year analytic time horizon.
These results are reported for the three treatment groups in LEAD-1 and for the three analytic time horizons.
To explore the potential indirect vaccine benefits among non-vaccinated populations, we extended the analytic time horizon from 5 years to a lifetime.
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These parameter trends are then used to develop analytic time-varying BRDF (TVBRDF) models.
We also show the developed analytic Time-Varying BRDF functions for a number of interesting natural phenomena including the drying of various paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabric), and the accumulation of dusts (household and joint compound).
Based on the Boltzmann model with external forcing term, the UGKS uses an analytic time-dependent (or scale-dependent) solution in the construction of the discretized fluid dynamic equations in the cell size and time step scales, i.e., the so-called direct modeling method.
In the first simulated dataset, the time course of all three methods follows the analytic time-varying coherence, although some differences can be observed (Figure 3).
Panel A represents the analytic time-varying coherence in Example 1 obtained from Equation (13) at amplitude a = 0.5, and panel B shows the desired T-F coherence for simulated data of Example 2. We then sought an exploratory assessment of the different estimators of T-F interdependence in characterizing synchronization in an exemplar physiological dataset, namely human EEG.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com