We validate several predictions made by our model using empirical measurements on an instantiation of a highly-threaded, many-core machine, namely the NVIDIA GTX 480.
The findings of the research are as follows: (1) The observed temporal changes in the snowpack depth were well reproduced by our model using observed and estimated densities.
Validation analysis was performed by comparing the number of outpatient visits predicted by our model using current vaccination uptake rates with the excess consultations attributed to influenza estimated on the basis of German surveillance data [ 21].
Figure 8 presents the fluid temperature at position kmax in the receiver obtained by solving our model using a high heat transfer coefficient (a heat coefficient of 10,500 W m−2 °C−1 is taken, to guarantee that the plate temperature and the fluid temperature at position k = 1 are equal) and varying the fluid thermal conductivity.
By fitting our model using 3 years of data in seven counties in North Carolina (Alamance, Chatham, Durham, Guilford, Johnston, Randolph, and Wake), we found that increased PM2.5 exposure is related to increased risk of cardiovascular mortality on the same day and the next 2 days.
Since the success of a predictive model is determined by its verification, we tested our model using several independent datasets collected from multiple countries (Fig. 6).
Second, by empirically testing the predictions of our model using longitudinal firm-level data for Spain, which has been often considered as an epitome of dual EPL in southern Europe.
1 2 8 By contrast, our model used country-specific trends for diabetes risk factors including obesity and smoking in addition to the demographic trends, whereas the Global Burden of Disease GBDD) used urbanisation as a crude proxy measure for obesity and physical inactivity. 2 The estimates of the model and observed diabetes prevalence reported in national surveys were comparable.
However, by fitting our models using multifractional models, no non-linear model outperformed a standard linear model in our sets of data.
These residuals suggest that it is difficult to predict the spatiotemporal evolution of afterslip by our simple model using the early postseismic data (the first 3 months in this case).
A final demonstration for the predictive power of our model was made by experimentally measuring the phenotypic switching rates between the OFF and ON states of the GAL network, followed by predicting the rates by our model without using any additional fit parameters.
