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The turbulent kinetic energy and velocity predictions were compared between the turbulence models and with experimental data, then interpreted on the basis of the recirculation length.
(22) The turbulence model constants are obtained through a data fitting exercise.
The computational results of recirculation length for each model are then compared with the experimental data for similar investigation carried out using diesel fluid [9] in order to determine the best turbulence model.
Comparison of the predicted recirculation length (Z=1.25m) and experimental data (Z= 1.35m) for similar study done using diesel [9], showed that the Reynolds-stress model under predicted the recirculation length (7.4 % error).
However, it is important to add that exact experimental data for turbulent air flow from sudden expansion in circular ducts could not be found in literature for thorough validation of the computational results.

Less robust sensitivity indices have also been used to account for cases with lack of data, common in the oil sector.
The bathymetric data were obtained from different sources, depending on the region of the mesh.
Data for the coastal regions and the Patos Lagoon were obtained from the base of the Hydrographic Center of the Brazilian Navy6.
Figure 2 compares the data time series of the two best simulations (T1 and T2) performed in the physical calibration stage.
The field data were provided by LOCOSTE.8 4, for the initialised oil spills, respectively in ZP-19 and ZP-20 respectively.

The conclusion and the experiment data will be of significant value for future work of robot design and evaluation.
Motion capture system collected data at its maximal frequency 120HZ.
The same subject was tested 2 times in each foot state to ensure the accuracy of the experimental data.
Data processing.
The data was processed by programming and analyzed by comparison.

This results in a reduction of the available nucleation sites for precipitation as well as a decrease in the driving force for recrystallization along time.
Some of the aforementioned physical parameters were back determined from the comparison between experimental data and model calculations.
The activation volume, Va, is back calculated from experimental data.
As the softening kinetics are provided by stress measurements, so as to be able to compare the predictions of the model with the experimental softening data, a hardening model incorporating the contribution of precipitation, strain and solid solution hardening is considered.
From the data the following relationship can be written: [ ] 5480 2 10862 .

The data link and interaction are both achieved by information process through the WWW mechanism.
Then, the application server receives the request and gets the data from the database server.
Ajax provides a good solution for Web application which has many interactions, frequent data manipulation and fine data classification.
The process speed is increased by eliminating downloading redundant web data.
By loading data on demand, the redundant request and response are both decreased effectively.

Gongde Guo et al. [9] suggested a method by constructing a KNN model for the data, which replaces the data to serve as the basis of classification, thus the value of K is automatically determined and is varied for different data.
Step 3 Now, we have two new data sets M and P.
Experiments and Discussion The data set.
The data set is composed of 19 thousands instances with 11 attributes.
Table 1 The description of experimental data set Id The training data size The test data size 1 8000 1000 2 9200 1000 3 10400 1000 4 11600 1000 5 12800 1000 6 14000 1000 7 15200 1000 8 16400 1000 Results and Analysis.

For optimum weight reduction new materials and their fatigue behavior under real cyclic service loads have to be taken into account (Gassner test).
Figure 3: Stress-strain curves for TRIP with different cooling strategies and the reference material 42CrMo4 As the experimental gathering of cyclic material data (stress-strain curves) using strain controlled fatigue tests is very time consuming, so the much shorter Incremental Step Test is very attractive [11].
Tech., (1995), Vol. 11, 874-881 [10] Wang, J.; Van der Wolk, P.J,; Van der Zwaag, S.: Journal of Materials Science 35, (2000), 4393-4404 [11] Wagener, R.; Esderts, A.: Estimation of the strain-life curve from Incremental Step Test data, 6th International Conference On Low Cycle Fatigue, 2006, Berlin [12] Traint, S.; Pichler, A.; Heuzberger, K.; Stiaszny, P.; Werner, E.: Influence of Silicon, Aluminum, Phosphorus and Copper on the Phase Transformations of low alloy TRIP-Steels, Steel research 73, Vol. 6+7 (2002), 259-266 [13] Sonsino, C.

Data agents are responsible for collecting and processing diabetes data from a variety of sources, such as electronic medical records and wearable devices.
The integration of multimodal data, such as genomic, proteomics, and medical imaging data, can be explored to improve diagnostic accuracy.
Result and Discussion Analysis of Discrete Function Model on Diabetes Data.
SVMs, with their ability to map data to high-dimensional spaces, also show good performance.
Machine learning and data mining methods in diabetes research.

The article deals with the methods and calculations made to obtain the data on solar radiation effects on sloping surfaces oriented in different cardinal directions in regard to Saint-Petersburg weather-related conditions.
However reduction of energy consumption due to disclosure of existing energy saving opportunities is only a part of harmonization between architecture and nature.
It is possible to determine whether it is feasible to apply solar technologies if there are any data on energy efficiency (electric power generation) of solar photovoltaic modules (PV modules) and solar-based electric power system (EPS).
With this background a calculation method has been developed performing good results in regard to compliance of estimated and observed data (discrepancies do not exceed 8%).
These correction factors have been obtained on the basis of estimated and observed data made at 50 scientific stations located within the territory of the Russian Federation.

By the use of multi-sensor technology, the positioning accuracy can be improved in some degrees by data fusion, such as GPS and DR data fusion.
The height data can be calculated by path wise interpolation method, and the data would be used to amend the coordinate calculated from single GPS receiver.
Fig. 7 shows the error of raw GPS data and matched GPS data, which was compared with RTK.
The average error of matched GPS data is 3.2m.
(A) Indicates errors of raw GPS data, (B) indicates the average errors of matched GPS data.