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At higher temperature, our oxidation data support the existence of a sudden variation of the recrystallization process that rapidly reduces the residual amorphous region and, consequently, the oxide thickness.
Filled circles and triangles represent data obtained through Rutherford backscattering spectrometry in a channelling geometry (RBS-C) and TEM, respectively.
The large difference between the activation energies of the oxidation and recrystallization rates, 1.6 and 3.4 eV, respectively, explains our experimental data.
The curve shows a maximum at 910 °C, in excellent agreement with the experimental data.
A sudden variation of the recrystallization rate is necessary to explain our data.

A conventional creep equation based on tensile test data was adopted as a material model for the simulation.
Modeling Details 2.1 Material data The flow stress behavior during superplastic forming can be represented by the power law given by equation (1).
The data for the simulations were extracted from tensile test results with the corresponding strain rate of 0.003 s-1 and temperature of 400 ºC [9].
The bounded data are summarized in Table 1.
Table 1 Material data for AA5083 (unit: Pa) Parameter Value Parameter Value K 4.05×108 A 8.51×10-19 n 0.151 � 2.093 m 0.327 M -0.315 2.2 Simulation model The model used for the simulation is presented in Fig. 1.

Fig. 1: Navigational Data Transmission.
The VDES deals with VHF data communications such as AIS including ASM and VDE.
Data Communication functionalities for ASM, VDE and AIS are as indicated in table 1 below; Table 1: Functionalities of VHF data exchange systems.
ITEM ASM VDE AIS 1 Marine safety information General purpose data exchange Safety of navigation 2 Marine Security information Robust high speed data exchange Maritime locating devices 3 Marine safety related messages VDE satellite communication Satellite detection of AIS in long range 4 General purpose information communication Locating during SAR in long range Fig. 3: VHF data communication exchange system.
These attacks gain access to a network, acquire data, and then secretly monitor the network for a considerable amount of time.

The current work addresses both tasks: improvement of efficiency and reduction of noise emission.
Modifications of the stripper geometry with the objective of pressure drop linearization along the stripper are evaluated with respect to efficiency, pressure rise and noise reduction.
The goal of this work is to reveal approaches for side channel blowers that lead to a noise reduction as well as a rise in efficiency.
The data was recorded with a telemetric system at a sampling frequency of 100 kHz and transferred to the non-rotating laboratory system.
A network of hydraulic resistors was setup for the original configuration and was trimmed with the measurement data (see Fig. 3a).

PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data.
PCA PCA, Principal Component Analysis, is a linear input dimension reduction technique.
Experimental results This experiment used PCA to reduce the dimension of characteristic parameters of statistical data on water injection, and then FNN was applied to train and predict the data for further research.
The experimental processes are shown in figure 2: Fig. 2 Experimental process Data preparation.
The fuzzy membership function center C and width B obtained randomly. 116 groups of data samples were randomly divided into two parts of the training set and test set, the training set for 100 groups of data samples, the test sets for the rest of the 16 groups of data samples.

In particular the effectiveness of the selected signal characteristics will be assessed using dimensional reduction methods (PCA) and the so called averaged damage indices will be described.
The preliminary results of the data collected from the subcomponents tests with the model description, as well as approach for the SHM system design will be delivered.
This is clearly visible on this plot (Fig. 1(b)) but it is worth to notice that data are separated for individual generators (Fig. 2) for both types of specimens.
The following plot (Fig. 3(a)) shows classification regions for 5-nn model based on collected data.
Friedman: The Elements of Staistical Learning: Data Mining, Inference, and Prediction, second ed., Springer Science+Business Media, New York, 2009

The PCA method is used for dimensionality reduction of the features to obtain human facial age.
In theory, information fusion is usually divided into three levels: data fusion, feature fusion and decision fusion.
In 1995, Dietterich et al. proposed an extension of two-class classification to multi-class classification using ECOC [16]: for m-class data classification, binary coding with the length of n is implemented for each class to form a mÍn-sized code matrix.
We randomly divide test data of each group into 5 portions, and iteratively use 4 of them as training set and the last as a test set.
The PCA method was used for dimensionality reduction of the features to acquire human facial age features.

The reduction of OPC content leads to a high reduction on the compressive strength of the mortars.
The slow hydration characteristics of fly ash contribute to explain that reduction.
Since this mixture has a higher percentage reduction on OPC content this could mean that the addition of metakaolin and calcined sodium hydroxide could have compensated that reduction.
So the differences between the several mixtures could be explained by the scatter data because they are small enough for that.
Compressive strength results were also presented showing that the reduction of OPC content in the mortars leads to a high reduction on the compressive strength.

Real-time curve of some parameters about primary frequency regulation in the load-reduction process Test data of some parameters about primary frequency regulation in the load-reduction process is shown in Table 1.
Test data of some parameters about primary frequency regulation in the load-reduction process Parameter Before Experiment 3 Seconds After Disturbance 15 Seconds After Disturbance 30 Seconds After Disturbance Unit Speed Deviation （n-n0） -2 14 14 14 rpm Unit Load 253.127 250.979 230.212 221.644 MW Valuve Stroke 17.564 15.819 15.716 15.460 % Main Steam Pressure 16.450 16.608 16.827 17.030 MPa Main Steam Temperature 539.329 539.329 539.329 539.559 ℃ Drum Water Level -17.257 -20.974 -46.041 -73.121 mm Furnace Draft -92.511 -69.909 -84.749 -132.939 Pa Condenser Vacuum 94.927 94.927 94.927 95.034 kpa Table 2.
Result of performance index calculation about primary frequency regulation in the load-reduction process Time (s) Speed Deviation (rpm) Frequency Deviation (Hz) Unit Load (MW) Change of Load (MW) 0 -2 -0.033 253.127 0 3 14 0.233 250.979 2.148＞0 15 14 0.233 230.212 22.915＞ΔP*75% 30 14 0.233 225.644 27.483＞ΔP*90% From Fig.5 and Table 1 and Table 2, we can conclude that both static and dynamic index or amplitude of load change are meet the performance requirements in the action process of PFC.

Introduction Reduction, fixation and rehabilitation are the 3 major principles of fracture treatment.
From a large number of data, the use of artificial biomaterials intervention after bone injury healing and rehabilitation means is also increasingly mature and diversified.
The 3 major principles of fracture treatment are reduction, fixation and rehabilitation.
And in which the early accurate reduction, fracture healing process is the necessary conditions for smooth.
It is accumulating a large number of data and experience for treatment and rehabilitation of the bone injury.