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The data for self- (Cu) and solute (Ag, Bi) GB diffusion in nano-Cu are compared with the corresponding diffusivities determined for the coarse-grained (cg) material.
However, the older indirectly determined literature data on copper without any reference to its purity were used for the comparison.
This value of δ is used for the comparison of diffusion data derived in different kinetic regimes.
These data can thus be combined with the direct data on Dgb (determined for the nanocrystalline material).
Simultaneous detailed investigation of the microstructure is imperative for the comprehensive evaluation of the data derived.

With the deepening of disaster reduction and prevention research, people began to realize the important role of the disaster receptors vulnerability analysis in the disaster reduction and prevention work [5-7].
Study area and data collection Dongtou County is located in southeastern Zhejiang province and is the Wenzhou Municipality County.
The collected data included the high-resolution RS images and the in-situ survey data.
The in-situ survey data included GPS data of the control points, sample spectrum data, sample characteristic spectrum, other graphics data and text data.
The Data Conversion method and ArcGIS version 9.2 (ArcGIS 9.2, ESRI, Inc.) were used to generate the thematic maps indicating the spatial distribution of vulnerability grades divisions based on Equation (1).

In order to reduce the error due to temperature instability, the data are recorded after the temperature has stabilized for 10 min.
The experimental data obtained under atmospheric pressure is listed in Table 1.
The linear fitting curve of the first set of data in Table 1 is shown in Figure 3.
The data are recorded after the pressure has stabilized for 5 min.
In the same experimental environment with the same experiment process, the data are recorded and the linear fitting curve is drawn, as shown in Figure 7.

Based on the acquired data an Artificial Neural Network Multi Layer Perceptrons(MLPs) and Radial Basis Functions(RBF) Neural Network has been designed to recognize each one of the aforementioned defects.
After training the Neural Network, it was checked by new data gathered by new experiments and the results showed that the designed network can predict the faults with more than 75% reliability, and it can be a good assistance to an ordinary machine operator to guess the problem and hence make a good decision.
Two third of the data is used for training of the network ( a 11×56 matrix) and one third of the data is used for testing the network ( a 11×28 matrix).
Error reduction rate and the coefficient modification procedure are deducted from the following relations: Ei=12dℇDtid-oid2 D=training set (13) Where ο is output transfer function and t is the desired output.
Amount of new coefficients obtained from the following equation: Wij=Wij+∆Wij (15) For the categorization of the extracted data Radial Bases network have been used.

Faults occurrence is inevitable in real world and a practical alternative approach is the reduction of fault consequences.
In [24] a prototype of the holonic supervisory control and data acquisition (HSCADA) is presented and applied for IB, and according authors, it has the characteristics of flexibility, scalability, reconfiguration and integration.
It is based on predefined priorities such as: reduction of the performance, shorter recovery time, etc., and on historical data, from where it is possible to measure the statistical significance of each type of fault in terms of frequency rate, recovery time, and operational cost.
The last phase is the “learning phase”, which involves the storage of relevant data for use in further cases.
“Holonic supervisory control and data acquisition kernel for 21stcentury intelligent building system”.

What remains elusive is good quality data that confirms this choice of compression (or stretch).
Residual stress characterised by x-ray diffraction. b) Data plotted using a Zener Wert Avrami function to linearize the data.
In this case the fWhM data is a simple average of all measurements made on the blocks.
This increases the uncertainty in the data.
In summary the trend observed in the cylinder peak width data does correspond to that seen in the blocks.

The two cases used in the present study have been selected based on the reliability of the data produced by researchers using different CFD codes.
Experimental data from Mabey’s 7402-series shock tunnel tests [4], Van Driest [5] and numerical data from a 2D simulation of the flow-field are used for this validation exercise.
Data from each X-Z row of cells was compared to investigate the effect of the 3D extension.
Comparison with Mabey’s experimental data [4] suggested boundary layer transition was delayed in the Eilmer simulation.
The deviations in temperature and velocity accumulated into a cf error of 4%, but within the experimental uncertainty of Van Driest’s data [5].

The measurements fall within the ISO 230-2, ASME B5-52, VDI 3441-DGQ, JIS and GB international standards, and permitting also the memorization of the measured data and the obtaining of the tabulated results, or in graphical form too, using XL 80 soft Laser, by forwarding the information to the computer.
With the help of spirit leveling device (2) we can check the horizontality for the laser and mirrors. 4 Z axes 5 1 2 3 Fig. 6 The linear measurement on Z Axis at the Horizontal Machining Centre TMA-AL-550 realized by XL 80 laser Fig.7 View of graphic positioning errors realized by Renishaw Analysis on Z axis at the Horizontal Machining Centre TMA-AL-550 The linear measurement on Z axis at the Horizontal Machining Centre TMA-AL-550 realized by XL 80 laser with interferometer is according to international standard ISO 230-2. [8] As it results of the measurements realized by laser XL 80, the positioning errors can be observed on the data table, presented in the figure 8.
Fig. 8 View of table data for the measured positioning errors With the laser XL 80 software we have the possibilities to view the error compensation table presented in figure 9, which is transmitted through the stick to CNC of machine TMA-AL 550.
J., “Reduction of machining errors of a three-axis machine tools by on-machine measurement and error compensation system,” Journal of Materials Processing Technology, vol. 155-156, (2004), pp. 2056-2064

Iterative method is superior if the projection data contains high noise or is incomplete.
Aside from the relatively less data needed to be processed to generate an image, half-scan methods also have the advantage of potentially reducing the time of data processing and reconstruction.
The reconstruction input data are selected from them.
A half-scan error reduction based algorithm for cone-beam CT.
Feldkamp-type VOI reconstruction from super-short-scan cone-beam data.

This article attempts to conduct empirical research on this issue by using spatial panel data.
In addition, the studies are mostly using time series, the ordinary panel data, the least square method, and there is relatively little literature by using the spatial panel data to research.
The data contains 30 provinces in Chinese mainland except Tibet.
Meanwhile, in order to ensure comparability of data, the currency data has been deflated according to price, and the base period is 1978.
The conclusion is mainly as follows: (1) the spatial panel data model which compares with the ordinary panel data model has the better explanation to it.