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Therefore, an additional annealing step (8 hours at 300 °C ) was introduced after two passes of I-ECAP at 250 °C, which should have resulted in a reduction of internal stresses with a concurrent microstructure homogenisation.
Temperature reduction to 200 °C was possible only after four passes at 250 °C, which resulted in grain refinement to ~6 µm.
As this approach turned out to be successful, further temperature reduction in the last pass was attempted for sample 15 but massive damage was observed at 125 °C.
In some cases, fracture was observed only at the beginning or at the end of billet, when a gradual temperature reduction was applied (Fig. 8a).
The experimental results were compared with data obtained for conventional ECAP [2-3, 9].

However, the more difficult reduction of Co2+ and Fe2+ is carried out by adding NaBH4.
The occurrence of superparamagnetic Fe nano-particles was inferred from Mössbauer and EPR data [1].
For low PAN-content (Fig. 9c), the reduction of Co2+ and Sn2+ ions drives to nanocrystalline CoSn2 (Lc= 24 nm) and minor impurities (CoSn and SnO2).
Zhecheva, Electron paramagnetic resonance, X-ray Diffraction, Mössbauer spectroscopy, and electrochemical studies on nanocrystalline FeSn2 obtained by reduction of salts in tetraethylene glycol, Chem.
Tirado, Nanocrystalline Fe1-xCoxSn2 solid solutions prepared by reduction of salts in tetraethylene glycol, J.

The weight reduction of the vehicle will not reduce the safety performance due to the decrease of the weight of the vehicle body, but oppositely will improve the safety performance, the handling performance, comfort and other aspects of the performance of the vehicle due to the application of some advanced materials, advanced structure design and advanced manufacturing technology.
It was known that 10% weight reduction can save 6%~8% fuel consumption.
The lightweight of automotive is not only a direct and effective mean of energy saving and emission reduction, but also a basis for realizing electrization and intelligence as well as embodiment of the national automotive industry development level and competition ability.
[7] Marlen Bertram.Improving sustainability in the transport sector-through weight reduction and the application of aluminum[R].
[9] Aluminum Standard and Data[M].

A quantity of latent heat is released from the body causing reduction of energy, generating thermal oscillation of the molecules and consequently the formation of the solid.
Thus, mathematical modeling is a powerful resource that can contribute to predict the phenomena, allowing the reduction of defects and improving the quality [23-24], especially in cases where the shape of the solidified product is complex.
Figure 5 illustrates the physical problem, and the simulation data are the reported in Table 1, for the solid alloy.
From the analysis of this figure we can see that, until approximately 400 s, the temperature drops abruptly, thereafter the reduction becomes to slow.
At this point the temperature drops abruptly until approximately 400 s, and then the reduction becomes slower.

Data process and statistical analysis 1 mL samples from every flask were collected every two days, and preserved in Lugol’s solution.
All the data of the growth assays in this study were analyzed by ANOVA and Tukey’s test.
Data represent average values (n=4) with corresponding error bars (1 S.
Data represent average values (n=4) with corresponding error bars (1 S.
Data represent average values (n=4) with corresponding error bars (1 S.

Data source.
Data analysis.
The Factor Analysis (FA) allows a considerable reduction in the number of variables and the detection of structure in the relationships between the GAP and other indexes [7].
The first two factors, in which the Eigen values were higher than 1, contribute more than 80% of the total variance in the data, and the high communality estimates suggested that the high portion of variance was explained by the first two factors (Table 2).
The factor analysis result of 2011’s data indicated that the factor 1 explained 84.3% of total variance with a high positive loading from GAP, NEPA, CCF, SAG and SAV.

As more land is converted to urban uses, the question arises as to whether this trend represents a systematic reduction in our ability to produce food by placing our infrastructure on the most productive soil resources.
Data and methods.
The remote sensing data used in this study is a set of Landsat satellite TM images in 1986, 1995, and 2000 (resolution at 30 m), and CBERS-2 data (resolution at 20 m) in 2005.
Tucker, Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988, Science. (1993)1905-1909
Elvidge, A technique for using composite DMSP/OLS “city lights” satellite data to accurately map urban areas, Remote Sensing of Environment. 61(2003)361-370

Overall, the current project evaluation of mathematical methods commonly used are: multi-objective decision making(Chankong and, Haimes, 1983) [5] , data envelopment analysis method and model , AHP (analytic hierarchy process) (Saaty [6], 1980; Liu Bao et al [7], 1984) , Fuzzy Comprehensive Evaluation FCE) methods of mathematical statistics.
Data envelopment analysis by the U.S.
Data envelopment analysis has been advances in technology assessment, evaluation and fields of returns to scale enterprise efficiency evaluation is widely applied.
Assignment using expert scoring method, the experts should be based on project feasibility report, analysis and design documents, construction documents and completion of the inspection reports, audit reports, evaluation of environmental data and survey results of the local government and surrounding residents, and through on-site situ observations are given for each quantitative assignment.
(This analysis using Spss data analysis software, provides Bartlett test of sphericity in Spss, reflected as the correlation matrix test, KMO test.)

Experimental data Through the pipeline system under different flow rate and pressure slurry pipeline to gather data, test data, see table 2 below.
Rheological properties of slime According to Table 2, the test data and the measurement principle of the wall rheology, wall shear stress and shear Department rate values shown in Table 3 Table 3 Rheological characteristics of slurry rheological parameters of 3 Discharge Rate Transmission distance Pressure Shear stress 2.5 7.074 2.45 498 2.875 3.0 8.488 3.18 646 3.034 3.5 9.903 3.8 772 3.176 4.0 11.318 4.8 976 3.303 4.5 12.732 5.64 1146 3.42 5.0 14.147 6.16 1252 3.529 5.5 15.562 7.27 1478 3.63 6.0 16.977 8.31 1689 3.724 6.5 18.391 9.28 1886 3.813 7.0 19.806 10.12 2057 3.898 7.5 21.221 9.85 2002 3.978 Flow index Consistency coefficient Rheological equation Effective viscosity () Viscosity equation Because, as the rheological characteristics of slime dilatant fluid, which is to absolutely avoid transport phenomenon.
When the transmission distance, the fitting formula to calculate the pressure values measured error is greater in individual cases, some even 40%, these data both for the experimental research or engineering is desirable, but can be as a reference for a number of other high concentration of relatively viscous material pipeline.
Material in the pipe due to high pressure, the particle density, viscosity, molecular interaction between the surface charge volume and other basic parameters and morphology of fundamental changes have taken place, the direct performance of the rheological equation for the regular greater reduction of flow patterns change.

Therefore, many methods were proposed for surveillance and reduction of chatter vibration, such as vibrating cutting, control of instantaneous tool-workpiece relative position [3] and varying the spindle speed cutting.
Figure 4 depicts the experimental data.
(a) (b) Fig. 4 Experimental data of x-axis direction: (a) The dynamic cutting force; (b) The relative tool-workpiece vibration The cutting process transfer function was derived from the experimental data with the Fourier Transform (FT) method, as shown in Fig. 5.
A circular curve was fitted with the measured data.
Tobias, PREDICTION OF DYNAMIC CUTTING COEFFICIENTS FROM STEADY STATE CUTTING DATA., Annals of the CIRP 21 (1972) 97-98