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Silicon carbide (SiC) powders have been prepared at 1200-1500°C by carbothermal reduction of two kind precursors of carbonl/silica mixtures: coked rice husk with high-carbon content, and gasified rice husk with low-carbon content mixed with carbon powders as an external carbon sources.
The external carbon source is commercially available carbon black powders (CBp,specific surface area[m2/g], average diameter 40 ~ 50 nm).Table 1 presents data on the chemical composition, SBET obtained of these ashes.
To clarify the effect of the nature of carbon on carbothermal reduction, BRHA,a inherent precursor without CBp was prepared in the same way.
The difference of silica structure in two systems would also affect the rate of SiO(g) formation from the reduction of SiO2.
XRD pattern of the powders from system 2 synthesized at various temperatures for 4 h area of the amorphous carbon, and helps in the carbothermal reduction of silica.

It was found that increase the target size leads to a reduction in the maximum impact force recorded during the test.
This is due to the reduction on flexural rigidity of the larger panels.
For 50 mm and 200 mm diameter panels impact with 1.75 Joule, the peak force reduced from 2000 to 800 Newtons, which is approximately 60% reduction.
It is clear that the results offered by the two mathematical models perfectly matched with the measured data due to no obvious damage occurred on the large diameter samples.
[8] EP121-C20-53 Data Sheet from Gurit Ltd

An agreement of simulation results with experimental data is shown.
To ensure equivalent strain at 2.0-3.5 during asymmetric sheet rolling with a 75% reduction, shear angle of metal layers should be 65-80°.
According to experimental data of [10] dislocation density after asymmetric rolling of Al 7075 is (5.5±0.5)×1014.
Thus, simulation results for = 2 agree well with available experimental data [10].
A good agreement of simulation results with experimental data is shown.

Z.Pawlak in 1982, as a data analysis theory[1,2], it can effectively analyze and deal with the inaccurate, inconsistent, incomplete and other incomplete information, and find hidden knowledge, reveal the potential of the rules[3].
Based on the effective information extracted from the large numbers of transaction data of the enterprise, rough set theory can effectively prevent the occurrence of enterprise risk.
But dealing with massive data by hand is obviously unreachable, so we designed a computer program based on the thought of rough set to find out the rules.
In this paper, the rough set-based customer risk model is based on a large amount of transaction data, through the analysis of the data, while retaining critical data, reduce the data and get the smallest expression of knowledge, thus to find the inherent rules of customer risk factors, and finally provide reliable, objective and fair reference to circumvent customer risks.
It has been proved that the rough set theory can identify potential and unknown knowledge from the large numbers of customer transaction data, and such knowledge can effectively prevent the occurrence of enterprise risk.

Despite the same test data, there are many obvious differences ,compared the test date with each other.
The mechanical properties of reduction is larger between20 ℃ and 500 ℃; The experimental data conforms to changes of mechanical properties at high temperature, It shows that it changes smoothly in less than 400 ℃and obviously in more than 400 ℃.
Test results of high temperature mechanical properties of steel strand Taking the limitations of test data into account, this paper processed test data on average, and then analyzed the processed data, finding that the relation between the yield strength and elastic modulus reduction of pre-stressed cables and varied temperature.
The equation can be written as: Yield strength reduction factor: fT= -2.01×10-6T2 -4.05×10-4 T+1.0354 (1) Elastic modulus reduction factor: ET = -1.99×10-6T2-0.82×10-5T+0.9967 (2) The corresponding degree of equations (1), (2) with the experimental data is shown in Fig. 2, the regression equation and processing of experimental data better degree of fit.
Experimental data regression analysis Methods of calculation for steel member temperature This paper uses the air elevation of temperature equation [4]: T(x,z,t) – Tg(0) = TZ [ 1 – 0.8exp ( - βt ) - 0.2exp ( - 0.1βt ) ][ η + (1 – η )exp (- ( x – b ) / u ) ] (3) Where T(x,z,t) is the air temperature (°C) and in which t is the time, x (m) is the horizontal distance from the center of the fire, z (m) is the vertical distance from the ground.

The hot rolled sheets were pickled and subsequently cold rolled up to 0.5mm end thickness with 70% reduction.
The MTM-FHM software [10] was used to calculate the orientation distribution functions (ODFs) from the pole figure data obtained from the X-ray texture goniometer.
These materials were further cold rolled down to a reduction of 10% reduction.
After slight rolling reduction the surface was again examined in the x-ray texture goniometer and the phi2-45° sections are shown in figure 4.
The critical amount of light cold reduction @ 10% triggers the recrystallisation kinetics to lead the inward growth of surface grains along with their orientation at 800 0C.

Growth kinetic parameters of E.coli and S.aureus affected by silver nanocomposites were calculated by modeling of absorbance data according to Gomperz equation.
The results indicated that a modified Gompertz equation (Eq. 3) was fitted well to all experimental absorbance data.
Parameter values obtained by fitting Equation (3) to the experimental data of LBL deposited nanocomposites for E. coli.
Parameter values obtained by fitting Equation (4.4) to the experimental data of LBL deposited nanocomposites for S. aureus.
The growth curves result from fitting the Equation (3) to the experimental data for E. coli.

Table 3 The elongation, shrinkage of section, tensile strength, Brinell hardness of ZL101A alloy Quenching water temperature Elongation/% rate of reduction in area /% tensile strength /MPa Brinell hardness /HBW 25℃ 2.49 2.96 236 69.5 50℃ 4.80 3.65 318 71.3 70℃ 6.45 4.95 325 72.8 90℃ 4.85 3.28 305 70.6 Figure 2 Metallographic microstructure of the sample Figure 3 Fracture morphology of the sample Test results By comparing the metallographic structure and fracture analysis under different multiples, according to the data of tensile strength, elongation, reduction of area and hardness.
(3) Elongation and reduction of area: the relationship between elongation, reduction of area and quenching water temperature is shown in Figure 4.
The reduction of area is the highest at 70℃.
When the quenching water temperature is lower than 70℃, the reduction of area increases with the increase of temperature, but when the quenching water temperature is higher than 70℃, the reduction of area gradually decreases.
In summary, the following conclusions are drawn: 70°C elongation, reduction of area, and tensile strength are the highest, and Brinell hardness is slightly lower.

This potential refers to the reduction of the investment costs and the reduction of the cycle time of the press hardening process which directly influences the unit costs of the components.
The reduction of the cycle time by one second, for example, leads to a reduction of the unit cost of 5 %.
A reduction of the contact areas by means of section points leads to a low heat transfer; in particular, because the "air gap" represents a "good" insulator.
The data base for the programming is composed of the CAD data of the tool.
Investigations about the extent of the cycle time reduction due to an optimal cooling order are currently being carried out.

Spatial Data Publishing Platform.
Data Standardization.
The standardization of cultivated land quality includes two aspects, such as spatial data standardization and attributes data standardization.
The standardization of cultivated land quality information data are achieved through the classification and coding of data, it is the basis for managing effectively the temporal and spatial data.
Data Management.