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In order to plot similar graphs for each rolled metal we use the data obtained from experiments.
а b Fig 5 Slip lines field at CR: scheme (а) And at area reduction 1.10 (b) Special attention should be given to a unique situation observed in the axis of a sample during cross rolling.
With constant reduction ratio the stresses existing in the axis are invariable by the magnitude and direction (with respect to the tool) and deformations are accumulated proportionally to the rotation of a billet.
At that, reduction ratio change can give a change in stresses on the axle in a certain range.
Fig. 6,a shows experimental dependence of a number of loading cycles N before failure (curve 1) at rolling at room temperature of aluminium AD-1 on reduction ratio =D/H, where D is an initial diameter, and H is a distance between the tools.

The 15Kh2MFA and 15Kh2NMFA steels have been used as a RPV material in VVER nuclear power plant and it is necessary to have an extensive data base of the mechanical properties and the influence of water environments on crack initiation and growth by both stress corrosion cracking (SCC) and corrosion fatigue (CF) processes.
For each test, a full stress-strain curve was recorded and the parameters ultimate tensile strength Rm, yield strength Rp0.2 and ductility loss (reduction in area RA and elongation Z) were determined for the evaluation of the results.
The other typical features of DSA are a peak in tensile strength (ultimate tensile strength Rm) and a minimum of ductility (reduction in area RA) in the DSA temperature range and a negative strain-rate sensitivity.
The ultimate tensile strength Rm and reduction in area RA, reflect, more or less, the hardening effect best and distinguishes the effect more clearly.
Taking into account the reduction in area as a function of temperature and its coincidence with steel behavior in HTW with declared oxygen content [5], the stronger minimum of ductility at DSA temperature range can partially explain the differences between the C and D steels.

Damping capacity was tested by INV9824 vibration data collector with V10 vibration signal processing software (china orient institute of noise & vibration), excited force was 200 N, sampling frequency was 5120Hz,the e composite loss factors and vibration acceleration level were calculated by linear mean method, overlap coefficient was 15/16.
The change rates of tensile strength were -29.01%, -32.82%, -39.70% and -45.04% respectively, the change rates of elongation at break were -3.81%, -12.26%, -18.21 and -22.95% respectively, the change rates of tear strength were -14.70%, -24.55%, -30.47% and -36.74% respectively, Shore A hardness reductions were 5, 2, 0 and 2.
The absorption peaks between 1600~1800cm-1 had a remarkable reduction, which proves that the C=O bond had broken apart.
By the effect of corrosive medium, the active groups of molecular would have hydrolysis reaction and oxidation reaction, the basic bonds of macromolecule would break apart, which caused softening, changes in color or mechanical property reduction of material [14].
According to the reduction of composite loss factors, salt fog aging only had minor effection to energy dissipation capacity of material, the structure still had high loss factors, it is mainly because the material itself does not focus on salt fog aging, but its constrained damping structure is great in aging resistance.

(1) Where, is total urban road network capacity(pcu·km);Ci is the theoretical value of traffic capacity of one of the i-class road(veh/h); is reduction factor of multiple lanes traffic capacity of the i-class road;ti is the unit operation time of the i-class road, usually take an hour of rush hour(h);i is road category, respectively are,freeway,main road,Secondary road,branch;Lij is the mileage of the i-class,the j-road;Nij is the lanes of the i-class,the j-road;n is the total lanes of the i-class road;αi is intersection reduction factor of the i-class road;βi is the average saturation of the i-class road;γi is trunk road with uneven coefficient of the i-class road;ki is the accident interference coefficient of the i-class road. 2.2Determination of urban traffic demand based on road network capacity (1) The city traffic demand The formula the city traffic demand:
Table 1 Total road network supply table of The city M parameter category theoretical traffic capacity of one road intersection reduction factor trunk road uneven coefficient accident interference coefficient average saturation average lane lanes mileage [km] Capacity [pcu·km] freeway 1800 0.75 1 0.85 0.90 7 9 150 154912.5 main road 1730 0.55 1 0.80 0.88 6 60 890 596171.84 collector road 1640 0.45 0.8 0.75 0.85 4 98 840 316159.2 branch 900 0.35 0.7 0.70 0.75 2 1650 1880 217633.5 total - - - - - - 1817 3760 1439789.54 (2) The analysis of city M total traffic demand Taking an example of city M,according to the survey ,the number of city sunrise line per day is 3500 thousand persons.
We have statistical data of passenger transport structure in motorcycle, taxis, private cars, buses and the unit car, and peak hour factor is 0.11.According to the formula (2), we know, the city traffic demand is D1=705474,detailed in the table 2.
Table 2 The table of city M total traffic demand mode of transport total travel [persons· times/day] Proportion [[%] average real road [persons/ pcu] vehicle reduction factor average mileage [km] peak hour factor demand D1 [pcu·km] motorcycle 3500000 2 1.25 0.15 8 0.11 7392 bus 19 25 3 19 0.11 166782 taxi 5 2 1 10 0.11 96250 private cars 17 2 1 10 0.11 327250 official car 2 2 2 14 0.11 107800 total 45 - - - - 705474 In order to calculate foreign and transit transport demand, the peak hour factor is 0.11.According to the formula (3),we calculate D21+D22=446420.5,detailed in the table 3.
Table 3 The total foreign and transit transport demand table vehicle model demand category bus medium bus passenger car big truck medium van minivan total foreign demand foreign traffic 4675 13495 66797 13790 10075 9765 - mileage 15 15 15 15 15 15 - vehicle reduction factor 3.0 1.5 1.0 3.0 1.5 1.0 - peak hour factor 0.11 0.11 0.11 0.11 0.11 0.11 - demandD21 23141.25 33400.125 110215.05 6860.5 24935.625 16112.25 214664.8 Transit demand transit traffic 3397 7980 38896 7856 7989 8735 - mileage 20 20 20 20 20 20 - Vehicle reduction factor 3.0 1.5 1.0 3.0 1.5 1.0 - peak hour factor 0.11 0.11 0.11 0.11 0.11 0.11 - demand D22 22420.2 26334 85571.2 51849.6 26363.7 19217 231755.7 total - - - - - - 446420.5 According to the formula (4), we calculate that city M total traffic demand is D=705474+446420.5=1151894.5

Rough set theory can be used to mine dependence relationship among data, evaluate the importance of attributes, discover the patterns of data, learn common decision-making rules, reduce all redundant objects and attributes and seek the minimum subset of attributes.
In the same way, we also take the six enterprises as examples, the index data shown as Table 2.
The index data of evaluation indexes Enterprise X Index jI 1I 2I 3I 4I 1x 78 60 70 85 2x 80 80 65 80 3x 75 65 30 65 4x 60 50 56 70 5x 55 45 78 40 6x 20 56 80 45 Constructing unascertained measure functions and calculating the single index measure evaluation matrix of each logistics outsourcing risk assessment index.
[6] Aboul Ella Hassanien, Intelligent Data Analysis of Breast Cancer Based on Rough Sets Theory, International Journal on Artificial Intelligence Tools, Vol.12, No.4 (2003), p.465-479
[7] Masahiro Inuiguchi, Attribute Reduction in Variable Precision Rough Set Model, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 14, No. 4 (2006) p.461-479

A range of 4-hours duration data over this period has been considered [15-18].
Figure 1 – Wind rose based on the meteorological data Figure2 – Average monthly wind speed diagram based on the meteorological data According to the meteorological data the average wind speed at the height 10 m above ground accounts for 6.12 m/s, and the prevailing direction of the wind is the southern one.
The computation results have been performed using the software package WindPro on the basis of the data observed at the meteorological station in Amderm.
Source data, presented in the Table 2, have been entered into HOMER software.
Source data simulation computations: Facility name Quantity Capacity, kW Investments, K.

The influence of moisture to concrete strength is assumed to be related to cement surface energy reduction by water.
It can be seen that comparing saturated concrete with dry concrete, for the crack length in concrete did not change, the strength reduction of saturated concrete is mainly due to variation of surface energy and elastic modulus of material.
Fig.1 Initial modulus of concrete Fig.2 Stress-strain relationship of concrete 4.2 Stress-strain relationship of concrete In caclulation stress-strain relationship of concrete,the following data were assumed: damage threshold and =1.5, =800, E=32Gpa.

For the same data, generated by the grid is unique, ie, not the data or attributes of the order of one of the advantages of this concept lattice.
Association rule mining algorithm Association rules in data can be divided one-dimensional and multidimensional.
One-dimensional association rules, we only involve one-dimensional data, such as the user to purchase items; multidimensional association rules, data to be processed will involve more than one dimension.
The data mining algorithms based on improved-Godin and Apriori are following
The concept lattice model used as a data organization and formalization of data analysis tools; both theoretical research and practical applications are of great significance, it has a broad and successful application in many fields.

Using survey information about pollution sources and water quality monitoring data, water quality condition was analyzed systematically.
During the Eleventh Five-year plan, a special research has done for pollution treatment against the major river systems and branches, and then accumulated the massive data and model technology.
Based on water quality monitoring data and survey information about pollution sources, water quality situation and change trend of Yitong River were studied systematically.
Water quality change Combined with pollution sources survey information and water quality monitoring data in sections of Xinlicheng Reservoir, Changchun City, Nong’an County and Kaoshan Village in 2004, 2006 and 2007a in the Yitong River basin, water quality change through investigating DO, COD, CODMn, BOD5, NH3-N was analyzed, and the results are shown in Fig. 1.
Through the analysis on varying parameters of N, P fertilizers and land area change in the upper reach, application rate and loss of fertilizers reduction in paddy field were similar to the dry land’s, in accordance with smaller change of NH3-N in three sections of Xinlicheng Reservoir.

It is therefore found that KIC are nearly constant and there is no reduction in air up to 1973K.
Eq. (3) was fitted to the data on Al2O3/YAG eutectic composite, Al2O3 and sapphire by a least-squares deviation regression analysis of KII /KIC on KI /KIC.
The empirical equation provides fairly good fits to the data on Al2O3/YAG eutectic composite, Al2O3 and sapphire specimen with the constants C=2.18(1773K), 1.70(1573K) and 2.64(1773K), respectively.
On the other hand, Eq. (4) also provides good fits to the data on Al2O3/YAG eutectic composite, Al2O3 and sapphire specimens with the constants m=4.00(1773K), 2.00(1573K) and 1.00(1773K), respectively as shown in Fig. 4(b).
(3) The empirical equation provides fairly good fits to the data on Al2O3/YAG eutectic composite, Al2O3 and sapphire materials.