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Owing to missing data in impact index and response index, in the empirical research, a partial analysis, including only drivers, pressures and state changes, was implemented.
The data mainly came from: - China statistics yearbook; - China environmental statistics yearbook; - China energy statistics yearbook; - The indicator bulletin of energy consumption per GDP in each province, municipality and municipality directly under the Central Government; - Statistics yearbook in Shanghai, Jiangsu and Zhejiang; - Statistics yearbook in Yangtze River Delta The data about carbon emission did not be announced.
So in the research, all the data about carbon emission were calculated in the following steps.
Firstly, all data of energy consumption by coal, fossil oil and natural gas were translated into the unit of standard coal, and then multiply by the discharge coefficient of carbon dioxide, which were identified as =2.6873，=2.0438, =1.5495.
The evaluation of low-carbon city development was fulfilled for cities in Yangtze River Delta in two steps, including DPSIR model and factor analysis of available data.

The main aspects regarding the solidification of metallic composites processed by casting method based upon theoretical concepts, general knowledge about casting of composites and experimental data are discussed.
Table 2 The diminution of melt surface tension for different magnesium contents in aluminium melts Alloy Reduction of melt surface tension, [%] 2% Mg 4% Mg 6% Mg 8% Mg Unoxidized 3.61 6.14 8.10 9.70 Oxidized 3.99 6.66 8.68 10.3 On the other hand magnesium can react with alumina from melt surface forming different compounds like magnesia (for Mg>1.5%) or MgAl2O4 spinel (for Mg<1.5) [4].
The data presented in Table 3 show relatively small effect of this parameter and therefore, at usual temperatures, nonwetting to wetting transition does not occur.
Thus in Al-SiC system, using Rohatgi’s data, the contact angle reduction is only 4.8%.
Table 3 The diminution of melt surface tension for liquid aluminium at different temperatures Relationship Reduction of melt surface tension, [%] T = 700°C T = 800°C T = 900°C Shen 0.78 2.70 4.63 Keene 0.82 2.95 4.91 - heat treatment of dispersed material.

Table 1 ISO 15765 Data Frame Structure Address Information Protocol Control Information Data Field N_AI N_PCI N_Data The data frame includes 3 parts.
The third part N_Data is the data area.
FF_DL defines the data length of all the data to be transferred in multi-frame situation.
System will check whether the data is 15765 diagnostic data when the flag sets.
Some key data when SCR systems works can be read in real time by “Data Flow” model.

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.

A reduction of 50% to 67% and a rolling speed of 0.2 m/s were applied.
Furthermore, texture measurements were performed on an X-ray diffractometer in the normal direction at 80% sheet reduction.
These results are in accordance with literature data [13].
Mechanical properties (in RD) of the initial materials and after one ARB pass with varying parameters (ARB process temperature and reduction).
With decreasing deformation degree also the related bond strength drops from 0.51 to 0.34 at 60% and 51% sheet reduction, respectively.

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