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Table 2 Energy coefficients of different industrial sectors in 2010 Industrial Sectors GDP (billion RMB) Energy Consumptions (thousand tce) Energy Coefficients (tce/million RMB) CO2 emissions (million ton) CO2 Coefficients (ton/million RMB) Primary Industry 4053.36 64770 15.98 771.7 190.4 Secondary Industry 18758.14 2373280 127.75 6169.6 328.9 Tertiary Industry 17308.7 465760 26.91 1299.7 75.1 The latest official data showed that China’s CO2 emissions reached 8240.958million ton in 2010 [10].
With the data of energy consumption of the three industrial sectors and the total amount of CO2 emissions, the CO2 coefficients of the different industries are calculated (see Table 2 and Fig. 2).
Industrial restructuring contributes to 0.87% and 0.9% of CO2 emissions reduction in 2015 and 2020.
This indicates that energy efficiency improvement has more effects on the CO2 emissions reduction.
This target on CO2 emissions reduction is likely to be achieved in the optimal condition of scenario 4.

Of Nanjing,New & High (N&H)—Nanjing New & High-Tech Industry Development Zone,HuaGongYuan—Chemical industrial park(NCIP),DES-Direct environment supervision. 1.3.1 COD emissions and enterprise emissions, life emissions connection According to the data table 1, The following correlation coefficients is obtained by using Data processing system(DPS)： r(Y0,X11)= 0.65236, r (Y0,X12)= 0.61206,r (Y1,X21)= 1.00006, r (Y1,X22)= 0.49057, r (Y2,X31)= 0.95374,r (Y2,X32)= 0.54285.
GM (1, 1) Model GM (1, 1) Model is 1 order equations 1 variables Grey Model, Hypothesis: n observation value of the original data set of the sequence is: .
Here the original series represent for Nanjing enterprise COD emissions and SO2 emissions respectively, data see table 7.
Table 7 Nanjing enterprise COD emissions and SO2 emissions Unit:Ten thousand tons index 2002 2003 2004 2005 2006 2007 2008 2009 2010 COD 3.1 3.12 2.96 3.03 2.84 2.68 2.56 2.25 2.0213 SO2 12.65 14.16 14.44 14.91 14.56 13.95 13.76 13.4 11.55 Data sources: Nanjing Environmental Protection Agency,《Nanjing Environment Bulletin(2002-2010) 》 According to the data table 7, The following results is obtained by using Data processing system(DPS)(GM(1,1)model): 2.2.1.
[6] Data Information on http://www.njhb.gov.cn

A System Dynamics-Based Scenario Analysis of CO2 Emission Peak and Emission Reduction Paths - A Case Study of Chongqing’s Cement Industry Sheng Wang1,a*Min Tang2,b Shiyong Zhang2,cChunyan Li1,d Liping Wang2 1.
A comparison with historical data indicates a desirable goodness of fit of the simulation results, which show that the cement output in Chongqing will reach the peak of about 95 million tons in 2020, followed by a slow decrease to 90 million tons towards 2030; the energy consumption will reach a maximum of some 7.8 million tons of standard coal in 2020; the CO2 emission will reach about 76 million tons in 2020, followed by a drop to 60 million tons towards 2030, equivalent to the 2015 figures.
Based on the data, this paper analyzes the influence of different technical paths and policy options on emission in various developmentalscenarios, and proposes specific paths for mission reduction.
At the provincial level, Mao Ziwei (2010), Gao Cailing (2012), Feng Bingxun (2003) and other scholars examine the potential and measures of emission reduction in Shandong, Henan and Taiwan provinces[2].
Tong Hefeng (2012） and Wang Xianghua (2007) systematically examine the paths of carbon emission reduction in cement industry using the system dynamics-based simulation model.

Introduction Data mining is the process of extracting knowledge from large amounts of data.
Privacy Preserving Data Mining Architecture Privacy Preserving Data Mining Technologies There are many methods for privacy preserving data mining.
Moreover, DRBT may cause loss of accuracy due to dimensionality reduction in the original data.
Data Utility.
Zaiane, Privacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation, Proc. of the Workshop on Privacy and Security Aspects of Data Mining (PSADM’04) in conjunction with the Fourth IEEE International Conference on Data Mining (ICDM’04), 2004, 21-30

Moreover, the following input data were taken for simulation: tool temperature, 60°C; ambient temperature, 20°C; friction coefficient, 0.3; friction factor, 0.7; the coefficient of heat exchange between the material and the tool, αnarz = 3000 [W/Km2]; and the coefficient of heat exchange between the material and the air, αpow = 100 [W/Km2].
Effect of the relative rolling reduction, ε, on the magnitude of the strip curvature, ρ, for different values of the asymmetry factor, av, and a constant strip shape factor value of h0/D = 0.035 It can be stated from the data in Figure 2 that for the 35 mm-thick strip (h0/D = 0.035), in the examined range of rolling reductions ε, a straight strip will be obtained for the following rolling reductions: ε≈0.18÷0.19 (at av=1.01÷1.03), ε≈0.21÷0.22 (at av=1.05÷1.08), ε≈0.25 (at av=1.15) and ε≈0.28 (at av=1.10).
The data shown in Fig. 6 indicate that straight strips, for h0/D = 0.016, on exit from the deformation zone can be obtained for a relatively wide range of rolling reductions and peripheral speed asymmetry factors: for ε≈0.25, at av=1.01÷1.03 and at av=1.08÷1.10, and nearly straight strips (with a very small curvature) for rolling reductions of ε≈0.30÷0.50, except for the cases, when ε≈0.30; at av=1.08÷1.10 and when ε≈0.50; at av=1.15.
It follows from the data in Figures 1 to 7 that the rolling process parameters examined significantly influence the magnitude of strip curvature and the direction of strip bending upon exit from the deformation zone, and the relationships between the process parameters examined and the strip curvature have a periodic character, as confirmed by the author’s previous results obtained from the investigation of the asymmetric sheet hot rolling process in the continuous Rolling Mill [7, 8].
It can be seen from the data shown in these figures that as the thickness of rolled strip decreases from 50 mm to 14 mm (h0/D = 0.05÷0.014), the magnitude of rolling reductions, for which a straight strip is obtained, changes.

It showed that drag reduction technology with DRA would be the inexorable trend of drag reduction of the nature gas pipeline transportation.
The testing, collection and analysis of the field data were accomplished.
The drag reduction effect is obvious.
Drag Reduction in Gas Pipeline Coating Technology[M].
(In Chinese) [6] Reduction in Crude Oillines.

(2) The analog to digital begins to sample noise signal and executes the data conversion for which the micro control unit keeps waiting
Enclosed space noise source sampling data diagram The curve describes the noise data in confined space when the system is not started, the coordinates of the horizontal axis represents the sample time length (300 seconds) and the vertical axis represents the sound pressure space (unit: dB).
When the system runs for some time, we sample sound data during 3 minutes in the confined space.
Enclopsed space noise reduction sampling data diagram It’s obvious that the effect of noise reduction is significant by comparison of the data before and after the noise reduction, the average amplitude decreases to 6 db (ref Fig 7).
Enclosed space noise reduction noise reduction diagram The black solid line represents the sample data from noise source in the confined space, the dashed line represents sound sampling data through noise reduction processing.

We have developed the catalytic process of the NO reduction by AC over CuO [23], and the influence of pretreatment method of activated carbon on the catalytic reduction of NO by AC was studied.
Catalytic reduction of NO by carbon The catalytic reduction of NO by AC was carried out at atmospheric pressure in a fixed bed microreactor (Φ10 mm × 300 mm).
Effect of Gd2O3 loading on the catalytic activity of Gd2O3-CuO/AC for NO reduction by AC.
These data of XPS above are in agreement with the results of XRD, that is, with the increase of loading of Gd2O3 in the CuO catalysts the Gd2O3 and CuGd2O4 phases strengthen.
However the distinguish between Cu 0 and Cu1+ based on the XPS data is difficult, because the BE of Cu0 (932.7 eV) is very close the BE of CuI (932.6 eV) [32]. 930 940 950 960 970 Intensity /a.u.

This paper discusses the impact factors of the energy-saving and emission reduction benefits of electric vehicles.
It calculates how much energy cost electric vehicles save and how much carbon emissions reduction value they get each year.
The number of electric vehicles according to the twelfth five-year plan in Guangdong Year 2011 Year 2012 Year 2013 Year 2014 Year 2015 large-scale commercial vehicles 2500 5700 7010 8520 10260 light commercial vehicles 0 0 1320 3030 5250 taxies 1600 3800 6050 8960 12760 the other passenger vehicles 11000 19900 41370 71440 113530 Total 15100 29400 55750 91950 141800 (1)Energy saving benefits Table 3 shows the estimated relevant parameter of the energy-conservation benefits, according to the combined fuel consumption of all vehicle kinds in the Ministry of Industry and standard power consumption on the current market, among these data, the standard coal consumption is converted by the corresponding fuel consumption or power consumption, It assume that the price of unit standard coal consumption in Guangdong province is 800 Yuan/t.
Conclusion The case show that, the method we provide can measure the directly benefits of energy conservation and emissions reduction of the electric vehicle per year effectively.
Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles[J].

To evaluate the effect of the cold reduction rate on ferrite recrystallization behavior, hot-rolled sheet specimens are cold-rolled at cold reduction rates of 40% and 67%.
To evaluate the effect of the cold reduction rate on ferrite recrystallization behavior, the results obtained in the present study are compared with those obtained in a previous study (reduction rate: 67%) [6].
In addition, it should be noted that the effect of the cold reduction rate on the ferrite recrystallization behavior of specimen M was smaller than the effect of the cold reduction rate on specimens P and B.
Ferrite recrystallization behavior of each specimen during annealing (data at a reduction rate of 67% was taken from a previous report [6]).
As shown in Figs. 2 and 4(a), although the dislocation density of specimen M did not depend on the cold reduction rate, the progress of ferrite recrystallization of specimen M was more rapid at the larger cold reduction rate.