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Experiment apparatus and procedure The experiment data were obtained at the Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, DLUT.
Through the comparison of the grain coating permeability model and the pore filling permeability models with the experimental data, the results are better agreement with the pore filling mode that hydrate will form.
Similar to above, the experimental data are correlated using Masuda's permeability correlation [9], as Eq. (3).
Comparison between the experiment data and Masuda permeability model.
In the absence of experimental data, many numerical simulations have shown that the gas production rate is correlation with permeability of the hydrate zone [6, 9].

The data are acquired spirally at the sampling interval of 1 mm.
Result and discussion LS WT by filtering frequency, amplitude and both (combined method) are applied on the sample data to study their performance of noise reduction.
Fig. 12 shows the distributions of GBIR of (a) raw profile data and three profile data filtered at (b) frequency domain, (c) amplitude domain and (d) combined method.
Average values of all noise data are nearly zero.
Standard deviations of filtered noise are equivalent that of raw data.

The developed finite element model has been verified with the experimental data.
Fig. 2(b) shows the effect of the reduction on strip shape, with an increase of reduction, the strip shape varies from middle waves to edge waves.
A Pentium III computer was used for data collection by using Lab Window Software in the experiment.
(a) (a) (b) (b) 0.150 0.200 0.250 0.300 0.350 0.400 0 10 20 30 40 50 60 70 80 90 100 Distribution across width (mm) Thickness (mm) Unlubricated 30% reduction trendline Lubricated 30% reduction trendline Unlubricated 35% reduction trendline Lubricated 35% reduction trendline Unlubricated 45% reduction trendline Lubricated 45% reduction trendline Unlubricated 60% reduction trendline Lubricated 60% reduction trendline Fig. 5 Effect of reduction on strip thickness distribution 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 Strip width (m) Exit thickness (mm) Calculated Measured Fig. 6 Comparison of calculated strip thickness difference with measured value Conclusion An elasto-plastic FEM model has been developed to simulate asymmetrical rolling of thin strip.
The calculated thickness along the strip width is consistent with the experimental data.

Mechanochemical Reduction of MoO3 Powder by Silicone to Synthesize Nanocrystalline MoSi2 H.
Further milling resulted in a gradual decrease in MoO2 peak intensities because of its continuous reduction.
Milling time [h] d(MoO2) [nm] d(Si) [nm] d(βMoSi2) [nm] d(αMoSi2) [nm] Strain η [%] 6 16.6 49.2 --- ---- 0.275 12 --- 204 --- ---- 0.687 17 --- 15.2 32 48.2 ------ 22 --- --- 17.6 26 0.36 28 --- --- 17.6 25.7 0.52 33 --- --- 12.5 19.2 0.64 50 --- --- 11 9 0.93 Table 2. shows the thermodynamic data for the starting materials and the reactions occur during the mechanical alloying [10].
Right after, due to release of the reactions heat, temperature rises up to which the reaction between molybdenum and silicone starts concurrent with MoO2 reduction reaction.
Thermodynamic data for starting materials and reactions occur during the mechanical alloying at room temperature [10].

Data mining is a class of in-depth data analysis[4].
Data mining is in-depth analysis of financial data.
Data management includes the following: (1) data selection.
Search all the financial analysis of object-related internal and external data, according to the purpose of financial analysis and choose the data for data mining. (2) Data reduction.
Data reduction is in the discovery task and understanding to the content data itself, based on search depends on the data found target characteristics of a useful to reduce the data size, Conger data as much as possible the original appearance of the data possible on the premises. (3) Data conversion.

In order to evaluate and mitigate the province from the agricultural natural calamities, the panel data from 1990-2012 of four agricultural natural disasters covered and affected areas are analyzed.
These data refers to the total of the above four main kinds of disasters, therefore other agricultural damages have not been included, for example typhoon, plant diseases, insect attacks, grass evils, rodents and rat harm, etc.
With the statistical data and information, we can give an initial analysis for the cases of natural disaster reduction and prevention.
DCT DC DAT DA Figure 4 Floods According to average disasters covered and affected areas data in table 1, the flood disturbed the province agriculture after drought and wind hail.
FA FAT FC FCT Figure 5 Wind hail WAT WA WCT WC The table 1 data indicates the wind hail is the second natural disaster covered and affected areas after drought.

The energy consumption data were derived from China Energy Statistic Yearbook 2002-2011 [6].
Shanghai’s population and annual GDP data were derived from Shanghai Statistics Yearbook 2002-2011[9].
Data of ESn and β were derived from Shanghai energy-saving report [10].
The historic data on carbon intensity reduction showed that the measures taken by Shanghai worked effectively in the past few years.
The data in Fig. 2 shows that industry sector accounts for over 90% of the total carbon emissions over the period 2002-2010.

Reduction by silicon If silicon is used as a reducer for barium reduction, the process of reduction progresses proportionally to the amount of reducer up to 0.06 kg (Fig. 3).
The degree of barium reduction amounts to 70%.
However, its reduction degree is 40–50 % only.
Reduction by silicon and aluminum To assess the possibility of combined reduction by silicon and aluminum the case for reduction of 1 kg ВаО and 0.2 kg Si followed by aluminum addition was calculated.
The data of calculation are shown in Figure 7.

Based on the data, this paper analyzes the influence of different technical paths and policy options on emission in various developmental scenarios, and proposes specific paths for emission reduction.
Based on the above analysis, this research develops a simulation systems of carbon emission for electric power industry as shown in Fig. 1: Fig.1 Simulation system flow chart of carbon emission in Chongqing electricity industry Based on the real data of Chongqing’s electric industry from 2000 to 2011, this model aims to examine the major factors which influence electricity-related carbon emission in Chongqing from 2000 to 2030.
As the regional total population refers to the regional permanent residential population, and based on the data from 2000 to 2011, the increment of urbanization rate is from the linear fitting relationship between 2000 and 2011, also the increment of GDP from 2000 to 2011 is the historical data.
Reduction of unit coal consumption, increase of off-region supply, bigger share of coal-sourced power, and greater use of clean energy can increase the energy efficiency and reduce energy consumption. 3.
[5] Li,Li., Design model and methods in energy conservation and emission reduction in electric power industry, Beijing,2011: 89

Based on experimental data, the constitutive equation was established, and the predicted peak stresses by the developed model agree well with the experimental data.
Based on the experimental data, the constitutive equation was established.
(5) (6) Then based on experimental data, the lns-ln and s-ln relation curves are plotted by linear fitting method, as shown in Fig. 3.
(8) Based on experimental data, the ln[sinh(αs)]-ln and ln[sinh(αs)]-1000/T relation curves are plotted by linear fitting method, as shown in Fig.4.
Based on the experimental data, the constitutive equation is established using Arrhenius relation.