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And which can simplify and calculate knowledge by introducing reduction and summation [1].
Fig. 1 Prediction model of rough set neural network Sample collection According to the existing research results and national standards[5][6], 30 measured data of caving mining method are used to be training and testing sample, after the measured data is pretreated by front-end processor of rough set theory, then it is trained and predicted by the BP neural network algorithm.
Discretization of Sample information data Before the sample is reducted by rough set theory, it must be discretized, and the essence of discretization is space division by selected breakpoint.
Table 1 General situation of a copper mine Mine number Category Rock consistence coefficient soundness Ore body dip angle (°) Mining thickness (m) Minging depth (m) Footwall Upper plate Footwall Upper plate A copper mine General situation of a copper mine 6.2 8.0 Middle stable Relative stable 75 50 850 Input samples of neural network 1 2 2 1 3 3 4 According to the discretization of rough set data and attribute reduction theory, the date is preprocessed.
Table 2 Comparison to calculated results of displacement angle Displacement angle of upper plate（°） Displacement angle of footwall（°） Rough set neural network method 62.57 71.23 BP neural network method 56.12 69.25 Measured values 63.81 73.65 Conclusion In the comprehensive analysis of influence factors of stratum movement, based on the intelligent data analysis of rough set theory and nonlinear relation between the approximation ability of artificial neural network theory, this paper puts forward and designs rough set neural network model in selecting stratum movement angle.

All data were collected at the passenger breathing height in the rear seat.
All Data were collected under five different ventilation settings: (1) all windows closed and ventilation off
In the Fig.2, PM2.5 data of SHS with and without air purifier under parking condition was compared.
Obviously, the PM2.5 concentration at 30 mph driving condition was higher than the data at 60 mph speeds, which means the higher driving speeds caused the higher air exchange rate between indoor and ambient.
If only through the vehicle air-conditioning, and its reduction of PM2.5 was only 20%.

The data reduction and correction for background scattering and instrument sensitivity functions was performed using the ILL (Institute Laue Langevin) software package.
These lognormal distributions were fitted to the given Q-range available from the data in the equation 2 2 3 2 0 4 ( ) ( ) ( , ) 3 d Q R N R F Q R dR d σ η π ∞  = ∆   Ω  ∫ (4) where ∆η and R are the scattering contrast (η=scattering length density) and the radius of the spheres, respectively. 0.01 0.1 1E-3 0.01 0.1 1 10 100 1000 1.6 GPa 20 oC 100 oC 200 oC 300 oC Intensity, I(Q) (arb.)
On the data fitting, however, none of the experimental curves could be satisfactorily approximated with just a single lognormal distribution, which means that in all cases the distribution of the pores is different from this basic distribution type.
This coarsening process can also be directly seen in Fig.1, where the scattering intensity curve for 300 o C reveals a reduction of the intensity at large-Q, which means small size of scatterer, as compared to the other curves.
Numerical data for pores at different temperature.

Data reduction, background description and the final structural refinement were carried out with TOPAS 4.2 and its program package.
The refinement was performed within the space group Im3 (R-Bragg 4.182) yielding the crystal structure data collected in Table 1.
The dashed and dotted line is a CW fit to the data in the temperature range 200-300 K and 2.5-6 K, respectively.
The lower insert displays a close-up of the lowtemperature data with the magnetic transition temperature Tmag and the temperature T0 at which correlations between the 4f moments set in.
The solid line is a linear fit to the data below 0.1 K emphasizing the non-Fermi liquid behaviour ∆ρ = AT with A = 2.77 µΩcmK−1.approximately 5% with respect to the value −2.05 obtained for Ce4Pt12Sn25.

Fields covered by Grey theory include systems analysis, data processing, modeling, prediction, as well as decision making and control.
The Grey-forecasting Model (GM) is the core of Grey Theory, the grey model represented by GM (n, h) predicts its own trend by a dynamic different equation established on the history data.
Supposing the history data series is represented by, and the model GM (1, 1) is as follow:
So the accumulated generating operation (AGO) result of the history data series is as follow:
Therefore, the grey assessment method can provide an initial prioritization of future dam safety investigations, potential structural and non-structural risk reduction measures.

We suggest that a similar gettering effect of implanted ions into 4H-SiC could explain the data obtained.
These data confirmed the improvement of the structural perfection of the 4H-SiC CVD epitaxial layers near the Al ID p +n junction position after its formation indicated above.
Also TEM data showed the absence of amorphous layers usually created by high dose Al ion implantation [7].
SIMS data shows that an Al box-profile was formed with Al atom concentration of about 5×1020 cm-3 after Al ion implantation and annealing (Fig. 2).
Hall data for Al ion implanted p +-layer after a short high temperature annealing: (a) free hole concentration and (b) its mobility, (c) resistivity of the p+-layer.

Original 3D CAD data of desired model are sliced in dedicated software into layers of a specific thickness.
Last step in data preparation was slicing the resulting models into layers 50 μm thick in the AutoFAB software.
After the data were prepared, the job was submitted to SLM 280HL machine.
According to the freely available data which concerns AlSi12 material [4], its standard mechanical properties are as following: E = 72 000 to 75000 MPa, Rm = 150 to 230 MPa and εmax = 5 to 12%.
The data obtained in this article corresponds to more hardened structure which is a consequence of SLM method.

First order phenomenological model Neo-Hookean model is a proper material constitutive model based on single uniaxial tension test data.
Fig.2 Uniaxial tension test data Fig.3 Uniaxial tension test results of silicon rubber of semi-conductive layer 3 Results and discussion Figure 4 shows the von-Mises stress distribution, the maximum value of von-Mises stress 0.27 MPa is occurred at the boundary of silicon rubber and inner semi-conductive layer because expansion ration and hardness of them are different.
High stress or stress concentration of rubber can cause the reduction of long-term performance of rubber since the stress relaxation.
Higher interfacial pressure could result in a reliable electric strength but also lead to larger stress of silicon rubber which can cause the reduction of long-term performance of rubber.

The case of Dow: significant reduction of global energy intensity in the past two decades [2]).
The workbook utilizes a front-end flowchart to specify the steps and tasks involved, and then logically integrate all the relevant entities such as training materials and instructions, data collecting tables, procedures of analysis and calculation, and worksheets to support task execution, project management and documentation.
· Working data/tool sheets that can be populated for data collection and task execution · ALL of ISO50001 requirements in organization, personal, task planning, task execution, and documentation Since it is based on a widely used computing environment, the workbook can be easily implemented and adopted by any manufacturing site within the organization’s global network, regardless of location.
These include training and knowledge acquisition, organizational and operational procedures, project management, data collection and documentation management.
Wu, Promoting Awareness of Industrial Energy Efficiency and Waste Reduction in the University Students Population, Annual Conf. of American Society for Engineering Education, Hawaii, July 2007

Fig.1 The Tourism Highway Network of Mount Jinggang Methods and data Model of accessibility analysis.
Data source.
The actual speed data is obtained by survey.
DEM data of 30m resolution is downloaded from Geospatial Data Cloud in China.
Inter-relation of Slope-Terrain-Speed reduction coefficient Slope（degree） Area threshold （km2） Terrain Speed reduction coefficient High speed Other roads 0-7 2 Flatground (utmost gentle slope) 1 1 7-15 4 Moutain (gentle slope) 0.9 0.75 >15 4 Moutain (steep slope & cliff) 0.8 0.6 Fig.2 Classification of slope and terrain in Mount Jinggang Results analysis As about 70% area of Mount Jinggang are steep slopes or cliffs, which are practical impassable regions.