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Moreover experimental research of this phenomenon should be based at trustworthy excitation data if the fatigue life tests have to be performed at the laboratory stands.
Installing at it accelerometers and performing a few tests allows collecting necessary data to continue subsequent long lasting tests at the laboratory stand.
Identification of the modal model components from test data was the topic of number of publication, e.g. [1] where similar methods were used for assessment of the material strength.
Presented method of the identification of kinematic excitations functions from real data can be made applicable for the measured data in terrain by applying a low-pass FIR filter, which limit influence of the higher-order modal coordinates.
Acknowledgments The authors kindly acknowledge firms vMACH Engineering GmbH of Markt Indersdorf, Germany, and Macross GmbH of Munich, Germany, for providing the research with some experimental data concerned modal model of the car body, as well as – real kinematic excitations of chassis vehicles.

As this paper researches mainly the corn futures market at recovery phase, so sample interval is from January 2009 to December 2011, the sample data as shown in Table 1.
The explanatory variables as follows: - corn futures price, according to relevant data in DCE, - soybean futures price, according to relevant data in DCE, - exchange rate, -money supply, (hundred million yuan), -consumer confidence index, -consumer prices index.
Multiple Regression Analysis The econometric model with above data is solved in regression analysis by Eviews software, the output as shown in Table 2.
Research on international relevance of The corn futures marke between China and the U.S.A.: the empirical analysis based on the day data.
Dimension reduction and coefficient estimation in multivariate linear regression.

Recently, rough set theory developed by Pawlak from Poland has become a popular mathematical framework for pattern recognition, image processing, feature selection, neuro computing, conflict analysis, decision support, data mining and knowledge discovery process from large data sets.
Until now many advantages of rough set theory application have been founded , some of them are listed as follows: 1) It is based on the original data only and does not need any external information, unlike probability in statistics or grade of membership in the fuzzy set theory. 2) It accepts both quantitative and qualitative attributes and specifies their relevance for approximation of the classification; 3) It discovers important facts hidden in data and expresses them in the natural language of decision rules; 4) It contributes to the minimization of the time and cost of the decision making process; 5) It is easy to understand and offers straightforward interpretation of obtained results; 6) It takes into account background knowledge of the decision maker.
The steps are as follows: Fist, deletes redundant data and inconsistent part; then, withdraw essential information; third, produce decision-making rule; last, provide support for scientific management and making decisions.To more fully form the evaluation model of low-carbon based on the rough set theory we introduce some notation and definitions.
Definition 3.Dependence of Attributes: The purpose of discussing Dependence of Attributes is to analyze the inner relation among data.
Therefore, China has to carry out the strategy of energy saving and emission reduction to promote the harmonious development between economic growth, social progress and environmental protection.

The use of EOR technologies developed in combination with thermal-steam treatment methods permits reduction in crude oil viscosity and increase in oil displacement factor.
As is seen from the data presented in Table heavy oils contain substantial amounts of vanadium (133–288 nmole /g) and nickel (41–71 nmole /g) petroporphyrins.
The variation in the contents of nickel and vanadium porphyrin complexes of crude oil in period by about two and three months might be due to the effect of EOR systems, the data are presented in Fig. 1 (a, b).
The data presented in Table and Fig. 1 (a) suggests that the nickel porphyrin complexes content has decreased in samples collected from No. 2752 well, but has increased in samples collected from Nos. 2805, 3418 and 4243 wells in period by about two and three months after the EOR system injection.
Variation in the nickel (a) and vanadium (b) porphyrin complexes content of the crude oil samples collected from Nos. 2752 and 2805 wells with increasing time after the EOR system injection The data suggests that the vanadium porphyrin complexes content has increased in samples collected from Nos. 2752, 2805, 3418 and 4243 wells in period after the EOR system injection (Fig. 1, b).

The experimental data was further analyzedusing pseudo-first-order and pseudo-second-order kinetic models in order to identify the rate limiting step.
Based on Table 1, pseudo-first-order model did not correlate well with the experimental data as a very low correlation coefficient was recorded.
The pseudo-second-order model showed good linearity and the calculated adsorption capacity (qe,cal) is closed to the experimental data (qe, exp).
Table 2 shows Langmuir and Freundlich parameters obtained from the experimental data.
Based on the correlation coefficient valuesfor both models, Langmuir model correlated with the experimental data better.

At present, the research that optimizes the parameter of the injection and production of the steam flood is mainly focused on the single parameter [1], and it needs mass data and takes much time calculate.
Calculate the fitness function value ( ) of the new individual, if then accept the new solution, where is parent individual fitness value, is current temperature, and random is a random data from 0 to 1
Basic data of the oil property , thermal parameters and situation is same for the four well groups are the same block which is the following: oil saturation is 0.65, residual oil saturation is 0.26,oil viscosity is 781,oil density is 0.87,dissolved oil/gas ration is 5, K-factor of reservoir is 256, K-factor in the upper and lower reservoir is 274, heating capacity of reservoir is 2860, heating capacity of upper and lower reservoir is 2860.The different basic data of the four well groups shows in the table 1.
Table 1 Basic data of different well groups Name [MPa] [%] [10-3μm2] [m] [km2] group1 2.1 26.5 473 15 0.12 group2 2.1 24 200 17.4 0.10 group3 1.9 26.6 210 13.4 0.93 group4 2.5 28.6 510 4.2 1.03 Making the maximum value of the vapor-liquid interface factor as the target optimize the injection reduction parameters including dryness fraction of steam, injection rate of steam and temperature of steam.
Compared to numerical simulation, this method needs less data and computation speed is faster

Seen from the point of application, life time predictions based on data from mechanical tests results with expensive natural, artificial aging and weathering elements were the important information requested [1].
The monitoring of mechanical data during weathering leads to the accumulation of plenty of data.
From the graph, the data shows that at 20˚C the samples have higher rate of stress.
From the graph that has been plotted, the data shows that the temperature 70˚C produced higher rate of strain than the lower temperature.
Thus the data show the value of the Young modulus is proportional to the value of the stress and inversely proportional to the strain.

Introduction Main factor analysis is a very useful statistical analysis method,it can through the dimensional reduction and data simplification to research the inner dependent relationship of many variables.
Moreover,this mathematical method is to find a basic structure relationship of the observation data and to use a few abstract variable to express the relationship of data.This few abstract variables are called factors,furthermore,these few factors are capable of reflecting the main information of many of the original variables.The original variables can be observed in the show variable,however,the main factor is potential variables that cannot be observed.
Factor analysis and modeling process Factor analysis steps The main factor analysis steps is as follows,firstly according to the research problem and aim to select the original variables and then standardize the original variables and calculate the original variables and data,then in search of initial common factor and factor loading matrix,and the important step is factor rotating and factor scoring.
zx16-0.052zx17-0.032zx18 Z3=-0.075zx1-0.216zx2-0.013zx3-0.022zx4-0.051zx5+0.001zx6+0.081zx7+0.260zx8+0.196zx9-0.206 zx10-0.336zx11+0.072zx12-0.001zx13-0.001zx14+0.141zx15-0.001zx16-0.042 zx17-0.043zx18 Z4=-0.018zx1+0.012zx2+0.042zx3+0.339zx4+0.357zx5+0.187zx6-0.104zx7-0.085zx8-0.133zx9 -0.181zx10-0.030zx11-0.050zx12+0.053zx13+0.053zx14-0.032zx15+0.053zx16-0.053zx17-0.036zx18 Conclusion Through the main factor mathematical analysis method and respectively testing the eighteen index of physical and mechanical performance,internal structure performance,filtration performance of the automobile engine oil filter materials,such as nominal filtration precision, thickness, weight, average pore diameter,maximum pore diameter,actual filtration precision, maximum fiber diameter, minimum fiber diameter,average fiber diameter,porosity,air permeability, breaking strength, breaking elongation,elongation at break,fracture work,breaking time,bursting strength,bursting elongation.After modeling and data
[4] Sorensen B.L.and Sorensen P.B.Applying cake filtration theory on membrane filtration data.

Disaster recovery is through the post-disaster reconstruction, make the region back to the normal state, and disaster recovery and disaster risk reduction should be combined. 2.2 Water Resources Emergency Management Water resources emergency management serves for water resources management at the sudden disaster events, according to different types of emergencies put forward the corresponding emergency response plan and disposal measures, to ensure water source security, water supply safety and water environment security in the greatest degree.
Cloud computing provides a intensive mode for a number of information resources with physical focus but application logic separation, such as real-time acquisition of rainfall, weather, water quality and project information, through the construction of resources pool that takes water resource data center as the carrier, to achieve the intensification and scale of water resource information, promote public and sharing of information, provide Infrastructure As A Service, Platform As A Service and Software As A Service, so as to realize high availability and scalability.
By the technology of cloud storage, data storage is no longer limited by disk space, such as vast amounts of real-time monitoring data, historical data storing in the database and business management data of water resources, can provide strong technical support for smart water resources emergency management. 3.3 Geographic Information Technology Geographic information technology is the powerful support for smart water resources emergency management, which makes the emergency management system modeling based on the three-dimensional visualization possible.
Conclusions The proposed concept of smart earth will drive the development of water resources emergency management system, derive the urban smart water resources emergency management system, integrate a new generation of information technology including Internet of things, cloud computing, digital earth and geographic information technology to solve the existing problem of data and system fragmentation in the information support system in water resources emergency management in the present stage.

Growth of TMI with external pressure is in agreement with the data reported by Itoh et al [14], who had studied pressure dependence of ferromagnetic transition temperature TC (coinciding with TMI) of La0.85Sr0.15MnO3 single crystal.
The data given in [14] are almost identical to our results for Y097 sample.
According to the data of induced-couple plasma spectroscopic and redox titration, two possible expressions of the chemical formula of the obtained samples were given by the authors, La0.86Sr0.15Mn0.95O3 or La0.85Sr0.15Mn0.93O2.95.
We hadn’t defined the composition of single crystals obtained in our work, but since the data of pressure dependence of TMI and TCO presented in [14] and the same data for Y097 are in good agreement, it can be supposed that compositions of those samples are very close.
In this sense, reduction of a vacancy concentration in the sample is similar to the increase of x.