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This theory while maintaining the original decision table without changing the classification ability, through knowledge reduction, value reduction, export classification decision tables and decision rules.
It is a mathematical tools to deal with the information and knowledge use the way of quantitative analysis to process the data of imprecise, inconsistent, incomplete.
Data preprocessing For the attribute set, we can get the importance of the classification for attributes subset, then get the both dependence. is mean we  classified the object, to remove some attributes subsetfrom set , then we can get the Influence.Fig 1 shows us the knowledge representation system.
That the weights have different sensitivity on the environmental.In the Rough set theory, attribute importance reflects of the impact on the current data on the environmental attributes decision.
The rough set theory to determine the importance of property is a manifestation the objective existence relative importance (which is objective existence) of large amount of historical data for each attribute.

ABSTRACT Up till now, the design code for the determination of fire resistance of reinforced concrete (RC) columns is essentially based on tabulated data.
The reduction factor of effective rigidityis derived by the authors using SAFIR.
For uniaxial compression, Pp can be calculated as: (8) where As is the total area of steel reinforcement; ; ; and are the respective reduction factors of concrete and steel strength under fire conditions

Based on a defined product model, a workflow with all operations data will be generated according to the rules from the knowledge base as follows.
First is the order of piping products with different due dates, described in a fixed data format and delivered by the customer.
A complete order is made by six data tables which can be divided into three groups as being common information, assembling information and parts information.
Various information of each product is easy to be located during data processing.
The plan collects a total data of 3,342 operations within 25 working days.

aHANAMURA.Toshihiro=nims.go.jp, bTORIZUKA.Shiro=nims.go.jp Keywords: high strength steel, total balance, reduction of area, uniform elongation, local elongation, true stress-true strain curve.
This data gives a good proof that cooling rate, 1K/sec, is stable down to room temperature even though there is a martensitic transformation starting around 340ºC.
However, when the size of the grain, within which the micro void exists, is small enough, the stress required for the micro void growth increases and then the crack progress is retarded, leading to the enhancement of the local strain and the reduction in area.
　 [1] As has been reported elsewhere, also in the low C-5Mn steel, Ms and Mf are found to decrease with decreasing the austenite grain size.　[2] With the refinement of austenite grain size, the packet size and the block length decrease, while the lath width and the block width change only slightly, suggesting that the HV value for each specimen is almost the same.　[3] A reduction in austenite grain size increases both fracture strength and fracture strain, showing a possibility of having excellent total balance properties Acknowledgement This study was supported by the Japan Science and Technology Agency under “Heterogeneous Structure Control,” a collaborative research initiative based on industrial demand.

The implementation of data mining software into a company’s intranet or a web search engine is no longer a problem due to rising software performance.
In the vision of CPPS data access will be possible in real time and with high resolution.
According to Hilbert, the data processing performance of hardware devices pH is growing with a yearly rate of 8%.
Further, the performance growth of data compression capabilities of new software pS is growing with a rate of 11% per year.
· fSS: the utilised ERP-System does not allow the integration of data which is generated with isolated software applications by employees in different departments

The paper discusses the reduction of carbon taxes of the energy saving projects.
For example: the historical data show that the annual energy price indices are i1, i2, ……, in-1, in, then the price index can be calculate in formula 4
Here is the relevant data: the flow rate of heat medium water is 500 [t/h], the temperature of the intake gas separation unit is 85 degrees, the temperature of the outlet gas separation unit is 65 degrees, the pressure of the low-pressure steam is 0.8 [MPa], and the temperature of the low-pressure steam is 295 degrees.
The historical data of price indices of coal is in table 2.
Discussion The analysis just considered the coal saved due to the reduction of steam.

allx2242@126.com, bhyyu@ustb.edu.cn(corresponding author), cdbsun@mater.ustb.edu.cn,dluwanheng@gmail.com *Corresponding Author: Hongying Yu, Professor, Corrosion and Protection Center, University of Science &Technology Beijing, Tel: 86-10-62332548, FAX: 86-10-62332567, email: hyyu@ustb.edu.cn Keywords: Co-Ni nanomaterial; soft template; 1D structure; magnetic property Abstract: Co-Ni alloy nano array was formed in applied magnetic field via liquid phase reduction method using different surfactants as soft templates.At last, The microstructure and the size of Co-Ni alloy nano-powder were illustrated with FESEM, and the powder’s composition was analyzed by XRD.
All the reflection peaks could be indexed entirely to the face-centered cubic(fcc)structure of Co or Ni.Three peaks all correspond to the diffraction from the {111}, {200}, {220} facets, and these statistics are in good agreement with the reported data{JAPDS File No.04-0850 Ni,No.15-0806 Co}, and there are no impurity peaks, so we can say that the powders we made were pure.
The XRD pattern also shows that, the more contents of the Ni in the powders,the higher the intensity of the peaks,which indicates that Co-Ni alloy nano-powders have preferred orientation; Because there are no PDF data cards which can correspond to CoNi alloy in the JCPDS database.
Through the analysis of data, the linear Co-Ni nanoalloy powders has the highest coercivity of the three, which can reach 313.67 Oe.
Conclusions In summary, we have got Co-Ni nanoalloy powders via liquid phase reduction method using different surfactants as soft templates.

In addition, the use of a disk laser allows to obtain significant reduction in focus diameter and increased beam quality compared to traditional rod or slab lasers.
Technical data of the welding system, which consists of a 6-axis robot and optics, are listed in Table 1.
Table 1: Welding system technical data Maximum output power [kW] 2.0 Laser light wavelength [nm] 1030 Beam Parameter Product [mm × mrad] 8.0 Focus diameter [μm] 300 Focal length [mm] 200 Maximum energy density [kW/mm2] 28.3 Table 2: Nominal chemical composition of AA 2024 (wt.%) Si Fe Cu Mn Mg Cr Zn Ti Al 0.50 0.50 3.80 ÷ 4.90 0.30 ÷ 0.90 1.20 ÷ 1.80 0.1 0.25 0.15 Bal.
Table 3: Parameters and average data of BOP beads Thermal input [J/mm] Power [kW] Welding rate [mm/s] Penetration depth [mm] Porosity content [%] 200 1300 6.50 1.71 1.59 200 1400 7.00 2.63 4.93 200 1600 8.00 3.20 1.10 200 1800 9.00 3.20 0.00 160 1300 8.13 1.75 0.36 160 1400 8.75 2.10 0.80 160 1600 10.0 3.20 5.21 160 1800 11.3 3.20 0.23 Figure 3: Penetration depth and porosity content as a function of power for given thermal inputs Parameters and average data of the beads are listed in Table 3.
Peix: Reduction of porosity content generated during Nd:YAG laser welding of A356 and AA5083 aluminium alloys, Materials Science and Engineering A, Volume 363 (2003), pp. 40-52

The reduction in toxin content of all analyzed toxins reached 45.6%, 30.2%, 44.5%, 55.9% and 37.6% under the corresponding carboxymethyl chitosan concentrationsis.
The series three API 3000 quadrupole mass spectrometer (United States Applied Biosystems) was equipped with a turbo ion spray source, a needle pump, the Analyst 1.4 data processing system and a gas source for high-purity nitrogen.
The reduction in toxin content of all analyzed toxins reached 45.6%, 30.2%, 44.5%, 55.9% and 37.6% under the corresponding carboxymethyl chitosan concentrations.
Fig.2 Reduction in PSP toxin composition profiles of the soup steamed with different carboxymethyl chitosan concentrations compared with the steamed soup.
Data are presented as mean ± SEM.

In the present research, a series of AZ80 magnesium alloy billets were compressed with 60% height reduction on hot process simulator at temperatures of 473,523,573,623,673,723K under strain rates of 0.001, 0.01, 0.1,1 and 10s-1.
Cockcroft and Latham hasn’t expounded whether the critical damage factor depends on the temperature and strain rate [9-10].In this study, for AZ80 magnesium alloy, the relations between critical damage factor and temperature and strain rate were analyzed based on a series of compressing tests data and damage data by numerical computation.
Furthermore, a concept about incremental ratio of Cockcroft-Latham damage in plastic deformation () is brought out and defined as the ratio of the damage increment at one step () to the accumulated value (): (2) True stress-strain data collected at different temperature under different strain rate from the thermal compression experiment of AZ80 alloy were inputted into the custom materials library of the finite element software Deform-3D.
Fig.2 shows the damage distribution at the last step (height reduction of 60%) at 573Kand strain rate of 0.0l s-1.From the simulation results, it can be seen that the maximum damage value appears in the region of upsetting drum, while the minimal value appears in the middle region.
Fig.2 Damage distribution at last step (height reduction of 60%) at 573Kand strain rate of 0.01 s-1 Table.1 Critical damage factor at different temperatures and different strain rates (a) =0.01s-1 (b) =0.1s-1 Fig.3 Incremental ratio varying of Cockcroft-Latham damage during compressing process at different temperatures and different strain rates The mechanical behavior of the material during the hot processing can be described by constitutive equations containing flow stress (σ), strain (), strain rate () and deformation temperature (T).