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The corresponding simulation model was established to verify the thermal conductivity data.
The test data is automatically collected by the test system.
The data in Table 5 can be plotted.
The experimental data of the sample is used as the data of the known training set to train the designed BP neural network prediction model, and then rely on the trained BP network to predict the thermal conductivity of bentonite under different influence factors.
The experimental data (including experimental data of bentonite thermal conductivity and influencing factors) was imported by software to simulate the thermal conductivity of bentonite.

The data collected for five consecutive years in a degree level technical institute was recorded.
It provides the visual summary of data and it reveals whether the process is centered on a target value of 75%.
The histogram in Figure 5 clearly shows that the data was not centric and the mean is far beyond the target value of 75% which shows the problem of centering.
Conclusions Based on the data collection and analysis the following conclusions were derived
Chakraborty and Gautam Majumdar, Selection and evaluation of supplier by decision model of hybrid data envelopment analysis, International Journal of Applied Engineering Research. 10(62) (2015) 123-127

Finite element modeling was used with experimental data obtained from tension and compression testing.
In addition, compression tests were conducted at similar temperatures and strain rates to obtain flow stress data for flow localization analysis.
Barreling effect during compression test from friction was corrected to increase the reliability of the stress-strain data.
The constitutive equation is from regression of hot compression data (see in Fig. 2).
The modeling was performed using experimental data obtained from the tensile and compressive testing of the alloy. 2.

Those temperature and humidity data in the corresponding time with measuring the physical properties of snow were picked out from the meteorological data recorded continuously in weather meter (Fig. 2).
The values of air temperature were just a datum at a given time, while the data of snow density and hardness function was a general indication of the formation and component features of snow.
There was a relatively reduction as to the extent of discreteness of the values of snow depth, pressure, density and hardness with the duration of snow increasing, especially the observed data on the left side (the sunny side) was particularly more concentrated than on the right side.
Nevertheless, the cost of field observation is quite high, and there are many environmental factors influencing the test results, a further accumulation of data and detailed study is necessary to be performed.
Advancing Knowledge Discovery and Data Mining Technologies, edited by Q.

The tasks are summarized as, in planning stage: make prediction for future power system and power energy demand; collect technical and economic data of equipment; draft the reliability criteria and design standards, evaluate the system performance according to the criteria, and identify the weak links of system; select optimal scheme.
(5) the optimized model of load curtailment When the problems arose in system caused by the outage, it is need to apply the special optimization power flow (OPF) model to reschedule the generation, thus can eliminate the illegal limitation and constraints of system; meanwhile, should avoid the load reduction as far as possible, or make the load reduction be minimum when cannot avoid it happening.
Focus on the transmission system, using the state enumeration method for actual example analysis, the system wiring diagram is shown in Fig.5. the generation and load data of each bus node is shown in Tab.1; the data of transmission lines is shown in Tab.2; the events statistics that lead to system faults is shown in Tab.3; the data statistics of interruption, state probability, and state frequency of the actual system is shown in Tab.4.
Fig.5 The wiring diagram of actual system Tab.1 Data of generation and load bus Number of bus Generation Load Rated capacity（MW） Regulation output（MW） % MW 1 100 100 10.5 80 2 0 0 31.6 240 3 200 200 5.2 40 4 0 0 21.1 160 5 0 0 31.6 240 6 750 460 0 0 SUM 1050 760 100 760 Table 2 Data statistics of transmission lines Number of lines Number from start to end of the buses Length (Km) Impendence Max. transmission capacity (MW) R X 1 1—2 40 0.1 0.4 100 2 1—4 60 0.15 0.6 80 3 1—5 20 0.05 0.2 100 4 2—3 20 0.05 0.2 100 5 2—4 40 0.1 0.4 100 6 2—6 30 0.08 0.3 100 7 2—6 30 0.08 0.3 100 8 2—6 30 0.08 0.3 100 9 3—5 20 0.05 0.2 100 10 3—5 20 0.05 0.2 100 11 4—6 30 0.08 0.3 100 12 4—6 30 0.08 0.3 100 13 5—6 61 0.15 0.61 100 Tab.3 The events statistics that lead to system faults Number of lines in outage condition Node number of the bus LSC Insufficient power supply capacity of the system（MW） Normal —— 875 0 1 1—2 867 0 2 1—4 865 0 3 1—5 879 0 4 2—3 879 0 5 2—4 874 0 6 2—6 736 24 7 2—6 736 24 8
2—6 736 24 9 3—5 803 0 10 3—5 803 0 11 4—6 649 111 12 4—6 649 111 13 5—6 791 0 Tab.4 Data statistics of interruption, state probability, and state frequency of the actual system Number of lines in outage Original data MTTR(h) MTBF(h) Outage rate Normal —— —— 0 0.645002 —— 1 8 200 0.04 0.026875 0.0034 2 8 133.3 0.06 0.041170 0.0051 3 8 400 0.02 0.013163 0.0016 4 8 400 0.02 0.013163 0.0016 5 8 200 0.04 0.026875 0.0034 6 8 266.7 0.03 0.019949 0.0025 7 8 266.7 0.03 0.019949 0.0025 8 8 266.7 0.03 0.019949 0.0025 9 8 400 0.02 0.013163 0.0016 10 8 400 0.02 0.013163 0.0016 11 8 266.7 0.03 0.019949 0.0025 12 8 266.7 0.03 0.019949 0.0025 13 8 133.3 0.06 0.041170 0.0051 According to the reliability indexes calculation formulas introduced before, the deterministic reliability indexes can be calculated as follows: ① utilization coefficient of generator FGU: ② coefficient of transmission equipment FTR: ③ minimum load supplying ability min LSC: ④ maximum non-sufficient capacity

At the end of 2009 the UN held the Copenhagen Climate Conference ,and reached a non-legally binding agreement in Copenhagen, it maintained the "common but differentiated responsibility" principle of the United Nations Framework Convention on Climate Change and the Kyoto Protocol, arranged developed countries take mandatory emission reduction and developing countries take independent action to reduce emissions, and reached general consensus on the global Long-term objectives, financial ,technical support, transparency and on the focus issues.
[Table 1 Wood Construction Architecture Accounted For The Proportion Of The Total Residence Of Partial Countries and regions] Country Percentage / % Country Percentage / % Country Percentage /% USA 90～94 England 60 Netherlands 6～7 Canada 76～85 UK 20 French 4 North Europe 80～85 Germany 10 Sweden, Gustavsson L, Madlener R, Hoen HF finger that use wood replace the standard concrete, heavy concrete, lightweight concrete block and brick materials, it can reduce emission of CO2 that per m3 792,1013,725 and 922kg respectively, CO2 store in the wood and CO2 replace other materials which reductive emission, the total reduction is about 2t[7].
Based on the above summary of knowledge and its development trends at home and abroad ,on the basis of the carbon emissions when the actual building architecture of China , estimating wood construction architecture from material processing, use to waste treatment process changes in carbon sequestration for the follow-up study the early data reference.

Similarly, the steels that have special requirements in Z-direction performance are called Z-direction steels.At present, the reduction of area ψ along the thickness direction is used to evaluate Z-direction performance of steel plate, and the performance indicators of Z-direction steels are established according to the value of ψ.
Table 1 Technical indicators of Z-direction steel Grade Condition Ceq Sulfur content, % Reduction along Z direction,% ≤50mm 50～100mm Q345GJ-Z15 TMCP ≤0.38 ≤0.40 ≤0.010 ＞15 Q345GJ-Z25 TMCP ≤0.38 ≤0.40 ≤0.007 ＞25 Q345GJ-Z35 TMCP ≤0.40 ≤0.40 ≤0.005 ＞35 Earthquake resistant performance To reduce the loss caused by earthquakes, higher requirements have been put forward for the mechanical properties of the steels used in buildings, in which yield-strength ratio (YR=ReL/Rm) is an important indicator.
To understand the main characteristics of the steels for high-rise buildings, the composition, room-temperature properties and high-temperature properties of these steels were tested, and valuable data was obtained[4], as shown in Tables 2 and 3.

Table 2 Literature data of sludge stabilization with earthworms in vermicomposting processes The organic wastes or the feed Main results Controls References Solid textile mill sludge spiked with anaerobically digested biogas plant slurry Intial C:N=69.2-129.2 TOC↓ 25-28% C:N=26.1-40.6 TKN increase in 100% TOC↓ 13-22% C:N=43.2-83.4 Garg et al. (2006)[10] Crop residues, farm yard manure, cattle dung Organic C ↓ 21-29% Total N ↑ 91-144% Available P↑ 63-105% Exchangeable K ↑ 45-90% No control Suthar (2007)[7] pH ↓ 67.0-15.0%, Organic C ↓ 46.1-28.4% Total N ↑ 137.7-67.8% Available P↑ 107.9-16.9% The tranditonal monoculture as control Suthar (2008)[8] The sewage sludge mixed with sugarcane trash Organic C↓ 4.8-12.7% Exchangeable K↓ 3.2-15.3% Total N ↑ 5.9-25.1% Available P ↑ 1.2-10.9% Exchangeable Ca ↑ 2.3-10.9% Exchange Mg ↑ 4.5-14.0% - Suthar (2009)[9] Discussion Not only would this application of the earthworms in the waste management reduce the amount of waste that
From a comparison of these two scales: the performance of the sludge reduction and the adaptabilities of earthworm, truer and more precise conclusions may be reached in respect of determination of the key design parameter in the reactor.
Sludge reduction with Tubificidae and impact on performance of the wastewater treatment process

U/C=U/Ind(a,b,c,d)={(x1),(x2),(x3),(x4),(x5),(x6),(x7),(x8)} U/D={( x1 ,x4 ,x7),(x2 ,x3 ,x4),( x6 ,x8)} ={x1,x2,x3,x4,x5,x6,x7,x8} when remove attribute a we can get ={(x1),(x2,x4,x6),(x3),(x5),(x7),(x8)} and then obtain the weighting of attribute a by Eq.2 in definition 3: ; Similarly , we can find the weighting of attribute b, c, d : According to the EQ.3 in definition 4, after the properties of a, b, c, d were normalized, we can draw the corresponding weighting: ; ; ; Through deeply analysis we can find the result of a single attribute weighting based on rough set exists the following problems: as shown in information table 1, all the condition attributes are the reduction and there is no redundant condition attributes; so all the condition attributes have an effect on the result of the decision-making.
That is to say, the condition attribute c have no effect on the decision results, which is obviously inconsistent with the principles of attribute reduction.
Yao, and L.Luo: Data analysis based on discernibility and indiscernibility, Information Sciences, Vol. 177(2007), p.4959 [7] Y.Y.Yao: Three-way decisions with probabilistic rough sets, Information Sciences, Vol. 180(2010), No. 3, p. 341

After cathodic reduction (at −1V for 2 min), the curves of capacitance as function of potential were measured.
The elements composition of sample (A)700 ℃,(B)800 ℃, (C)900 ℃, (D)1000 ℃ After cathodic reduction, the work electrodes were used to measure the variation of current with time at the potentiostatic potential.
(3) where ω1, ω2, and b are unknown constants that are to be determined from the experimental data.