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Fu Yun, Ma Yonghuan think, low-carbon economy based on low energy consumption, low pollution, low emission and high efficiency, high efficiency, high energy (three high) as the basis, to low carbon development is the direction of development, energy saving and emission reduction are the way of development, developing methods to carbon neutral technology.
Therefore we should use objective data to support index, index data, or by the corresponding calculation can be obtained indirectly and indexdata sources should be reliable, should be scientific and reasonable evaluation standard (3) The integrity and hierarchy principle: index system as a whole, should more fully reflect the specific characteristics of the development of low carbon economy, that is, to reflect the social and cultural, economic industry, policy and law, science and technology development of the main characteristics of dynamic changes of specific indicators, various aspects of development trend, determined, must be based on certain logic rules level, reflecting the structure reasonable, (4) Dynamic and stability principle: the construction of low carbon economy is a dynamic process, which is mainly manifested in two aspects: one is the dynamic index setting, namely the index should be with the development of society, economy, science and technology and make appropriate
both static index and dynamic index of balance, not only reflect the status quo of the economic development, but also reflect the dynamic changes of the, (5) The principle of 3R: low carbon economy is focused by saving energy, improving energy efficiency, enhance the rate and reduce carbon emissions or zero emissions using material circulation, promote the coordinated development of man and nature, therefore, we must follow the principle of the 3R is a low carbon economy index system principles The evaluation index system of low carbon economy 科学性原则: 指标体系的构建必须具备一定的理论基础，要能够准确$客观地反映出低碳经济的实质和内涵# The scientific principle: the construction of index system must have a certain theoretical basis, to be able to accurately and objectively reflect the essence and connotation of low carbon economy Feasibility principle: constructing the index system of the main purpose is to review the development status of low carbon economy, therefore to be feasible, especially to be convenient for data
collection, data index is not easy to be collected should not be included in the system Representative principle: the selection of indicators can not be too much, more representative, can accurately reflect the characteristics of different economic development level evaluation , low-carbon aspects.
Because the index data selection method and different dimension, not a comprehensive evaluation on its directly, so it is necessary to carry on the standardization process: ,X is a reverse index, X'index is positive.

This reduction occurs because when HCl concentrations are extremely high, the acid easily becomes volatile, which is not conducive to the reaction in aqueous phase.
Although, the range analysis can easily determine the optimal value of different factors, it does not use all the data information.
Thus, ANOVA was utilized to assess the OAD results, which can reflect the volatility of data[13], which is dispersion of the data.
So, to verify whether the effect of individual factors on yield efficiency is statistically significant, an ANOVA was used to interpret the experimental data obtained from OAD optimization.
[13] Bo Deng, in: Statistical Processing method of Analytic Data, edited by Tsinghua Publishing, Beijing (1995).

Intorduction High-purity germanium dioxide is mainly used in germa-nium manufacture and electronics industry, such as production of reduction germanium, organic germanium, catalysts, germanium tetrachloride for optical fibers, BGO crystals, crystalline compound [1] and Gd5Ge4 alloy [2], etc., and its purity must be at least 99.999 % (5 N).
The related weighing data was recorded to calculate the GeO2 moisture removal rate
Microwave ×100 c. oven ×10000 d. microwave ×10000 Fig. 3 Surface morphologies of GeO2 by oven (microwave) drying 2.2 Microwave power influence to GeO2 moisture removal rate The datas of microwave power influence to GeO2 moisture removal rate are in Table 3.
Table 4 and Table 5 are the experimental datas of material thickness influence to GeO2 moisture removal rate at 400 W and 550 W.
Table 4 The data of material thickness influence to GeO2 moisture removal rate at 400 W Material amount / g 100 200 300 400 500 Material thickness / mm 10 20 30 40 50 Drying time / min 9 11 12 16 21 Unit drying time min/kg 90 55 40 40 42 Drying efficiency kg/kw×h 1.67 2.86 3.75 3.64 3.57 Chlorine content ( £ 0.05 % ) 0.016 0.022 0.030 0.039 0.047 Table 5 The data of material thickness influence to GeO2 moisture removal rate at 550 W Material amount / g 100 200 300 400 500 Material thickness / mm 10 20 30 40 50 Drying time / min 6 9 11 15 20 Unit drying time min/kg 60 45 36.67 37.5 40 Drying efficiency kg/kw×h 1.82 2.42 3.00 2.91 2.73 Chlorine content ( £ 0.05 % ) 0.013 0.021 0.028 0.036 0.045 3 Conclusion In the process of drying GeO2, the two drying processes can both gain GeO2-06 products with national standard, and microwave drying has nearly 40 times higher efficiency than oven drying.

The neutron diffraction data were analyzed by the Rietveld method using Fullprof software in order to determine the position of the deuterium atoms.
According to X-ray data analysis, the hydrogenated TbNi0.4Co0.6 compound is single-phase but the initial TbNi0.4Co0.6 compound underwent a structural transformation during hydrogenation, turning into another structural type: TbNi0.4Co0.6 hydride has an orthorhombic structure CrB-type S.G.
The obtained data were used for refinement of the structural parameters and interatomic distances.
As it had been shown by neutron diffraction data deuterium atoms in TbNi0.4Co0.6 occupy 8f, 4c and 4b positions.
Those calorimetric data allow us to propose that hydrogen fills up interstitial site 4c first of all and then occupation of 8f site takes place.

General aviation market data show steady increase of sales and production[1].
This inherent variability drives up the cost of composite testing and tends to render smaller data sets than those produced for metallic materials.
In this data reduction method, the data from all environments, batches and panels are utilized together to generate statistical information about the corresponding test.
This approach utilizes essentially small data sets to generate test condition statistics, and simutaneously considers population variability and corresponding basis values to pool results together for a specific failure mode across all environments[7].

Introduction The design and manufacturing of dies and trial products, which require a long lead-time due to repeated trail-and-errors during the development of these products, holds the key to the reduction of the lead-time and cost [1].
Fig. 1 Design of the die Fig. 2 Defects of the stamped part 3-D Elasto-plastic Finite Element Analysis and Stamping Experiments Finite elements of the die are created from surface data of the die.
The surface data are generated from the IGES transformation of solid data and the surface filling of the IGES data.
Machining data of the die set have been generated from the commercial software WorkNC CAM.

It can be noticed that after the one – two milling cycles, the efficiency of milling decreases – particle size reduction stabilizes (Fig. 2).
Table 2 TiC-NiMo cermet powder particles angularity parameter at different milling cycles Parameter Number of milling cycles, N 1 2 3 4 5 16 SPQmean1 0.619 ± 0.042 0.526 ± 0.056 0.416 ± 0.039 0.316 ± 0.047 0.366 ± 0.048 0.179 ± 0.032 SPQmedian2 0.619 0.549 0.384 0.293 0.366 0.152 SD3 0.145 0.192 0.134 0.156 0.154 0.101 n4 46 46 46 43 39 39 1 SPQmean – the mean value of the SPQ data set 2 SPQmedian – the median value of the SPQ data set 3 SD – standard deviation of SPQ data set 4 n – number of studied particles (data set size) On Figure 3, dependence on SPQmean from milling cycles and uncertainties of measurements of TiC-NiMo powder particles is shown.
Data are approximated by logarithmic trendline SPQ= −0.162ln(N) + 0.6079.

Surface nodes temperature simulation(ap=0.005mm) is shown in Fig. 2,surface nodes temperature data is in Tab 1.
Tab 3 The surface node temperature of different wheel speeds vs(m/s) 20 26.38 32 T(℃) 225 240 250 2.2 Effect of Workpiece Speed on The Grinding Temperature If vapor cooling, ap=0.015mm, vs=26.38m/s is selected in simulation conditions, surface nodes temperature simulation data will be shown in Tab 2, corresponding to different workpiece surface speeds.
The heat source intensity increases with increment of workpiece speed and reduction of heat action time on workpiece. 2.3 Influence of Wheel Speed on The Temperature Field If vapor cooling, grinding depth ap=0.015mm, workpiece speed vw=1.5m/min is selected in simulation conditions, surface nodes temperature data responding to different wheel speed will be organized into Tab 3.
Simulation and test data show that grinding depth impacts more on grinding zone temperature, and the datas of experiment 1 and 2 show that in a large grinding feed conditions, the water vapor cooling can controll the surface temperature within 400 ℃, and compared with dry grinding, the cooling effect is obvious; The influence of workpiece speed on the grinding temperature is not clear, and in various combinations of the grinding parameters, the surface temperature may increase, reduce or substantially not change with the workpiece speed increasing; The comparison with different grinding depths shows that with grinding depth increment the temperature increases jumpily. 4 Conclusions (1)Simulation and experiment results show that with water vapor as coolant, the change of the moving speed (1m/min≤vw≤3m/min)has little effect on the cooling effect
Simulation datas show that: when 0.01mm＜ap≤0.02mm and 0.02mm＜ap≤0.025mm, the water vapor cooling can controll the grinding zone temperature ranging from 300 ℃ to 400 ℃or less, reduced by about 65%, 50% or more, especially compared with dry grinding

Then principal component analysis was used for data reduction and finally quadratic analysis was used to grade the apples [4].
They can be viewed as a family of generalized linear classifiers which find the desired function based on given empirical data.
Thus it can maximize predictive accuracy while automatically avoiding over-fit to the training data.
The smaller the pairwise misranking error is, the better the prediction fits the training data.
Perhaps the data set is too small.

Work Expert system works can be described as follows: For a large forgings, the first in the forging process the metal structure of dynamic test data, such as encoding parameters, encoding and fault category corresponding to the formation of learning samples, respectively, assigned to the knowledge base, through the learning samples to get the network weight matrix.
Accused of human-machine interface to the data object into a dynamic database, inference engine based on the knowledge in knowledge and dynamic real-time data in the database of reasoning, whether the system is obtained and what the failure fault, and then to evaluate the diagnostic results and decision-making, troubleshooting, and then repeat the process.
(3) Knowledge acquisition databases: reasoning used to store the initial data, intermediate results and final conclusions of the memory
Condition Conditions Conditional Conclusion 1 1 Module W / Module high <0.5 and irrational processes and Irrational process 2 W / module high <0.4 and 3 reduction rate <10% and Expert knowledge acquisition is to establish a sound system, sound and effective knowledge base.
Commonly used method has specific sort, sort order, data order, the nearest sorting, size sorting, the system uses the order of ranking method, combined with recombinant technology knowledge base, the rules of the higher frequency of use as a priority rule.