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Data from UNEP (United Nations Environment Program) Roise Center disclosed, 100 developing countries took part in CDM, meaning 91% Kyoto Protocol of non Annex 1 countries involved in emission reduction.
The averaged RERs amount of all CDM projects in each host country, data came from UNEP Roise Center.
Investment data was collected from the CDM Project Design Document ( PDD), data posted on UNEP Roise Center.
Data was from the World Economic Forum “World Competitiveness Report ( 2010)”.
Data was from World Bank open database (2010).

The base data of power plants are shown in table 1.
Tab.1 Base data of power plants Power Plant Net coal consumption rate/(g/kWh) SO2 mg/m3 NOx mg/m3 EAF Net loss/% Water consumption t/h Power plant 1 340 390 380 0.90 6.7 800 Power plant 2 330 380 350 0.91 6.9 820 Power plant 3 310 61 310 0.92 7.1 200 Power plant 4 320 55 120 0.91 7.2 80 Power plant 5 303 65 250 0.90 7.3 350 The indexes of energy-saving and emission reduction and the comprehensive evaluation indexes are shown in table 2.
In order to compare the effect of energy-saving and emission reduction, the basic data of power plant 3, 4, 5 should be calculated.
Tab.3 Calculation data of power plants Plant Coal consumption t/h Flue gas/(m3/h) SO2 kg/h NOx kg/h Water consumption t/h Order Power plant 3 186 1116000 68 245.5 300 2 Power plant 4 192 1152000 57.6 138.2 80 1 Power plant 5 181.8 1090800 81.8 272.7 350 3 According to the table 3, although the coal consumption of power plant 5 is low, compared to power plant 4, the SO2 and NOx emission per hour is higher.
According to the comparison of the computational data, the proposed evaluation method of this paper can fully reflect the situation of energy-saving and emission reduction.

The knowledge discovery process has the following steps: data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, knowledge representation.
The data mining module according to the model library system model, the data warehouse multidimensional data sets for data mining, in order to obtain the required Knowledge.
On physical education comprehensive quality index by, through data mining based on rough set attribute reduction algorithm.
The C2 attribute significance degree is 0, so do not select C2 joined the reduction set, i.e. the last attribute reduction set is Red = { Ratio of height and weight, Fitness function } Insect.
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data.

On the above basis, an attribute reduction algorithm is presented.
This kind of decision table with definite form has been widely used in data mining, machinery learning, decision analysis, process control, data analysis, artificial intelligence, and so on[6-12].
Yang, “Dominance-based rough set approach to incomplete interval-valued information system,” International Journal of Data & Knowledge Engineering, vol. 68, no. 11, pp. 1331-1347, 2009 [7] H.
Masamoto, “Application of data mining to quantitative structure-activity relationship using rough set theory,” International Journal of Chemometrics and Intelligent Laboratory Systems, vol. 99, no. 1, pp. 66-70, 2009 [8] N.
Gerhard, “Adapted variable precision rough set approach for EEG analysis,” International Journal of Artificial Intelligence in Medicine, vol. 47, no. 3, pp. 239-261, 2009 [9] Tuan-Fang Fan, Churn-Jung Liau, Duen-Ren Liu, “A relational perspective of attribute reduction in rough set-based data analysis,” European Journal of Operational Research, Vol.213, no.1, pp. 270-278,2011 [10] Yuhua Qian, Jiye Liang, Witold Pedrycz, “An efficient accelerator for attribute reduction from incomplete data in rough set framework,” Pattern Recognition, Vol.44, no.8, pp.1658-1670,2011 [11] Qiang He, Congxin Wu, Degang Chen, Suyun Zhao, “Fuzzy rough set based attribute reduction for information systems with fuzzy decisions,” Knowledge-Based Systems, Vol.24, no.5, pp. 689-696,2011 [12] Yan-Qing Yao, Ju-Sheng Mi, Zhou-Jun Li, “Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems,” Fuzzy Sets and Systems, Vo.170,no.1 pp. 64-75,2011 [13] Lifeng Li, Jianke Zhang, “Attribute

To correct the reduction modes, it is necessary to have experimental data on the geometry and wall thickness deviation ends of the roughing tubes after the continuous mill.
Some data in relation to TPA-80, having in its composition 8-stand continuous and 24-stand reduction mills, published earlier [5,6,7].
Processing of Initial Data For improving the technological modes of rolling in order to reduce the length of the thickened ends, at the first stage, the analysis and processing of actual data on the end wall thickness deviation were carried out.
However, an analysis of the actual data showed that Eq.1 gives understated values of thickening due to the fact that it was obtained for reduction in one stand and does not take into account the features of the TPA-80 multistand reduction mill.
A distinctive feature of the technique is the calculation based on actual data on the volume of the end thickening, which must be reduced by a specified value when rolling the end areas of pipes in the head stands of the reduction mill.

assunpp@263.net,b1039716857@qq.com,cshayuzhang@163.com,d373752621@qq.com Keywords: Slurry pipeline transportation, Drag reduction technology, Micro-bubble drag reduction, Vibration drag reduction.
Introduction Slurry pipeline transportation has greatly advantages such as less investment, construction period shortly, less environmental pollution and ease of management,causing various aspects of attention.It has been used widely in many fields.With increasing depletion of energy and intensification environmental pollution,energy-saving and emission reduction have to be solved.Thus the research of drag reduction technology plays an increasingly important role.There appears a variety of drag reduction methods.For instance,soluble high molecular weight polymer,fiber material, spiral flow, changing pipe geometry,heating drag reduction and adjusting the size of particles etc.With the analysis of the theoretical knowledge and experimental data to be established,the above drag reduction technologies had been made great progress, but there are some disadvantages and some of the drag reduction technology apply in reality still for a long time.This article will discuss micro-bubble drag reduction
The Technology of Micro-bubble Drag Reduction The Development of Micro-bubble Drag Reduction Technology.
The Technology of Vibration Drag Reduction The Development of Vibration Drag Reduction.
Li: Review of Research on Drag Reduction.

So, the indium need to pass to smelting process is complicated to extract[4],Based on this, this paper put forward thermodynamic analysis of reaction conditions of indium in thermal vacuum carbon reduction method under different conditions, The paper studied on the thermodynamics of the carbon reduction of indium ore preparation of indium in vacuum, which provides the basic data for research on post step by vacuum carbothermal reduction of indium.
In vacuum, the oxide reduction with increasing the reduction temperature, can be more completely reduction reaction,as the reaction temperature decreases, the reaction temperature decrease[5].
According to the Handbook of the thermodynamic data of data and calculation method, the formula is as follows[6]: ； ； ， Thermodynamic data According to relevant data check related thermodynamic data are shown in Tab1[7].
Thermodynamic data of the main related substances(/J·mol-1) In2O3 C InO In2O In CO(g) CO2 -925919 0 -271960 -167360 0 -110541 -393505 The basic process of indium ore carbothermal reduction The main reaction of indium mineral carbon thermal reduction process occurs as follows[8]: In2O3+3C=2In(g)+3CO(g) (1) In2O3+C=2InO(g)+CO(g) (2) In2O3+2C=In2O(g)+2CO(g) (3) Indium mine carbothermal reduction reaction process, the intermediate reactions may occur: InO+CO(g)= In(g) +CO2(g) (4) In2O3=In2O(g)+O2(g) (5) 2In2O (g)= 4In (g)+ O2(g) (6) Analysis of the results Indium mine carbothermal reduction process analysis of the main reaction According to the relevant thermodynamic data with reaction formula (1) complete reaction in the vacuum
Practical Handbook of inorganic thermodynamic data[M].Beijing: Metallurgical Industry Press, 2002:6-754

Oxide impurity inhibited the reduction kinetics of the ilmenite, yielding a lower reduction extent[1].Suresh’s study showed that reduction rate decreased with increasing weathering degree of ilmenite[2].
Temperature had great influence on the reduction rate.
Fig. 3 Effect of temperature on the solid state reduction degree of ilmenite To determine the solid reduction mechanism of ilmenite, shrinking unreacted core model was used to analyze the isothermal reduction results.
Experimental data at the interface was used as follow: where x is fractional reduction, t is time and k is rate constant; The plot of curve-fitting was shown in Fig.4.
Temperature had great influence on the reduction rate.

., Huwei, Yunlin 63208, Taiwan ason.summer@msa.hinet.net, blochsieh@nfu.edu.tw Keywords: Electric vehicle, gear data, helical gear reducer, prototype manufacture Abstract.
Then, based on the involute theorem, the gear data of helical spur gear pairs we obtained.
Finally, according to the gear data, its corresponding engineering drawings were accomplished and its corresponding prototype was manufactured.
Tables 3 and 4 showed some important gear data of this two gear reducers.
According to the gear data, the corresponding engineering drawings were accomplished and their corresponding prototypes were manufactured.

Naive Scaler algorithm is used to discrete the risk index data, then an algorithm of attribute reduction based on mutual information entropy is used in decision table to reduce index and build risk index system optimal model.
Naïve Scaler algorithm array the data of decision table in ascending or descending order according to conditional attributes.
The risk index data table is shown in Table 2.
From the result of reduction, “backward purchase method and operate error”, “backward storage technology and operate error”, “backward goods of management technology and operate error”, “a deviation of information in data transfer” are the main factors on the risk index system of distribution obviously.
Conclusions For the distribution risk index of distribution of supply chain in retail enterprises is redundant and it is difficult to determine the decision rules through the complex index, this article provide a risk index reduction model from risk analysis to risk index data discretization ,then to risk index reduction.