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Further using rotating disk electrode (RDE) measurements and Koutechy-Levich analysis, the overall electron transfer number in the catalyzed ORR on 40%NiPc/C was found to be 2.8, suggesting the catalyzed ORR proceeds via both the 2e- transfer reduction and the 4e- transfer reduction.
The RDE voltammograms for ORR were performed at various rotation rates and the data were using the Koutecky-Levich (K-L) equations [8]: ik = i·id/(id - i) (1) id = Bω1/2 (2) B = 0.2nFSCo2Do22/3ν-1/6 (3) Where B is the Levich slope, ω is the rotation rate, n is the electron transfer number per oxygen molecule, F is Faraday constant, S the electrode surface area, CO is the concentration of oxygen, DO is the diffusion coefficient of O2 in different electrolytes, ν is the kinetic viscosity and the constant 0.2 is adopted when rotation speed is expressed in revolutions per minute.
It can be calculated the electron transfer numbers per oxygen molecule in the overall reduction process are 2.93, 2.76 and 2.61, respectively.
These data suggest that the ORR on NiPc/C-800 electrode process via both the 2e- transfer reduction and the 4e- transfer reduction process.
For NiPc/C-non, NiPc/C-600 and NiPc/C-900, the electron transfer numbers are all determined to be about 2.2, which suggests that the oxygen reduction catalyzed on these electrodes are the 2e- reduction process leading to great deal of H2O2.

The reduction of WO3 by Al powder is investigated thermodynamically.
The influences of water, ambient air on the reduction of WO3 were also studied.
During the reduction of WO3, the incomplete reduction should be considered.
Fig. 2 gives the calculation results of the incomplete reduction of WO3.
Hu, Handbook on the thermodynamic data of inorganic materials (2nd edition); Metallurgy Industrial Publishing House, Beijing, China, 2002

Among the technologies, reburning is a promising one for NO reduction in furnace during coal combustion.
They found that Fe or Fe2O3 could catalytically reduce NO to N2 when CO gas existed and proposed the following reactions: Fe2O3+3CO®2Fe+3CO2 (4) 2Fe+3NO®Fe2O3+1.5N2 (5) According to the thermodynamic calculation using the basic data from JANAF tables (1985), Fe could be completely oxidized to Fe2O3 at 700-900 °C and when the concentration of NO is lower than 500 ppm.
FeO is therefore believed to be the critical oxide of iron for the reduction reaction of NO over iron.
However, very little attention has been given to the heterogeneous reduction of NO with char.
Little work was reported on the catalysis of Fe or its oxides on NO reduction during reburning.

China, 310023 aE-mail: zhangdiping163@163.com Keywords: Logistics industry; Energy saving and emission reduction; Countermeasures.
Based on the analysis of major problems on energy saving and emission reduction for China’s logistics industry, this paper proposes the strategic objectives and strategic focus to energy saving and emission reduction of the logistics industry, and discusses possible ways for the achievement of energy saving and emission reduction in the logistics industry from multiple aspects.
Strengthening energy saving and emission reduction is in urgent need to answer to global climate change.
Building a socialization logistics information platform under the government's support can achieve the resource sharing, data sharing and information exchange of different logistics departments and different logistics enterprises, and create conditions for the smooth flow of logistics information exchange and make effective convergence among different logistics activities with the achievement of resources integration and configuration optimization.
According to the requirement of the new situation of global energy saving and emission reduction, China should increase the policy support for the development of logistics industry and focus on the incentives of energy saving and emission reduction, to reduce the economic benefit of logistics cost and by the logistics enterprises, make it be in line with the social benefit of energy saving and emission reduction and make the owners of logistics bear the low-carbon responsibility initiatively, through the formation of policy incentives such as energy saving and emission reduction subsidies, tax support policies and preferential loans and the guidance of behaviors of logistics owners.

The reduction performance were examined up to 700°C and reduction was continued for 60 min at 700°C in a stream of 20 vol. % CO in nitrogen.
Temperature-programmed reduction (TPR) method was used to analyse the completeness of catalyst reduction.
The standard diffraction data (JCPDS) file was referred to identify the crystalline phase composition of recorded diffraction patterns.
The TPR profile signified with two reduction stages for reduction MoO3 to MoO2, in which Stage I signified by peak showed at early reduction step of MoO3 to Mo4O11 species.
Meanwhile, the addition of zirconia to MoO3 has a significant influence in the reduction behaviour of MoO3 catalyst by delaying the reduction process.

The plastic strain ratio r value reached the maximum with cold reduction ratio of 50%, when the cold reduction ratio was increased, the r value was gradually reduced, However, the strain hardening exponent n value was gradually reduced.
This paper is based on batch annealing production process characteristics, it studied the affection of the cold reduction ratio and annealing process to the high plastic stain ratio structural steel sheets on microstructure and property, assured the optimal cold reduction ratio and annealing process, which has supplied the theoretical guidance and reference data to the industry.
cold reduction ratio:40 %; heating speed: 20˚{TTP}730 C/h cold reduction ratio:50 %; heating speed: 20˚{TTP}730 C/h cold reduction ratio:60 %; heating speed: 20˚{TTP}730 C/h cold reduction ratio:70 %; heating speed: 20˚{TTP}730 C/h cold reduction ratio:40 %; heating speed: 40˚{TTP}730 C/h cold reduction ratio:50 %; heating speed: 40˚{TTP}730 C/h cold reduction ratio:60 %; heating speed :40˚{TTP}730 C/h cold reduction ratio:70 %; heating speed: 40˚{TTP}730 C/h cold reduction ratio:40 %; heating speed: 60˚{TTP}730 C/h cold reduction ratio:50 %; heating speed: 60˚{TTP}730 C/h cold reduction ratio:60 %; heating speed: 60˚{TTP}730 C/h cold reduction ratio:70 %; heating speed: 60˚{TTP}730 C/h Fig.1 Effect of cold reduction ratio and heating speed on microstructure of experimental steel Mechanical properties Heating speed 20˚{TTP}730 C/h The effect of cold reduction ratio on mechanical property, n value and r value of experimental steel is shown in Fig2
The fig4 shows, with the cold reduction ratio increases, the value gradually reduces, about 0.21 to 0.24, and value was about 1.2 to 1.5, the value reached maximum with 50% cold reduction ratio.
Fig.6 Effect of cold reduction ratio on mechanical property of experimental steel（60˚{TTP}730 C/h heating speed） Fig.7 Effect of cold reduction ratio on n value and r value of IF steel （60˚{TTP}730 C/h heating speed） The optimal cold reduction ratio and annealing Process under the 670˚{TTP}730 C batch annealing temperature,it is all spherical recrystallization microstructure conditions different annealing process and cold reduction ratio.

In this paper, a marketing data mart is constructed and isolates the business data and analysis data based on the customer and transaction of a large supermarket.
Based on the data mart, using data mining technology, the description model and predictive model of customer classification were constructed.
The first part is the supermarket customer and transaction data, it provides original data for the model; The second part will process the data of the first layer, load the data to data mart focusing on customer data, then on the basis of data mart, with technology as the core of data mining based on RS, store the analysis of the results in the classification model library, The third part predicts classification for new customers through the model library.
The following data mining steps are based on RS: (1) Data pretreatment (2) Attribute selection with reduction algorithm (3) Generating rules.
The structure system of customer classification model based on Rough Set According to the customer data in the database and sales data, one of 15 customer historical data is selected as test data sets through data cleaning.

In the four processes the first pass rolling reductions were uniform while the total reduction ratios were 10%, 30%, 50%, 70% respectively.
Large size of interface defects could be found in the rolling-bonding with reduction ratio of 10%, this is because the reduction ratio is small and the reduction near the interface is small, leading to incomplete close rolling-bonding of some parts and causing microcracks.
The specific data could be seen from Table.3.
When the reduction ratio is 10%, the plasticity is poor for elongation and reduction in area is small, the tensile specimen suddenly ruptured at interface.
When the reduction ratio is 30%,the inclusions at interface still influence the interface properties, leading to brittle fracture at interface, however, both the reduction in area and elongation increase comparing to the reduction ratio of 10%.

Attribute reduction algorithm is helpful to extract rules of information system and discover knowledge in data.
Data analysis based on rough set does not require any prior knowledge.The result is objective andis easy to be understood.
The data in process is gotten by programming.
Rough sets: Theoretical Aspects of Reasoning about Data[M].
Fuzzy-rough data reduction with ant colonyoptimization[J].

Here, basic unit of food waste generation was applied as 332.8g/(person∙day) according to the national waste statistics data of Korea.
Concrete with Carbon Footprint Labeling Certification was analyzed as the major cause of GHG emission reduction.
They respectively showed reduction per unit area of 29.23 kg-CO2/m2 and 27.44 kg-CO2/m2.
Also, “3.1.1 Flexibility” and “3.3.2 Reducing Food Waste” respectively showed reduction per unit area of 17.22 kg-CO2/m2 and 13.36 kg-CO2/m2, and this was analyzed as slightly superior GHG emission reduction performance.
However in the future, once case analysis studies reflecting diverse building environments and conditions are carried out, the results of this study can be utilized as basic data for adjusting weighted values among categories of G-SEED and for configuring weighted values among certification criteria within “materials and resources” category.