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In order to achieve the emission reduction targets in China, one of the basic ways is energy conservation and emission reduction.
According to the statistics from Liaoning Statistical Yearbook, China Electric Power Yearbook and China Energy Statistical Yearbook from 2005 to 2010 and the data provided by China's power industry statistical data analysis of 2010, combine with raw coal translation of standard coal coefficient and the electrical equivalent of standard coal coefficient, we can get the raw coal emissions CO2 coefficients 1.876 kgco2/kg.
Having transformed, the equation of per capita electricity carbon dioxide emissions can be written as: = * (4) Data Processing.
According to the statistics from Liaoning Statistical Yearbook, China Electric Power Yearbook and China Energy Statistical Yearbook from 2005 to 2010 and the data provided by China's power industry statistical data analysis of 2010, we have some calculation and analysis.
It is estimated that, compared with the data in 2005, till 2020 the target of the 45% reduction of the unit GDP CO2 emissions from power consumption could be able to achieve in liaoning province.

Lossless data compression is a technique which is used in data compression algorithms to compress text data and also retrieve perfect original data from the compressed data.
The test data obtained will be having huge data.
The hybrid approach is combination of Dynamic Bit reduction method and Huffman coding to compress the text data.
Improved dynamic bit reduction algorithm was designed to improve the compression ratio and memory saving percentage for text data.
For instance, it could be interested in finding representatives for homogeneous groups (data reduction), in finding ‘natural clusters’ and their unknown properties (natural data types), in finding ‘useful’ and suitable groupings (useful data classes) or in finding unusual data objects.

First, the exception handling model process m environmental indicators, if this data is anomalous, the results of the direct output is 0;If the set of data is normal, then put the data into the statistical reduction model for the first level of perfection; Then give the input clustering reduction model a second level of perfection; Ultimately, we can get the main property sheet of the index system according to the reduction criterion .The basic framework of indicators is as follows[3]: Figure2 Multi-level reduction model of complex indicators Unit Model Introduction Exception Handling Model Let , , is the i-th input environment indicators values of learning sample, also is -m dimensional vector.
The process of exception handling model: , Among them ,Namely, judge each index of this group of data, if the value of the data is in the normal range, then the data is normal, otherwise it is abnormal data.
In exception handling model, as long as there is an indicator of the data does not meet the judgment, then the set of data is abnormal data, otherwise is normal data.
As for the data on the diagonal, we can set a threshold to exclude some data, so that the number of error will not be too much after rounding coefficients.
The steps diagram for statistical reduction model is showing below[4]: Figure3 Procession of the statistical reduction model The specific process is as follows: Suppose after exception handling model, each indicator collect N groups of normal data ,based on statistical data description, for each set of this data selected mean, mode, standard deviation, maximum, minimum, sum, number of observations, etc. seven describe methods for each indicator building a multi-dimensional statistical vectors is as follows: ,among them, the calculation formula of average: ;The number is the most times appearing in the sets of data; standard deviation formula: ; the max and the min is the maximum and minimum sets of data; Sum is adding of N sets of data; Number of observations is N.

Additionally, the urea and fuel consumption and ammonia leakage have been compared and analyzed respectively, the experiment data indicates that the urea water solution consumption ratio is only 5.7% of fuel for this SCR system, while its average ammonia slip is below 5 ppm.
The engine is operated with a comparably high NOx raw emission, after using SCR system, a large reduction in NOx emission.
However, the experiment data indicates that urea water solution consumption ratio is only 5.7% compared to fuel used for the SCR system.
Marti, NOx-reduction in diesel exhaust gas with urea and Selective Catalytic Reduction, Comb.
DaCosta, Urea thermolysis and NOx reduction with and without SCR catalysts, Applied Catalysis B: Environmental 46 (2003) 17-34

Leakage Process and Minimization--Transistor Stacking Effect, Data Retention Gated Ground Cache, Drowsy Cache Debasis Mukherjeea and B.V.R.
Data Retention Gated-Ground Cache and 3.
Data Retention Gated Ground Cache Data Retention Gated Ground cache is based on transistor stacking effect.
Data Retention Gated Ground Cache.
Obviously very low power supply is needed for passive operation where only data storage is done.

Stiffness Reduction of CFRTP Spring without Loading.
These experimental data of the CFRP at room condition express a log-linear relationship between logand log.
Prediction Model of Stiffness Reduction of CFRP Spring.
Prediction Model of Stiffness Reduction of CFRTP Spring.
This experimental data suggested that the material constantshown in Eq.7 was almost 0.

The comparison results found good agreement between the numerical model and wind tunnel experimental data with an error of 7.8% in the wind reduction ratio, indicating the present numerical model can be used to undertake study on butterfly and non-planar porous fences.
The rationality and the validity of the numerical method developed in the present work have been evaluated by comparing the numerical results with the experimental data.
The validity of the present numerical method has been evaluated by comparing the numerical results with the experimental data which contain the mean velocity profiles behind the porous fence and the wind reduction ratio.
Fig.5 Comparison of non-dimensional mean velocity profiles at Z/H = 0.8 and 1.2 Fig.5 shows that numerical values are higher than experimental data in the downstream region.
As a result, the numerical predictions show good agreements with the experimental data.

It is recommended that the conductor should have a temperature-reduction value of 25℃ according to the conductor temperature-reduction value analyzed by amount of creep, which can be used for reference during line design and construction.
The software specially developed for a creep test is used to collect data in this test.
Data Analysis Method The relationship between creep elongation and time of stranded conductor changes exponential, and it is a linear relation on log-log coordinates.
Analyze test data to get the coefficient in creep equation; use the creep equation to calculate amount of conductor creep for 10 years or 30 years operation, and then offer a regression curve[7].
It can see temperature-reduction value of 5 types of conductors from Table 3.

The strength reduction method is applied in the analysis of slope stability, strength reduction, into the finite element program for calculating, until computations convergence.
According to the survey data of landslide soil mainly for silty clay clip of gravel, and sliding bed for approximate circular arc shape due to landslide, groundwater is meager, it does not consider the effect of hydrostatic pressure on groundwater dynamic.
Set, as the initial strength parameters, safety factor, strength reduction definition form.
This article uses the finite element strength reduction method of a landslide, undertook systems analysis, confirmed the strength reduction method used in slope engineering feasibility, obtained the corresponding safety coefficient of stability of landslide
The finite element strength reduction method in soil and rock slope, 2004, 23 ( 19): 3381 - 3388(In Chinese) [3]Shifei Lou.

The analysis results are also compared with the experiment data from published references and they show a good agreement with the experiment data.
As shown in Figs. 4a the calculated values from two material models agree well with the experiment data.
And the results from the Model 2 are lower than the experiment data until the wear depth ratio becomes 0.8.
It means that Model 2 gives conservative pressure values comparing with the Model 1and the experiment data.
And the calculated results are compared with the experiment data in the references [4, 5] except the case when the wear length is 50.8 mm because of absence of appropriate experiment data.