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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.

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.

Energy saving and economic growth: Empirical Analysis Based on Panel Data Dongheng Hao1, a, Guozhu Li1,b and Dianru Wang1,c 1 School of economics and trade, Shijiazhuang university of economics, Shijiazhuang,050031,.China ahaodh@sjzue.edu.cn, bliguozhu@sjzue.edu.cn cwangdianru@126.com Keywords: Energy Saving, Economic Growth, Panel Data.
Abstract. we analyzed the relationship between energy conservation and economic using panel data. the reduction of energy consumption per unit of GDP and energy consumption per unit of industrial value-added will promote economic growth, however, lower electricity consumption per unit of GDP may inhibit economic growth.
Methods and variables When the sample time is short and the provinces situations are very different, panel data can combine time series and cross section, it can control the factors that are unobservable due to difference between provinces, there minimizing the errors of results.
Specifically, there are three main advantages for the panel data.
Because of the short time dimension and the large cross-section, the data of this study is a short panel.

Recently, data mining approaches have been successfully applied to industrial data analysis to derive useful and comprehensive knowledge [4].
One of the new data mining theories is the Rough Set Theory (RST) (Pawlak, 1982), which can be used for reduction of data sets, finding hidden data patterns and generation of decision rules.
In this study, we propose a systematic approach, including data preprocessing, data reduction, and rule generation, for selecting a group of attributes capable of representing mould risk assessment.
In our work, basic event’s historical data is organized into a decision table.
Some of those attribute data are incomplete, vague or ambivalent in Table 2.

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.

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.

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.

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.

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.

The Model of the Al reduction cell Mathematical Model.
The finite element method (FEM) model of the reduction cell is shown in Fig.1, but the air around the Al reduction cell is not shown.
The element Source 36 is used to provide current data, and need to predefined geometry; the element Solid 117 includes 20 nodes and is used for the static magnetic analysis.
Fig. 1 Schematic diagram of pre-bake anode Al reduction cell Fig. 2 FEM model of Al reduction cell Calculated Results and Analysis.
Fig. 3 X magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 4 Y magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 5 Z magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 6 Sum magnetic intensity vector of the Al of the Al reduction cell (Tesla) Fig. 7 X magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 8 Y magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 9 Z magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 10 Sum magnetic intensity vector of electrolyte of the Al reduction cell (Tesla) Fig. 11 X magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 12 Y magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 13 Z magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 14 Sum magnetic intensity vector of the cell wall of the Al reduction cell (Tesla) Fig.12~Fig.15 are the X, Y, Z and the magnetic