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Reduction of GO by NaHTe.
These XPS data indicate that the oxygen-containing functional groups have been partially removed after reduction.
XPS of Te-GO2 Summary A mild and efficient reduction system by using NaHTe as reducing agent for reduction of graphene oxide is described.
This reduction was carried out at room temperature.
Cheng, The reduction of graphene oxide, Carbon 50(2012)3210-3228

If we can extract this information from the database, then we will create a lot of potential profit and value for the owner of the data, and this kind of technology of mining information from large database is data mining. 1.1 Theoretical basis of data mining The basic theory of data mining can be attributed to the following several aspects: 1) Data reduction: according to this theory, the basis of the data mining is to reduce the description of the data.
In a large database, data reduction can translate fast approximate response to queries.
Data reduction technology mainly includes the singular value decomposition behind the main component analysis of drive elements, wavelet, regression log-linear model, histogram, cluster, sampling, and the index tree structure. 2) Data compression: according to this theory, the basis of data mining is to compress the given data.
For example, model found can be seen as a form of data reduction and data compression.
It mainly includes the following main components: Visual user interface Model knowledge assessment Data mining engine Database or data warehouse server Data cleaning Data integration Data filtering Database Data warehouse Knowledge base Figure1.

By comparing the diffraction lines in Fig. 1 with the tabulated diffractometric data [17], three compounds may be identified in the observed system, namely NiO, WO3 and NiWO4.
The standard enthalpies and free energies of reduction of oxides, shown in Table I, one may calculate on the basis of the handbooks of thermodynamic data.
In accordance to literature data [8, 9], NiO is reduced at the lowest temperature, while pure WO3 requires the highest temperature to be reduced.
By the way, from the X-ray diffractometric data, the electrochemically obtained alloys are different in structural sense from alloys of this study.
[17] Powder Diffraction File, Joint Committee on Powder diffraction, International Center for Diffraction Data, Swarthmore, PA, 1987

The component of reduction gas is shown in Table 2.
According to some data, the influence of fine ore particle size is great on sticking behavior.
Therefore, sticking problem under fluidized state is a peculiar phenomena in reduction process or reduction atmosphere.
Degree of metallization or reduction.
The reduction degree of fine ore reduces slowly with the improvement of reduction temperature while the sticking is happening

Many approaches for dimension reduction exist in the field, e.g. by feature selection using some objective criteria [18, 8,11].
In the following, we will use only the good data (we will refer to them as 'the data').
What is the shape of the multidimensional data cluster containing data points representing subsequent data vectors, 2.
It is obvious that dimension reduction works in favor for further processing.
Low dimensional visualization using PCA and self-associative neural network Principal component analysis (PCA) PCA is a well established method used for reduction and low-dimensional visualization of data [16,17,4].

Subjective evaluation by the observer on the assessment of the effect of image noise reduction, on the other hand, objective evaluation is used to contrast with the original picture parameter data, including Mean Square Error (MSE) [2], Signal Noise Ratio (SNR), Peak Signal Noise Ratio, PSNR [2][3], Entropy and so on.
The Table 1 also shows that the data is non-linear growth when we choose large windows.
With the increasing of the window size, the data tends to be a constant.
Analyzing from the data recorded in Table 2, the effect of reduction is not simply proportional or inversely proportional to the size of filter window, and the 5×5 window is the best.
MSE Entropy PSNR Noisy image 1.4722e+3 7.1873 16.4510 Method (1) 674.8619 6.4043 19.8387 Method (2) 748.3711 6.3485 19.3896 Table 7 Results of data for Simulation (III) MSE Entropy PSNR Noisy image 1.4722e+3 7.1873 16.4510 Method (1) 674.8619 6.4043 19.8387 Method (2) 748.3711 6.3485 19.3896 Table 7 Results of data for Simulation (III) Fig. 20 Noisy image Fig. 21 Step 1 and 2 by Method (1) Fig. 22 Step 1 and 2 by Method (2) As the data shows in Table 7, if a noisy image contains a large Salt & Pepper noise, the Method (1) is the best choice for noise reduction.

“Energy saving and emission reduction” problem has drawn worldwide attention.
Most of these studies are based on the technology of energy saving and emission reduction in a certain machinery or industry, while studies concerning energy saving and emission reduction conditions of the whole society are few.
Modeling Optimization Model of Carbondioxide Emission Reduction.
The model to evaluate the optimal energy consumption structures is: (2) Results and Discussions Based on the relevant data of China [7], two optimization models can be solved.
Based on the data in Table 1, the carbondioxide emission reductions of each major sector are evaluated in Table 2.

The key parameters of soft reduction technology includes: total reduction, reduction position and reduction ratio, reduction rate for the designated areas.
On the premise of determination of position in soft reduction, we analyze the macrostructure test, central segregation index methods according to the measured data.
The relation between total rolling reduction and reduction ratio[7]: Total rolling reduction = (1) Where:— total rolling reduction/mm; —reduction ratio/mm·m-1; —length of soft reduction/m。
Reduction rate means the rolling reduction during unit time.
Reduction rate is an important parameter for devices of soft reduction.

Based on the data availability and the total amount of different countries’ CO2 emission, 49 countries’ data between 2001 and 2007 are taken as the samples.
CO2 emission amount in Africa is not included in this research because of low CO2 emission and lacking data in Africa.
The data of energy and CO2 are from Energy Information Administration (EIA) in 2009.
The capital stock is obtained by roughly calculating the relevant data in Penne World Table (PWT6.3) with perpetual inventory method.
The data of labor force and GDP are also collected according to PWT6.3.

This paper presents an empirical Gramian balanced reduction method which efficiently solves nonlinear power system model reduction problems.
Model reduction is one of effective solutions to solve the above problems.
Nonlinear dynamic power system model reduction is the key research hotspot.
The system sample data got by the empirical method must be in stable region, and reflect the perturbation as much as possible.
The more data samples of system, the more accurate Gramian matrices.