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The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.
In the present investigation, the experimental data were tested with respect to the Langmuir isotherm.
The data obtained from the adsorption experiment conducted during the present investigation was fitted using different COD concentration into the isotherm equation.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947 in Fig. 6.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.

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 standard Gibbs free energy change can be calculated by using basic thermodynamics data [8] when the vanadium oxides were reduced by reducing agent ferrosilicon
Then it is more beneficial to the ferrosilicon reduction reaction.
After 3~5 minutes, prepare tapping after the self-reduction agglomerate are fully melting.
In the self-reduction agglomerate, the amount of reluctant ferrosilicon is surplus.
(In Chinese) [8] Yingjiao Liang, Yinchang Che: Thermodynamics Data Book of Inorganic Compound.

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.

LFDA can preserve both manifold of data and discriminant information.
Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality.
In addition, LPP can only preserve the manifold of data and lose the discriminant information because LPP is an unsupervised dimensionality reduction algorithm.
Dimensionality reduction is performed to solve the problem of dimensionality curse which arises by high dimensional data at the stage of feature extraction.
[9] Masashi Sugiyama: Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis.

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.

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 optimize vibration digging shovel’s drag reduction performance parameter, building the regression model of traction resistance between vibration frequency and amplitude, penetrating angle and the traction rate based on the vibration reduction simulation experiment results, moreover, it is optimized.
Domestic and foreign related research on this technology is mostly based on theoretical mechanics modeling and experimental research, simulation study on vibration drag reduction technology is less.
The data were processed by Design-Expert analysis.
Building regression model Application Design-Expert software on traction resistance data (Table 2) are analyzed, getting the code representation of the traction resistance of distribution coefficient Y of 2 regression model: Y=1464.00+19.17X1+30.00X2+6.67X3+10.83X4+10.00X1X2-2.50X1X3-5.00X1X4- 7.50X2X3-7.50X2X4+5.00X3X4+15.50X12+19.25X22+114.25X32+15.50X42 (1) According to the size of regression coefficient of each model factor, the available sequence the influencing factors: the vibration frequency X3, vibration amplitude X2, traction rate X1, penetrations angle X4.
[5] Qiu Lichun: Bulk Subsoiler System Dynamic Model and Study of Drag Reduction and Energy Saving (Ph.D., Shenyang Agricultural Uinversity, China, 1998)

First, the pre-system uses principal component model to convert properties of the source data of the basic window, and it plays a role of dimensionality reduction; Second, the post-system uses the density model to execute clustering operation; Finally, it uses the summary of data, generated in before two steps, to execute simply second clustering and update the clustering result, which fit the requirements of streaming data features.
The Description of a Dynamic Data Stream Model Data Stream.
,an), as the data come constantly , the old data are out from one end of the window and the new data enter from the other one end.
Dimension reduction has been one important research topic in the field of pattern recognition, machine learning, and multivariate data analysis.
Especially with the arrival of the information age, people get large amounts of data, high dimension, unstructured data has become easy increasingly, which makes data reduction has become more urgent.

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.