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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.

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.

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 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.

The percentage of storage reduction and data anomalies are investigated for every normal form and database system.
For the advantages, Normalization avoids data modification (INSERT/DELETE/UPDATE) anomalies as each data item resides in one place.
Data are organized and structured more logically.
Also, it enforces Referential Integrity on Data, namely the enforcement of relationships between data in joined tables.
ACM SIGMOD International Conference on Management of Data, May 31-June 1, 1979, Boston, Mass.