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An approximate power function relationship between the roughness and the fatigue life was established through the fitting of the experimental data.
On the whole, there is a trend that the greater the roughness is, the lower the fatigue life is, although some individual data is biased because of intrinsic discreteness for the fatigue test.
Ten group data, displayed by the solid circle dots, scatter in the figure.
A power function curve was used to fit these data.
An approximate power function curve was fitted using the experimental data.

In order to verify the reduction of the vibration, a test rig was set up.
On the test rig, the reduction effect of two different dampers was studied by adjusting the amount of the magnetic fluid.
It was accepted by the data acquisition and transformed by analog-to-digital conversion, then inputted into the connected computer, and then converted into acceleration signal by Matlab[5].

That is, we fit to the relevant experimental data, in a least-squares sense, (1) where is photon energy andrepresents absorption coefficients.
For evaluation of Eg, we perform our fit of Eq. (1) to the experimental data shown in Fig. 4.
This results in a reduction in carrier concentration.
The reduction of structure defects implies a decrease of recombination centre of photo-generated carriers, and thus leads to an improved photo-sensitivity.
For R=0.1, we have inferred that hydrogenated InAsSb films did not crystallize from the XRD data (Fig. 1).

In addition, it is of great significance to produce hydrogen from organic wastes by fermentative hydrogen production, because it plays the dual role of waste reduction and energy production[1-3].
Hydrogenases that are able to catalyze the oxidation of hydrogen or the reduction of proton contain one or more [Fe-S] clusters to store and transport electrons [4].
Data shown are representative results of independent tests that were triplicate.
On this figure, the squares represent the data points and the curve represents the corresponding fitting curve.
In this study, the Han-Levenspiel model (Eq.1) was used to fit the average hydrogen production rate data.

It was reported in 2010 that PEM fuel cell can reach ~60% electricity conversion efficiency and 80% energy co-generation (electrical and thermal ) efficiency with more than 90% reduction of carbon and pollution emissions [1].
Within an operating circle of the PEM fuel cell, the diffusion of reactants, the oxidation and reduction of reactants and the conduction of protons, they all take place in the membrane electrode assembly (MEA).
The same step is conducted to obtain the properties of electrode by using the re-modified parameter of nafion membrane as the input data.
The accuracy of the material definition must be verified by experimental data [6].
The stress-strain curve acquired from the model fit the experimental data well as shown in Fig 3.

Introduction Aluminum reduction cell is the main equipment for the aluminum production, and whether it can provide a stable production is directly related to current efficiency, power consumption rate and the of primary aluminum and other technical indicators.
So it is essential in the aluminum electrolysis that the effects of aluminum reduction cell control is good or not.Aluminum electrolysis process is a nonlinear, multivariable time-varying and large delay system for industrial processes because of its own internal complicated physical and chemical changes, and various external conditions of interference determine a number of uncertain parameters during the cell operation process.
The cell resistance deviation x1 and resistance deviation variance ratio x2 getting from the analysis can be regarded as input data to calculate the spacing interval deviation of point feeding as y.
Fig.2The structure of fuzzy neural network The entire network data changes as follows: The first layer is the input layer.
The formulas described as follow: （k=1,2,…n） (2) (j=1,2,…n) (3) The third layer is the fuzzy rule layer, where each node represents a fuzzy rule, which is used to multiply the input data.

The collected data were implemented with data reduction using Graphical Reduction and Analysis SANS Program (GRASP) and analyzed with analysis software (IGOR Analysis Pro 5.04.8).The produced ZnO nanostructure was characterized by powder X-ray diffraction (XRD, Philips, X'pert, Cu Kα).
Log-log plots of SANS data on ZnO powder prepared by solvothermal method at temperature of 150˚C for 18 hours.

Material Specimens The average adhesion value of [N/5sm] The decrease of adhesion [%] Vinyplan 6580 Arctic «dry» 230 3 «soaked» 225 ORCA TECH 289 4588 «dry» 280 18 «soaked» 230 Prolonged soaking of the materials has different influence on the reduction of film adhesion.
However, such statistical data is needed to develop requirements for existing materials and materials under development as well as for estimation of their strength properties.
Table 2 shows data of strength tests of these specimens.
Data of strength tests.
The reduction in strength is about 30% compared to the new materials.

The crystalline phases were identified using standard cards from the International Centre for Diffraction Data (ICDD, Newtown Square, Pennsylvania) Powder Diffraction File database.
The adsorption isotherm data are characteristic of each system and its analysis is crucial for understanding the adsorption mechanisms.
Lovley, Dissimilatory Fe(III) and Mn(IV) reduction, Microbiological Reviews 55 (1991) 259-287
Ballester, Bio-reduction of Fe(III) ores using three pure strains of Aeromonas hydrophila, Serratia fonticola and Clostridium celerecrescens and a natural consortium, Bioresour.
Data 45 (2000) 575-584

The present noise reduction methods, such as wavelet decomposition, empirical mode decomposition, acoustic emission technique, etc., achieved some effect but simultaneously lead to the reduction of fault signal.
Let noise intensity D=2.31, the data length L=4096 and sampling frequency is 10Hz.
During the experiment, the data acquired by an accelerometer mounted on the top of the bearing holder on left side.
As the sampling frequency is 25.6kHz, the experimental data should been preprocessed by scale transformation [11].