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The increase in concentration of MBISA in coating material results in consequent reduction of corrosion current reaching its overall decrease of more than two orders of magnitude between the coatings with the lowest (0.0025 M) and the highest (0.25 M) concentration of the inhibitor.
The equivalent circuit analysis of EIS data was conducted after 30 days of constant immersion in dilute Harrison's solution.
The equivalent circuit model of coated metal used for analysis of EIS data.
Results of equivalent circuit analysis of EIS data.

If plastic package can realize the reduction, recycle and reuse, it can still be a very possible solution for green packaging material.
Actually, with the daily increased environmental pressure, plastics are all the way controversial, but due to recent data in package industry, plastic package is still one of the fastest growing and most needed materials.
Recent before the environmentalist proposed a data showed that there discovered 96% of the plastic wastes in their stomach of dead sea gulls nearby seashore of Scotland.
And lots of environmentalists ever advocated to take back and recycle of those plastic bags, but the actual data revealed that 80% of the consumpted plastic bags were just as same transportation and stacking as the normal wastes, only 7% of them can be genuinely recycled.

Rough Sets Rough set theory is proposed as a new mathematical approach to imperfect knowledge by Pawlak in 1982 [3,4], and is used in the imprecision, vagueness and uncertainty data analysis.
The attribute reduction can be performed by the concept of significance of attributes.
According to the rough set theory, the engineering samples pre-process should be performed firstly, i.e., continuous attribute data discretization.
Pawlak: Rough sets: theoretical aspects of reasoning about data (Kluwer Academic Publishers, 1991)

This approach is very promising in the framework of the reduction of fatigue testing time; however, it proved to be rather unreliable, working only for certain materials, and should, to the opinion of the present authors, rely on more physical bases to be able to relate this change of thermomechanical regime to the microstructural changes taking place in the tested material.
The three terms on the left hand side of Eq. 7 are calculated independently from the recorded data.
Seven 5 s acquisitions (1750 images) of data have been performed, 30 s separating two consecutive acquisitions.
The first step in the processing of the data is to subtract the thermoelastic effect from the )(tθ signal, assuming the following reasonable assumptions: the thermoelastic heat sources are independent from the dissipative ones and the deformation rate of the material is sufficiently important to consider the thermal regime as adiabatic.

MPU-6000 (6050) integration of the 3 axis gyro, 3 axis accelerator, and with digital motion processing accelerator, with second I2C ports connected to other brands of magnetic sensor, and other sensors (DMP: Digital Motion Processor) engine hardware acceleration, mainly from I2C port to a single data stream to the application form, the output complete 9 axis six axis sensor fusion technique MPU6050, angular velocity MPU6050 full scale range is ± 250, ± 500, ± 1000 and ± 2000 ° /sec (DPS), which can accurately track the fast and slow movements, with angle compensation, the error of output angle is small, the accuracy of <1 °, high flexibility.
Display 6 independent buttons on the screen, were used for start/stop (start / stop), set (set), up (supp.), enter (down9) reduction, determine), back (return control.
Kalman filter function is as follows: Void Kalman_Filter(float angle_m, float gyro_m) { 　　angle+=(gyro_m-q_bias) * dt; // priori estimate 　　Pdot[0]=Q_angle - P[0][1] - P[1][0];// Pk-' A priori estimate error covariance of the differential 　　Pdot[1]=- P[1][1]; 　　Pdot[2]=- P[1][1]; 　　Pdot[3]=Q_gyro; 　　P[0][0] += Pdot[0] * dt; 　　P[0][1] += Pdot[1] * dt; 　　P[1][0] += Pdot[2] * dt; 　　P[1][1] += Pdot[3] * dt; 　　angle_err = angle_m - angle; //zk- priori estimate PCt_0 = C_0 * P[0][0]; PCt_1 = C_0 * P[1][0]; 　　E = R_angle + C_0 * PCt_0; 　　K_0 = PCt_0 / E; K_1 = PCt_1 / E; 　　t_0 = PCt_0; t_1 = C_0 * P[0][1]; 　　P[0][0] -= K_0 * t_0;// posteriori estimation error covariance P[0][1] -= K_0 * t_1; 　　P[1][0] -= K_1 * t_0; 　　P[1][1] -= K_1 * t_1; 　　angle += K_0 * angle_err;// posteriori estimation 　　q_bias += K_1 * angle_err; 　　angle_dot = gyro_m-q_bias; } Data Time: second Angle: degree Table I The data of the angle Setting value Left angle Error of left

Introduction Considering the greenhouse gas emission reduction and efficient utilization of coal, the coal gasification technology is more utilized in some chemical plants.
Here, for the first time, we aim at exploring the ash fusion properties of Ningdong coal blending and analyzing the mechanism, the obtained data will provide valuable information on the influence of minerals on combustion in existing and developing systems, as well as on the utilization of process residues.[5] In addition, the results will be helpful for improving slagging and promoting the effectively use of feed coal, and be useful to improve efficiency, capacity and availability of the facilities.[6,7] This research endeavors to add to the existing knowledge in the field of coal science, further research on the experimental and theoretical work of coal blending combustion is required if more instructive information is to be understood.
Especially, the data suggested that FT tendency is different.
Moreover, these data are consistent with the notion that the blended ash temperatures are non-linear related to the blending ratios of the low AFT coal ashes.

Expressed by the following relationship: (2) Where: S ─ probability of survival; τ0 ─ maximum dynamic shear stress amplitude; z0 ─ where thedepth of maximum dynamic shear stress; N ─ stress cycles to count a million times; V ─ a stressed volume; a ─ Hertzian contact ellipse long axle; l-rings running the length of the raceway; e ─ Weibull distribution slope parameter; c, h ─ material constant, regardless of the state of contact; Since the eighties, with the improvement of the quality of steel, originated in the cracks under the surface flaking resulting reduction surface cracks which stems from flaking produced more common, which began two concerns contact fatigue crack expansion mode: surface fatigue crack growth under way and surface fatigue crack propagation mode.
(8) Where: F (N) - Defect life distribution; The formula (8) into equation (6) have bearing survival probability expression: (9) Where: S (N) —containing bearing survival probability volume defects; A defect life factor; τ 0 — the ultimate fatigue constants (MPa); ζ—stress index; β—life discrete index; ZS—stress measuring depth (usually: surface) (mm); h—value constant; m—defect density; t crit —Dimensionless critical stress; Pmax — the maximum Hertzian pressure (M Pa); l—raceway length; Z0 —the maximum Hertzian shear stress range in depth (mm); a— Hertz contact half length (in the direction perpendicular to the rolling) (mm); A new prediction model of rolling bearing fatigue life In 1996, T.E.Tallian is presented based on the L-P theory of the rolling bearing life data fitting a new prediction model[6] (T model),in which this model introduces the smelting process, surface defect, surface roughness, residual stress, elastohydrodynamic lubrication
No. 3: 7 [4]Wan chang seng,Anlysis methode of the rollings bearing (Chineses) [M]．Bajing: mechanical industrial press ,1985.139-163 [5] T.E.Tallian，A data-fitted rolling bearing life prediction model-part I: mathematical model[J]．Tribology transactions 1996,39 (2):249-258 [6] T.E.Tallian， A Data - Fitted Rolling Bearing Life Prediction Model - Part Ⅱ: Experimental Database[J]．Tribology transactions. 1996 ,39 (2):259～268

Firstly, the relationship among the properties indices was calculated, which showed certain relations between the indices; hence, principal component analysis can be used to make dimensionality reduction.
Making factor analysis on the data in Table 2 with principle component analysis as extraction method and without rotation, the results are shown in Table 3 and Table 4 [3,4].
For principal component analysis, the data in table 4 were divided by the square roots of there corresponding initial eigenvalues.
The transformed data are the coefficients of the four new components, from which, Eq. 1~Eq. 4 were obtained, they are F1=-0.151ZX1 + 0.206ZX2 + 0.31ZX3 + 0.306ZX4 + 0.577ZX5 - 0.55ZX6 - 0.332ZX7, (1) F2=-0.026ZX1 - 0.703ZX2 + 0.215ZX3 + 0.442ZX4 - 0.072ZX5 - 0.235ZX6 + 0.451ZX7, (2) F3=-0.623ZX1 + 0.063ZX2 + 0.299ZX3 - 0.462ZX4 + 0.27ZX5 + 0.106ZX6 + 0.470ZX7, (3) F4=0.628ZX1 + 0.103ZX2 + 0.678ZX3 - 0.323ZX4 -0.073ZX5 - 0.089ZX6 + 0.132ZX7, (4) where Fi (i=1~4) are the four principal components, and ZXj (j=1~7) are the standard values of Xj (j=1~7).

The model output was then compared to the experimental data and a simple parameter tuning approach was employed to refine the fit between experimental data and the model.
The data were logged by a computer for analysis.
Measurements of the refractive index of the base resin gave a value of 1.589 at 589 nm and 20oC, while additions of propylene carbonate led to a significant reduction.

These determinations were performed to obtain enough reduced or oxidized products for analysis of S/Fe/Cu speciation by S K-edge and Fe and Cu L3-edge XANES and Raman spectrometry to define the thermo-reduction process of chalcopyrite at 65°C [3]. 2.
For thermophiles, the thiol group concentration in S0 grown cells was in the range of 2-4 time that in Fe2+ grown cells (data as yet unpublished).
These data indicate that extracellular –SH groups take part in the activation of S0.
Both Fe2+ and Fe3+ were bound to extracellular –OH groups (data unpublished as yet).