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The low melting temperature of plastic based fibers leads to the creation of new expansion channels in concrete structure that allows transport of an excessive water vapor and consequently reduction of the interior pore pressure.
Looking at the data, there is almost no distinct impact of temperature load up to 200 °C for all studied parameters.
Based on DSC and TG data published in [6], this concrete behavior corresponds to the disintegration of portlandite (CH), C-S-H gels, calcite (CaCO3) and β → α transformation of quartz.
The presented data will find use in further research aimed at the development of UHPFRC with enhanced resistivity to high temperatures.

The processing of test data is same with the test of cube compressive strength, and results are shown in Table 3.
The processing of the data is same with cube compressive strength test, results are shown in Table 3.
While the ratio of rubber powder is between 40% and 60% the strength of it reduced slowly compared with the above data.
Conclusions The cube compressive strength, splitting tensile strength, axial compressive strength and flexural strength of rubber cement concrete have different degrees of reduction with the increasing of the content of rubber, but it also reflected the good preference which is difficult to achieve for ordinary concrete, such as high density, toughness, crack resistance, deformation ability, and mechanical properties of recoverable.

The dent causes a reduction in the spacing between fibers in the thickness direction and this causes an increase in fiber contact leading to decrease of electrical resistance in the thickness direction.
The commands and the data acquisition are carried out with a code developed in Matlab 2011b.
Barsanescu, Ultrasound and eddy current data fusion for evaluation of carbon-epoxy composite delaminations, INSIGHT, 51(1) (2009) 25-31
Sabbagh, Inversion of eddy current data and the reconstruction of flaws using multifrequencies, Int.

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.

We hope that the research results will instruct the numerical simulation of the forging process and provide experimental data for the optimization of process parameters.
Isothermal compression tests were conducted in a Gleeble-3500 thermo-mechanical simulator at the deformation temperatures ranging from 1060 to 1120°C, the strain rates of 0.001, 0.01, 0.1, 1s-1, and the height reductions of 30% and 60%.
The values is obtained by fitting the data and αn is 0.05347 and n is 4.7120675, and then the value of α is 0.0113475MPa-1.
From Eq. (7) and the experimental data, the activation energy can be calculated and the value is 439KJ/mol.

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

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.

Meanwhile, we have developed intelligent parameter adjustment device with the functions of monitoring the dynamic liquid level on-line and automatic balancing adjustment for the CQ-YQY-D type pumping unit, reaching the purpose of energy saving, consumption reduction and improving the efficiency of machine mining system.
It can’t be disturbed by gas and Foam layer and can avoid false liquid level data effectively.
Adopting power balance method (Q/SY 1233-2009), the data of current and power is acquired in real time in up and down stroke by EDA power monitoring module according to the torque balance principle.
Tab.1 Frequency conversion and line frequency comparative data table Condition Active Power (kW) Reactive Power (Kvar) Power Factor Current (A) Voltage (V) Daily power consumption (kWh) Active energy- saving rate％ Reactive energy- saving rate％ Integrated energy -saving Rate％ A phase B phase C phase A phase B phase C phase before application 9.78 42.13 0.24 66.3 66.9 67.5 222 221 222 234.72 25.66 96.75 32.32 after application 7.27 1.37 0.98 4.4 4.3 4.9 220 220 220 174.48 difference -2.51 -40.76 0.74 -61.9 -62.6 -62.6 -2 -1 -2 60.24 From the table above, we could see that, after applying centralized control of energy-saving device at Wang x-x well group, daily electricity consumption drop from 234.72 kWh to 174.48 kWh, daily power save 60.24 kWh, and the rate of comprehensive energy-saving is up to 32.32%.