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Figure 1 Flow chart of SA algorithm System Development Data Input.
When a user inputs the number of workpieces or machines, the character entered may not be a numeric character and an integer character because of human mistakes, Delphi abnormal state processing sentence (try… except) is added to display a prompt dialog box “The data you entered are incorrect, please input correct data!!!”.
The edit boxes are generated in a dynamic way according to the scale data entered in the input boxes.
When the user inputs the process matrix and time matrix, the characters entered probably are not numeric characters and integer characters because of human mistakes, Delphi abnormal state processing sentence (try… except) is also used to display a prompt dialog box “The data you entered are incorrect, please input correct data!!!”.
Figure 2 Data input Parameter Value Initial temperature 200 Markov chain Lk 1400 Temperature reduction parameter a 0.9 Figure 3 Parameter setting of SA algorithm When the user inputs the above data, the characters entered probably are not numeric characters because of human mistakes, Delphi abnormal state processing sentence (try… except) is also used to display a prompt dialog box “The data you entered are incorrect, please input correct data!!!”.

All three alloys demonstrated reduction of area values in excess of 50 percent over the entire range of test temperatures.
Figure 5 presents the mechanical property data, as well as the available retained austenite measurements, of the samples after annealing.
These data are for samples that were hot-rolled with a simulated coiling temperature of 593°C.
The data are presented as a function of peak annealing temperature, and include both hot-spot and cold-spot cycles.
The data show a minimum in yield strength (Fig. 5a) and ultimate tensile strength (UTS, Fig. 5b).

The modern market dynamics, furthermore, currently push for the costs reduction by minimization of the production times.
The obtained data are listed in the report.
In Table 2 are listed the data detected by Block 1 in the PP0 part – program (Fig. 2).
Table 2: Detected data in the PP0 part - program Section Tool ID TINI [s] FINI [mm/rev] SINI [m/min] 1 14 10.6 0.06 32 2 14 9.8 0.08 26 The optimization phase (Block 3, Fig. 2) has produced the results summarized in the Table 3 (it is a typical procedure results report).
It is evident that it has been possible to detect in the tool life curves data – base at least a curve whose parameters (F,S) maximize MRR and respect the VB constraint.

The shear bond strength data was statistically analyzed using one-way ANOVA at a significance level of 0.05.
Data was also evaluated using the Tamhane’s T2 post hoc test.
Fig. 1 Prepared specimen for shear bond strength testing and loading direction Results The data of shear bond strength was analyzed for normal distribution by the Komolgorov-Smirnov test and the data showed normal distribution in all group.
The bond strength increases because the cause of bond reduction is removed and P residue does not affect to the bond.
Yatani, Adhesion procedure for CAD/CAM resin crown bonding: reduction of bond strengths due to artificial saliva contamination, J.

However, there is a lack of data and legislation regarding potentially new applications as additives in biofabrication processes.
The load vs. extension data was recorded and calculated by the testing equipment.
The working curves of all four data sets intercepted the exposure axis at around 51 mJ/cm2, which is the minimum energy Ec of the suggested material composition and hence independent of the dye concentration.
The reduction in modulus went along with a decrease in compressive strength from 12.5 MPa down to 9.4 MPa, which amounts to 25%.
The strength data was however less consistent as indicated by slightly higher standard deviations, which was due to a noticeable difference in strength between the two specimen sets of three parts each.

Currently there are no unambiguous data confirming the clear correlation in the changes in the microhardness and plastic characteristics [26, 27].
Some data confirm the possibility of forming the certain properties on the local defect sites when exposed to a given number of laser pulses lasting several nanoseconds [8, 26-28].
The structural changes are confirmed by the data of the X-ray diffraction analysis, which made it possible to distinguish the metastable amorphous-nanocrystalline structures corresponding to the annealing temperatures: 748-823 K, 853-888 K, 903-943 K, 973-1023 K [20].
Fig. 1 shows the obtained experimental data on the dependence of microhardness on the distance to the center of the melting zone.
Bruening, Catalytic nanoparticles formed by reduction of metal ions in multilayered polyelectrolyte films, Nano Letters. 2(5) (2002) 497-501

It is evident that the deviations between the pressure loss with the no-slip boundary condition and the experimental data (△preal)[12] is increasing with the reduction of cross-section side length a.
The calculated standard errors σ between simulation results and experimental data are listed in Tab.2.
(a) a =500μm (b) a =300μm (c) a=200μm Fig. 4 The pressure loss profiles of polymer melt in microchannels compared the simulation results with the existing experimental data Table 2 The standard deviation σ of pressure losses between the simulation and experimental data 500μm F no-slip 8.0×105 7.5×105 7.0×105 6.5×105 6.0×105 σ 4.064 1.731 1.298 1.109 1.398 2.204 300μm F no-slip 7.0×105 6.5×105 6.0×105 5.5×105 5.0×105 σ 12.411 3.562 2.516 2.532 4.047 6.338 200μm F no-slip 6.0×105 5.5×105 5.0×105 4.5×105 4.0×105 σ 32.308 6.326 3.121 2.125 5.589 9.630 In order to analyze the relationship between the wall slip and the characteristic dimension of microchannel (length/diameter ratios of microchannel), the filling flow of polymer melt in microchannels are numerically simulated under the different process conditions.
The melt velocities at the center are all reduced in three microchannels, but the reduction is greater at the higher shear rates.

The cut off-filter for the 2D profiles was 0,8mm, where the pre-processing of the 3D maps was based on the low pass filtering (Gaussian noise reduction).
Multivariate analysis software Commercially available (The Unscrambler® X, CAMO Software AS) and custom made (LabView by National Instruments) software packages were used for post processing and data mining.
Therefore, multivariate analyses were performed on the full set of data collected within all measurements.
However, substantial improvements to the models and especially to the data set of reference values are necessary before real-life applications.
Chemometric analysis and numerical modelling of the data acquired allowed better understanding of the weathering mechanisms/dynamics and wood species/coating type resistance.

The purpose of this work is to study the joint effect of different conditions of twin roll casting-extruding and two-stage annealing on the structure and properties of rods from an alloy with a content of 0.3% zirconium and 0.2% iron and comparing results with previous research data.
The formation of primary Al3Zr intermetallic compounds is not desirable, however, single clusters found in the structure do not have a significant effect on the mechanical properties of the rods studied, which is confirmed by the data in Table 2.
Two-stage annealing ensures the decomposition of the solid solution supersaturated with zirconium with the release of dispersed Al3Zr particles (Fig. 2, b, spectrum 3, 6 and 7), leads to a less significant decrease in the ultimate tensile strength, increase in ductility and a significant reduction alloying elements in solid solution.
Table 3 gives comparative data on the properties of the bars with previous studies in [8].
Two-stage annealing allows to achieve a significant reduction in electrical resistivity to a minimum value of 0.0290 Ohm‧mm2/m after annealing the rods extruded with drawing ratio of 4.4, while the ultimate tensile strength is 120 MPa.

Chinese government actively takes its responsibility for carbon emission reduction and promises to reduce China’s carbon dioxide emissions per unit of GDP in 2020 by 40% to 45% compared with that in 2005.
It aims to reveal the long-term equilibrium and short-term fluctuation relationships between them, make clear the impact mechanism on carbon emission by urban construction land expansion in China, and offer reference for China’s efforts to promote sustainable urbanization and relieve the pressure on carbon emission reduction.
Study Method and Data Source Calculation of Carbon Emission Carbon emission mainly refers to that from energy consumption.
Data source The data needed for this study include the area of the land used for urban construction and energy consumption of China.
Fig.1: Changes in the area of the land used for urban construction and the carbon emission of China from 1997 to 2012 Unit Root Test To eliminate the heteroscedasticity of the data, we take natural logarithms for the area of the land used for urban construction and the amount of carbon emission and record them as log u and log c, respectively.