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It was shown that a comparison of the data of change patterns in crystal-chemical parameters phenomenologically predicted and obtained from experimental data, allows to predict some of the conditions of stabilization in the examined phase, the solubility limits of alloying elements and, consequently, its homogeneity region, as well as formation conditions of multiphase systems.
For example, studies of Ti-Ni-Pd, Ti-Ni-Pt, Ti-Ni-Au alloys are thoroughly described in [13-21], the data on Ti-Ni-Fe and Ti-Ni-Co systems - in [22-27] and the data on Ti-Ni-Zr, Ti-Ni-Hf, Ti-Ni-Ta systems - in [28-33].
The availability of experimental data allows a detailed comparison of the predicted and experimental results.
Therefore, according to experimental data, the B2 phase existence region in the ternary Ti-Ni-Zr system may be represented as shown in Fig. 6.
Barret, Structure of Metals, Crystallographic Methods, Principles and Data, New York, 1952

By comparing the simulation data, the success rate of finding the optimal solutions is improved, and the larger network scale, its advantage is more obvious.
One of the reasons, resulting in premature convergence, is premature reduction in the diversity of population, leading to genetic algorithms search space extremely limited, which mainly manifests as the fitness of the best individual in population cannot increase, after several iterations the population still cannot find the optimal individual.
Figure 2 Request success rate comparison chart Analysis of experimental results: the experimental data in figure 2 shows that as the network scale is more and more big, the IARGA performance advantage is more and more obvious.

Economic data for all road-construction climatic zones in Russia is included.
Properties of Type B asphalt concrete, produced using “wet” process, with crumb rubber added as a part of rubber-asphalt binder, MPa The presented data shows that the addition of crumb rubber using “dry” process leads to unpredictability of asphalt concrete operational properties due to the differences between chemical composition and original structuring degree of crumb rubber from different batches.
Core samples test results Asphalt concrete water saturation increase and density reduction, most likely, can be explained by the increased stiffness of the rubber following thermal vulcanization typical for “dry” process.

The results show that as a multi-criteria decision-making method, AHP can be used effectively in the project risk management even with less historical data.
The second criteria level should consider four areas, reduce the probability and the loss of the risk factor, increase the controllability, and control the cost which is must be considered when we evaluate the risk reduction measures.
Even with less historical data, AHP can also reflect the evaluation results of the engineering practice.

The method of deal with chromium containing wastewater include: chemical reduction- precipitation method[1], electrolytic reduction method[2], filter method[3] bioaccumulation[4], [5] as well as adsorption.
Analysis of experiment data Study factors of this experiment is removal rate ( table 4) and absorption, respectively, then analysis of the data.

For medium and small stands, the effective way to reduce the roll bouncing is to reduce the length of the rolls, i.e. to reduce the ratio of roll length L and roll diameter D (L/D) [2].The reduction of roll life dues to shortage of roll length which can be mitigated by improvement roll surface hardness and application of comprehensive roll (High strength steel core with high hardness coat) etc.
Table 1 Dividing shear technical data Technical data Low temp. cutting capability High temp. cutting capability Max. stocks size [mm] Φ40 Φ65 Lowest temp. of stocks surface [℃] 300 800 Speed range of stocks [m/s] 4~18 1.8~18 Max. tensile strength of stocks [MPa] 540 150 Movement Trace of Blade under Crank Mode.
At the same time, many requirements about roll neck and stiffness are raised because of big reduction process is used.

This approach results a reduction in computational complexity.
This causes a considerable reduction in the amount of data in an image while preserving its important overall structural features.
In addition, that data in each of three channels of color La*b* color model were considered as texture for which the mean, standard deviation and skewness were calculated and considered in the feature vector.

Thus, one can obtain [14,15] = (g|| + 2g^)/3 , g|| = g0 + 8kzd /D1 + kzd2/D22 + 4kzd2/(D1D2) - g0 zd2[1/D12-1/(2D22)] + k zd3 [4/(D1D22)-1/D23] -2k zd3[2/(D12D2)-1/( D1D22)] , g^ = g0 + 2kzd /D2 - 4 k zd2 / (D1D2) + k zd2 [2/(D1D2) - 1/D22] + 2 g0 zd2 / D12 + kzd3(2/D1-1/D2)(1/D2 + 2/D1)/(2D2) , (1) where g0 (» 2.0023) is the pure spin value. k is the orbital reduction factor. zd is the spin-orbit coupling coefficient of the 3d9 ion in crystals.
Using the data f(O2-) » 1.0 and f(F-) » 0.9, one can approximately obtain Dq » 960 cm-1 for the Ni+ center in MgO here.
The above orbital reduction factor may also be adopted for MgO:Ni+ in view of the similarity in covalency for the ligands O2- and F- [22].
Substituting these data into Eq. (1) and fitting the theoretical to the experimental value, we have DZ » 0.05 Å . (4) The calculated g factors are listed in Table 1.

But there are still some remained problems which involved detailed quantitative description of the data of the structure of under expanded jet.
PIV system is a commercial products from LaVision Corp., following are the parameters: sheet light thickness: 0.5mm-2.5 mm (adjustable), laser divergence angle: 20°, laser Max. input pulse energy: 400 mJ@532 nm(5ns pulse, 10Hz), pulse time interval 200ns～0.1s; the CCD camera parameters: pixels: 2048(H)×2048(V), max. frame rate: 16fps, integration time: 0.06ms～121ms, data transfer rates: 400M/s.
The Mach Disk is a such strong shock that creates a strongly discontinuous flow region and produces a huge reduction in velocity which turn to below the sonic.
Comparing with the Mach Disk, the flow pass through the oblique shocks will not have a smaller reduction and their velocity is still supersonic.

Table. 1 R&D performance management model R&D Performance dimension R&D performance Indicator Reference R&D Output Quality R&D output ratio about target quality achievement [1, 5] R&D output level about quality improvement Cost Success in keeping within budget [1, 2, 5] R&D project failure ratio Labor costs reduction through R&D Materials and energy costs reduction through R&D Delivery Success in keeping within R&D project schedule [2] Cycle time through R&D from collection to prototype release Cycle time through R&D from prototype release to mass production Production lead time reduce level through R&D Use & Diffusion Use ratio of organization of developed new technique [2] Number of developed new technique’s use and share external partner Number of patents Patents maintenance ratio Internal customer Satisfaction R&D internal customer satisfaction Added by specialist Internal work efficiency improvement through R&D R&D Outcome Customer value Number of securable new customer [1,
This study data analysis was written based on CLT(Central Limit Theorem).
The weight values need to be derived from the resurveyed data periodically.