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Surface characteristics of the samples were analyzed using Atomic Force Microscopy(AFM).Data collected were in terms of Surface Roughness (RA), Peak Vs Valley (P-V) and Surface Root-Mean-Square Roughness (RMS).
Result and Discussion Analysis of the surface characteristics are done using data collected from AFM which are in terms of surface roughness (Ra), maximum peak-to-valley height (P-V), and root-mean-square height (RMS).Surface roughness(Ra) is defined as the arithmetic mean of the surface roughness profile from the center line within the measuring length.
Durmaz, Variation reduction by the use of designed experiments, Quality Engineering 5 (4) (1993) 611–618

Combined with China’s general idea of comprehensive transport development, the key aspects of construction of comprehensive transport system is analyzed, the evaluation indexes of comprehensive transport system construction are constructed by the using of Data Envelopment Analysis model, providing a reference for subsequent work to construct a comprehensive transport system.
(7) proportion of input in green traffic It refers to the proportion of relevant input in aspects of traffic environmental protection, resource conservation, energy reduction in total input in traffic.
Evaluation Model based on Data Envelopment Analysis According to Slaving Principle of Synergetics from Haken, the system’s t internal variables in the phase transition point can be divided into fast relaxation variable and slow relaxation variable, slow relaxation variable is the key to determine the phase transition process of the system, it is also known as an ordinal variable of system, the number of such kind of variable is small, its attenuation changes are slow.

The optical absorption coefficient (α) of the films was calculated from the optical transmittance (T) data using the relation, α = - (1/t) ln T (2) where t is the film thickness.
The refractive index (n) of the films was determined from the optical transmittance interference data employing Swanepoel’s envelope method [25] using the relation, n(λ) = [N + (N2 – n02n12)1/2]1/2 (4) and N = 2n0n1[(Tmax - Tmin)/TmaxTmin] + (n02+n12) / 2 (5) where no and n1 are the refractive indices of air and substrate, and Tmax and Tmin the successive optical transmittance maxima and minima respectively.
A small peak observed at around 0.13 V may be attributed to Mo6+ reduction.
The presence of molybdenum leads to the existence of an additional peak related to the oxy-reduction of the molybdenum ions.
The presence of two reduction peaks was attributed to the existence of two predominant forms of tungsten and molybdenum oxide.

The Frequency Response Function (FRF) plot of the accelerometer A3 is reported in Figure 13, while experimental modal data, extracted with the circle fitting technique, are presented in Table 1.
Details on the test set-up and the full set of experimental data are included and commented upon in [6,7].
The 3D model, taking into account 5 numerical frequencies, permitted a more complete exploitation of experimental data, even if an increased number of unknowns was present.
Rotational data acquisition resulted more effective for static than dynamic tests.
Requiring the solution of an inverse problem, static and dynamic model updating results appeared to be very sensible to available quality of experimental data.

Two different sets of EBSD data were acquired from the partially heated sample before roll forming.
The second set consisted of data from five different areas in the normal direction in the centre of the heat affected zone.
The black dots schematically show where the EBSD data were acquired.
Prior to the characterization of the microstructure a noise reduction of the EBSD data was performed including replacement of incorrectly indexed isolated points followed by removal of some of the zero solutions.
Data are taken from [1].

Graphs and tables were drawn based on the data.
All tables and graphs, which are shown below, have been selected and are representative to indicate and support the analysis not all of data is listed, but results drawn below from analysis of the data below.
The R-Sq values of both Ra and MRR are greater than 90%, which mean the model fits the data in the experiment well.
The normal probability plots of Ra and MRR are shown in Fig. 2 and Fig. 4 respectively it can be seen that the data shows a normal distribution.
So the data collected in this paper are reasonable, and worth to analyze.

Information about displacements and strains in the form of digital data is available for each registration moment.
Figure 4a shows a loading plot with a marked moment, for which data are presented.
Data presentation for the 160th step of the registration: a) loading plot with a marked moment, for which data are presented, b) strain distributions along the line, c) visual representation of strain maps Strain distributions in the welded joint for the selected points of loading are shown in Figure 5a.
The presented strain data were read in the WM from the face side (line 0) and in the centre of the weld cross-section (line 1).
For the BM, strain data that were registered in the place of the occurrence of the highest strain level (along line 0) are presented.

In this paper, the background of cloud computing technology to study how to optimize cloud computing data center energy efficiency mechanisms.
Implementation of the energy consumption is defined as: the task to run on a computer, computer hardware instruction and data-driven energy generated during operation.
Cloud data center to the virtual machine as a unit for the user to allocate resources application, the user by running a virtual machine execution services.
Deployment and migration of virtual machines is an important issue cloud data center energy optimization of virtual machine management, you can clearly see the migration process as shown in Figure 1 virtual machine.
Quiroz, et al., Energy-efficient application-aware online provisioning for virtualized clouds and data centers, in Proceedings of the International Conference on Green Computing. 2010. p.31-45.

However, traditional diagnosis of bottleneck is carried out relying on designer's experiences, theoretical calculation or data analysis after system run.
With the visual results and simulation data based on the evaluation factors, the bottleneck process can be diagnosed.
These databases can integrate other system database by data interface in order to carry out intelligent design of production line.
(3) SDBPL can provide many simulation data.
Using those data, redesigning and reconfiguration of production line in SDBPL are quicker than those of traditional methods.

Sectional loads by three load caculation methods are examined by the analysis results of BO105 and the flight test data of the SA349/2 helicopter.
Lim [3] investigated structural loads data of UH-60A using both 2GCHAS and CAMRAD/JA.
Results and Discussion Two sets of loads data are used to examine the load calculation methods.
The other is the flight test data of SA349/2 Gazelle helicopter [10], which is outfitted with an advanced geometry rotor.
To compare with flight test data, it is necessary for the moments to be rotated to the deformed coordinate system.