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According the experiment data, the parameters of thermal deformation was calculated, in which the activation energy is 261.2KJ/mol.
The experiment temperature was 900℃,950℃,1000℃,1050℃ and 1100℃, and the strain-rate was 0.01,0.1,1.0 and 10(1/s), and the reduction was 50%.
(3) Analysis of results According to the experiment data, the curves of and and can be obtained, seeing Fig.2.
According to equation (1)—(3), the coefficients (n,α,Q,A) can be determined by experiment data.
According to the data of stress and strain for AMT100 steel, the strain-rate sensitivity coefficient and temperature sensitivity coefficient have been put forward, m=0.0998, s=7912.

XRD data was collected between 10–70° 2θ value by powder diffraction method in an XRD using Cu Kα target (1.54 Å) at 0.03°/s scan rate.
Further, as the electrical conductivity of Zn(OH)2 is very low, it retards the cathodic reduction of oxygen (reaction (2)).
Therefore, the anodic reaction that took place by the coating dissolution can be balanced by oxygen reduction as the cathodic reaction.
The corresponding equivalent circuit model which showed best fit for the impedance data obtained was Rs((QEDLRCoat)(QFeRFe)) circuit (Fig. 9) [14, 22, 47].
[17] JCPDS Powder Diffraction Files, PCPDFWIN Software, Joint Committee on Powder Diffraction Standards – International Centre for Diffraction Data, Swarthmore, USA, 1996

It’s feasible to carry out research on InSAR atmospheric correction methods based on MODIS water vapor data.
This paper attempts to correct atmospheric effects in Taiyuan ASAR interferogram using MODIS water vapor data, thereby to evaluate the accuracy and reliability of using MODIS water vapor data to correct InSAR atmospheric effects.
In order to fill the data vulnerability caused by the presence of cloud, taking into account the large differences between the MODIS water vapor data and ASAR image in resolution, MODIS-PWV need to be interpolated.
Meanwhile, in order to better validate the effect, we made a comparion between the leveling data and InSAR monitoring results before and after atmospheric correction, the result showed that InSAR monitoring results after atmospheric correction were close to the leveling data, and the RMS difference between leveling data and InSAR decreased form 1.40 cm to 1.08 cm, the degree of improvement reached 22.1%, confirming the correction of atmospheric water vapor based on MODIS data validity and reliability.
In this paper, MODIS water vapor data was applied to reduce the atmospheric effect on ASAR interferogram.

INTRODUCTION Data Compression refers to a process, in which the given size of data is compressed to a smaller size.
The compressed data when reconstructed, looks similar (or exact) to the original data.
If the re-constructed data is an exact replica of the original one then the algorithm used in compression and decompression is lossless.
On the other hand, if the reconstructed data is perceptually good quality (but not exact replica of the original data) then the compression algorithm is lossy.
On the other hand lossless compression methods generally do not give a very good compression ratio (typically in the order of 3-4 times of the original data size), but the reconstructed image is exact replica of the original image In some application areas exact replica of the original data is preferred over high compression ratio.

The production data showed LVC work roll could improve the strip crown control precision evidently. [11] The high-precision shape control technology of hot-rolled high-strength yoke steel proposed in this paper includes the optimization of rough rolling back-up roll contour, the load distribution optimization of rough rolling multi-pass, the optimization of the parameters of the finishing rolling shape model, and the symmetrical variable taper (SVT) work roll contour and smart shifting technology.
For the load distribution optimization setting, we can get the input quantity: the incoming data, roll data, various model coefficients, rolling mill equipment data and limit values of various process parameters.
The unknown quantity is mainly the thickness setting parameters: outlet thickness of each pass, rolling force, forward slip, torque, power, roll gap, reduction amount, etc.
The reduction was 293.8 μm, and the reduction ratio was 47.44 %.
It can calculate the appropriate work roll bending force and the shifting value according to the current rolling mill state data.

Lean manufacturing aims to increase efficiency on operations through the reduction of costs, times and non-value added activities [16].
And recurring interactions can bring more data that brands can use to create even more interesting experiences that will generate even more loyalty [23].
This data relates to the season of AW21/22.
To understand the viability of these undyed and unprinted fabrics for a business-like co-creation data with information from suppliers that provide dying or printing services was also gathered.
Popp, Swamped in information but starved of data’: Information and intermediaries in clothing supply chains, Supply Chain Manag.

Up to today, based on statistical data of 60 years, the annual average damages caused by typhoon each year were 3.5 times than those caused by others and those caused by floods were even dozens.
This will be the basis for planning the advanced disaster prevention and disaster reduction of each city.
Therefore the study is give experimental data that whether the sandbags are piled up high enough to be effective and to sustain the water pressure caused by the flood.
The experiment data will provide the disaster managers with their work during the mitigation phase of the disaster management.

(1) Thereby, x is the current time value, y is the correspondent value of the derivative major strain rate, and x and y are the average values in the data set.
Based on the consideration that the standard deviation measures the dispersion of data around an average value, a slope equal to two times and four times the standard deviation respectively is considered.
Even if the gap between the results depicted by the two procedures still be rather significant, the data dispersion by adding a slope of ±2σ is comparable with the dispersion data of the line best fit method, which presented a dispersion error of about 0.08 in case of tension-compression.
In both case the data dispersion is significantly improved in comparison with the unmodified coefficient of correlation method (figure 8a).
Key idea is to add a linear function to the major strain values dependent on the standard deviation of the data of the so called “correlation coefficient method”.

Table 2 shows the results referring to Figure 1 and Table 3 shows the data from Figure 2.
Through the data obtained, we can see that adding pseudoboehmite promotes the reduction of the final deformation (50% for pure nylon [4], 42.8% for the composite; Table 2) and reduction of the flow tension (52MPa for pure nylon 6.12; Table 1, 30.88MPa for the nanocomposite; Table 2).
Figure 2: 3 points bending test Figure 2 shows the 3 points bending strength test of the pseudoboehmite-nylon 6.12 nanocomposite and Tables 3, the data of this analysis.
Figure 3: Pseudoboehmite scanning electron micrograph The x-ray diffraction data shows that the sample has the pseudoboehmite structure (Figure 4). 2θ Figure 4: X-ray diffraction data of the pseudoboehmite sample used for the production of the nanocomposite Tables 5 shows the results of HDT and Vicat analysis for the composites and pure nylon 6.12.
Conclusions Pseudoboehmite synthesized along the sol-gel pathway results in an increase of the elasticity module, reduction of final deformation and increase of flow tension.

Data collection platform software comprises of two major modules (data collection and data report).
Data collection module provides data receiving, analysis, storage and other functions.
Therefore, data reporting module is necessary to include information extraction, data compression packaging, data monitoring, data reporting, data transmission, data receiving, data unpacking, data integrity monitoring, data processing storage, data submission and management functions, etc.
Kong (2011) Internet of Things in Disaster Prevention and Reduction [J].
Natural Disaster Reduction in China 11:32-37