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These data show that crystallite size decreases gradually with increasing milling time, and a nanocrystalline structure appears in alloys milled for 25 h.
These data show that all of the investigated alloys can be activated easily, and are completely activated after one cycle.
Fig. 3 Variations in discharge capacity with cycle number for the sintered and milled alloys Table 3 Electrochemical performance data of the sintered and milled Mg2Ni alloys Sample Na a Cmax b (mAh/g) C60 c ( mAh/g) S60 d (%) Sintered 1 77.68 12.44 16.01 milled 10 h 1 117.33 16.80 14.32 milled 25 h 1 112.92 19.29 17.09 milled 40 h 1 149.80 20.88 13.95 milled 50 h 1 154.80 27.20 17.57 a The cycle numbers needed to activate the alloy electrode.
The reduction in particle size increases the surface area of the alloy and the refinement in crystallite size, leading to an increase in volume of grain boundaries.
The sintered alloy milled for 25 h leads to a reduction of the crystallite size to nanometer scale.

For an image compression, we test the JPEG and found the proposed method can endure 80% data loss.
Furthermore, data absence was not influence the deriving result strongly as shown in Fig. 8.
Fig.7 Extracting result: (a) JPEG compressed with 75% reduction, (b) JPEG compressed with 80% reduction (a) (b) Fig. 8 Extracting result: (a) watermark embedded image with data absent, (b) derived watermark image (a) (b) Attacking tolerance.

To enable EBSD data acquisition, sample surfaces were directly electropolished using a Struers LectropPol-5® device, with a H2O-HNO3-HCl electrolyte under an applied voltage of 35 V for 15 seconds.
This demonstrates that post-processing enables reliable EBSD data under fast acquisition conditions.
This strain level was chosen to obtain sufficient data in the plastic regime, where the material behaviour can be considered monotonic.
FCC phase reduction becomes more pronounced during stage B with the generalization of twinning mechanisms, following an almost linear trend with the logarithm of strain.
The contribution of these three types of misorientations decreases during the transition to stage B, which is consistent with the progressive reduction in FCC phase fraction as strain increases.

Official Istat data define a farming surface to be about 1.500 ha in 2010 whereas, according to the information provided by the sector representatives, 1.100 - 1.200 ha would be a reliable estimate of the bergamot farming area (INEA).
This meaningful reduction of farming areas has caused a consequent reduction of a 1/3 of the produced essence that currently equals 100.t.
With reference to prices, analyzing official data released by the Consorzio between 1951 and 1990 and comparing it to data compiled by companies and producers, it is interesting to note their gradual increase in terms of current prices while fixed prices in euro (2012) appear to report a continuing recession.

Multiple regression analysis of the data obtained from more comprehensive simulations, suggests estimating the heat flux through the double layer of slag as: qs=*A*exp1+Bt+C in watts for t≥5 seconds A=6383+43.1*h-39*T-181*K B= -346.41-0.109*h+0.156*T C=62676-120*h+614*K+(80*T) where qs is the heat loss through the slag layer by mechanisms of conduction, convection and radiation, is the metal/slag area in m2, T is the liquid steel temperature in K, h is the initial thickness of the slag layer in m; K is the heat transfer coefficient in m2K; t is the time in s.
There is a reduction in the driving force for heat diffusion and because of reduction of radiation losses at the solid slag/environmental interface. 
Data also highlight the effect of soaking temperature of the refractory lining.
Heat losses through slag layer however depends on metal-slag interfacial area() , temperature(T), slag thickness(h), heat transfer coefficient (K) and time (t) and can be quantified by the correlation that resulted through multiple regression analysis of the data obtained from various simulations through the following equation.

Table 3 showed the hardness data from all samples.
From the data, oil quenched sample has only 83.87% volume fraction of martensite.
In summary, hardness value reduction after tempering process were 225 HV, 252 HV, and 341 HV for air, liquid nitrogen, and oil quenched sample respectively.
From previous data, sub-zero treated sample has more reduction and increment in martensite and carbide percentage respectively, compared with the oil quenched sample.

TieNiu 3DOMS is adopted to acquire their surface data, the digital point cloud data of one single vision within 10s, automatically registering complete point cloud by rotary platform, so that the efficiency of process inspection and final quality control of the work pieces of complicated surfaces are improved.
Then automatically register the both data, shown in Fig.8e.
All the data are registered together, and the result is shown in Fig.8f.
Remove the noise from the result and we get the whole cloud data, as shown in Fig.8g.
Export CSV and Unicode data for use in other applications such as SPC, shown in Fig.16.

It is suitable to medium scale data storage.
Non RAM based Round-Robin Mapping Scheme The NAND Flash memory is divided into three zones according to data update frequency, i.e., Hot Data zone, Warm Data zone and Cold Data zone [3].
However, this method will cause capacity reduction.
The MCU chip select output (NSS), data input (MISO), data output (MOSI) and clock input (SCK) connected to the Flash chip select input (CS), data input (DI), data output (DO) and clock input (CLK) respectively [8].
The PROGRAM LOAD RANDOM DATA command can be issued to update bytes of data in the page.

Drying, heating and shrinkage lumped models were proposed and fitted to experimental data.
The fitted results presented good agreement with experimental data.
Table 2 – Parameters of the Eq. 1 obtained by fitting to experimental data.
Table 3 – Parameters of the Eq. 2 obtained by fitting to experimental data.
The applicability of the models was examined through the statistical parameters and found that all the theoretical data agreed well with experimental data.

Then the signal is imported to the processor for data preprocessing including image data rearrangement, synchronous signal extraction and other operations.
After that, the image data will be stored in the SRAM1 by the input buffer control module.
Then the Nios II CPU starts to read the data stored in SRAM1 to transform 14-bit image data into 8-bit image data, at the same time the CPU starts to implement real-time image enhancement in order to improve the quality of the image, then stores the results in SRAM2.
The signal preprocessing algorithm dealing with a mass of data requires a high-speed processing capability.
In this figure, CLK, Reset and INT signals are inputs, while Dvalid, the sign of valid data, and Dout, image data bank, are outputs.