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Compared with the conventional thermal reduction process, the hydrogen palsma reduction process would require merely 1/10 reaction time.
It means the reduction of both the required energy and the processin time.
Results and Discussion The XRD patterns of JCPDS (39-1256) data for Sr2SiO4 powder and Sr2SiO4:Eu thin films deposited on Si (100) substrates at substrate temperature 600 o C and the oxygen pressures of 50, 100, 150 and 200 mTorr are shown in Fig. 1.
All peaks of the XRD patterns of the as-deposited thin films are consistent with the JCPDS (39-1256) data of Sr2SiO4 as shown in Fig. 1.
That is, most of Eu 3+ ions was reduced to Eu 2+ ions by the hydrogen plasma reduction process.

Fig.1 Flow chart of ferrite rolling test Fig.2 Temperature distribution photo at 800°C Fig.3 shows the temperature distribution data along both rolling and transverse directions of the strip.
(a) Rolling direction (RD) (b) Transverse direction (TD) Fig.3 Temperature distribution data of strip before rolling at 800°C Results and discussions Rolling Parameters.
Fig.4 shows the reduction effect on the rolling force in the ferrite temperature range.
It can be seen that reduction has an obvious effect on the rolling force.
A higher reduction always achieved higher rolling force.

The unfrozen water content reduction of No.3 sample was the slowest, because it had the lowest water content and the least frost-heave and thawed amount.
(5)When the temperature of the sample met the test requirement, the NMR test was started and the test data were collected.
Fig.2 Changes of magnetization vector From NMR test data, the relationship of unfrozen water content and frozen temperature was obtained for 4 kinds of Mohe permafrost samples, as shown in Fig.3, Fig.4, Fig.5 and Fig.6.
In Fig.5 and Fig.6, under the condition of similar initial water content and density, the reduction rate of unfrozen water content of No.3 sample was lower than that of No.4 sample with the same amount of frozen temperature reduction.
Compared to the relationship in Fig.3, Fig.4 and Fig.6, the unfrozen water content reduction of No.3 sample was the slowest, because it had the lowest water content and the least frost-heave and thawed amount.

The variable parameters of the process were: rotational speed asymmetry factor, av ; strip shape factor, h0/D; and cross-sectional area reduction ε.
The paper presents the results of rolling 10 mm stock with a rolling reduction of ε=0.15.
Strip curvature for a constant strip shape factor of ho/D=0.0091, rolling reduction of ε=0.15 and changing asymmetry factors: a) av=1.01, b) av=1.03, c) av=1.05, d) av=1.08, e) av=1.10, f) av=1.15, g) av=1.20 As indicated by the data in Figure 1, a straight strip was only obtained for the asymmetry factor values of av=1.01 and av=1.08.
av ld [mm] Upper roll Lower roll Strip curvature ρ [1/m] Lde [mm] Lde/ld [mm] Lad [mm] Lde [mm] Lde/ld [mm] Lad [mm] 1.01 28.72 13.64 0.48 15.08 16.49 0.57 12.23 -0.04773 1.03 28.72 15.13 0.53 13.59 17.75 0.62 10.97 -0.47981 1.05 28.72 14.09 0.49 14.63 19.03 0.66 9.69 -0.53971 1.08 28.72 11.49 0.40 17.23 23.24 0.81 5.48 -0.11136 1.10 28.72 9.45 0.33 19.27 26.31 0.92 2.41 0.527612 1.15 28.72 3.30 0.12 25.42 28.72 1 0 1.392111 1.20 28.72 3.56 0.12 25.16 28.72 1 0 1.453841 Based on the data shown in Figs. 2 and 3 and provided in Table 2 it can be stated that introducing a roll peripheral speed asymmetry has an effect of differentiating the lengths of the advance and the delay zones in the rolled strip on the upper and lower roll sides.
Distribution of rolling moments on the lower and the upper rolls during rolling 10 mm stock with a rolling reduction of ε=0.15 for the varying magnitude of speed asymmetry factor av The data shown in Figs. 4 and 5 indicate that as the speed asymmetry factor av increases, both the roll separating force magnitude and the rolling moment magnitude decreases.

Simulation modeling can be used in such cases to efficiently create reliable KPI data.
This data need can be satisfied by simulation of the current and future system versions
- Determination of the input and output data: In this step, there is necessary to define the input and output data of the simulation model to be created.
In many cases the requested data are not available; consequently, it is only possible to create these on the basis of estimation and/or on-site measurement
- Creation of the simulation model: The simulation model has to be created on the basis of the input and output data and working principles

Data were analyzed by Kruskal-Wallis and Mann-Whitney U test at p<0.05.
Evaluation of biofilm formation was assessed through the XTT reduction assay at 492 nm [12, 14].
By using XTT reduction assay, the amount of biofilm formation are expressed as the optical density (Table 1).
But, within these AgNPs groups, the reduction was not dose dependent.
They also showed the reduction of flexural strength of PMMA with the increase of AgNPs in heat polymerized PMMA resin.

In this paper, we propose a novel MB-OFDM structure and use the frame to transmit data in vehicle-to-vehicle (V2V) communication scenario.
Such services require dependable wireless V2V communications providing robust connectivity at moderate data rates.
The N-point frequency-domain data is divided into G block of length .
Where N corresponds to the IFFT data block length of conventional OFDM.
Finally, choosing data from the last sub-block as guard interval of whole MB-OFDM symbol: (1) Fig. 1 Time-domain frame structure of novel MB-OFDM Fig. 2 Steps to produce a novel MB-OFDM symbol, where [•] denote the size of the data As mentioned above, the difference between MB-OFDM and conventional OFDM is the IFFT-pointer.

Simulation experiment data come from KDD Cup 1999 data set, which collects 7 million network connection records, covering a variety of intrusion data types and normal data.
Simulation experiment select data from training data and 10% subdata set of testing data, of which 2000 data strips forming the training set and test set of 3000 data strips, attack types included in training set are less than that of the test set.
Discrete data performs box dividing process according to data characteristics, which divide data into different boxes, taking the median value of the same box data as their value and data in different boxes has no intersection.
Attribute reduction - dimension reduction treatment Data of intrusion detection data set comes from network packet information captured, some feature data of which has great contribution in determining if there is an intrusion behavior, and some has no contribution to determine intrusion behavior.
Attribute reduction is conducted in KDD CUP 99 data set.

Locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimension reduction.
By LLE algorithm, original sample data is directly mapped to its' intrinsical dimension space,which data still keep primary nonlinear form. then via kernel fisher discriminant analysis(KFDA), the characteristics data in intrinsical dimension space are mapped into knernel high-dimensional linear space,and then different fault data are discriminated based on a criterion of between-class and insid-class deviatione ratio maximum.
Since 2000 two papers[1～2] about manifold learning were published in Science, THis has become a hot research topic by using manifold learning methods to carry out dimension reduction and data analysis,find instrinstic geometric structure of nonlinear high-dimensional data set.
Differential monifold believes that high-dimensional geometric distribution of observational data is determined by it's intrinsic nature, pattern recognition problems need reference to data set's specific geometric constraint.
LLE-KFDA algorithm bases on original observational data,and performs local linear embeding and map sample data to intrinsic dimension subspace,thus avoids from problems caused by improper feature parameters choice.Because of embedded subspace mostly hold the original data nonlinear feature, the fault identification through LDA will have a lot of limitations.

For percentage thickness reduction of 59.6%, minimum roughness 2.09μm, and maximum hardness 41.7 BHN, the confirmatory test showed values of 64.78 % thickness reduction, roughness of 2.14μm and hardness of 44.82 BHN that were in agreement with the predicted value.
Experiment parameters % Thickness reduction Roughness, Ra Hardness, BHN A B C D E 1 0.46 6.0 0.50 2000 2300 47.10 3.59 42.77 2 0.46 6.0 0.50 2000 2500 53.62 3.68 44.35 3 0.46 6.0 0.50 2000 2700 54.35 3.79 43.15 4 0.46 8.0 0.75 2500 2300 55.80 3.20 41.97 5 0.46 8.0 0.75 2500 2500 50.72 3.22 41.84 6 0.46 8.0 0.75 2500 2700 56.52 2.93 40.10 7 0.46 10.0 1.00 3000 2300 55.07 2.74 30.36 All values for the experiment varied between following ranges: 8-27 0.56-0.78 6-10 0.5-1 2000-3000 2000-2700 47.1-64.29 2.62- 7.65 32.84- 43.54 *Only first seven sample data shown in above table.
Besides, it provides an efficient solution to the uncertainty, multi-input and discrete data problem.
No Experiment parameters GRC GRG A B C D E % thickness reduction Roughness Hardness 1 0.46 6.0 0.50 2000 2300 0.4488 0.7001 0.8712 0.6734 2 0.46 6.0 0.50 2000 2500 0.5675 0.6881 1.00 0.7519 3 0.46 6.0 0.50 2000 2700 0.5847 0.6727 0.3463 0.5346 4 0.46 8.0 0.75 2500 2300 0.6224 0.7629 0.8179 0.7344 5 0.46 8.0 0.75 2500 2500 0.5078 0.7594 0.8100 0.6924 6 0.46 8.0 0.75 2500 2700 0.6431 0.8109 0.7156 0.7232 7 0.46 10.0 1.00 3000 2300 0.6029 0.3644 0.4333 0.4669 *Only first seven sample data shown in above table.
· Better ' % thickness reduction' is observed with increase in size of forming tool