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The blocks were then homogeneized at 1050 °C for 36 h in order to insure chemical homogeneity and hot-rolled at 1050 °C using 5% reduction steps until a total reduction of 65%.
The EBSD data were acquired with an Oxford® HKL Channel 5 software package.
The data analyses were carried out by means of the Oxford® AZTEC software package.

Nanomodified reinforced concrete out-performs an ordinary reinforced concrete in many features, such as: 1) Reduction of own weight of the structure due to low density of nanomodified cocncrete. 2) Modification of the reinforcement system and reduction of amount of reinforcement used in concrete. 3) Reduction of footing loads of the structure, so foundation design is simplified and quantity of earthworks is reduced. 4) Exclusion of general and special damp course from the structure. 5) Price reduction and acceleration of en bloc reinforced concrete building construction. 6) Increase of safety and reliability of antiseismic construction. 7) Reduction of number of aseismic isolation elements in building construction in areas with high earthquake activity. 8) Increase of fire-resistance of buildings and structures [1].
“Adding of rice husks and metakaolin coupled with superplasticizers, which are effective for this concrete, provides increase of compressive strength up to 70%, increase of concrete modulus of elasticity up to 15%, increase of value of time-yield measure of concrete from 0,44 to 1,19 measure of concrete equal in strength without modificators, reduction of the contraction shrinkage up to 30%, which influences positively on early crack resistance of reinforced concrete; it practically does not influence on drying shrinkage”.
The use of microspheres allows to get composition with density up to 800 kg/m3, and wherein strength characteristics could achieve datas 40-45 MPa.
Economy is also shown in reduction of an amount of used cement and plasticizers. 4.

In addition, rigid-plastic finite element simulation was performed for the high-temperature compression test using the flow strain data by the thermal deformation characteristics test.
An increase in the temperature increased the similarity between experimental data and finite element analysis results.
Table 2 summarizes the data of Eq(1), which is extremely similar to the test results in each temperature zone based on the true strain rate.
Additionally, rigid-plastic finite element analysis was performed with the material constant data calculated from the high-temperature compression test results.
The following conclusions were obtained: A rigid-plastic finite element analysis was conducted with the material constant data calculated from the experiment.

Section 2 presents an interaction model framework and the methodology of structure equation models and. section 3 explores data process.
Data and Descriptive Analysis Date Collection and Processing.
After getting rid of the incorrect data according to effective and logical test criteria, data of 1235 nuclear families heads with complete household and personal attributes are available from database.
Second, since Suzhou is a relatively highly-developed city in china, the research will go deeper if other scale and types of cities can be introduced to the studies with more data available.
Choi..A structural equation model of activity participation and travel behavior using longitudinal data.

This new method is more accurate than the traditional method and much closer to the actual production data.
Thus, comparing to actual production data, the deviation is inevitable in burden calculation with coefficient of major elements recovery.
The burden calculation table can be obtained by sorting out the data above, and is shown in Table 11.
In the two methods for burden calculation, quantity of ore and coke is not far off the actual production data in factory A with maximum deviation of 5.5%.
The specific data is shown in Table 13.

There is a 50% reduction in raw material usage and a significant reduction in machining time when compared to the conventional method.
And if the lack of fit data is not significant, then this proves that the model is a good fit.
Dev. =0.0055 R2 =0.8766 Radj2 =0.7533 C.V. % =5.13 The multiple correlation coefficient Radj2 of the regression model reaches 75.33% according to the analyzed data in Table 5, which demonstrates the high precision of the response model.
The data can be interpreted intuitively to show that the model is highly significant with P < 0.0001, the lack of fit with P> 0.05 is not significant, and the two items with lower significance for the objective function are removed.
The F-value, Radj2 and lack of fit values of the modified response model are somewhat improved, and R2 reaches 87.1% while comparing with the data in Table 5.

Wireless terminal send monitoring data to AP inserting point in wireless way, and the latter upload data to monitoring center, and thus insert WLAN into wired Ethernet.
The designed terminal can read device data of sensors in monitored area and valve condition and control, so as to switch data between WLAN and wired networks.
Generally, drivers do not preserve buffers for transmission data, instead, it transmit data using transmission functions of hardware directly.
Data are received by interrupt requests of hardware.
The optimized plans should be draw up and carried out In conjunction with data.

One-way ANOVA was used to examine significance of data variations with SPSS.
Lee Di Milia in their study also showed that shift work models and prolonged daytime driving under monotonous conditions usually led to a continuous reduction in drivers’ vigilance [4].
Results The SSS score, RTs and PECLOS data recorded during the driving were analyzed with one-way ANOVA using the “SPSS” program, and Person’s correlation coefficient was employed to explore the correlations among the three indicators.

The field data obtained from the Muppandal site in India such as Mean time between failure (MTBF), Mean time to repair (MTTR), failure rate and repair rate are used to compute the WT availability, using ITEM Toolkit version 8.0.2 as a measure of performance.
Mostly, the tips get open and it causes reduction in the power generation.
Table 2 Failure data of WTS LOC NUMBER YEAR TOTAL TIME MTTR GDT MTBF YAW ROTOR BRAKE GEAR GENERATOR TOTAL PK1/ 225 kW 1995-1997 26304 19 121 7 254 9 410 1306 24588 1998-2000 26304 9 60 36 24 123 252 672 25380 2001-2003 26280 34 258 56 432 12 792 556 24932 2004-2007 26304 12 14 23 4 21 74 1531 24699 2007-2010 26304 26 454 15 19 18 532 1610 24162 D24/ 250 kW 1995-1997 26304 12 1466 7 5 13 1503 1306 23495 1998-2000 26304 18 327 11 821 826 2003 672 23629 2001-2003 26280 12 78 10 292 248 640 556 25084 2004-2007 26304 16 48 13 49 4 130 1531 24643 2007-2010 26304 12 256 8 258 21 555 1610 24139 SPA 2/ 400 kW 1995-1997 26304 14 12 7 5 13 51 1306 24947 1998-2000 26304 107 1338 14 821 842 3122 672 22510 2001-2003 26280 15 288 42 12 11 368 556 25356 2004-2007 26304 31 13 25 6 4 79 1531 24694 2007-2010 26304 10 139 8 7 14 178 1610 24516 The total MTTR of 250 kW WT varies from 130 to 2003, for 225 kW WT the total MTTR varies from 9 to 123 hours and 400 kW WT has 4 to 842 hours of MTTR.

For the thermal conductivity good agreement is found while for the CTE a transition of the experimental data from Schapery's upper to Schapery's lower bound is observed as volume fraction increases.
The data on CTE of the composites can be compared to the classical bounds by Schapery [16].
This reduction may be limited by intelligent interface design.