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Introduction The diffusivity of vacancies (V) and of self-interstitials (I) in silicon is known from electron irradiation data [1-3] to be remarkably high even at cryogenic temperatures.
The data on the size and density of grown-in voids [4, 5] allow to deduce the vacancy diffusivity at the void nucleation temperature Tn (typically around 1100oC); the deduced value, » 3x10-5 cm2/s, is close to the value of 2.7x10-5 cm2/s resulting from Eq.(1).
There are however some other data which indicate a very low diffusivity of V.
The vertical bar shows a range of apparent vacancy diffusivity deduced from RTA data.
The corresponding profiles (solid curves in Fig.2) provide a good fit to the data.

Wang[3] proposed energy-consumption benchmark state concept under the varying operation conditions and determine it with data mining method, such as fuzzy rough set (FRS)-based decision table reduction and fuzzy C mean(FCM)-based clustering.
A large mass of information which reflects the systems and its related equipment performance is hidden in the historical operating data of thermal power units.
Based on the actual data of the unit, is achieved and shown in Eq (9): (9) Determination of Energy-consumption Benchmark State Figure 2 shows a simplified system diagram.
Table 1 ASFC of All States state M P T Tre P1 … P8 Pc bpipe bH bI bL bc bboiler b 1 473.6 23777 561 547.7 6064 … 11.88 5.54 0.85 11.69 1.141 4.96 14.97 131 290.3 2 473 23719 558.3 543.3 6033 … 15.57 5.5 0.84 11.67 1.141 4.65 14.83 130.5 289.2 3 474.6 23745 558 541.9 6043 … 18.45 5.49 0.85 11.7 1.146 4.45 14.81 130.8 289.3 4 478.7 23557 560 541 6128 … 19.11 5.35 0.86 11.86 1.152 4.46 14.67 131.6 290.2 5 480.9 23758 560 543.6 6170 … 19.19 5.31 0.86 11.92 1.156 4.48 14.66 132.1 290.8 … … … … … … … … … … … … … … … … n 460.7 23581 561.9 547 5989 … 12.83 5.12 0.82 11.26 1.111 4.85 13.99 127.7 285.4 … … … … … … … … … … … … … … … … 48 466.2 23946 560.4 541.1 6124 … 18.85 4.77 0.83 11.72 1.078 4.89 13.44 128.1 285.6 49 483.3 23942 558.3 539.4 6333 … 20.14 5.03 0.86 12.22 1.112 4.88 14.17 131.5 290.5 50 485.2 24495 563.1 545.2 6345 … 19.98 5.11 0.86 12.34 1.119 4.91 14.4 132.5 291.9 THA 466 24200 566 566 5977 … 18.74 5.88 0.84 11.11 1.023 4.68 14.89 130.5 288.7 Fifty sets of actual data under
Adjusting the unit based on benchmark state, FSC would be reduced by 3.3 g/(kW·h), 3.5 g/(kW·h) and 4.3 g/(kW·h) and the percentage of reduction are 1.14%, 1.19% and 1.44%.

In order to clearly know formation pressure，taking Sabei area of Daqing oilfield for example，On the basis of researching the relation between Formation pressure and logging data,By studying the relation between test data of ninety-three small stratum pressure and well-logging data with logging unit of water-out interval logging series of nine RFT test well, with statistical regression we come into being a theory model making use of water-flooded zone log reading to calculate formation pressure quantificationally  .To improve the calculation accuracy of the formation pressure using logging data.
Found by study: When calculating the formation pore pressure, the effect will be very good to join the natural gamma logging data.
Based on 178 measured RFT test data analysis in 15 well, only 93 small layers in nine Wells measured pressure is real and effective.
Using logging data to predict formation pressure method is very feasible, so it will have a very broad application prospects.
Logging data to detect formation pore pressure of traditional method [J].

Hence we apply the cross product on the sets: Roles and Data items.
Table 1 lists access permissions on data items for each of the roles.
In this paper, we present results of using Formal Concept Analysis and dimension reduction methods on access control data.
Our aim is to allow processing of larger amount of data.
Identifying the dependencies existing in a domain allows efficient data analysis. 6.

The model used to adjust the data is presented in Fig. 1.
(a) Experimental data plot.
(b) Adjusted plots, with the electrical circuit in fig. 1 and the data from table 2.
(a) (b) Fig. 3 Results for all the samples without water in the electrolyte (a), Experimental data plots.
(b) Adjusted plots, with the electrical circuit in fig. 1 and the data from table 2.

It means that welding process may lead to a great reduction in fracture toughness and the fatigue property for titanium alloy.
The fatigue crack growth data is important for an assessment of reliability and safety of structures containing crack-like defects.
This means that the welding process may lead to a great reduction in the fatigue property of the Ti-alloy.
It means that welding process may lead to a great reduction in fracture toughness and the fatigue property for titanium alloy.

Then the data were analyzed using statistic methods, ANOVA, and Duncan’s Multiple Range Test (DMRT).
Seeing the data should have implication the highest calorific value and final heating temperature of AA among all samples.
The trend of methane reduction is the same as carbon dioxide reduction.
The lowest methane reduction implies the highest calorific value and final heating temperature although AZ3 showed the lowest carbon dioxide reduction.
It was caused by a decrease in carbon dioxide also accompanied by methane reduction.

Starting from the actual standardized methodology for design of the control gauges, this paper presents a new algorithm for the design, which is taking into consideration also the number of serial production parts which are verified, by increasing the efficiency of the gauges number used and the reduction of the control costs.
Furthermore, based on the experimental data presented, the mathematical methods of the tolerance execution field of the gauges and respectively the wear field, of the GO, NOGO gauges for interior and exterior diameter.
The new methodology permits the optimization of the parameters values z1, y1 by taking into consideration the production plan against minimization of the gauges number, especially the shaft gauge, what gives reduction to the product cost by cutting down the gauges fabrication costs. 2.

Applying the method of finite element to research the composite damping vibration reduction effect of circular saw blade.
Extract three circular saw blades to finite element simulation analysis as relevant data plotted as shown in Figure 1 and Figure 2.
Contrast Figure 6 and Figure 7, cost the same time the C saws outer axial vibration speed is less than B outer axial vibration speed, it also explain the effect of damping vibration reduction.
The research progress of circular saw blade noise and noise reduction technology [J].
Viscoelasticity damping materials on damping noise reduction applications [J].

These models form the basis of a number of methods and schemes for the organization of data collection and calculation for different types of impacts of industrial purpose objects.
During the technical re-examination of the building structures the experimental data on the displacements at the nodal points of the frame appears as results.
Herewith, the data on the displacements at the nodal points of the calculation scheme of industrial object are starting materials for the in-situ measurements.
For further processing the data are arranged in chronological order.
The choice of the trend equation is defined by the highest value of the coefficient of determination indicating a high correlation between the fitting curve and the actual data.