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Authors also present different data on carrier lifetime recovery upon defect annealing [2,3,5-7].
Thus, analysis of the above data shows that there are large scatter in the characteristics of defect which arises in Si and solar cells under light action.
In this paper the new data on the defects, appearing under illumination with the light with a spectral composition close to solar radiation, in silicon with a high content of boron and oxygen are presented.
The data obtained are shown in Fig. 3.
The data obtained also suggest that by the action of light or heat treatment a recombination-enhanced diffusion of oxygen or oxygen-containing defects takes place according to the mechanism proposed in [32].

Then the whole Rankine cycle 4 basic processes are realized. 3.3 Data reduction and analysis The whole ORC systems was taken for reaching the steady condition when all the sensors do not change.
The test data are recordered by data acquisition systems, in which the sample rate is 0.2HZ.
To more clear analyze the thermodynamic process, the data reduction are expressed by the list equation: 1）The total quantity of engine waste heat ,Q Q=Qw+Qe (1) 2）The working fluid absorb heat quantity, Q: (2) 3）The work expander do ,W： (3) 4） Isentropic efficiency of expander ： (4) 5) Heat rejection for the condenser, Qc： (5) 6) Generating efficiency of ORC (6) 7) Net generate efficiency of ORC systems (7) In the above equation, the Qw,Qe are the heat quantity of coolant and
From the ORC systems beginning to generate elecrity to the steady condition, the test data recording time is about 2 hours and 24minutes.

Q=vh (1) The calculated data are puts in table 1.
Using a formula (2): (2) Where, =268, =270, It is possible to receive values of number of Froude for each L/h values, calculation data are puts in table 2.
Calculation data L/h 186 233 280 350 F 0.031 0.029 0.027 0.024 Figure 4.
Processing of experimental results When carrying out natural experiment for data acquisition buildings 28 meters high, with various gaps of an air layer were considered (0.08 m, 0.10 m, 0.12 m, 0.15 m).
For descriptive reasons processings of experimental data, having used a formula (3) it is possible to receive theoretical values of parameters of an air flow in the ventilated air gap, for the buildings having height other than the experimental.

In recent years, financial evaluation was blamed for one-dimensional approach in terms of recording data in previous periods.
Continuing the activity on the same parameters will lead to a significant decrease in profit, as seen from the accounting data presented above.
Expenses reduction.
Target 100% Actual 60% Fig.3 Balanced Scorecard of the Omega company Given all the above data, the Balanced Scorecard model for the Omega company is shown into Figure 3.
Thus Balanced Scorecard is a performance measurement system that balances the historical accuracy and integrity of financial data with future performance factors, while enabling the organization to successfully implement the strategy.

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial.
Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.
Methods Information extraction and classification.Based on the TM remote sensing images about Huadian city district in 1991 and 2011 and dem resolution of 70 m as the main data sources, using TM image computing NDVI value,Eq. 1[2]
The terrain is indirect effects on vegetation distribution,the influence of vegetation light, heat, water and other conditions play a role.The terrain is indirect effects on vegetation distribution ,it is mainly affected by vegetation, light, heat, water conditions and terrain factors .Terrain information is extracted from DEM data,the common terrain factor mainly has the altitude, slope, relief, gully density[3].
NDVI change intensity.In the study of NDVI variation in strength, NDVI change rate of the NDVI area is defined as the area difference and unit area ratio,Eq. 2 (2) In the equation, and are two NDVI at all levels of the area, as evaluation unit area.[4,5] Analysis of multi-source spatial data.For study on the relationship between the work area of vegetation cover,the two classification data of NDVI are analyzed with the application of GIS spatial analysis function[6,7].In order to to effects of topographic factors on NDVI change,the NDVI changes in classification data and elevation, slope, slope direction data were discussed.

Properties “Kfoam” “Pocofoam” Pore diameter (mm) 0.5 0.31 Porosity (%) 78 75 Bulk thermal conductivity (W/m×K) 55 135 Density (g/cm3) 0.34 0.58 Data Reduction and Experimental Uncertainties.
All temperature and pressure signals were acquired by a PC-based data acquisition unit (Yokogawa MW100).
To minimise data reduction uncertainty, the time-averaging method was employed to reduce the data derived from the experiments.
The present experimental data show that a larger pore diameter may help bubble generation and detachment at the pore level.

The main topic is to build and test a software tool with Visual Basic Excel to predict the PEMFC performances starting from operating conditions and with different technical data.
Also called mass transport losses, they relates to the reduction of the fuel’s concentration in the gas channels.
However, we have considered studying the performances of a PEM fuel cell with this tool [8]; the data input are shown in Table 2.
The data input of the PEM fuel cell.
The FCvb tool, by referring to the technical data in Table 2 taken from [10, 11], gives the graphical results presented in Fig. 5, and Fig. 6.

The data display an Arrhenius behavior with a relatively high degree of linear correlation (correlation coefficient ~ 0.965) and an apparent formation energy of ~8.6±1.2 eV is deduced from the slope of the Arrhenius plot, which is indeed higher than that deduced for VC formation under thermodynamic equilibrium [4,5].
No evidence is reported for the influence of the D-center on the minority charge carrier lifetime, but such data are strongly needed in order to assess the significance and challenge of thermal formation of D-center during high temperature processing.
The data are compared to SIMS depth profiles of B in the same samples (SIMS detection limit ≈ 1014 cm-3). .
An apparent formation enthalpy of ~8.6 eV is extracted from the slope of the data.
Summary The formation of D-center after high temperature processing in the range 1700 to 1950 °C was experimentally demonstrated, and the data obey an Arrhenius behavior yielding an effective formation enthalpy of ~8.6±1.2 eV.

For a high level scientific exploitation of experimental data obtained from the XRD analysis, a procedure which determines the network parameters of the phases, the crystallite size and the microstrain at network level has been used.
PEAKFIT defines a diffraction line hidden as if it is not responsible for a maximum in data flow.This does not mean that a hidden diffraction line is not visible in the collector design.
As a result of the fitting process, PEAKFIT report the amplitude (intensity), the area, the center and the width of data for each peak.
As for the diffraction patterns discussed above, the diffraction angle chosen for data processing with PEAKFIT program was in the range of 30º- 80º, whereas the Voigt function was chosen.
Fitting was performed for each sample and the resulted data are presented in tables for maximum diffraction angle (2θ) and width at half peak height - β.

The identity of the simulation results with experimental data on the example of a complex traffic pattern is shown in Fig. 2.
This situation consists in non-specificity of the existing criteria for the efficient priority driving of route transport, the lack of data on admissible volume of passengers flow, combined traffic volume on the main and conflicting directions.
Mironchuk, Investigating Intermittent Bus Lanes using Simulation Data.
Dailey, A prescription for transit arrival/departure prediction using automatic vehicle location data.
Callas, Analysis of Transit Signal Priority Using Archived Tri-Met Bus Dispatch System Data.