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For the data reduction and examination, a routine was written in the program environment IDL ©.
Another possibility is the use of a smoothing or filtering routine during the data evaluation.
This figure shows the data quality of the images used for the evaluation.
The stress at this position is 40 MPa ± 10 MPa (data from image analyses).
Rothkirch for their assistance during the measurements and during the data analysis.

All the data were averaged from 9 replicas.
All data were calculated by Excel 2003 and subjected to statistical analysis of variance (ANOVA) and correlation analysis in the SPSS 11.5 statistical package.
Data shown are means and standard deviation of nine replicates (μg g-1dw h -1).
Data shown are the means of nine replicates (%).
Data shown are the means of nine replicates (%).

The first step to optimize production process is to find out the relationship between and, according to the analyses of data.
Moreover, the historical data, the production plan are also needed in model building, as shown in Figure 5.
Historical data plays a main role in the model which can be obtained by measuring the secondary equipment of DMS when all the equipments are operating normally.
It is forecasted according to the historical data, because the production parameters do not changed significantly.
It is forecasted according to the historical data, production parameters adjustment and production progress.

According to statistics, the measured or forecast data, evaluation indexes are assigned values and standardize.
Table3 The results of performance indexes of example structures[6] Structure ID internal damage index number of inelastic cycles maximum inter-storey drift energy-concentrated quotient Equivalent vibration cycle/s 1 0.783 10.564 0.019 4.201 0.411 2 0.762 3.338 0.025 5.334 1.968 3 0.861 2.278 0.043 4.616 1.191 4 0.726 6.073 0.017 4.238 0.752 The data of the internal damage index, number of inelastic cycles, maximum inter-storey drift and energy-concentrated quotient in the table3 are standardized according to equation (3) and equation (4), and the results are shown in the table4.
Table4 The normalized values of performance indexes of example structures Structure ID internal damage index X1 number of inelastic cycles X2 maximum inter-storey drift X3 energy-concentrated quotient X4 1 0.783 0.789 0.380 0.457 2 0.762 0.933 0.500 0.619 3 0.861 0.954 0.860 0.517 4 0.726 0.879 0.340 0.463 Selecting the normalized formula of the butterfly catastrophe model, using complementary criteria, mutation membership function values of reinforced concrete structures for the three level earthquake resistant design and earthquake damage of four kinds of structures are calculated using the indexes data in table2 and table4.The results is shown in the table5 and table6.
It is convenient to deal with data by the method, that decreases the subjectivity impact.
But due to the incompleteness of selection of indexes and data, and the uncertainty determining the index weight order, extension of the index system, the accuracy of obtaining data, and the index weights determined need further study and improving.

The analysis of the experimental phenomena, experimental law, and statistical results of the experimental data are as follows.
The detailed data can be seen in the following statistical figures.
Fig. 4 shows the bending angle after the test piece failed: test data indicates that the bent degree of the test piece is small and the bent degree is up as angle between weld line and drawing force becomes bigger.
Test data of the change of weld line’s skewing trend indicates that the whole curve tends to horizontal and every data doesn’t deviate far.
On average, these data have markedly decrease compared with base metal.

The current approaches, to evaluate the failure probability or frequencies of equipment, including the Measurement Statistical Method, the Probability Assessment Method and the Fuzzy Assessment Method, need abundant sample data for the modeling and parameter estimation.
The traditional assessment method derives the probability density function or membership function of equipment voltage tolerance capability in uncertain area from sample data.
With personal computers (PCs) as sensitive equipment, the IPDF of voltage tolerance capability of PCs is derived from the sample data obtained in the laboratory tests.
It is assumed that three-phase faults occur on a selected transmission line, and the distribution of fault positions obeys uniform distribution [6].The sample data of voltage sags magnitude at the supply bus 29 are obtained and the probability density function fsu(u) could be derived.
In the laboratory tests of PCs, 60 sample data of voltage sag magnitude and duration tolerance are obtained, and the IPDF are calculated by the method mentioned in section 2, shown in Fig.3 and Fig.4.

· All data can be saved and stored in electronic form without of the need of paper.
Data can be transported via the storage medium and sent by means of mail or other electronic ways
The coordinate systems for CL data definition Coordinate systems are one of the elements of operation and NC sequence in CAM system.
They define the orientation of the workpiece on the machine and act as the origin 0(0,0,0) for CL data generation.
Generally can be find two types of coordinate systems in CAM programs: [6,7] · machine – acts as the default origin for all CL data.

See Table 2 for detailed data.
According to the test data, anti-skid performance degradation model is set up to evaluate pavement performance degradation.
Table 1 Friction coefficient (BPN) data of the experimental section within 15 months 0.5 mouths 3 mouths 6 mouths 9 mouths 12 mouths 15 mouths 0+005 78 74 73 71 70 69 0+010 79 75 74 73 71 69 0+015 80 77 71 72 70 64 0+020 79 74 73 75 67 65 0+025 78 74 72 71 68 65 average value 78.8 74.8 72.6 72.4 69.2 66.4 Establishment of Anti-skid pavement performance degradation model To select the best regression model to express related data relation, Curve Estimation process provided by IBM SPSS Statistics19.0 software (Chinese version) is applied for linear fitting.
In view of anti-skid performance standards of asphalt road (BPN of expressway and first-class road≥45) and BPN (27) of blank comparative test piece (see the friction coefficient data measured through pendulum instrument method in this study), it is believed that under the traffic volume of the experimental pavement section, anti-skid performance index of coating-type color anti-skid pavement after 5 years will not satisfy requirements of expressways and first-class roads.
Cost reduction (especially colloid cost) and development of functional fillers are two development directions.

Data concerning Plastic Industry in Libya was collected to which some projection of the future production rate and waste rate has been estimated.
Data concerning Plastic Industry in Libya was collected to which some projection of the future production rate and waste rate has been estimated.
The basis for such interest is energy content of these polymeric materials compared to domestic fuels and energy resources as shown below in Table 1 [6]*: Table 1: Plastic materials Latent Energy compared to traditional energy products Polymers [MJ/kg] Fuels [MJ/kg] Biomass [MJ/kg] PE -46.3 Coal -32.5 Glucose -15.55 PP -46.4 Crude Oil -46.3 Wood -18 PET -23.5 Biodiesel -42.2 PVC -18 Natural Gas -53.6 PS -41.4 Methane -55.6 Hydrogen Gas -143 Gasoline -46.4 *reproduced from raw data available in UNEP Compendium [6] Polyolefin (LDPE, HDPE, PP) is a major type of thermoplastic used throughout the world in different applications such as bags, toys, containers, pipes (LDPE), house wares, industrial wrappings and film, gas pipes (HDPE), , battery cases, automotive parts, and electrical components (PP).
Some preparation of the waste plastics feed is required before pyrolysis, including size reduction and removal of most non plastics.
Data concerning Plastic Industry in Libya was collected to which some projection of the future production rate and waste rate has been estimated.

The interface slip between steel and concrete was considered approximately by multiplying strength or stiffness reduction factor.
The experimental data were obtained at the peak displacement load of the first cycle in each load level to compare with the numerical results.
The FE model was verified by the test data of composite frame.