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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.

We performed the neural network training using the 97 measured data in CASS materials by Aubrey and Chopra.
Fig. 2 (b) shows the target value that is ferrite content from the input data.
Therefore, we believe that if the trained neural network described in this study is used, we can effectively predict the ferrite content. 0 5 10 15 20 25 0 20 40 60 80 100 Data number Chemical composion(%wt) C Mn Si Cr Ni Mo N 0 10 20 30 40 50 020406080 Data number Ferrite content (Vol.%) 100 (a) Input data (b) Target data Fig. 2.
Neural network architecture for prediction output and Aubrey equation results of Charpy impact energy 0 100 200 300 400 500 600 700 0 40 80 120 160 200 240 280 Data number Ferrite(vol%), Aging temp( ℃), Aging time(×10 2hr) Ferrite content Aging temp Aging time 0 50 100 150 200 250 300 0 40 80 120 160 200 240 280 Data number Aged Cv (J/cm 2) (a) Input data (b) Target data Fig. 7.
Training data for prediction of Charpy impact energy For Charpy Impact Energy Prediction.

(b) The TGA/DTA data for s-ANTs.
The Langmuir-Hinshelwood model was used to fit the data to  C /C0  et / where the parameter is the time required for 67% dye reduction and was used to compare the results.
The complete photocatalytic data is presented in Fig. 3.
Table 1 summarizes the results of the Raman data.
The commercial software that came with the instrument was used to smooth the data.

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.

Such as the classic C4.5 algorithm which evaluated from ID3 algorithm .Its shortcoming is the demand for the tree data scanning and sorting of data sets, which lead to inefficient algorithms.
ID3 algorithm uses a method called window that randomness to select a subset from the data set to avoid access to the entire data set.
Data Mining Concepts and Techniques[M].Fan Ming, Meng Xiaofeng translation.
Principle and algorithm of data mining[M].
The attribute reduction algorithm based on attribute similarity [J].

A novel semi-supervised Model For a given m-dimension data set , , the main propose of semi supervised clustering algorithm is to find , the membership degree of belonging to the cluster , by integrating the cluster information of labeled data, and the structural information of unlabeled data.
is a weight factor indicating the importance of the cluster information of labeled data.
The performance of critically depends on the shape of data set.
Different percent of instances from the whole dataset are randomly selected as labeled data.
Data set after dimemsion reduction Figure 3.

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.

And the data of design condition generally has a certain margin.
Generally, it puts design condition as the scale, and fluctuates around the design condition, in other words, the changes of operating condition data in specific range are normal state.
The data about main technical and economic index in each condition are collected in WISCO and shown in Table 2.
In Table 2, the evaluation index data which come from actual collection and calculation shows that prediction condition is the best condition, its efficiency of waste heat recovery and efficiency of power generation are the maximum, and design values of each evaluation index are better than the index value of operating condition.
The data in Table 2 are real data by actually collecting, so the data have greater objectivity.

The analysis of room temperature XRD data revealed that A1, A2 and A3 samples exhibit a complete solid solution between the crystal structures of LSCF cathode and BCZY electrolyte.
Introduction Reduction of the operating temperature of solid oxide fuel cell (SOFC) to intermediate temperatures solid oxide fuel cell (IT-SOFC) has greatly widened the materials selection for cells fabrication and accelerates the commercialization of SOFC technology.