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It is shown that the strength reduction of rock mass is governed by the geometric configurations as well as the mechanical properties of the joints, and that the presence of joints results in the non-linearity of the pre-peak region.
The program of DIPS was employed to analyze and visualize the geological structural data via the same techniques used in manual stereonets.

The proposed model was validated against experimental data and it showed simulating and experimental results are in very good agreement at three different rotational speeds, in particular near the surge line, though the deviation begins to increase as mass flow rate goes up.
Anyway, in many cases, a limited operating domain of the turbocharger device is really available, and various maps data extrapolation techniques are commonly included within commercial 1D codes [8, 9, 10].
As shown in the figure, the simulation results match very well with experimental data in the neighbourhood of the surge line.
Figure 4 Comparison between the model 1 simulation results and experimental data SIMULATOIN RESULTS The main geometry data and other numerical values used in Centrifugal Compressor Model are presented in Table 1.
The proposed model was validated against experimental results and the simulation results match very well with experimental data except the high mass flow rate area.

The result of this modeling was compared with experimental data taken from literature and good agreement was observed.
For validation, all the modeling result is compared by experimental data from literature [12]. 2.
The result of modeling was compared with the data taken from literature and observed good agreement which testify the model validation.
As the gas velocity increases, the removal efficiency decreases due to reduction in resident time.

The research result shows the fatigue design data at different temperature may be corrected by elastic modulus with room temperature curve.
Table 1 Tensile test result at room temperature and 380℃ 0.2% yield strength[MPa] ultimate tensile strength [MPa] Total elongation [%] Percent reduction in area [%] Specimen No.
Table 2 and Table 3 show test data and the number of cycle to failure.
The fatigue data used to construct the design curve s are plotted using room temperature E values.

Simulations of the Lankford parameters were carried out with the help of the viscoplastic self-consistent scheme (based on the global texture) and compared with the experimental data.
The strain was measured with the optical system Aramis (GOM mbH, Germany) yielding the elongation dεx and the width reduction dεy simultaneously.
All data given for the initial material B are taken from [5].
However this requires precise data on the local texture in each layer in order to get an exact separation of the global texture.

By calculating the information entropy for the scale of construction of energy transportation channels with the data of annual investment in the fixed assets from 1986 to 2008, we analyzed the relation between transportation channels and the information entropy combined with the maximum entropy methods.
When Ki is normalized, the formula for pi is, els as, (4) According to the data of annual investment for conventional energy in fixed assets in China from 1986 to 2008 and utilizing the formula noted above, the information entropy Si for the four kinds of the construction scale of energy transportation channels can be achieved.
In the present work, we further study the formula (2) combined with our data and it is found that the base value is pi=0.33, i.e., when pi>0.33, pi decreases with Si, but, when pi<0.33, pi increases with Si.
According to the calculated date from 1986 to 2008, as shown in figure 1 and figure 2, it can be summarized as follows, With regard to rail transportation, between 1986 and 1996, the ratio of annual investment to the total investment is kept above 50% and p1 tends to total reduction.

According to survey data, the paper calculated the probability of the top event, as well as the probability importance degree coefficients and critical importance degree coefficients of basic events, and quantitatively analyzed the cause of accidents.
The basic events X3, X4, X5, X17, and X18, namely poor safety awareness, weak sense of responsibility, high control office noise, unsound rules and regulations, and inadequate enforcement of rules and regulations are the main reasons for the accident; the basic events X21 and X22, namely low precision of radar and poor stability of radar easily lead to the accident; other basic events can cause accidents too, but not seriously. 5 Quantitative Analysis of the Fault Tree According to the survey data, this paper used the Delphi technique to get the ratio and weight of each basic event.
Taking into account the uncertainty of the event and the fuzziness of the expert’s estimation, the fuzzy evaluation of survey data was completed to get the probability of each basic event, as shown in Table 2. 5.1 Calculation of the Top Event Probability.
At the same time, with the reduction of their critical importance degree, the influence of these basic events becomes smaller and smaller gradually. 2) The basic events X21, X22, X8, X13, X11, X7, X10, X16, andX20, namely low precision of radar, poor stability of radar, poor cognitive ability, poor memory, lack of relevant control training, poor language expression ability, high similarity degree of flight number, poor sense of space, and lack of coordination awareness, whose critical importance degree coefficients are relatively bigger, can also lead to the accident easily.

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, China a hydrowang@163.com, b lubaohong@126.com, c hanwenzhan05015134@163.com, d 1176445330@qq.com, Key words: Hebei province, Precipitation, Trend, Abrupt change point, Periodic variation Abstract: Based on the precipitation data observed monthly of 19 weather stations in Hebei province from 1960 to 2011, three methods, linear trend estimation, Mann-Kendall test as well as Morlet wavelet transformation, were adopted to analyze the characteristics of precipitation trend, abrupt change points and cyclical variations under the circumstance of multi-time scales in the past 52 years.
Data series and methodology In the study, 52 years’ precipitation data in Hebei province was chosen from National Weather Centre, which were in great representative and distributed in towns or cities of Zhangbei, Weixian, Shijiazhuang, Xingtai, Fengning, Weichang, Zhangjiakou, Huailai, Chengde, Zunhua, Qinlong, Qinhuangdao, Langfang, Tangshan, Leting, Baoding, Raoyang, Huanghua as well as Nangong.
The reduction amplitude was 86.0mm, accounting for 24.08% of summer precipitation.

Data on PV and TEG voltage, current, and solar irradiance were collected and analyzed.
These setups were tested simultaneously using a solarimeter and multimeter attached to the different configurations to record data.
Immediately after the setups were made, the data was collected for over 8 hours (10:30 am to 6:00 pm), with 25-minute intervals.
This data collected includes PV voltage and current, TEG voltage and current, and solar irradiance value.
ηsys= Ppv + PTEGP (5) Results and Discussion In this section, the acquired data, as well as the experimental outcomes, are presented and discussed.

The discussed problems result the lack of scores or access records ,which means the collected data sets are often high dimensional and sparse, leading to the Dimension Reduction(DR) before using the traditional clustering techniques[7].
However, DR makes the noise data and valid data closer to each other in lower dimensional space and the loss of some important correlation, which decreases the clustering quality.
As for CS based on context, because of introduction of more context factors, the high-dimensionality and sparsity turn to be more serious, dataset capacity also becomes greater, so how to keep or improve the quality of CS based on preservation of original correlations between data items is a significant problem needed further research.
Finally 85 customers’ respective scores for random 20 comedy films and associated context data are collected, after the calculation of, awith 85 vertices and 5 hyper edges is constructed.
[2] M.Gorgoglione, C.Palmisano and A.Tuzhilin,in:Personalization in context: Does context matter when building personalized customer models,Proceedings of the Sixth International Conference on Data Mining, Boston(2006), in press