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Its main mineralogical, chemical and physical features are summarized in Table 1 (data made available by the producer company) [20].
Data analysis methods.
The data obtained for the compressive strength and flexural strength of blended mortars (0, 15, 25 and 35% of NPs) after 28 and 90 curing days are given in Fig.8a and Fig.8b.
Table 8 presents the constants (a, b, c and d) with regression coefficients (R2) of the correlation between the experimental data and the equation (Eq.2, Eq.3 and Eq.4 respectively).
The equation of water absorption by capillarity vs time (Eq.1) was found to provide a very good fit to the data with correlation coefficients of over 0.99 (Fig.11a).

Furthermore, we propose a method to improve the quality of the traditional A* algorithm by eliminating unnecessary intermediate points, which is Shortcut Path Reduction (SPR).
Δxk and Δyk is the horizontal and vertical difference at the k th element in P (n): Δxk=xk-xk-1,Δx1=0Δyk=yk-yk-1,Δy1=0 (6) First, select the kinked nodes from P(n) and filter the set of intermediate points, forming the set P '(m): Δxk≠Δxk+1∧Δyk≠Δyk+1 (7) The last shortened version of P’’(q) is filtered from P’(m) according to the following algorithm, naming Shortcut Path Reduction (SPR).
Fig. 7 Simulation results (from left to right) BFS and DFS (row 1); Dijkstra and A * (row 2) Respond to Start = [30, 50]; Goal = [95, 95], we obtained simulation results (Fig. 7) and data tables (Table 2).
Table 2 Data table of simulation results of algorithms BFS DFS Dijkstra A* Open points 5731 7871 5770 2068 Closed points 5623 7242 5635 1889 Path length 100.2 398.0 100.2 100.2 Evaluation of SPR on traditional A* Evaluation on simulated map.
Table 3 Data table of simulation results A * before and after processing SPR Start à Goal Closed points Path length Before After Before After [30, 50] à [95, 95] 90 4 100.2 94.5 [30, 50] à [5, 5] 115 4 123.9 118.1 [90, 15] à [5, 95] 121 5 145.3 135.3 [5, 50] à [90, 50] 138 6 151.1 141.7 Evaluation on real map.

Model estimation is defined from a set of observed data fitting out a proper mathematical model.
4 Clustering As a matter of fact, the PS of data point can be viewed as an attribute of the points, denoted by , its essence is the concept description.So it can be used to cluster data points based on the conceptual attributes of the data point.
is a child node of , denote the ratio of data point of ; is the probability that the data points with property value is clustered into ; denote the ratio for the data points with property value , this is an invariant, and can be determinated before the cluster.
By clustering, the data points are classified into several clusters.
Firstly, each data point is described as PS, and the Jaccard distance of PS-PS will attributes as the concept of a data point.

Fig. 2 : XRD data for samples prepared in air at 700oC, 750 oC and 800oC.
Fig. 3 : XRD data for samples heated between 800 and 1000oC in O2.
Fig. 4 : XRD data for samples heated between 800 and 950oC in N2.
Fig. 8 : Refined XRD data for samples that heated at 800oC in N2. .
Fig. 7 : Refined XRD data for samples that heated at 800oC in N2. .

The behavioral data means the collected data from user`s everyday life using mobile device such as call log, SMS log and GPS coordinates.
For experiments, we collect the behavioral data of 5 users and path data of them.
These collected data have following formats.
Also, the previous method 2 considers the additional context data such as adjacent people data for calculating the path similarities.
[2] Saygin, Y., Ulusoy, O., “Exploiting data mining techniques for broadcasting data in mobile computing environments,” IEEE Transactions on Knowledge and Data Engineering, vo.14, no. 6, pp.1387-1399, 2002

The object pixel data would be labeled as 1 and the scene pixel data will be labeled as 0.
The object pixel data, scene pixel data and the corresponding labels are employed as the training data of the adaboost classifier which was obtained after the training procedure.
After the improvement, the adaboost classifier would label the pixel data as 0, 0.25, 0.5, 0.75, 1 and these data constructed the confidence map.
Then we get data set for one rectangle.
Boosting as a dimensionality reduction tool for audio classification[J].

Data from the redox potential development was used to program a redox potential controller in an electrochemical vessel to reproduce the same leaching conditions in the absence of microorganisms.
As a result, data comparing biotic and abiotic systems does not conclusively support a bioleaching mechanism.
In this study, redox potential data from a moderately thermophilic batch bioleaching of chalcopyrite were used to mimic the redox potential development in an electrochemical cell in order to reproduce the same leaching conditions without the presence of microorganisms.
The electrical current kept the redox potential on the set value by reduction of ferric ions to ferrous.
XRD analysis of the electrochemical leaching residue confirmed that beside chalcopyrite, the only detectable iron compound was jarosite though it was not detected in the bioleaching experiment residue (data not shown).

The first category includes stress-strain relationships in the form of analytical expressions, coming directly from the evaluation of tests data.
These data, such as the majority of the experimental tests available in the literature (Nisticò et al. 2014) [22] refer to plain concrete.
Note that the experimental data are average of three experiments.
With the reduction factor of 0.50 the test data can be reasonably well reproduced.
Fig.7: The relation between relative strength of confined concrete columns as a function of corner radius (shape of the cross-section) for: (a) 1-ply and (b) 2-ply CFRP The numerical results are compared with test data and it can be seen that the results are in reasonably good agreement.

Comparative Analysis on Emission Between hybrid and Conventional Vehicles WANG yong-ding1a, MA chen-qi1b, PAN hai1b 1 College of Engineering Science & Technology, Shanghai Ocean University, Shanghai, China aydwang@shou.edu.com bsmilemuch@sina.com Keywords: HEV; Energy saving; Emission reduction; parameter match; advisor.
Data shown in Figure 1and figure 2: Fig. 1 Honda Insight petrol engine data map Fig. 2 Toyota Prius engine data map The motor selection.
Data shown in Figure 4 and figure 5: Figure 4 Honda insight motor data graph Figure 5 Toyota prius motor data graph Determine the type and parameters of the battery pack.
Conclusions By using the vehicle simulation software ADVISOR we can match the hybrid cars of all key parts of the parameters, and ADVISOR can do the second development and modular design [11],Can also add more variables are calculated which is more close to the real data.

This amounts a reduction in heat transfer film thickness and enhanced heat transfer to the propellant with consequent enhancement in the dynamic burn rate resulting the undesirable starting pressure transient.
These SRMs do not lend themselves to the costly empirical techniques, and the radical differences in the size and design of these SRMs defy extrapolation of the previous data obtained from more conventional motors.
The accuracy of the final computation will be a function of the accuracy of these empirical data.
The situation is more uncertain for turbulent flows (Re > 106), since at the level of the Reynolds-Averaged Navier-Stokes equations, the uncertainty connected with the semi-empirical turbulence models will require a control of the accuracy of the computed flow properties by comparison with experimental data, in particular for the validation of the sensitive variables such as wall shear stresses and heat transfer coefficient.
The present numerical study is expected to aid the designer for conceiving the physical insight into problems associated with the prediction and the reduction of the pressure spike, pressurization rate and thrust oscillations during the starting transient period of operation of high-performance solid rocket motors.