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Therefore, this paper adopts the related panel data analysis method .
data sample This paper is focused on the panel data of the energy consumption and GDP of the OECD countries and the BRICs from the year 1986-2009, within which,it converts the annual nominal GDP into the actual GDP(unit average: billion dollars) according to the global GDP constant prices of 2005; annual energy consumption (mainly includes:crude oil, natural gas and raw coal) is converted to million tons of oil according to the related convertion standards of the BP world energy statistics annals(2010) .
Rroot of panel unit test the panel unit root test means making a unit root test on the cross section sequence of variables of panel data as a whole.
The test of the unit root of panel data is similar to that of the time series, with the prior consideration that whether there exists an auto-regressive constraint in the panel data structure.Considering the AR(1) process based on the panel data structure: (2) i=1,2,…,N means the cross section sample; t=1,2,…, Ti means the time span of the panel unit but represnts all the fixed effect and rare trends in all modes; is an auto-regressive coefficient; is error and assumes to be interdependent.
In order to avoid the “fake regression”of the panel data sequence, we should first carry out a panel unit root test on the panel data sequence.

The aim of the current study was to assess effectiveness of automatic braking system quantitatively using real pedestrian accident data selected from IVAC database.
The current study is an attempt to predict the benefits of automatic braking system based on real-world accident data.
Data Set In 2006, a special team from Hunan University carried out a vehicle traffic accident investigation in Changsha located in middle of China.
Drivers of 78 cases did not brake before the collision, accounted for 94% of the data set.
A Method for Estimating the Benefit of Autonomous Braking Systems Using Traffic Accident Data.

Modeling and Experiment of Deformation Texture Development for Pure Al by CPFEM with EBSD Data Huachun Pi1, a , Jingtao Han1, b, Chuanguo Zhang1, c , A.K.
Both models predictions in tensile deformation are reasonable agreement with the experimental data.
Early studies connecting EBSD data to crystal plasticity modeling used manual measurements to define initial orientations.
More recent work has taken advantage of automated data collection on samples as a means of collecting detailed EBSD data for modeling.
The purpose of this study is to model deformation texture development for pure Al using crystal plasticity finite element method with EBSD data.

The test is divided into a rubber content of 0%, 5%, 10%, 15% and 20%，under the conditions that the SBR content 5% of cement mass, study the ratio of tension and pressure and fracture pressure performance of latex rubber modified concrete, and studies the noise reduction properties of concrete, test ultrasonic propagation velocity in the concrete.
Tests show that rubber latex can improve the interface effect between rubbers and cement base material, improve the bonding strength between the two, improve the ratio of tension and pressure and fracture pressure ratio performance of concrete, and reduce the propagation velocity of ultrasonic in concrete, enhance the noise reduction performance of concrete.
It also shows that the elastic is good material, the ultrasonic velocity in this kind of material is smaller, the noise reduction performance is better, and the ultrasonic velocity in concrete can be used to indirectly measure the concrete performance of noise reduction, noise [2].
In order to facilitate comparison, the unified test adopts the ordinary concrete mechanics performance test method standard "(GB/T50081-2002)[4] of the provisions of relevant test operation, the test data are shown in Table 1: Table1 Basic Mechanics Performance Index of Concrete Specimen Number Rubber Content（%） Compressive Strength Tensile Strength Flexural Strength Strength Increment (Mpa) (%) Strength Increment (Mpa) (%) Strength Increment (Mpa) (%) DC MDC 0 60.35 14.1 5.25 15.6 6.63 21.3 68.88 6.07 8.04 NRC MRC 5 55.42 11.5 4.86 14.8 6.37 19.0 61.79 5.58 7.58 NRC MRC 10 49.61 10.3 4.49 15.1 5.83 23.3 54.74 5.17 7.19 NRC MRC 15 46.45 7.4 4.29 14.7 5.67 21.2 49.88 4.92 6.96 NRC MRC 20 40.79 12.3 3.88 20.9 5.11 28.0 45.81 4.69 6.54 We can see from Table 1, addition of latex, improves compressive strength, splitting tensile strength and bending strength
Anti noise and noise reduction performance of Rubber cement concrete [J] Yangtze River.2010, 41 (10): 71-74

Madurani4,d 1,2,3,4Chemistry Department, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia a*harmami@chem.its.ac.id, bitau@chem.its.ac.id, cbaswara.18012@mhs.its.ac.id, dkartika.madurani@its.ac.id Keywords: Adsorbent, Adsorption capacity, Rice Straw, Cr(VI), Wastewater Treatment Abstract.
The highest percentage reduction of Cr(VI) reached 66.90%.
The highest percentage reduction of Cr(VI) occurred at a contact time of 2880 minutes.
All chemicals were used without any purification. 1 M H3PO4 solution as adsorbent activator and 1 M HCl solution were prepared in Laboratory of Instrumentation and Analytical Sciences, Chemistry Department, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember.
The reduction reaction of Cr(VI) ions can be seen in Fig. 5.

Surface Characterization and CO2 Reduction Using Electrodeposited Silver Particles over TiO2 Thin Film Luisa F.
Furthermore, photo reduction of CO2 by TiO2 is enhanced by metallic silver [5].
On the other hand, electrochemical reduction of CO2 upon TiO2 surfaces [6] has been studied briefly.
Here, the average rugosity (Ra, Table 3) diminishes as scan size increases and the maximum high (Zrange) diminishes as well due to a smaller area size where the effects caused by large cumuli is eliminated; therefore, data is related to small Figure 3.
Therefore, on Table 4 we present particle size data for the 1 μm scan size only.

In which, building Time period, Response Reduction factor and Importance factor alters design base shear majorly.
Pushover analyses are carried out to find its effects on over strength factor and response reduction factor.
This equations comprosies of Zone Factor (Z), Importance factor (I), Response Reduction factor (R), Spectral acceleration based on building fundamental time period (Sa/g) and total gravity weight of the structure (1) Many studies have reported the appropriate values for Zone factor, Response Reduction factor and Spectral Acceleration based on building fundamental time period.
In other words, it is a force reduction factor used to reduce the linear elastic response spectra to the inelastic response spectra.
Push over curves are idealised to bilinear curves and corresponding data are caluclated as per procedure explained above.

Fig. 3 Data acquired with the laser stripe sensor (left), result of the crack inspection (right) In both digitalization steps the acquired surface data has to be transformed into a common workpiece coordinate frame.
Fig. 6 Steps to compute the robot path based on digitalization data Crack Identification.
After removing false positives, the remaining data can be used for path computation as detailed in the following section.
All robot paths and all digitalized data are relative to that work piece coordinate system.
Since all data is described in the same (work piece-dependent) coordinate system, the programed repair path points can be compared directly to the digitalized data, without the need to transform the data first.

Rough set theory is a new tool of data analysis to deal with imprecise, incomplete and uncertainty.
The fault tolerance and generalization of RS are not strong enough and the limitation of quantify the data are the advantages of SVM as complementary.
The system offers its two innovations: first, reducing data of input side of SVM by attribute reduction of the data with RS process in order to improve the operating speed of system.
The large mount of data need to be computed in facial expression recognition and the reduction the classification accuracy caused by the data of noise.
In this paper, RS-SVM has been employed in classification of facial expression recognition due to the good anti-jamming and reduction of data dimensionality and computation.

These resulted in a considerable reduction in the data and a reduction in the computational time to perform correlation.
However, this approach causes computational difficulties in the application of the tremendous AE data.
The reconstructed signal retains main information about modal dynamics and results in a considerable reduction in the data.
Settingis the discrete sampling data of the signal,，orthogonal wavelet decomposition of signalcan be expressed as
Reconstructed signal considerably reduces the data, which results in the reduction of the computational time for correlation filtering.