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This paper takes the Northern slope of Qinling Mountains which have four administrative districts, and selected the DPSIR Model to analyze the ecological security index of the four districts to study the human activities impact on these areas, and chose the 2009 and 2013 economics and environmental data to establish the DPSIR Model.
Selection of research area and data sources This research chose Northern slope of Qinling Mountains as the research object, and selected related indexes including society, national economy, population, natural environment and environmental protection investment to analysis the ecological security data in Zhouzhi, Huxian, Chang’an and Lantian Districts of the Northern slope of Qinling Mountains where the human activities especially the urbanization activities are very popular in those four districts, then establishing an ecological security evaluation model, to compare the different ecological security level in the year of 2009, and 2013.
The index R all had been showed an increasing tendency in the four districts, which indicated that energy conservation and emission reduction were stressed as much as economic construction in piedmont area in Xi’an.
Tab.3 The ESI index of the four districts in 2009 and 2013 Zhouzhi Huxian Chang’an Lantian 2009 2013 2009 2013 2009 2013 2009 2013 ESI 18.25 36.55 48.03 76.37 112.69 224.81 13.01 13.40 In Tab.3, it can be seen that Zhouzhi and Lantian were still in a relatively unsafe situation, in a temporal comparing the ESI of the four districts were increasing under the new environmental policy and large number of environmental investment, but the ESI of Lantian was nearly the same, because of its economic foundation is too weak These data showed in Tab.3 indicated that with the continuous development of the third industry, the pace of Chang’an development was faster than other 3 districts, at the same time as the investment in environmental protection, control of population density, and with the continuous development of the third industry, the ecological environment in Chang’an gradually got stabilized.

The adsorption data were better fitted for the Freundlich model，indicating that the adsorption of Cr(VI) preferably follows multilayer and heterogeneous adsorption process.
A variety of methods have been developed for the removal of Cr (VI) from wastewater, such as adsorption, ion exchange, chemical precipitation, electro-deposition, photocatalysis reduction, solvent extraction, and reverse osmosis [2].
The values of Langmuir and Freundlich parameters obtained from the experimental data are shown in Fig. 5 and Table 1.
The value of Freundlich adsorption intensity (0.3 < 1/n < 0.5) indicates that the adsorption of Cr(VI) on composite materials is favorable. 2.3 Kinetics of the Cr(VI) adsorption on composite materials To identify the rate-controlling mechanisms during the adsorption of Cr(VI), two simplified models are applied to assess the experimental data.
As is shown in Fig.6, with the increase of adsorption time, the data of the pseudo-first-order reaction gradually deviate from the fitted curve.

There was a good agreement between the calculated values and the experimental data, which indicated that this model can be used for predicting the agglomerates size distribution at any desired levels of the variables studied.
The reduction in size was followed by an increase in the number of agglomerate.
The experimental data as shown in Fig. 7 clearly show the regions where clusters dominate and where agglomerates dominate.
Fig. 6 Effect of reaction temperature on Fig. 7 Comparison of the model values the particle size with experimental data Conclusions Ferric phosphate, one of the most important precursor of lithium iron phosphate, was prepared by controlled crystallization process in a continuous reactor.
Good agreement between the calculated values and experimental data suggest that the population balance equations can be used to predict the PSD of agglomerates.

The substructure of a single grain in an electron backscatter diffraction (EBSD) data map is studied, focusing on the influence of the grain boundary configuration on the misorientation to the average grain orientation of data points close to the grain boundary.
For each boundary segment, one zone contains only data points belonging to grain 1 (this will be called the inside zone), while the other zone will consist of data points belonging to one of the neighbouring grains (the outside zone).
White spots are due to data points which could not be unambiguously indexed by the EBSD system.
The shading of the data is related to the position along the boundary.
Parts of the surface that are left out are due to data points which could not be unambiguously indexed by the EBSD system.

Beyers[1] tried to get the data with several three-cup anemometers on a six meter mast in Antarctic.
Each three-cup anemometer is about 20cm high, so the data is rough.
A Wind Rake is adopted to get the data at different height.
The expressions of mean velocity profiles and turbulence intensity are proposed from the data get by the wind rake.
Scan valve and DSM3000 are adopted as data collection dealt with program.

Simulated data is shown in Table 1: Tab.1.
Simulated data P(UO) L(UO) RE event a 0.1 0.9 0.09 event b 0.9 0.1 0.09 event c 0.1 0.1 0.01 event d 0.9 0.9 0.81 From Table 1 we can conclude that the larger the risk value is, the more attention should be paid.
Simulated data are shown in Table 2: Tab.2.
Five groups of simulated data Operating capital Technique & Equipment Cost per capita Number of employees Price of product Profit 5 12 12 20 262 13.04 10 10 9.6 15 181 23.7 12 20 10 13 160 11.4 8 5 6 5 50 10.35 15 30 9 10 140 25.1 We take ten thousands as quantity units except one index(Number of employees), selecting data in the first group to analyze the sensitivity.
Specific data is shown as follows: Tab.3.

PCA-CCPSO PCA Principal component analysis (PCA) [5] is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables.
In PCA, it will be easy to analysis the data and reduce the noise when is as small as possible.
But is the object data dimension after dimension reduction, so that it will lose some useful information if become too small.
Therefore, the contribution rate of the main elements of the data must be considered.
Contribution rate is the weight of the principal component in all the data analysis.

Fig. 3 Schematic circuit for SCI communication interface Software design of the acquisition system The main function of the software includes acquisition of the lung sound signals, setup of the data sampling rate, initialization of serial communication ports, saving of lung sound data as well as the timer and the interrupt handling of the timer.
And then start the timer, while waiting for an interrupt occurs. if an interrupt occurs, you can transfer data to a PC through the SCI port.
Fig. 4 The flow chart of system software Experiment and result analysis Fuctional test of the designed system At last, the experiment of acquisition of lung sound signals is done based on the designed system, the acquisition data is send to MATALB software, in the experiment, the six sounds (wheezes, velcro, coarse crackles, fine crackles, pleural rub and rhoncus) is collected and the result is shown in figure 5.it is showing that the acquisition system is working properly, and reliable design.
After collecting data on the software system, the data is sent via SCI interface to personal computer, and analized in the upper machine. based on this system, the relevant experimental results demonstrate that the system meets the design features and reliable work.
Lung Sound Noise Reduction Using Gabor Time-Frequency Masking[C]. proceeding of World Congress on Medical Physics and Biomedical Engineering, 14:971-974. (2006) [5] G Ntoumenopoulos, Y Glickman.

A formula was developed to calculate the theoretical limit pressure of rupture discs, and the theoretical predictions were compared with the measured data.
Some assumptions such as spherical deformed shape and uniform thickness reduction are adopted, and the relationship between limit pressure and material parameters has not been clearly realized.
Table 1 Calculated results of C* by the data of references Ref.
Material K [MPa] n d [mm] s0 [mm] p* [MPa] C * [3] Aluminum 24-T 689.50 0.168 162.56 1.016 8.27 0.9595 [3] Hard-rolled copper 413.69 0.066 162.56 1.016 5.17 0.9998 [3] Aluminum 75S-O 375.76 0.145 162.56 1.041 4.69 0.9745 [9] Aluminum L2 134.36 0.196 31.40 0.204 1.72 0.9852 Table 2 Calculated results of C* by the data of bulge tests for 316L Material state K [MPa] n d [mm] s0 [mm] p* [MPa] C * Mild 1207.37 0.3570 106.50 0.075 1.68 0.9879 1287.17 0.3698 106.50 0.155 3.71 0.9902 1226.23 0.3908 106.50 0.395 9.04 0.9938 1276.78 0.4198 106.50 0.700 16.48 0.9819 1323.19 0.4559 106.50 1.160 28.82 0.9998 Solution treatment 1252.50 0.3950 106.50 0.155 3.59 0.9847 1195.72 0.4201 106.50 0.395 8.74 0.9854 1146.54 0.4400 106.50 0.700 15.02 0.9966 1318.17 0.4721 106.50 1.160 28.08 0.9779 Limit pressure expressed in Eq. 13 is derived on the tensile instability condition.

Correction of the RSA data both in reflection and scanning experiments allows a significant improvement in the reliability of RSA under different conditions.
Reduction of the processing time generally results in lower resolution, which already is of magnitudes of orders lower than in AD systems (approx. 150/500 eV at 5.9/122 keV).
(b) Corresponding E vs. sin 2 ψ distribution with uncorrected and corrected data according to Eq. 1.
The uncorrected energy positions show an undefined distribution whereas the corrected data represent the theoretic considerations precisely: Almost linear increase -∆E/E = ∆θ/tanθ ≈ ∆z/a when scanning the layer through the gauge volume and steeper gradients before and after due to the "shadow gauge" with a high secondary beam divergence ∆θ.