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With the reduction of land resources, it has become the main content of current city construction that how to exert the most economical benefit with the limited resources.
Moreover, it also concludes that the social, economic and ecological influences must be considered in the evaluation of benefit of land resource use through the data analysis.
For a comprehensive evaluation of benefit of urban land resources use, we should focus on the three aspects of economy, society and ecology to design the index system, build the comprehensive evaluation model and carry on the data processing and analysis.
Because of the lack of data in this aspect in our country, we learn from international experience to use the perpetual inventory method to calculate the annual capital stock, using Formula (3) to complete the calculation: （3） where kit and Iit are the capital stock and the investment in the year of t period; kit-1 is the capital stock of t-1 period (base period); is the geometric depreciation rate.
So the entropy-based weight of the index is: (8) On the basis of using AHP and entropy weight method to determine the weight, respectively, the comprehensive weight of benefit indexes of land use can be obtained through the weighted summation of index data by the following formula: (9) mi is the index weight determined by AHP, is the index weight determined by the entropy method, n is the index number.

The lower computer software collects site signals, realizes the regulating and control algorithms, deliver real-time data to the upper computer, etc and the upper computer software reads the data block of the lower computer directly and mainly realizes the parameters configuration of the man-machine interface, the display system states and the off-line machine as well as the on-line system monitoring, control and maintenance.
However, the reduction of △H will greatly reduce the additional load on the horizontal steel.
Fig.2 Cable-supported full-model in wind tunnel Measurement and Data Acquisitions System.
Data acquisition is carried out by the parallel data acquisition module PXI-4472 of National Instruments (NI) of the United States.
Generally, 4-6 speed pressure steps are selected at each driving number and the stable period of each speed pressure step is no less than 10s so that sufficient vibration signal samples can be recorded down to enhance the precision of data processing.

Kodama and Aoki [23] estimated the number of bound water layers from DSC melting data.
Analysis of the DSC data indicates that as the fructose concentration increases, the fraction of the bound water decreases.
The surface potential (Ψo) and Debye length (κ -1) values obtained from best fit for the experimental data presented in Figure 5.
This particular set of data should be considered anomalous.
The retraction data showed clearly that pull-off forces in the fructose solution were significantly lower than in pure water.

To investigate this, artificial analytical data were processed in MATLAB environment to estimate the modal parameters.
Platt in [4], [5] explain how to fit experimental data using SDOF and MDOF techniques.
In the current work, this data will be used to find the modal parameters of the system [3].
To do this, the next step is to estimate the roots and residues from the sampled data.
The simulated analytical data is very useful because the exact values are known.

Table 1 - Data for the as-received powders and for Nimonic ® 90 alloys.
Table 2 presents measured Archimedes densities and Vickers hardness data for products sintered for 3 hr at 1310 ºC.
This is also attested by data in Table 3.
Fig. 4 - Typical σ x ∆L data and fracture morphology of W/A tensile bars sintered at 1280 ºC / 2 hr, at three different final heat treatment states.
Table 4 - Measured mechanical properties for lot B samples and typical data for Nimonic® 90.

The results showed significant reduction in the ability to carry out basic activities of daily living (ADL) in patients aged 80 - 90+ years compared to patients aged 45-59 years (p = 0.05) and those of 60-69 years old (p = 0.043).
Patients older than 80 years showed significant reduction ability to conduct IADLs compared to patients aged 45-59 years (p = 0.0047), aged 60-69 years (p = 0.0049) and aged 70-79 years (p = 0.029).
Data analysis Statistical analysis of results was done using Microsoft Office Excel program.
Results and discussions The results revealed significant reduction in the ability to carry out basic activities of daily living (ADL) in patients in the age group 80 - 90+ years compared to patients belonging 45-59 years age group (p = 0.050) and 60-69 years group (p = 0.043), see Table 1.
Patients older than 80 years showed significant reductions ability to conduct complex activities of daily living compared to patients aged 45-59 years (p = 0.0047), aged 60-69 years (p = 0.0049) and aged 70-79 years (p = 0.029), see Table 1.

Treatment causes some changes in the properties of the substrate such as a reduction of the % water uptake (up to 15%) as well as an increase in the elastic modulus (up to 10%). 1.
The addition of inorganic nanoparticles into silica matrix seems to contribute to the reduction of shrinkage and cracking after solvent evaporation [8-10].
The smaller increase in mass of RH, shows that the particular stone has less absorptive capacity in PMC, which also agrees with the data of % weight gain during application.
On the other hand, the rate of the water uptake for RH is less affected by the treatment where the % reduction ranges at 3-4%.
The results also showed a reduction in the average pore radius, mainly in RH, where in some cases the average pore radius was reduced from ~30 to ~8 microns.

Statistical Analysis+Data and Images Presentation.
From the present study, the group that treated the acarbose was the highest percentage reduction of FBS.
Furthermore, P2 was the highest percentage of FBS reduction in the group treated with an herbal infusion (Table 1).
The data revealed that the average concentration of MDA in healthy adult rats was determined as 28.90% nmol/ml.
This effect was associated with a significant reduction in the FBS and MDA levels as a biomarker in the occurrence of oxidative stress.

However, to facilitate the data collection, only four dominant factors were considered in the planning of experimentation.
This is due to the reduction of the cutting temperature and contact length.
This reduction is often followed by a reduction in frictional forces at the tool-workpiece interface.
Reduction of the tool–chip contact length is expected to occur due to the contamination of tool rake face, promotion of the plastic flow at the backside of the chip and overall reduction of temperature.
The data used for plotting Figure 10, were obtained by measuring thermal conductivities for several specimens under different temperatures.

Digital audio watermarking technology can realize owner identification, copy control and data authentication functions by hiding extra information into the audio products.
In order to guarantee the robustness and good imperceptibility of the algorithm, the watermark is embedded in the low frequency components of wavelet transform domain, and at the same time, the algorithm adopts the quantization scheme [3], the extraction of the watermark doesn't need original signal, which greatly enhance the practical application of the algorithm. 1 Digital Watermarking Embedding 1.1 Watermark image preprocessing The preprocessing algorithm includes scrambling encryption, dimensional reduction, and adding synchronous code sequence before the watermark is embedded.
This paper adopts the watermark with the size of , if it is embedded in the audio signal, it is needed to be converted to one dimension signal, that is, (2) V is the watermarking sequences after dimension reduction, W is the watermark image after scrambling
(3) Structure of the embedded data.
(2) Establish a SVM training samples and train, generate a fixed {0, 1} sequence and form a map relationship with part of the coefficients extracted in step 1, establish the training data of SVM: (3) Then use SVM to train samples, get classification model SVM '