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Relative data is collected from National Bureau of Statistics of China.
Data analysis shows the proposed models, especially based on LS-SVMs, have more steady performance and higher accuracy.
We are given the training data set of d points, Here is the i-th m-dimensional feature vector obtained from the above data mining by PCA and inputted into the LS-SVM regression model for training and forecasting, d is the sample number of training dataset.
The output data zi∈R assumed as the construction worker scale.
,in: Credit Scoring using Least Squares Support Vector Machine based on data of Thai Financial Institutions.

The model has been validated by numerical/experimental comparisons in terms of stiffness reduction over the number of loading cycles, considering three different applied loads.
Salkind [8] suggested to present the fatigue data in the form of S-N curves.
The calibration has been performed for both loads P1 and P2, to minimize the total error (meant as the sum of the errors for both curves) between numerical and experimental data.

More recently, studies on the building energy reduction technology are actively in progress.
Aimed to collect each element technology and weather data, temperature and humidity measuring sensors were installed in a total of points and portable equipments are used to collect and saved data.

The eco friendliness and reduction in wear and tear aspects in machineries with the use of natural fiber composites also has been captured in this paper.
This paper is an earnest compilation of the data regarding a variety of natural fibers, their physical and mechanical properties, their resilience and strength.
Considerable effort has been put in bringing the data on various natural fiber composites in one place by cutting out the details from various sources so as to make it as a ready reckoner for any researcher for future research in this area. 1.

Xie1 1 Tianjin Meteorological Bureau, Tianjin 300074, China a tjwbg@yahoo.com.cn b tjwzy@yahoo.com.cn Key words: Radiation fog; turbulent flux; easterly flow; meteorological tower Abstract: The air flow and turbulent fluxes features during the radiation fog formed on the dawn of 17 October 2007 is discussed in order to study the mechanism of an unexpected night fog, based on the meteorological and turbulent data obtained from the 250 m height tower in Tianjin, as well as the NCEP reanalysis data and other observational data.
Data acquisition and processing The North China Plain is surrounded by Yan Mountain in the north, Taihang Mountain in the west, Bohai Sea and Yellow Sea in the east.
Acknowledgements NCEP data is provided by NOAA.
The tower data is provided by The Institute of Meteorology of Tianjin and AWS data by Tianjin Meteorological Bureau.
The tower data is provided by The Institute of Meteorology of Tianjin.

Drying and heating lumped models were proposed and fitted to the experimental data.
Non-linear regression analyzes were performed to verify the consistency of the models to predict the experimental data.
The fitted models presented good agreement with the experimental data.
Table 3 – Parameters of the Eq. 1 obtained after fitting of the drying model to the moisture content experimental data of the sisal fiber.
Table 4 – Parameters of Eq. 2 obtained after fitting of the heating model to the surface temperature experimental data of the sisal fibers.

/cover and got the area data of different land use types.
Data sources and preprocessing This study used remote sensing data that covered five different times ( 1986, 1993, 2000, 2008 and 2012).
The image data ofe 1986 (July 30), 1993 (September 3) and 2000 (September 3) are obtained from used Landsat-5 TM image; The ture color Quickbird and WorldView image are used as the analyses data in 2008 (February 20) and 2012 (January 17), respectively..
Before being classified, source data of the five time nodes were first preprocessed to minimize errors in the following process[26].
Firstly, respectively use ENVI4,the remote sensing data processing software, to preprocess the remote sensing data of five periods, including image mosaic, geometric correction, data registration, clearing system errors and other data preprocessing[20]; Secondly, use the topographic map(1:15,000), administrative district planning map and other basic maps, corresponding with the reserve, to classify the land use type of various periods remote sensing date according to the classification standard in Table 1 through visual interpretation[27]; Finally, use the MAPGIS system to extract the characteristics of land use/cover in different times, get the area data of different land use types (Table 2).

A principle component model is then built up using the measurement data of sediments from the continental slope and shelf in southern South China Sea.
Theory of Principal Component Analysis Principle component analysis is the most important way in multivariate statistics, it is one way to make a large dimension data matrix dimensional reduction.
The research target is the data matrix formed in test procedure.
Cumulative contribution rate h of principal component can be obtained by the sum of covariance matrix X’s former k eigenvalue divide the sum of all eigenvalue, it is that: , clearly the cumulative contribution rate express the proportion of k principal component analytical data changes by all data changes.
According to the principle of principal component analysis, let , , , as the column vector data matrix to form a 124 dimensional data matrix, calculate the data correlation coefficient matrix R, then calculate the characteristic root of R.

The analysis of the recorded test data aimed on the distinction between cumulative material response (creep deformation, material hardening / softening) and spontaneous material response (material hardening / softening).
The subsequent analysis of the data used literature information to distinguish between cumulative material response and spontaneous material response [5,6].
Every 100 cycles peak/valley data pairs of force and displacement were recorded.
The analysis of the data was done automatically using a self-developed Matlab tool (MathWorks Inc., Natick, Massachusetts, USA).
However, to prove the latter further examination of the test data in the form of a detailed and quantitative dynamic mechanical analysis is required.

Incineration-power generation is making the combustible matter in garbage for incineration treatment by incinerator, which eliminates large amounts of harmful materials in garbage through the high temperature incineration, to achieve the purpose of harmless and reduction, meanwhile, it uses recycled heat energy to supply heat and electricity, to realize the beneficial utilization of waste resources[1].
The Technology Advantages of Industrial Residue Incineration Power Generation Environmental Aspects. 1) Volume reduction effect is good.
The burning boiler replaces the original 5 x35t / h medium temperature and medium pressure chain type boiler which is inefficient and high pollution, and the project with high efficiency bag filter can play the effect of energy saving and emission reduction.
the project Serial number Project Unit 1×130t/h+1×C12 average 1 Heat load 0.98Mpa t/h 50(151GJ/h) Heat load 3.82Mpa t/h 22.33(74.47GJ/h) 2 The evaporation capacity of boiler t/h 130 3 The inlet steam volume of turbine t/h 107．67 4 The standard coal consumption rate of heating kg/GJ 43.98 5 The standard coal consumption rate of power generation g/kWh 480 6 The power consumption rate of power plant % 5.5 7 The power consumption rate of heating kWh/GJ 6.303 8 The power consumption rate of Integrated plant % 17.3 9 Annual heating capacity GJ/a 1623.38×10³ 10 Annual generating capacity kWh 0.864×108 11 Annual power supply capacity kWh 0.715×108 12 Annual consumption volume of standard coal t/a 143280 13 The standard coal consumption rate of power supply g/kWh 508 14 Annual utilization hours h 7200 15 Heat-to-electric ratio % 313.6 16 Thermalization coefficient 100 17 The thermal efficiency of the whole plant % 58.48 18 Annual standard coal saving t/a 115488 According to the relevant data