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Based on statistical data, we analyzed the characteristics of changes in cultivated land, the influence of regional difference of economic development level for cultivated land resources in Suzhou and Wuhu of Anhui Province from 1991 to 2011 in this paper.
Table 1　Comparison of economy developing level between Suzhou and Wuhu in 1991, 2001 and 2011 GDP Gross industrial product per capita GDP Urbanization rate Suzhou Wuhu Suzhou Wuhu Suzhou Wuhu Suzhou Wuhu 1991 55.10 72.78 369.05 622.037 1083.78 3564.90 10.50 29.70 2001 193.22 218.77 421.32 1941.04 3314.80 9930.50 21.20 39.30 2011 802.42 1658.24 1171.63 3700.49 14970.00 43095.00 33.10 56.30 Fig. 1 Changes of GDP in Suzhou city and Wuhu City from 1998 to 2011 Cultivated land change process According to the statistics data, we drew the cultivated land change trends in Suzhou and Wuhu since 1991.
The regression models as follows: Ys=5443.6+28.605x+0.31x2（R2=0.939，F=41.05） (1) Yw=1582.7-20.33x2+1.741x2（R2=0.851，F=14.41） (2) Because quadratic term coefficient are greater than zero, With the development of economic structure, the reduction of cultivated land amount show quickly early and slow later, that is to say, speed of cultivated land non-agriculturalization is lager early, when the economy develops to a certain stage, along with the economic growth, cultivated land non-agriculturalization velocity decreases, which show that the higher economic development level, the smaller price of cultivated land is.

Table 1 Index system of risk measurement for IT project target strata factors layer index layer Risk measurement for IT construction project A Design risk A1 Risks of scheme and designing errors A11 Risks of multi disciplinary A12 Risks of importance degrees A13 Risks of data management A14 Risks of designing changes A15 Risks of creativity A16 Personnel technical force risk A2 Risks of techniques ability of crew A21 Risks of crew size A22 Actual experience risks A23 Risk management A3 Risks of systems A31 Risks of organization structure A32 Risks of controlling projects A33 Risks of quality
3.03 2.13 2.9 2.05 2.34 Medium 7 Risks of techniques ability of crew 1.93 1.78 3 2.7 2.35 2.05 2.29 Medium 8 Risks of controlling projects 2.08 2.23 3.18 2.28 1.95 2 2.27 Medium 9 Risks of multi disciplinary 2.43 1.65 2.65 2.35 1.95 2.88 2.21 Medium 10 Risks of designing changes 1.95 1.8 1.5 1.8 2.83 1.95 2.06 Medium 11 Risks of a lack of early warning mechanism. 2.2 1.8 1.68 2.45 1.95 1.83 2.04 Medium 12 Risks of resources management 2.13 1.68 1.68 1.68 1.95 1.85 1.88 Slight 13 Risks of measures of managing and controlling 2.35 1.6 1.98 1.98 1.08 1.95 1.81 Slight 14 Risks of systems 2.3 1.55 1.93 1.63 1.08 2.03 1.73 Slight 15 Risks of organization structure 1.93 1.33 1.33 1.33 1.95 2.03 1.67 Slight 16 Risks of experience accumulation 2.28 1.58 1.58 1.58 1.08 1.18 1.67 Slight 17 Risks of creativity 1.85 1.33 1.33 1.33 1.95 2.23 1.64 Slight 18 Risks of sharing information 1.5 1.45 1.33 1.33 1.95 2.73 1.55 Slight 19 Statement risks 1.88 1.43 1.43 1.43 1.08 1.83 1.48 Slight 20 Risks of data
The expert evaluation group evaluate sub-factors under factors layer, then the weight set of comprehensive evaluation factors level for all sub-factors can be gotten, such as table 10 to 14: Table 14 Risks response planning Sub-factors of risks Priority Risk response methods Measures adopted Main stages Risks of scheme and designing errors 1 Risk avoidance Risks must be aversed and the verification dimensions must be strengthened and meetings must be organized to discuss problems Proposal stage Risks of quality controlling 2 Risk reduction Pay attention to the quality controlling and details, adopting the methods of combining the regular maintenance examinations and strengthen regulation Implementation phase and starting stage Risks of coordination 3 Risks solution Strengthen communication and adopt forms of informal communication Proposal stage, implementation stage and acceptance stage and production stage Risks of importance degrees 4 Risks solution Members in the project group
should report the importance of the program at the beginning of the report, and encourage those involved in this work to pay much attention to the program from the perspective of evaluating the performance of individuals Proposal stage, implementation stage and acceptance stage and production stage Actual experience risks 5 Risks reduction Arrange proper work based on the amount of actual experience, avoid those inexperienced staff finishing important operations, and eliminate thoughts of luck Proposal stage Risks of safe measures 6 Emergency measures Carry out emergency measures and test them Proposal stage and acceptance and production stage Risks of crew size 7 Risk Contained Contain risks and add personnel Implementation stage Risks of techniques ability of crew 8 Risks reduction Every technical member must do the job related to his own ability to avoid the malposition of being arranged with jobs Implementation stage Risks of controlling projects 9 Risks contained The project manager
Implementation stage, acceptance and production stage Risks of data management 21 Risk contained.

The “Safe Hydrogen Fuel Handling and Use for Efficient Implementation 2 (SH2IFT-2)” [5] is just one of the numerous research and more applied projects regarding hydrogen-steels compatibility that will provide new experimental data, helping to bridge crucial knowledge gaps in the following years.
The following table provides relevant data using the SI units for some steel grades.
Hydrogen Compatibility with Steels As mentioned, hydrogen exposure can determine the loss of ductility of a steel, it can lead to reduction in fracture toughness and in general it triggers degradation mechanisms which may severely affect the performance of metallic materials.
More specifically, among the most relevant consequences of HE, the reduction of the following parameters is deemed to be critical: elongation to failure, area reduction to failure, strain hardening rate, tensile strength, fracture toughness and fatigue performances are crucial mechanical properties severely affected by hydrogen embrittlement [7,11,12].
Future experiments, conducted within the SH2IFT-2 project [5], will aim to partially bridge those gaps, thus providing new experimental data towards more reliable and safe hydrogen technologies development and utilization.

(5) The task of energy conservation and emissions reduction is heavy.
So it is great meaningful for energy conservation and emissions reduction to focus on developing energy saving transformation of existent public buildings.
(3) Strengthen the analysis and application of building energy consumption data.
The operational data of construction for building owners of the building units (including the owner individual), real estate development and construction units, design units, construction units, the property management unit is very meaningful.
It can make the property management unit get scientific data, handle and analysis the service condition of construction better, optimize the use of building energy, in order to maintain long-term effective energy saving building system.

The inner feature registration algorithm such as surface signature[2], spin-image[3], geometric histogram[4], harmonic shape image[5], splashes[6], is a dimension reduction approach.
Firstly, it is data processing, including removal of noise points, sampling and feature information extraction.
Firstly, the - neighborhood of point cloud data is determined, then the tree of the point cloud data is built, To traversal through the tree, the neighborhood points will be found, and then the normal vector at each point can be estimated.
The middle picture is the registration of both data.

In this paper, an evaluation model for capturing the right and sufficient data is used to evaluate that a new way of working on board is minimally as safe as an existing situation.
Fig 2.Evaluation Criteria Hierarchy of Ship Safety Manning. 3 Research methodology 3.1 Principal component analysis PCA is a standard data reduction technique which extracts data, removes redundant information, highlights hidden features, and visualizes the main relationships that exist between observations.
“A generalization of principal component analysis to K sets of variables”.Computational Statistics & Data Analysis, 35(4), pp.417-428 (2001) [6] KWONG C K,BAI H.

Polymers containing the Azo-benzene group have attracted attention due to anticipated uses such as materials for data storage, membranes with controllable permeability and materials with changeable solubility [1,2].
The foremost anticipated application for Azo-benzene in this respect is in optical data storage devices [2].
The shape of the group changes from planar to spherical, with a reduction of the C4-C4’ distance from 9 to 5.5.
The rate of the increase in the mean lifetime is in good agreement with literature data [3] on UV-light absorption (Fig 2b), as well as the time until the mean lifetime reaches a time-independent region.
The mean positron lifetime increased as a function of time, in good agreement with literature data on the decay of the excited cis-isomers of the Azocompound as measured with UV-spectroscopy.

This study is an attempt to combine accurate crystal structure data - obtained through high resolution Xray diffraction measured at the European Synchrotron Radiation Facility (SNBL beamline) - with UV-visible-NIR spectroscopy studies, on the basis of the crystal field theory.
Data were registered from 200 to 1100 nm, using an int-egrating sphere, BaSO4 as white reference and D65 as standard illuminant.
Crystallographic and colourimetrical data of perovskite pigments Cell parameters Cation in site A a (Å) b (Å) c (Å) Ln-O distance (Å) Al,Cr-O distance (Å) CIELab a* Nd 5.32593 5.32593 12.93236 2.5780 1.8937 13.3 Sm 5.29201 7.49107 5.28972 2.5067 1.9008 15.8 Eu 5.29838 7.46726 5.27702 2.4776 1.9036 16.3 Gd 5.30650 7.45314 5.25691 2.4594 1.9056 19.9 Dy 5.32294 7.40440 5.21161 2.4181 1.9116 18.0 Y 5.33124 7.37674 5.18376 2.4065 1.9074 23.5 Ho 5.32788 7.38374 5.18727 2.4090 1.9098 19.1 Er 5.33468 7.36542 5.16779 2.3943 1.9113 22.3 Yb 5.33719 7.32380 5.13267 2.3779 1.9083 21.2 Distance Al-O (A) Distance Ln-O (A) Dy Er Eu Gd Ho Nd Sm Y Yb 2.36 2.40 2.44 2.48 2.52 2.56 2.60 1.890 1.895 1.900 1.905 1.910 1.915 CIELAB a* Distance (Al,Cr)-O Dy Er Eu Gd Ho Nd Sm YYb 1.892 1.896 1.900 1.904 1.908 1.912 1.916 12 14 16 18 20 22 24 26 Figure 1.
In particular, a lowering of symmetry occurred from hexagonal (Nd-perovskite) to orthorhombic (Sm-Yb perovskites) confirming literature data [5,6].
This contraction brings about an octahedral tilting distortion, involving a reduction of the Al-O interatomic distance, which is inversely related to the crystal field strength ∆ according to the equation: ∆ = k (Al-O) -5.

An important contribution of ANNs is their ability to learn from incomplete and subject to noise data.
This adjustment allows the fuzzy systems to learn from the data they are modeling [6].
Nevertheless, the objective is basically the same, i.e., to generate digital images from sensed data.
Then, to convert it to digital, it needs to transform sensed data into digital form, with sampling and quantization [13].
Young, An efficient approach to converting 3D image data into highly accurate computational models.

In its radical form, DPPH• shows an absorbance maximum at 515 nm which disappears upon reduction by an antiradical compound.
The same data acquisition was used for the lemongrass ethanol-extract sample.
These data are consistent with the structure of eugenol, shown in Fig. 4.