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Jiang Jinhe (2011) proposed carbon emission measuring methods of different levels according to different energy consumption characteristics and available statistical data of the national, regional, industry.
The top-down way is more suitable for the level of country or region, while for the industrial park, the fuel consumption data are difficult to statistics; so many scholars adopted the bottom-up approach.
This paper using Hierarchical cluster process to analyze the carbon emissions condition of 36 parks,.In the case of complete data, clustering results can reflect the relative position and gap of 36 parks in carbon reduction, so as to specify the direction of further carbon emissions reduction of the industrial parks.
But there lack the data of carbon emissions intensity of buildings, traffic carbon emissions intensity and carbon sink, at present there are no relevant statistical data on this aspect, or it is not complete.
It is imperative to construct the system of building carbon reduction, traffic carbon reduction and carbon sink.

[3] found that climate change led to 5%-10% reduction in average productivity of China’s crops, among which, such three major crops as wheat, rice and maize chiefly experienced yield drops.
Data sources and Methods The data are provided by Shangqiu Metrological Bureau and Shangqiu Agricultural Bureau, respectively.
Metrological Yield where represents the yield component affected by short-cycle factors dominated by metrological factors, namely metrological yield; refers to per unit area yield; and represents a function of time tendency simulated from the original yield data, namely trend yield; e refers to the yield component affected by such random factors as pests, diseases and social turmoil.
For example, extremely-low temperatures in April and May are unbeneficial for winter wheat production (Table.1), which is in accordance with the result that food production often experienced reductions caused by extremely-high and extremely-low temperatures in critical growth periods.
Warm-and-dry climates characterized by increased air temperature and decreased precipitation aggravated the situation of water shortage and brought about reduction in productivity.

The assessment was based on monitoring data concerning the certain priority parameters obtained from the selected representative measuring points.
The values of correlation coefficients r for the relationship between ITS and major ecological factors Ecological factors The Neva Bay The Gulf of Finland Data number r Data number r Neva river flow, [m3/s] Depth, [m] Flow rate, [cm/s] Water transparency, [m] Water temperature, [0C] Degree of salinity, [0/00] Mineral phosphorus, [μg/l] Total phosphorus, [μg/l] Mineral nitrogen, [μg/l] N/P weight atomic ratio 36 224 382 101 450 227 450 446 227 - -0.98 -0.86 -0.79 -0.16 0.68 - 0.89 0.94 0.65 - - 78 - 30 217 227 224 246 - 343 - 0.91 - 0.54 0.86 0.68 0.95 0.61 -0.94 -0.86 The water areas of the Neva Bay and the Gulf of Finland under FPC construction are rather non-homogeneous as to climatic, morphometric, hydrological characteristics and the level of anthropogenic loads which predominates differences in water quality and priority factors forming the trophic status [10-12].
The recommendations on the possibility of application of data obtained during the experiment for the development of mathematical models of sanitary and ecological state of the Neva bay and the eastern part of the Gulf of Finland were formulated: · for correlation and regression analysis of the obtained data · for formulation of empirical statistical model of ecological condition · for verification of the forecast models of distribution of pollutants in the aquatic environment References [1] Karatygin P., Chronicle of Petersburg floods 1703-1879, Publiching House of Suvorin, St.
[12] Savchuk O., Wulff F., Modelling regional and large-scale response of Baltic Sea ecosystems to nutrient load reductions, Hydrobiologia. 393 (1999) 35-43.
DOI: 10.1023/A:1003529531198 [13] Savchuk O., Studies of the assimilating capacity and effects on nutrient load reduction in the eastern part of the Gulf of Finland with biogeochemical model, Boreal Environmental research. 5 (2000) 147-163

Calculation parameters of orthotropic slab analogue method The flexural rigidity of concrete cellular slab is slightly less than the same thickness of solid slab and λ is defined stiffness reduction factor as ratio of stiffness from concrete cellular slab to solid slab.
Because different of cross-sectional shapes in both direction and concrete cellular slab appeared anisotropy, the stiffness reduction factors of cross sections in both direction are different. 1) Stiffness reduction factor λx in direction of parallel to hollow tubes (1) Where: Bx=EcIx, B=EcI, Ix and I are moment of inertia in unit width of concrete cellular slab and solid slab in x-axis direction respectively.
According to classical definition of beam stiffness, Chaoyang Zhou[5] puts forward a kinds of method which can calculate equivalent bending stiffness for concrete cellular slab in direction of vertical to hollow tubes and gives reduction factor table of corresponding slab stiffness that shown as in Tab.1.
(15) The equilibrium differential equation of concrete cellular slab is (16) Where: , it is bending rigidity of isotropic thin slab, , it is stiffness reduction factor of concrete cellular slab.
Tab.2 The mid-span deflection coefficients f’ （×10-3） tw/d d/h 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 4.300 4.287 4.275 4.265 4.256 4.248 4.241 4.235 4.419 4.400 4.384 4.370 4.358 4.347 4.337 4.328 4.582 4.557 4.535 4.516 4.500 4.484 4.470 4.458 4.804 4.771 4.741 4.715 4.692 4.672 4.654 4.637 5.106 5.061 5.022 4.987 4.956 4.929 4.904 4.882 5.519 5.459 5.406 5.359 5.317 5.280 5.246 5.216 6.090 6.007 5.935 5.870 5.813 5.762 5.716 5.675 According to geometry sizes, it can be obtained that tw/d＝110/140=0.272 7 d/h＝110/140=0.687 5 The deflection coefficient at midpoint of cellular slab is 5.290×10-3 from Tab.3 and then takes this data into equation (20), the calculated deflection at midpoint of cellular slab is 0.2235mm.

If more machines are integrated in to the one system, their production is synchronized and material flow is fast. [1] Most important effects which are coming from flexible production are: · Manufacturing process time reduction · Working out manufacture reduction · Stocks reduction · Manufacturing costs reduction · Rigid reaction ability to the market requirements · Higher machine usage · Redistributing times reduction · Quality increase · Reduction the Material and manufacturing documentation circulation · Stock material volume reduction · Manufacturing space reduction Intelligent manufacturing systems Automation process is basic condition for manufacture intensification and flexible reaction to the customer requirements.
Such self control function will also ensure constant optimization based on actual data coming from system surrounding.

In addition, finite element method (FEM) analysis was carried out to track the strain states during roll-cladding, and the strain history data obtained by FEM were utilized to simulate the textures.
The total thickness reduction of the assembly was 22%.
Table 1 Changes in thickness before and after roll-cladding and the reduction in thickness.
Sample Initial thickness (mm) Final thickness (mm) Reduction (%) thicknessε Composite 3.98 3.11 22 0.25 STS 430, outer sheets 0.49 0.46 6.1 0.06 AA 3003, mid sheets 1.0 0.74 26 0.30 AA 3003, center sheet 1.0 0.71 29 0.34 Experimental results and discussion Table 1 shows the thickness reduction of each sheet in the assembly.
These values agree well with the thickness reductions of the two different Al sheets observed experimentally (Table 1).

From test data, the crack growth rate appears almost constant for a large percentage of crack propagation life and accelerates over the last stage due to the interaction of two approaching cracks.
More stiffness reduction in front of a crack could cause more interaction.
In addition to the presence of a crack, the MSD situation could cause more geometric stiffness reduction.
From the experimental study, the stiffness reduction in front of a crack has a significant influence on the crack growth rate of the crack.
The more stiffness reduction ahead of the crack, the more increase of crack growth rate of the crack.

The empirical research dataset includes 322 accidents data on a 15.27km continuous downgrades segment.
The accident data structure is listed in Fig. 1.
Further data analysis would be necessary before countermeasures.
Data preparation is implemented by SPSS Modeler.
Statistical and Econometric Methods for Transportation Data Analysis.

So S constructed from the data of m years to the n is shown below, ，Shorthand for , is the index in the year i.
Beijing energy risk index system optimization calculation model of decision-making system is as follows: (1)Tectonic system decision data matrix as shown in (2)The various data values of the data matrix standardization processing.
For, defining, making the various indicators of information fusion, then new data matrix is obtained
Balance the difference of absolute value for the "fixed" type indicators, and the rest for the "cost type" data.
According the new accounting methods , the data changed from 92,000 tons to 200,000 tons in 2010 (Source is from the Beijing Environment Agency).The phenomenon showed that the COD real emissions were more than statistics data.

It is assumed that the window length is fixed to be W, and data from period 1, 2, …W will form the first window containing NxW units, data from period 2, 3, …, W, W+1 will form the second window containing NxW units, and so on and the last window will consist of data from period T-W+1,…,T containing NxW units.
Ferruzzi: Benchmarking economical efficiency of renewable energy power plants: a Data Envelopment Analysis approach.
Tone: Data Envelopment Analysis: A Comprehensive Text with Models Applications (Springer Science, New York 2007)
Zhu: Handbook on Data Envelopment Analysis (Springer Science+Business Media, New York 2011) [12] H.
Cheng (2003), Analysis of panel data: Second Edition (Cambridge University Press, Cambridge, UK 2003), pp. 5-21.