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Introduction As a natural capital,soil has not  been comprehensive cognized recently.With the development of city modernization and industrialization,human activities affect the evolution of soil resource function profoundly and lastingly,including the shift between soil  bearing function and soil production function,and the reduction of ecological regulation function from sealing soil etc..The transformation of soil functions, reduction or even specific function disappear, these has caused the unbalance of many areas’ soil function,and these factors has become the shackles of soil utilization value maximization, so in order to achieve the optimized use of soil resources we  must carry on scientific planning.
We can select a scientific econometric model to choose soil advantage  function or function group in specific region with the specific data,change the simple ideas to determine the optimal use  and value only on the basis of land productivity and geographical position.
Factor analysis model also can make some complicated and high correlative indexes into a functional groups according to the objective representation of data.
The empirical analysis 3.1 The data source.On the base of Zhengzhou soil species, soil database, agriculture and land use database, landform characteristics,56 sample points in the territory were selected and corresponding soil function parameters were extracted .
Then this paper obtain soil value data matrix by two methods of reference and measuring formula.

The steel industry is no exception - an example of cost reduction is a technology of sorbitized wire rod production.
Introduction Tough economic situation directs industrial plants toward modernization of production processes, reduction of production costs, increase of efficiency of the operating equipment in conditions of considerably limited budget for R&D, making foreign methods of enterprise management popular, e.g.
One of such examples of production costs reduction is the process of rolling sorbitized wire rod, allowing to exclude expensive patenting operation at the metalware processing stage [3-4].
(2) In order to obtain the most certain calculation data, total heat removal factor aS is defined empirically at the similar (or close in construction) Stelmor line.
During R&D works in order to increase accuracy of mathematical model and its adaptation to actual production have been made the following changes in the software: implementation of empiric heat removal parameters; consideration of changing steel heat capacity along with the change of its temperature; consideration of massiveness factor for each zone of the air cooling line; addition of ability to create data base for heat capacities of different steel grades; creation of data base for air cooling line models; realization of data export as a table on MS Excel sheet; realization of multizone air cooling line model with ability of individual configuration of each zone; development of electronic reference book for the cooling process parameters and software user manual; calculation of wire rod cooling modes and motor speeds of air sections for each zone both in conveyor’s cross-section and longitudinal section of air cooling line.

The simulation is based on the meteorological data of monthly typical day which was summarized from a year round data of subtropical Guangzhou.
The meteorological data of monthly typical day was used to predict the performance of solar refrigeration system reasonably.
The hourly data of typical day is the average of monthly data.
The data covers from April to October because the solar cooling system mainly works in this period.
The simulation is based on a year round meteorological data of subtropical Guangzhou.

In the development of new processes to use the potential of iron reducing bacteria, Acidiphilium cryptum, the main bacterium involved in the reduction of Fe(III) compounds in acidic environments, could play an important biohydrometallurgical role.
Although iron oxide reduction can be performed by neutrophiles (e.g.
Comparing the evolution of total dissolved iron for inoculated vials and abiotic controls containing hematite and goethite, it is evident that A. cryptum promoted their dissolution due to the reductive environment generated inside the vials (data not shown).
Secondly, although based on thermodynamics any iron-reducing microorganism can also reduce Mn(IV) [10], directly or indirectly, via Fe(III) reduction (i.e. using dissolved iron as a redox mediator) [11], to our knowledge, this is the first time that manganese reduction (and bioleaching) by Acidiphilium cryptum is reported.
Although Figure 2 shows that oxalate caused the chemical dissolution of both solids due to its chelating and reducing nature (data not shown) [1], it had a marked stimulating effect on the microbial reductive dissolution, triplicating the concentration of dissolved iron obtained in its absence (Figure 1).

Current energy strategy of European Union is focused especially on reduction of operational energy of buildings.
The optimized alternative achieves very low embodied energy (218 MJ/m2) and high reduction of embodied CO2 (-114 kg CO2eq/m2).
Policies are being integrated in energy strategies and building regulations at several scales that are focused mainly on reduction of energy during the occupation phase.
The input data of environmental indicators are extracted from IBO database [13], only for straw are from Wihnan’s study [14].
This variant assures reduction of EE about 87% in comparison with B, about 75% in comparison with A and 58% in comparison with C.

As can be seen from the above equation EEOI is a comprehensive data, the calculation parameters include the ship's own data and ship operation data in two parts.
The ship’s own data is mainly about fuel consumption, such as: all kinds of fuel consumption, CO2 emission factor of fuel; ship operation data is the amount of cargo loading, voyage and other information.
After getting the variance of each cross-section sample collection, draw each characteristic wave height cross-section sets variance curve shown in Figure 3 access to data: According to EEOI curve obtained from each characteristic wave height simulation, as the wave height increases, EEOI curves converge slower.
Fig. 3 The curve of the cross sectional samples variance This phenomenon is due to the fact that the perturbation model is the random wave force, but the overall random wave force data has a certain statistical properties.
This paper is also an example to a 2000nm voyage, Given the relation curve between the engine speed reduction and EEOI changes.

X-ray diffraction data indicate that the compound is monoclinic, P21 / c space group.
The cyclic voltammograms indicate that the compound decreases the rate of oxygen reduction reaction (ORR) and induces the ORR more difficult in aqueous solutions.
Crystal data and structure refinement for the compound are shown in Table 1.
Empirical formula C12 H16 Co N2 O8 Formula weight 374.9 Temperature [K] 293(2) Wavelength [Å] 0.71073 A space group P2(1)/c a [Å] 9.783(2) b [Å] 5.1290(10) c [Å] 17.446(5) α [°] 90 β [°] 123.71(2) γ [°] 90 V[Å 3] 728.2(3) Z 1 Dc [g cm -3] 1.56 µ (Mo Kα) [mm -1] 1.711 F(000) 1.224 θ Range [°] 4.17-27.47 Reflections collected 6738 Independent reflections 1677 [R(int) = 0.0138] Data / restraints / parameters 1677 / 0 / 122 Goodness-of-fit on F2 1.094 Final R1, wR2 [I>2σ(I)] R1 = 0.0222, wR2 = 0.0614 R (all data) R1 = 0.0227, wR2 = 0.0619 Largest diff. peak and hole (e.
And except Fig.5B, the peak currents for oxygen reduction obtained on modified electrodes for 60 min are smaller than those obtained on modified electrodes for 30 min.

Baseline performance data is collected and the program’s goals are established.
The equipment operators start collecting data to determine equipment performance Planned Maintenance: The maintenance staff collects and analyzes data to determine usage/need based maintenance requirements.
Maintenance Reduction: The data that has been collected and the lessons learned from the TPM implementation are shared with equipment suppliers.
This analysis data is also fed into the maintenance database to develop accurate estimates of equipment performance and repair requirements.
Organizations all too often fail to collect useful ‘before’ data that can later be compared to ‘after’ data.

,Ltd, Qingzhou, Shandong, 262500,China afy58wang@126.com, b13589173172@163.com, csunruifeng_15@163.com, dyufeizb@sina.com Keywords: Wheel loader, Consumption reduction, Performance, Evaluation Abstract.
The performance of acceleration time, distance and the fuel consumption was evaluated based on the data from five tons wheel loader.
Based on the typical job cycle and the analysis on traction and speed, the author got the data of accelerated performance, efficiency and so on.
Acceleration Time and Acceleration Distance According to the test data of typical conditions, when making calculations, the main concern should on four stages of moving forward with no-load, moving back with load, moving forward with load, moving back with no load.

These algorithms for nonlinear structural characterization data demonstrated a very good effect.
In order to enhance the algorithm’s ability to make the sample data being more real-time, researchers made some improved algorithms later, such as the degree of isometric mapping, unsupervised discriminant mapping and linear local tangent space alignment (LLTSA) [7] algorithm.
A global geometric framework for nonlinear dimensionality reduction.
Nonlinear dimensionality reduction by locally linear embedding.
Laplacian Eigenmaps for Dimensionality reduction and data representation.