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This paper builds a concept model of low-carbon Scenic Area by referring to the UN Commission on Sustainable Development (UNCSD)'s DSR (Driving — Status — Response) model, and understands the connotation of low-carbon scenic area from five levels which include development stage of scenic area, resources and environment, emission-reduction technology, consumer operation, policy concept.
On the base above, to construct the evaluation system of low-carbon scenic area by choosing four level indicators including low-carbon resources evaluation, the development evaluation, the awareness evaluation and the experience evaluation , which can provide a reference for the development potential of low-carbon scenic area and provide the theoretical direction and the technical support for the inspect of future low-carbon scenic area, the cooperation of emission reduction projects and the establishment of discharge compensating system.
It provides not only scientific data for macro carbon emissions decisions, but also theoretical guidance and technical support for the inspection of future low-carbon scenic area, the cooperation of emission reduction projects and the establishment of discharge compensating system.
The structure of DSR index framework model is: the internal and external pressure forces the scenic area to make the transition (Driving) and simultaneously affects resources & environment, reduction technology and consumer operation (Status), and the scenic area made a response through environmental policies, general economic and sectional policies, and the changes among awareness and behavior (Response) when the low-carbon scenic area develops to a certain stage.
By referring to the conceptual identification of low-carbon economy and the building of evaluation index system [3], low-carbon scenic area and its developmental stage, Resources & Environment, Emission Reduction Technology, Consumer Operation, Policy Concept are closely associated.

The simulated results show a very good agreement with the measured data.
In order to improve the model predictive accuracy, the FMEP model used in this model based on the actual friction data on bench.
The results of engine performance has been plotted and compared with actual testing data.
The data show a good agreement between simulation result and actual test as illustrated in Figure 8.
The discrepancy of between simulation data versus actual measurement recorded at approximately 0.8%.

Based on land use data and social economy data in 1999 and 2005, this article analyzed the spatial-temporal characteristics and driving factors of the land use change in Shunyi district by GIS technology and logistic regression analysis method.
Data Sources and Research Methods The General Situation of Research Area.
Data Sources and Processing.
TM image data in 1999 and 2005 was obtained from remote sensing satellite ground station of China.
It would add social and economic variables data in the spatial data, then use the binary logistic regression model in the SPSS regression to analysis driving factors of land use change.

Based on the data from Architecture and Building Research Institute, Ministry of the Interior, the amount of CO2 emissions of the construction industry has exceeded one quarter of the national total.
It can be seen that the residential buildings play a key role on energy saving and CO2 emission reduction policy.
Therefore, it may influence the promotion of energy saving and carbon dioxide emission reduction policy.
Based on the data from Architecture and Building Research Institute, Ministry of the Interior, the amount of carbon dioxide emissions of the construction industry has exceeded one quarter of the national total.
Therefore, it may influence the promotion of energy saving and CO2 emission reduction policy.

B20E5 blend shows reduction in smoke opacity, CO and NOx emissions compared to baseline diesel fuel.
Data acquisition system, 7.
Smoke opacity meter To carry out the combustion analysis, the cylinder pressure was measured with a Kistler 6058A piezoelectric sensor and its signal was recorded with a high speed data acquisition system.
At high load condition, the reduction in CO is more prominent for B20 and B20E5 with 14-15% lower CO emission.
As a result, adding ethanol to biodiesel-blend had more significant effect on the reduction of smoke emission.

The use of this tool leads to a reduction in the amount of initial material and as a result - cost saving.
Optimization of the initial model allows to achieve a significant reduction in the weight of the part (also a reduction in the consumption of the raw material for its production) while preservation of the required strength characteristics of the part, as well as strength characteristic (and sometimes improving) of the optimized model as compared with the original one.
As can be seen in Fig. 3, and the quality of the computer 3D model is very rough, obtaining a high-quality computer model requires a product redesign taking into account the obtained data of material distribution in the process of topology optimization.
In fig. 4, the final 3D model is reprojected taking into account the data obtained in the process of TO.
Also one of the effective methods of weight reduction for such lightly loaded parts can be the use in the design of cellular structures.

It was then distributed to the 12 projects to collect data and project managers were interviewed.
Project coordination characteristics were analyzed and patterns were identified based on the scores calculated from the data.
The U&E scores were calculated from the collected data for the projects in Table 2.
This means that the projects need data acquisition and analysis more than problem definition and interpretation.
The formula is as below: Rs = 1 - The project equivocality and performance data are shown and ranked in Table 3.

The aim of the paper was to verify the results from Finite element methods with experimental data.
A Maximum reduction of 15.85% in diameters were measured.
Introduction In the automotive industry, the vehicle weight reduction is one of the major concerns.
In the second step, the structural analysis is carried with the result obtained from the step 1 and the final deformation in reduction in diameter is calculated as the final result.
It is observed from the experiment that by increasing the forming energy there is increase in reduction in diameter during compression process.

Thus, the original computer based solutions were developed for a wide range of problems: computer aided geometry for domain definition, data generators for finite element applications, output data file processors with visualization facilities, matrices defined as random access files with a wide range of subsequent applications in several disciplines and domains of science, FDM and FEM applications, customized solutions for heat transfer problems, computational fluid dynamics, experimental data reduction software applications, virtual reality facilities, semi-numeric modeling, computer based decisions.
Most of these applications present the output data in a graphical way, in order to be more relevant for a structural analyst.
Figure 2 – Screen captures of the results of the FEM applications which use the data type previously presented Several projects were developed using this data type: · Finite element method software applications, together with input data generators and output data visualization modules, Fig. 2, [2]; · Finite difference method software application for fluid flow through a manifold and for in-cylinder fluid flow, Fig.3, [3]; · Finite difference method software applications for heat transfer problems; · Experimental data reduction; · Virtual reality facilities; · Semi-numeric modeling; · Computer based decisions.
The input data are stored in a text file, which may be automatically created.
In this way, the applications may be linked either using original interfaces such as the ‘matrix’ data types, or neutral format data types, such as txt, csv, xml.

The Si particle size reduction for Alloy 2 also showed similar results.
The TEM images clearly demonstrate the reduction in the a-Al grain size.
Figure 4 also includes the 95% error bars for each separate datum point.
Close inspection of the data shows that Alloy 1 exhibits hardness values of ~61 Hv at the centre and ~68 Hv at the edges in the early stages of HPT processing but after 5 turns the hardness values are ~67 Hv at the centre and ~73 Hv at the edges.
Table 1 summarizes the measured mechanical properties as derived from using the BIT procedure: the methods for deriving the yield strength (YS) and the ultimate tensile strength (UTS) from BIT data were described in an earlier report [23].