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It was concluded that the existence of any imperfection, spiral shape in particular, in the structure of perfect CNTs results in a remarkable reduction in the stiffness.
Then the gathered data was transferred to a commercial finite element package, where C–C bonds were modeled as circular beam elements [21].
Therefore, it was concluded that the existence of any imperfections, spiral shape in particular, in the structure of perfect CNTs results in a significant reduction in the elastic stiffness.
It was concluded the existence of any spiral imperfection in the configuration of perfect CNTs results in a significant reduction in the elastic stiffness of these nano-structures.

Data such as domain thickness, number of layers, number of elements, number of nodes, number of integration points are then inputted.
INPT also initialize matrices that would be required to store values later in the program and finally reads in material data from an excel file which contains the energy and attenuation data from the XCOM database.
Thus from the results it becomes needless to analyze the shielding capabilities of materials with the Monte Carlo technique when the attenuation coefficients and other radiation properties of the material are readily known or can be obtained from theoretical data bases such as the NIST XCOM data base for photon crossections [9].
Linear Attenuation coefficients were calculated using the XCOM data base.
This is as a result of the reduction in the attenuation coefficient of the materials due to increase in the photon energy shown in Table 1.

The available data are the locked-rotor torque (starting torque), breakdown torque, and full-load torque.
The errors F1, F2, and F3 are computed using the available data from the sample motors, that is, 5 hp, 50 hp, and 500 hp.
The manufacturer-supplied data from the respective datasheets of the three motors are presented in Table 1.
Using the performance characteristics, majorly considering the torque and other nameplate data to minimize the deviation between the estimated and manufacturer data.
Rabelo, A High Precision Method for Induction Machine Parameters Estimation From Manufacturer Data, IEEE Trans.

To have a better understanding of the naturally occurring and phase transformation between CO2 and reservoir fluid and provide accurate data for designing an oil development plan, we must know the interactions between CO2 and reservoir fluids.
Hence, in the first part of this paper the CO2 and reservoir fluid phase equilibrium is measured with laboratory experiment, then the collected data is used to calculate the theory and practical CO2 Sequestration capacities.
According to the International Energy Agency data, over the next 25 years, development and industrialization, particularly in the Asian economies such as China and India, is forecast to derive a further increase in fossil fuel demand of some 70%[3].
The statistical data indicate that the injection of CO2 can lower reservoir oil viscosity by 40% at pressure of 18.93MPa which is lower than MMP.
The experimental data was also used to evaluate theoretical and effective CO2 sequestration capacities.

Based on analyzing secondary data, the different transport modes are presented.
More secondary data should be used to validate the research in the future.
The profitability can enhance by effective management of the transport function, through cost reduction and service improvements.
Initially, the reduction in lead-time has to be making for a reduction in in-transit inventory, especially speedier.
This research is limited, since the possibility of the train route is to develop and without practice; the Eurasian Land Bridge is still under instruction; more secondary data should be used to validate the research in the future.

Application of CDPM in Abaqus requires large data inputs although most of the data can be readily obtained using mathematical models.
Data evidences in Table 8 reject the null hypothesis of multivariate normality for the data, since test p-value 0.0075 is smaller than 5%.
Yande, Practical data analysis for designed experiments.
Wickham, ggplot2: Elegant Graphics for Data Analysis.
Tukey, “Exploratory Data Analysis,” in Section C, 1977

The analysis of the structural data is based on the assumption that similar underlying mechanisms control the formation and evolution of deformation microstructures.
This trend agrees well with experimental data.
The large amount of structural information which can be obtained by modern microscopical techniques requires organizing principles to facilitate the data analysis.
The many processes to produce nanostructured metals have resulted in a large variety of deformation microstructures but lack of reproducibility together with the inherent difficulty in the analysis of such fine structures has limited the amount of quantitative data.
In parallel to the increase in strength as the structural scale decreases a reduction in ductility is observed.

Some of them expresses a law of mixtures; A few of depends on theory of elasticity; and some try to adjust mathematical equation to the experimental data by reasonably selecting the current constants in the expressions [11,12].
The numerical data of FE modeling shows that the filler geometry of the 2-D model has a small effect on thermal expansion.
The thermal expansion results are averaged and plotted with respect to volume fraction and the linear best fit is added to the data point.
(b) Averaged data of relative expansion versus volume fraction.
The FE model predicts that filler material influences more and there is a slight reduction in thermal expansion.

Shiers (1999) analyses the energy costs of 14 BREEAM-certified buildings by comparing data on energy costs of green buildings to conventional buildings.
Results show reductions in energy use, and savings ranging between 6% and 30% [7].
If the company trade their carbon dioxide emission reduction index in the carbon trading market, the carbon trading income also brings additional benefits to company.
Actually, analyzing the costs-benefits of green building is a more complex process; one important drawback for the study was the lack of data.
For the statistical analyses and to reduce the variance of data, more detailed data collection is very important for similar studies.

This may turn into Sf lowQoS, such as low response to web queries, unsatisfactory VoIP communications and limited streaming of data.
Indeed, moving from the (On/Off, FIFO) strategy to the (On/Idle, FIFO) the response time T changes from 84 sec to 1.8 sec (an almost 97% reduction), while moving from the (On/Off FIFO) strategy to the (On/Idle, SPTF) the response time T changes from 84 sec to 16.5 sec (an almost 80% reduction)
Environmental Protection Agency ENERGY STAR Program, “Report to Congress on Server and Data Center Energy EfficiencyPublic Law 109-431”, August 2, 2007
[5] A White Paper from the Expertsin Business-Critical Continuity, “Energy Logic: Reducing Data Center Energy Consumption byCreating Savings that Cascade Across Systems”, 2012 Emerson Network Power
Harchol-Balter, “Open Problems in Power Management of Data Centers”, IFIP WG7.3 Workshop, University of Namur, Belgium Nov. 20, 2010