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Introduction Vibration reduction and isolation technology provide a new way for bridge antiseismic and has been used in railway bridge engineering in China, but there still are many problems not solved in calculation analysis and design theory.
Kyu-Sik Park et al. solved the contradiction between the reduction of the maximum forces and the increment of the beam displacement by introducing the weight coefficients [3].
According to the test datum of the lead core rubber bearings from China Academy of Railway Science, and [7]; =the relative displacement between beam and top of the pier at different time of the seismic excitation history; =the acceptable value of relative displacement between beam and top of the pier.
In order to acquire the reasonable parameters which meet both good vibration reduction effect and running safety requirement of vehicle, the dynamic optimization of the LRB dynamic controlling parameters is necessary and meaningful to the seismic isolation design for bridges

Reductions in traction-energy consumption contribute to direct energy-cost saving.
Based on specific exposures to emissions [84] provides country-specific data for emitted pollutants where average values have been used.
As it aids in the reduction of carbon emissions, the technology is intended to contribute to environmental causes.
Data availability Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Premkumar, S.Sivamani, E.Kirthees, V.Hariram, T.Mohan, Data set on the experimental investigations of a helical Savonius style VAWT with and without end plates, Data in Brief, 19 (2018) 1925–1932

Meanwhile, analysis of vibration types of transformation and rotation under seismic action is also carried out, in order to provide reference data for further elastic-plastic analysis.
However, the vibration of the structure often became the main reason of the engineering accident including damage of the structure, accuracy decreasing of a device or reduction of its reliability etc.

Laboratory experimental data are used to verify the model with nonlinear Temkin isotherm adsorption equation.
And the predicted solute concentration and observed data are presented in Fig. 4.
In Fig.4 the experimental data 1 is from Wang (2012)[7], while the experimental data 2 is the duplicate experiment in 2013.
Fig. 4 Comparisons of the theoretical results experimental data Model parameters analysis.
(18) where N is the quantity of the observed data, j is the number of the observed data, Cjc is the caculated data in the simulation , and Cje is the observed data in the experiment.

Then we obtained the data about the varying passenger flow density changing along with time and place.
We selected 8 locations including from pedArea1 to pedArea8 collecting the simulation data.
The area of each pedArea is 6 m2 instead of the unit area for the preciseness of the data collected.
Then we obtained the simulation data in first 40 seconds that was shown in figure 4.
From the density data of the table 2 and the density variety curves of the figures 5 and figure 6 we know: 1) The row data in table 2 shows the density changes over time, reflected in figure 6.

Reconstructing the geometry of the point cloud data from tests with the reversing engineering technology, a surface model that reflects the geometry morphology of the workpiece will be present.
By comparing it with the ideal point cloud data, the machining deformation of a workpiece can be easily found.
With such data communication and process control, a 3-D shape scan of a large-scale structure is completed.
By matching measurement data, extracting features of positions, coordinating system alignment and comparing with deformation, the result of measurement will then be output and displayed.
Error reduction methods for shape measurement by temporal phase unwrapping [J].

The strongest 3- peak data acts as the determinant and no others elements comprised to the samples which are refer to the card data.
Thickness and density of sintered samples Compaction Pressure (metric tonne) Sample Thickness of Sintered Sample (mm) Sintered Density (g/cm3) Average sintered Density (g/cm3) 1 A 0.98 3.610 4.129 B 1.16 4.347 C 1.21 4.430 1.5 A 1.14 3.740 4.108 B 1.03 4.296 C 1.00 4.289 2 A 0.95 4.269 4.276 Based on Table 1 it shows that the density of each samples increased but the thickness reduced after the sintered process due the reduction of porosity has occurred.
Figure 3 shown that the sequence of DC heating process and Table 2 shows that the data which duration to glow and break the sample with or without the Cu tape.
SEM images of crystal structure with different compaction pressure Summary The study on Zinc Oxide Crystal Growth is very interesting knowledge that shows us how to find the way to produce the data and aims the objectives or goal.
The overall of this project was achieved and it will be clearer if using different types of the method how to collect the data.

Because of this, the present study has the following objectives: To develop a methodology to estimate based on data of solar radiation and wind speed at a specific place, the energy potential available in the area.
The optimization method has been chosen to determine the annual energy supply from each source, [3] evaluates monthly the energy potential from these data and optimizes the best sizing system.
The data obtained are treated by the statistical distribution that best fits, which is the Weibull distribution [4].
For the evaluation of the solar resource it is necessary to know the exact latitude of the installation and have global radiation data in the area, which have been provided by the radar of the University of Piura.
Revenues of the project should be the reduction of the current billed amount by the consumption of electrical energy.

In general, a minimum of four wafers were used for each chemistry to ensure statistically significant electrical data.
In addition, process chemistry experiments were repeated at different via levels to acquire stacked via chain data.
SEM, XPS, TOF-SIMS, AES, and electrical data were combined to understand the surface mechanism and cleaning efficiency of wet cleans.
Electrical via chain yield data from several experiments indicated better performance of simple dilute acids as compared to proprietary solvent chemistries.
Multiplexed data showed significant concentration of C.

An organization needs integrated functional units which require inter-functional data flow for decision making, efficient procurement, management of inventory, accounting, human resources and distribution of goods and services.
In this context, management of organizations needs efficient information system to improve competitiveness by cost reduction and better logistics.
User acceptance & testing System integration & testing Mapping of business requirement Install ERP system Pre-implementation training Project team A END System maintenance & upgrade Post-implementation support Go-live Communication Post-implementation training Documentation Data migration & testing End-user B A YES NO Need ERP?
This step is optional ERP CRITICAL SUCCESS FACTORS Inter cooperation Organizational cultures Testing and troubleshooting IT support Educational Level User involvement Vendor support Inter communication Data accuracy Monitor and evaluation of performance Project champion Suitability of software and hardware Top management commitment and support ERP teamwork and composition Effective project management Clear understanding of strategic goals Project team competence Change management program culture SUPPORT ERP IMPLEMENTATION METHODOLOGY 1.
ERP system implementation failure in Company X Process Flow No project management Over budget Deficient process No scope management Data is not accurate The quality of data is bad Adopt the Big Bang method Insufficient training Software Complexity of Software Lack of product Software does not meet to business process No user manual Poor leverage of partner vendor Change Management No initiative from users to change their way of working Top management does not want to learn from experience Lack of internal communication Customer Mindset No recruitment for IT Staff / Technician Lack of IT/IS knowledge Insufficient education level Lack of internal cooperation