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Increasing porosity can often lead to the reduction in compressive strength and fracture toughness, although the crack bridging effect of particles (as described in Section 3.1) also plays a role.
The reduction in compressive strength of R2 and RS relative to R4 can also be explained by the higher porosity and increased content of larger capillary pores when smaller rubber particles are used.
The effects of the zigzag surface and interface lead to the reduction in tortuosity of R2 and R4.
With the remaining data, the average hardness values were calculated in Table 3.
However, due to the higher porosity, larger critical pore size and lower bridging stress, compared to R4, the compressive strength and fracture toughness of R2 and RS generally perform the larger reduction.

A standard procedure was used for data reduction including geometric corrections, radial averaging, flat field and dark field corrections, absorption and Compton scattering corrections.
The Bragg peaks in the diffraction patterns of crystalline samples were identified and indexed using the JCPDS data base [4,5].
Conventional data reductions were applied for absorption, incoherent and multiple scattering.
The SANS raw data were reduced to absolute scattering probabilities IQ= ∂SQ/∂W using standard software in the HFIR Wavemetrics Igor package [7].
Data were placed on the absolute scale by the ratio of the area detector counting rate to the beam-monitor counting rate in the empty-beam transmission measurement.

On account of the presence of a large number of qualitative analysis, five-point Likert scale is a measure appropriate to collect and analyze Quantitative data.
Data standardization.
By formula (2), we can do standardized transformation and get standardized data.
Similarity coefficient among the samples is calculated based on standardized data, and then the fuzzy similar matrixis obtained.
(5) whenis a finite data set.

The consequence is local debonding of the matrix from the reinforcement particles, which leads to an irreversible reduction of the thermal conductivity after multiple heating cycles.
Increasing compressive stresses during temperature increase are expected to generate creep deformation in the aluminum which leads to a reduction of the void volume fraction.
The voids in this size range could be identified and finally their volume fraction at different temperatures could be calculated from the data sets.
The void volume fraction decreases to 0.04 vol% resulting in a reduction of the CTE in this region.

Abstract Aluminum-lithium (Al-Li) alloys are of interest to the aerospace and aeronautical industries as rising fuel costs and increasing environmental restrictions are promoting reductions in vehicle weight.
Introduction Aluminum-lithium (Al-Li) alloys are of great interest to the aerospace and aeronautical industries, as rising fuel costs and increasing environmental restrictions are promoting reductions in vehicle weight with corresponding increases in payload capacity.
This enables the reduction of background current, and ultimately heat-input, while increasing the material transfer rate.
Furthermore, applying a linear regression to the data allows for the estimation of the approximate heat-input required to induce complete lithium burnout during welding, ~14kJ/in.

A new method of the data measurement and processing for the centerless roundness measuring of components with the use of corrective adjustment is proposed.
Each of the above mentioned approaches has its own peculiarities and requires using particular measuring instruments and data processing algorithms.
Model-made data have a specific character and are difficult in practical applications.
These data are used for simulation by the developed model, which result becomes a new device setting.
This result corresponds with similar data presented in the study [4].

The LabVIEW application is used to conduct several tasks including user interface design, publishing and sharing measured data, instrument control and remote access to a system in the Process Control Laboratory.
In this study, the first part of the experiment set-up associated with a data acquisition (DAQ) card and a computer for the development of process control virtual laboratory has been conducted to generate user interfaces on the computer by using LabVIEW.
For virtual and remote laboratory, computers are ubiquitous to integrate part of every engineer’s toolbox used to do computations, data collection and reduction, simulations and data acquisition, and to share information via the Internet.
A computer equipped with the appropriate interface circuits, DAQ systems and software, can provide a visual aspect to these quantities, can process the required data [4] and can be made available to the remote area user via Internet or Intranet link.
In the virtual simulation experiments, the web server is used to share simulation software without instrument hardware but in the ensuing real-time measurement it is connected with experimental instruments through the DAQ card to accomplish data transmission between clients and remote instruments.

The average microstrain as function of the milling time (1/2h, 1h and 8h) was determined from XRD data.
The data of Borges et al [9] was used as basis for Figure 1.
The stored energy in the deformation and residual microstresses can be estimated with Eq. (6) and (7) and the data of Table 1, since Ehkl values are available, but Ehkl data about these specific alloys are scarce in the literature.
Conclusions The proportionality factor P was redetermined, using more recent and reliable data.
Data Vol. 2 (1973), p. 531

This article takes “1+4” the aluminum belt hot tandem rolling line as the object of study, based on the scene deviation data, has researched the deviation regular of tandem rolling strap, has established the BP neural network forecast model of the rolling process.
From the actualdeviation data, the change of rolling parameters in F4 rolling system, often has a decisive impact on the final deviation of the strip.
Then we analyse to the actual rolling production deviation data in one aluminum company.
This aluminum had been rolled at 12:35:34 on June 25th, the average wedge of the product is 3.12mm,average wedge is 5μm, belt speed is 5.16m / s, rolling length is 764m.We can obtain the characteristic value of the steady-state deviation of these data
On basis of distribution rule of actual rolling data, tail deviation was divided into some kinds of patterns, obtained characteristic parameters and mapping matrix by BP neural network to predict tail deviation pattern.

Individual components of the model are constructed based on real vehicle data and mathematical dynamic model equations.
The relation for motor torque (Tm), drive shaft torque (Tdr), and motor angular acceleration (ώm) is given as (1) The torque of drive axle (Taxle) is amplified by the differential gear ratio (Gdf) with slight reduction due to the inertia as (2) Furthermore, the vehicle drive acceleration is dependent, based on the 2nd law of Newton, on the motor angular acceleration, the wheel radius (Rw), power transmission efficiency (κ), and the gear ratios as (3) From the equation (1), (2), and (3), the final wheel torque (Tw) is obtained as: (4) Using (1)-(4), the vehicle
For the high precision design in simulations and analyses, the SimPowerSystem / SimDriveline model was configured with real vehicle calibration data.