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For high (45℃)/low (-25℃) temperature experiments, the reduction of FIBC base materials’ mechanical properties were not obvious and woof fabric reduced a little faster comparatively.
Plastic aging causes breach of material’s inner structure and leads to reduction of its mechanical properties.
Because aging mechanism is complicated, it is difficult to study properties change caused by different aging phenomenon directly at present, but is analyzed through experimental data.
The main reason of sawtooth waveforms is that the same direction silks broke at different time, which led to reduction of strength efficiency and increase of elongation, at last tensile strength less than theoretical value.
As can be seen from Fig. 6, along with the natural exposure time increased, tensile strength holding ratio and elongation holding ratio of FIBC base materials reduced, but the reduction degrees were different.

Due to the effects of mass-customization there is an increase of the variance of the products combined with a reduction of the number of units per variation and a volatile costumer demand.
On the one hand side the continuous change of the consumers’ behavior, like mass-customization, lead to an increase of the variants of each product combined with a reduction of the number of units per variation and a reduction of the product life cycle time [1].
The variance of the products will increase combined with a reduction of the amount of units per variant.
Due to the approach that the intralogistics milkrun-system is substituted by an intralogistics taxi, the transportation sequence and the required data for the handling of remaining quantities has to be implemented to the library of the simulation model [3].
The reduction of the delivery lot size reduces the fixed capital at the production lines but increases the amount of needed vehicles.

But the data also show 2009 and 2010 relative to the previous year data growth are close to 50%.
The data isn’t growing.
The effectiveness of management reflected in the data of Veh & Veh that represented a decrease of 25% in 2011.
References [1] Qi Y, Smith B, Guo J, Freeway accident likelihood prediction using a panel data analysis approach, J.
[4] Li-Yen, Chang, and Wen-Chieh Chen, Data mining of tree-based models to analyze freeway accident frequency, J.

The whole Test Rig is equipped with devices for on-line data acquisition of main operating parameters such as temperatures, pressures and flow rates.
Measured values are recorded and processed by a data collection system including a multi-channel data logger and a personal computer for on line data acquisition and processing.
The experimental data are linked with the evaporator configuration.
During the test runs two series of performance data were derived: when using both R422A and R22.
The data derived by the experimental tests performed with R22 and R422A for wound coaxial coil evaporator having a rated duty of 25 kW (by Catalogue data), when working with R22, confirm that the rated cooling power reported in the catalogues is right for the case of R22, while, when using the evaporator with R422A, there is a leak of performance which is significant.

Liu et al.[2] used factor analysis of the multivariate measurement data to get the coefficient matrix of the process model.
Shan and Apley [3] used blind source separation techniques to build the data-driven process model.
From the history data, we get the covariance matrices of sensor noise, natural variation of the locators and process noise, where is an r × r identity matrix.
From the above calculation, we can get the specification of these data, as shown in Table 1.
Table 1 Specification of the quality data Item USL Target LSL Value 5.1 5 4.9 5 0.0458 So we can use Equation (11) to calculate the Cpis: The result shows that CP is not enough, We must improve the process capability.

The CT-scanned or MRI medical image data are processed first in order to obtain RP-acceptable STL file format.
CT Data in DICOM format was loaded into Amira (Mercury Computer Systems, Inc., www.tgs.com), a commercial image data processing software, in order to convert the images to STL file format CAD file for RP system input.
Noticeably, comparison data here did not include the binder cost, fabrication speed, surface finish, and post processing time of Z310 and Z510.
Fig. 12 The cardiac model made by Z310 (left and center) and compared with CAD data (right).
CT-scan data are loaded into commercial software, Amira, to convert to RP acceptable data format and solid cardiac models were built by Objet RP system.

BI versus compressive strength from a) Brazilian data (green balls); b) international data (red squares) [7] Fig. 3 shows the same tendency for Brazilian (Figure 3.a) and international (Figure 3.b) data: high dispersion of BI for low strength concretes, and lower dispersion for high strength concretes.
CI data calculated from benchmark are shown in Fig. 4.
Influence of BFS content on the CI for: a) Brazilian data; b) international data Fig.6.
Influence of BFS content on the BI for: a) Brazilian data; b) international data Fig.8.
Influence of FA content on the BI for: a) Brazilian data; b) international data Fig. 7, which analyzes the influence of BFS on BI, shows that there is no some important increase or decrease of BI from BFS content change.

Cell adhesion was observed qualitatively by using FESEM and cell proliferation was determined quantitatively by using AlamarBlue reduction assay.
Cell proliferation was determined via AlamarBlue reduction assay.
Data are expressed as mean ± standard deviation.
Cell viability was examined via AlamarBlue reduction assay.
This assay works by indicating whether the viable cells are able to reduce resazurin to resorufin and thus the percentage of reduction is directly proportional to the number of healthy cells.

With the analytical formulations of tooth pitting, the dependencies between damage levels of tooth pitting and the reduction of mesh stiffness can be obtained, then the damage dynamics of the gearbox system due to tooth pitting can be built.
Obviously, in the pitting area, the tooth pitting will result in the reduction of contact width, thus reduce the total mesh stiffness.
For simplicity, we assume that they reduce proportionally to the severity of pitting damage. kP is defined as the reduction of mesh stiffness due to tooth pitting, and it can be obtained: (2) So, when the tooth pitting occurs, the magnitude of mesh stiffness will reduce with the increase of pitting level according to Eq. (2) in the tooth pitting area, and the phase of mesh stiffness will shift according to the size of dp described in Fig. 1.
The trend analysis of tooth pitting growth The process of tooth pitting growth can be described by a damage index which is extracted from original data collected by monitoring parameters in MP, and a typical curve of tooth pitting growth over time can be plotted as shown in Fig.2.
We obtain the following conclusions: (1) The mesh stiffness model of tooth pitting damage proposed in this paper is effective to describe the dependencies between tooth pitting growth and the reduction of mesh stiffness. (2) The dynamic model of tooth pitting can correctly represent the dynamic response with the increase of tooth pitting levels. (3) The simulation results show that: the acceleration of gear can be used to track the development of tooth pitting effectively with the most sensitive way.

A wealth of new experimental data has been collected which add strong support to our understanding of plasticity problem.
On the base of this data, the plastic distortion tensor is evaluated for the deforming sample in the coordinates x, y and z [21] as , (1) where and are, respectively, symmetric and antisymmetric parts of the tensor , i.e. plastic deformation and rotation tensors.
These are indicative of insignificant hardening of the material with increasing degree of reduction.
However, the billet metal ductility cannot be estimated unambiguously and with a sufficient degree of certainty on the base of the above data (Table).
The experimental data [12] reveal a zone of jump-like rise in the internal residual stresses, which corresponds to the position of the jump in Fig. 4 a.