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The obtained data mean that Curie temperature of the Ni nanogranules is lower then 298 K.
Caused by a change of the conductivity mechanism there is a relatively sharp reduction of composites resistivity at insignificant increase of metal concentration in this region of compositions.
The calculation of interplanar spacing from the electron diffraction data (and also the comparison of composites electron diffraction with various nickel content) allows asserting with a high share of probability that two crystal phases: Ni and MgO exist in the Niх(MgO)100-x composites.
As a result of these two types of percolation the reduction of the magnetoresistivity down to complete disappearance takes place.

SEM data also should be considered to convince the phases that came out from the interaction between crown tube and high temperature combustion.
The mechanism of failure initiated by the differences of fluid temperature, color change on the crown surface, thickness reduction, and the force on the edge (fissure), until burning on its edge surface.
Reduction of thickness is the effect by the damage of oxide structures.
The continous deformation will take place easily on the crown tube with the damage oxide and thickness reduction on its edge.

In this model, a reference chaotic waveformis transmitted during the first half of each data bit.
Various noise reduction techniques have been proposed to solve this problem and limitations as well [5-9].
Then the decision variable for producing the output data stream is gotten.
In particular, the reduction of error rate is more evident in the Rayleigh channel model.
In: International Conference on Communication Technology, pp. 1361--1363 (2000) [9]Wang Jianming and Xu Zhenlin.: A New Version of DCSK Data Transmission Scheme.

Model Formulation There is a high degree of uncertainty associated with inspection data as well as material, environmental, load and structural effects.
The most reliable data on the corrosion rate can be obtained by measuring weight differences in the reinforcing steel with reference to its surface area exposed to corrosion.
According to available experimental data, after the initiation of corrosion the bond strength initially increases slightly (before cracking occurs), but then decreases as the corrosion propagates.
The other two types of element are used to model the reduction in bond strength between concrete and reinforcing steel as a result of general corrosion.
As expected, this is due to the decrease in bond strength between the concrete and the corroded reinforcement, while reduction of the crosssectional area of the reinforcement has an insignificant effect on serviceability reliability.

Modeling and Experiment of Deformation Texture Development for Pure Al by CPFEM with EBSD Data Huachun Pi1, a , Jingtao Han1, b, Chuanguo Zhang1, c , A.K.
Both models predictions in tensile deformation are reasonable agreement with the experimental data.
Early studies connecting EBSD data to crystal plasticity modeling used manual measurements to define initial orientations.
More recent work has taken advantage of automated data collection on samples as a means of collecting detailed EBSD data for modeling.
The purpose of this study is to model deformation texture development for pure Al using crystal plasticity finite element method with EBSD data.

Second, k-means algorithm is used to cluster quality data and extend the data base.
It enables intervention at an early stage within a specific machine as well as a reduction of defective parts during the production process.
Fourier Transform of Quality and Process Curves from Assembly Lines In general, data analysts doing data mining have too few data sets to generate reasonable and advanced information and dependencies.
Relating clustered quality data with assembly data provides insight in contributing factors of the assembly processes.
By applying this method on quality data from test benches, further information can be extracted from already existing data.

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.

Mechanical properties, namely, yield strength (YS) at 0.2% offset strain, ultimate tensile strength (UTS), and fracture elongation (El%), were derived from the data acquisition system of the machine.
The response of these data is characterized by the mean values of the yield strength (YS) at 0.2% offset strain, ultimate tensile strength (UTS), and fracture elongation (El%).
Fig. 4 (a-f) show the main effects plot for the mean values of yield strength (YS) at 0.2% offset strain, ultimate tensile strength (UTS), and fracture elongation (El%) data in terms of %Mg content, Sr-modification, Aging Temperature (AT) and Aging Time (At).
Fig. 4 (g-l) show the interaction plot for the mean values of yield strength (YS) at 0.2% offset strain, ultimate tensile strength (UTS), and fracture elongation (El%) data in terms of different combination of %Mg content, Sr-modification, Aging Temperature (AT) and Aging Time (At).
(g-l) Interaction plot for the mean values of yield strength (YS) at 0.2% offset strain, ultimate tensile strength (UTS), and fracture elongation (El%) data.

The results, in accordance with experimental observations, favor ductile fatigue behavior, i.e. damage fields remain small for most of the fatigue life and do not cause the macroscopic stress reduction.
Since damage does not affect macroscopic stress reduction, fatigue life of an entire structural element can be evaluated by benefiting solely single locations at which the fatigue damage decisively emerges.
Evaluation of the Approach The approach was calibrated to experimental data.
The approach results in some certainty, and the difference between data and predictions stems from the calibration being applied solely to low stress ratio loadings as well as to a slightly different PC applied in the experiments.
Black dotted curve, and black dashed and solid curves (overlapping) represent the model results, respectively, while the green, red, and blue curves represent the corresponding data extracted from [2].