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Using numerical approaches and experimental data, patterns describing the changes in agglomeration rate and the features of dispersing system stability were identified.
It was found that frequencies above 20 kHz are optimal for stable particle reduction to nanoscale sizes.
Agglomeration process based on a numerical approach From the presented experimental data it follows that the agglomeration process and the speed of particles decrease with increasing temperature.
Thus, the interaction between the components depends on the viscosity of the medium, and this must be taken into account when considering different types of mixing. 3 Conclusion From the obtained experimental data, which were described on the basis of numerical approaches, it follows that the agglomeration process and the particle velocity decrease with increasing temperature.
With the obtained experimental data of the dispersing process, we distinguish the following main factors, in particular: 1) temperature effect on agglomeration, the dispersing results show that with increasing temperature, the agglomeration rate of particles partially decreases.

The preparation was carried out by reduction of AgNO3 solution over TiO2 photocatalyst under UV light exposure.
From the AAS data, the Ag formed and deposited on TiO2 surface can be calculated.
The electron then reacts with Ag+ ion to proceed reduction to form Ag0 [21-22].
XRD patterns of a) TiO2, b) TiO2-Ag (1000), c) TiO2-Ag (1500), d) TiO2-Ag (2000), and e) TiO2-Ag (2500) The detail data from the XRD pattern are presented in table 1.
It is clear therefore that this data is in a good agreement with data from AAS analysis.

Initial preprocessing facilitates the classification task by reduction the number of data classes either by removing not relevant classes, or by making particular classes more concise in the multidimensional input data space e.g. by noise removal.
All neurons have common inputs, whose number, n, forms a n-dimensional data space.
Such model is suitable for analysis of complex data like for example the biomedical signals.
Example training data files.
Three example data files are shown in Fig. 2.

In this control unit data is classified and the best decision will be made based on the received data.
Data transfer by this model takes place via internet or GSM.
Observing and collecting data 2.
Decision making Observing and collecting data.
Also vehicles’ data is being updated continuously and finally all data is recorded and it is ideal for officials since they can access to full and precise data about owners of vehicles.

After data reduction, Do, D1 and n were obtained by fitting the creep data with the power-law expression shown in Eq. 1 without consideration of εp(σo, t) as its value was relatively small under low stresses.
Creep data of 45 oC composite coupons and the power-law curve fitting scheme Predictions for Other Orientations.
-6 MPa -n min -n , n = 0.238 (a) Creep data and predictive curve (φ = 15o, 5 h creep) (b) Creep data and predictive curve (φ = 34o, 5 h creep) Fig. 3.
Comparison of experimental creep data with simulation models for stress-strain responses under different orientation angles Prediction of Long Term Creep Data.
For example, consider the data for a 24 hour creep time shown in Fig. 4 (b), the optimal power law parameters, determined so as to fit the first 5 h creep data alone, yielded overestimated creep strains for the subsequent longer creep duration up to 24 h.

The main goal of developing environmentally friendly products is the reduction of the environmental impact of the product.
Tyteca et al [15] indicated that there are three main categories of environmental performance indicator (EPI) namely, physical indicators focusing on material and energy inputs and outputs from production process, business/management indicators focusing on linking physical aspects of environmental performance to information on business performance, and impact indicators relating physical output data such as emission to potential environmental impact like global warming.
This stage could provide a set of appropriate data to identify the fundamental environmental problems and main causes of the problem.
The main goal of this stage is to assess and study the collected data and information in order to identify the main causes and sources of variability.
Lim, Fuzzy FMEA with a guided rules reduction system for prioritization of failures.

Samples of liquids were taken out of the tank by the HIAC/ROYCO particle counting system: auto-Flow Chemical Sampler (AFCS) equipped with a Microcount 100S sensor, model 8000A liquid particle counter (LPC) using CountSpec 2.0 software, the data was processed and could be transported to Excel-file format.
The model (see Eq. 1) used to fit the data points uses linear diffusion rates and compensates for non-ideal continuous stirred tank reactor behavior and the fraction of particles immediately rinsed off during re-immersion.
[ ] ( ) ( )tktk tktk bc b i c cb e fefee kk k A tI − − −− − +−− − = 1))1( ( ][ )( *0 (1) Variables that have been fitted to the experimental data are the initial amount of particles in the carry-over layer ([A]0), f, and kb.
The effect of PEG addition goes from an almost 60% reduction in diffusion rate for the 0.43- µm particles to an 30% impact for 0.33 µm.
At pH 10 addition of surfactant has no effect, which probably means that the positive effect of the surfactant (reduction or shielding of surface charge and thus reduction of EDL-thickness) is reduced by the negative effect (increase of effective radius/mass).

In the telescope, the light-weighted mirror plays a critical role both in quality of images and weight reduction of satellite payload due to launch vehicle constraint.
Light-weight design and analysis of mirrors are very important issues for weight reduction and better performance of system.
In general, the format of FEA data is not acceptable for optical analysis software, and the displacements and stresses at arbitrary located finite element nodes can not be input directly into optical software.
Therefore, the computed displacement data provided by FEA codes must be translated into forms acceptable by optical software [1].
Usually, the structural displacement data must take coordinate transformation to the optical coordinate system based upon sag expression.

And kinematic viscosity at 40 ℃ and 100℃ was also analyzed, test data were showed in Table 2.
It can be seen from the table data, when the carbon chain length increased and the friction coefficient was gradually reduced, PB values did not change significantly.
The figures show the cumulative reduction ratio in each pass and the minimum thickness data which can be rolled.
Fig. 1 Effects of different rolling states on the thickness in rolling 202 stainless steel Fig. 2 Effects of different rolling states on the thickness in rolling 304 stainless steel Compare Figure. 1 with Figure. 2, the following results can be acquired: (1) Compare to lubrication-free state, the rolling oil containing alcohol or ester additives can significantly increase the single-pass reduction rate at constant roll gap and reduce the minimum rolling thickness
(2) When stainless steel cold rolling oils containing octadecyl alcohol as additives, the single-pass reduction rate at constant roll gap was larger and the minimum rolling thickness was smaller

Temperature of heating of 1050 °C exceeds Acm point of steel 150HNML according to data [3].
The metallographic picture of change of structure as a result of temperature increase of quenching in H12MFL steel considerably differs from steel 150HNML as all structural changes happen inside cells – reduction of amount of secondary carbides and appearance of sites of residual austenite while the size of the cells surrounded with an eutectic remains invariable is observed.
At first reduction of hardness is insignificant then owing to increase in amount of residual austenite after heating higher than 1100 °C – reduction of hardness makes from 53 HRC (at tq = 1100 °C) to 42 HRC (at tq = 1170 °C) (Fig. 1).
Noted features of influence of tq on wear resistance of 150HNML and H12MFL steels confirm literature data on a positive role of metastable residual austenite in relation to deformational martensitic transformation in ensuring high abrasive wear resistance [1].
Data of Fig. 2 demonstrate that there is a correlation between the maximum values of relative wear resistance and microhardness of a working surface of both steels after wear.