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The results from pulsed field gradient NMR data revealed that one of the peptides may undergo a more favorable self-assembly as temperature was increased, while other three peptides were found to form larger self-assemblies at lower temperatures and continuously dissociate into the monomeric form with increasing temperature.
However, the energetic reduction in peptide-solvent interaction may be more significant than the reduction in peptide-peptide interaction.
This result is presumably due to the energetic reduction of the interaction with solvent and the increased dynamics as a consequence of this.

The advantages of AASC in ecological aspect are related to reduction of CO2 emission due to application of by-products as well as waste products [11, 12, 13] and introducing industrial waste water in safe building materials [14, 15].
In addition, the reduction of AASC shrinkage deformation is possible due to surfactants.
The total effect on water reduction, slowing down setting time and increasing strength of such systems was accepted for determination the most effective CA’s formulations, which were further used to reduce AASC proper deformations.
It was noted that the greatest effect on reduction of proper deformations was determined for the systems with mineral compounds which belong to electrolytes Na2SO4 and NaNO3.
In case of Na2SO4 the data indicate the formation - hexagonal lamellar crystalline structures of minamiite ((Na,Ca0.5)Al3(SO4)2(OH)6) along with hydrosilicates and hydroaluminates.

The concept being that affordable superplastic magnesium sheet could be produced via twin roll casting with only limited rolling reduction to final gauge.
The superplastic deformation mechanisms for both alloys are discussed with reference to strain rate sensitivity data and the results of electron backscatter diffraction (EBSD) analysis.
Fig.1 Superplastic ductilities obtained from die cast AZ31 in the as-cast condition and after 55% warm reduction.
The roll cast material combines a rapid solidification rate, as a consequence of the forced contact with the rolls, with a hot rolling reduction.
It has also shown that small warm rolling reductions can be applied without detrimental effect.

PCA data set consisting of N observations, each of which includes K quantitative variables can be interpreted as a cloud of N points in k-dimensional hyperspace.
Finally, it means 630 data points in 8-dimensional hyperspace.
On the basis of the scree plot (Fig. 2), it was determined that the first two PCA factors, designated as PCA1 and PCA2, are responsible for 96% of the data cloud variability.
Some articles related to such problems of objectiveness and stability were published [13], especially about data driven methods [14, 15].
Zahiri, Unsupervised Data and Histogram Clustering Using Inclined Planes System Optimization Algorithm, Image Anal.

It can process information extremely rapidly and provide meaningful answers even when the data to be processed include errors or are incomplete [5].
Experimental data were divided into two sets, one for the network learning called learning or training samples and the other for testing called testing samples.
Tab.2 Part of experimental data for training neural network No.
The expression of relative error is as follows: , (1) where denotes the relative error, denotes the output data of shrinkage model based on BP neural network , denotes the test data of shrinkage experiment.
The 18 testing vectors of data were used to test the accuracy of prediction (Ac).

Data reduction and results According to the measured data, the energy balance between the tube side and annulus are generally within 1%, only those data that satisfy the above criteria are taken into consideration in the final data reduction.
(3) The experimental data consist of the smooth tube and the tube with five different porosity Φ twined coil inserts.

Creep was found to contribute to life reduction in in-phase tests when the peak temperature of cycling was above 600 °C.
Statistical Thermal Fatigue Creep Modeling of Stainless Steel Materials The elevated-temperature tensile, creep and rupture test data on stainless steel materials were being collected by a number of organizations and researchers [e.g. 6, 7].
Baced on these test data, statistical thermal fatigue creep modelling can be conducted to predict the falure life of stainless steel materials.
The cyclic material data are normally not enough, thus Manson’s universal slopes equation [8] can be used as an empirical correlation which relates fatigue endurance to tensile properties
The correlation can be achieved by comparing fracture data from tests against the prediction obtained using the model estimated based on materials test data and simplifying assumptions.

Each subroutine calls and data transfer realized in main module 0B1, 0B35 is interrupt service module, FB1 is fuzzy controller, whose child program blocks compose by FC6-FC9.
FC6 main job is to read the value of the temperature sensor and storage to data area, deposited in quantification factors ke, kec, ku in the DB5.
Fig. 3 Fuzzy control flow chart Fig. 4 Fuzzy PID control program flow chart The main loop self-inspection program blocks 0B1 run after PLC current and automatic check power, which realize to the function and function blocks calls and signal and data transfer [7].
KP, ki, kd assigned to the data block DB11 respectively, DB12, DB13 according to go up to fall, right to left.
KP, ki, kd data each have 169, which in turn for DBW0, DBW2,......

The rolling reduction ratio in thickness was 50 % and 0 % respectively for the first pair’s rollers and the second pair’s rollers. 5052Al was used as the work-piece material and was hypothesized as a plastic body.
Fig.3 Stress-strain curve of the 5052 Al alloy used in the finite element simulation Table 1 lists the geometry, materials and simulation data for the simulation.
Table 1 Data for the numerical simulation.
Geometry data Central distance of each pair rollers, (mm) Initial work-piece thickness, (mm) Initial work-piece width, (mm) Rolling reduction ratios Bending channel thickness, (mm) Output channel height, (mm) Die channel angle, (°) Die outer corner angle, (°) Die inner arc radium, (mm) Die outer arc radium, (mm) 80 4 10 50 % (first pairs rollers), 0 % (second pairs rollers) 2 2 99 228 8 10 Materials data Young’s modulus, (GPa) Poisson’s ratio, Thermal expansion, (˚C-1) Thermal conductivity, (N·s-1·˚C-1) Heat capacity, (N·mm-2·˚C-1) Emissivity, Work-piece (5052Al) 68.9 0.33 2.2E-5 180.2 2.433 0.7 Roller and others (H13) 206 0.3 1.4E-5 32 3.588 0.5 Simulation data Roller angular velocity, (rad/s) Time step, (s) Initial temperature, (°C) Environmental temperature, (°C) Friction factor, Mode A: 0.073(TR1)/0.1(BR1)/0.1(TR2)/0.1(BR2) Mode B: 0.1(TR1)/0.1(BR1)/0.1(TR2)/0.1(BR2) Mode C: 0.137(TR1)/0.1(BR1)/0.1(TR2)

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.