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Our results are consisted with the data reported in literatures [21-23].
The data obtained plotted as mass gain percentage (%) as a function of temperature applied.
The data obtained by thermogravimetric curves are composed of three main stages: the initial stage, steady – state (linear), and parabolic stage.
The data for the initial stage from 25°C to 250°C for Ti-Ta20-Al5HTSMA as cast and after CR+RE show small weight losses in the initial stage of the TGA curves.
Our results are consisted with data reported in literatures [10, 26, 27].

A variety of methods have been developed to estimate the fatigue life of adhesive joints, ranging from empirical approaches based on experimental data to advanced numerical simulations and fracture mechanics models.
These data points are then fitted to a logarithmic or power-law relationship to describe the fatigue behavior of the joint [3].
· Strain-Life Models: Combines experimental strain-life data with FEA results to estimate fatigue life based on the total strain experienced in the joint.
However, it requires significant computational resources and accurate input data, such as material properties and boundary conditions, to yield reliable results.
One common approach is the Weibull distribution, which characterizes the variability in fatigue life based on experimental data [9].

After receiving written consent to participate in the experiment, all subjects provided personal data regarding their age and underwent an IQ assessment (according to the Eysenck test) (Table 1).
The statistical package SPSS (version 21.0; SPSS, Inc., USA) was used for data analysis.
When analysing additional data that were determined during the implementation of the Halstead-Reitan Trailmaking test, such as the relative number of errors made by the participant, determined to the total number of points connected, a slight negative effect of the use of a half mask in women was found on the speed of visual search for points.
At the same time, some reduction in decision-making time was found when performing the task, distinguishing between words and colours (when wearing a half mask).
Trail Making Test A and B: Normative data stratified by age and education.

The optimization tool handles the data flow and the transferring of data between the different models.
The results from such a through process simulation are then evaluated using some kind of optimization technique, before a new set of input data is generated for the next series of simulation.
The optimizing software is an important constituent of PRO3, since it couples the models that are part of the value chain and organizes the flow of data between individual models.
The dispersoid data are important input to Alsoft, since these particles tend to impede the recrystallization through the so-called Zener-drag pressure.
Some key data extracted from the result database are given in Table 1 for the best solution within the green square.

However, depending on data availability, the general process could be reduced to just the construction phase as is the case in most developing countries where required pavement performance data is scarce.
However, to the authors’ best knowledge, these data and calculations have never been utilized during the design phase of pavements.
This type of data is scarce in developing countries as pavement management systems is currently not a priority for them.
Using reported data from other countries is risky given the differences in climatic and loading conditions.
As explained earlier, this decision was based on the lack of reliable performance data in developing countries.

A stitching sub-aperture interferometer is also carried by the Zeeko machine to give improved spatial resolution of edges, and to infill a small central obstruction in the OTT data.
After removing the specified low-order terms from the data to reveal the mid spatial frequencies (and hence smoothness), the surface error was 18.6nm RMS (Fig. 10).
This was traced to thermal effects in the 10m high air-column of Optical Test Tower, which caused random errors in metrology data.
No CMM data was available, so the part was given a rapid ‘flash pre-polish’ on the Zeeko machine, removing ~ 2 μm depth of material.
This comprises Test Tower data, with the CGH artefacts and central obstruction in-filled by stitching from on-machine sub-aperture interferometry.

Geometric information contained within a CAD data file guides the energy source in precise patterns over a layer of deposited powder.
As design tools become more sophisticated, they demand more material behavior data through-out the temperature range of interest.
This range of mechanical property data is critical to determining if a given material can satisfactorily perform in a targeted application.
Obtaining this data for a new material is often an expensive, lengthily process.
• Legal issues regarding contractual terms and conditions, such as protection of proprietary data, retard progress.

The main objective of material experiments was to observe successive stages of carbides forming and dissolving in the material in order to be able to work out, based on them, a set of training data for an artificial neural network.
Numerical analysis The main aim of the material experiments was to examine the precipitation phenomena and design a set of training data for an artificial neural network.
The most important task of a neural network was prediction, i.e. forecasting results for the data included in the problem domain, but outside the set of learning cases.
Based on the observations of the phenomenon and empirical data obtained during material experiments, a system of MLP-type neural networks (multi-layer one-direction sigmoid networks) was worked out [13-15].
In practice, it means a reduction in the duration and cost of the treatment during mass production.

Based on the test data, we suggested the temperature profile model to predict the slab temperature.
The data given in Table 1 were obtained using the method [23] taking into account the measurement period (Fig.2 and Fig5.).
The missing char and the remaining char were measured thrice in different places to get reliable data.
The obtained experimental data were processed using the method of least squares and are given in Table 1.
A specified area of degradation zones is given in Fig.10 and the obtained data are given in Table 2.

The values of the differential Avrami exponent are also determined from the isothermal data.
Structural and kinetic data have been analyzed with the aim of relating the microstructure evolution upon heating with the crystallization mechanisms and kinetics.
Thermal stability data of Ti40Zr10Cu34Pd14Sn2 rod: Tg = glass transition temperature, Tx = crystallization temperature, DTx = supercooled liquid region, Tliq = liquidus temperature, Trg = reduced glass transition temperature (Tg/Tliq) and parameter γ = Tx/(Tg + Tliq).
The data marked with * are taken from refs. [7, 14-17] and presented here for comparison.
Once the particles have grown to a certain size, the surrounding matrix, whose composition is enriched in the atoms rejected from the growing particles, approaches saturation and the associated reduction in the driving force makes diffusion the rate-controlling step [12, 13], slowing down and eventually stopping the transformation.