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Data collected based on factorial design and using SPSS software were analyzed.
Introduction Reduction of big diameter trees in natural forests caused plywood industry to reduce their capacities and some of them forced to stop production.
Adding tannin to PF resin causes considerable reduction (40%) in thickness swelling of all experimental panels while thickness swelling of control panels and panels made using 10% tannin and pressed at 150 Co were high (around 6%).
There is a relationship between increased addition of tannin and reduction in bond strength properties.

The latest data predicts that in addition to gas in the shale is also petroleum.
The structure of former austeniteafter deformation ε=0,05 and deformation rate 5s-1 in temperature 1180°C: A- X80 steel (zoom 200X); B- X100 steel (zoom 500X) From the data presented in the graph can be deduced that in the case of steel X80 is the critical strain (ε < 0.05), which results is a rapid growth of austenite grains.
Austenite grain size reduction was observed for strain ε > 0.2.
Local austenite grain size reduction was observed which resulted in a reduction in average grain size austenite in steel.

This study first compares the reconstructed data with data available from literature to evaluate the accuracy and reliability of our simulations.
The material properties used were those of SS400 steel, which were identical to the material data used by Vuong et al. [13].
This data suggests that the mesh layer can influence the accuracy of the simulation in predicting the final bending angle of the sheet metal.
The data presented in the figure primarily exhibits monotonic trends, signifying a consistent change in one variable angle as another is altered.
As the line graph reveals, a primarily monotonic trend dominates the data, with some lines exhibiting clear linear patterns.

A new control method of Fuzzy-PID controller with RSDA (Rough Set Data Analysis) is proposed.
The RSDA (Rough Set Data Analysis) theory can deal with imprecise and incomplete problems.
It is used to analyze the dependencies among the properties of the information decision system and the necessity of a data value without a priori knowledge.
a) Collect 100 experiments data, adjust the PID parameters by the classic method, make a decision table according to the ev, ∆ev and u.
b) Discretize the metrical data of decision tables, make cluster analysis and interval division by rough set theory.

CP-Ti plates were rolled to at various temperatures between 25°C and 600°C to achieve 60% reduction, and were subsequently annealed at 600°C for various time periods.
Microstructures developed during rolling and subsequent recrystallization were characterized via EBSD (electron backscattering diffraction) using a Hitachi 3400SE field-emission-gun scanningelectron microscope (FEG-SEM) and TSL-OIM TM software; any measured points whose CI (confidence index) is less than 0.1 were excluded from the analysis of the EBSD data; high-angle grain boundaries were assigned to the locations of lattice misorientation exceeding 10°.
When specimens were rolled to 60% reduction at various temperatures and reheated to 600°C for 1 min., recrystallization nuclei formed and the number frequency and the distribution of these depended on the rolling temperature.
In view of the harness data in the present study, it may be concluded that stored energy decreased with the rolling temperature.
Therefore, the slow recrystallization kinetics in the specimen rolled at 600°C can be explained on the same basis of the stored energy reduction, but not for the specimens rolled at 150°C~450°C that showed the opposite trend.

(a) Initial charge/discharge curve for cathode mass loading variable; (b) Cyclic voltammetry curve for cathode mass loading variable Improvement: Ultra-thin Gold (Au) Coating Previous data showed that anode-free LIBs using NMC material showed the potential for commercial fabrication with the 18650 cylindrical battery model.
When the dendrites can be controlled, the performance and stability of AFLB increase, as evidenced by the initial specific discharge capacity data, and the retention percentage after the first five cycles increases from 83.5% to 95.4%.
The data shows a significant increase in the percentage of capacity retention between Anode-free LIBs with Bare Cu-foil and batteries that have been modified with gold plating on the Cu-foil surface from 83.53% to values above 90%.
Peaks that occur in this reduction reaction indicate a reduction of Mn4+|Mn3+ at a voltage of 3.25 V.
Peaks at a voltage of 3.7 V indicate a reduction of Ni3+|Ni2+.

The I value (IC50) was determined by fitting of the Hill equation to the concentration response data.
Data are expressed as mean ± SEM (n = 4).
Data are expressed as mean ± SEM (n = 4-8).
Data are expressed as mean ± SEM (n = 4). ●: control; ○: 10 μM resveratrol.
Data are expressed as mean ± SEM (n = 4). □: control; ■: 10 μM catechin.

In this paper, we extend this approach to analyze 2-dimensional data.
Fig. 1 shows the resultant 3-D volume of data.
Principal component analysis (PCA) is a well- established tool in multivariate data analysis [11] that offers an efficient way to control the trade-off between loss of information and dimensionality reduction.
The remaining signals in the entire dataset were used as test data.
Initial results on ultrasonic data obtained from the inspection of welds indicate that the algorithm is capable of accurately classifying data from four different types of flaws.

The reflectance data were transformed into the Kubelka-Munk values.
Potentiometry The potentiometric titration data obtained in each case is depicted in Fig.1.
X-ray Photoelectron and FT-IR Spectroscopy The C 1s, Ti 2p and O 1s data of TiO2 and CMT is presented in Table 1.
They are based on the XPS and IR data presented earlier.
This hypothesis is supported by XPS and FT-IR data discussed earlier in this study for surface carbonate.

The base case of the system consists of the following data: Table 1.
Load Models Based on Consumption Profiles Description: This approach uses previously defined load profiles, based on historical data or market research, to model EV behavior.
Through training with historical data, neural networks can learn to identify complex patterns and predict future behavior.
Real-Time Simulation Models Description: These models allow for real-time simulation of the impact of EV load on the distribution network, using real-time data from sensors and monitoring systems.
Ziras, “A review of data sources for electric vehicle integration studies,” Renew.