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NiGa, CoGa, AuCd, or AuZn were intensively investigated due to the availability of convenient radioisotopes, see for example the respective handbook on diffusion data [6].
The experimental data along with a linear fit in coordinates of the logarithm of the diffusion coefficient vs. the inverse temperature are presented in Fig. 3.
The agreement of the two independent data sets is impressive both with respect to the activation enthalpy and the absolute diffusivities.
Thus, these data represent true volume diffusion of Ni in equiatomic NiTi.
Mehrer et al.: Diffusion in Solid Metals and Alloys, Landolt-Börnstein, Numerical data and functional relationships in Science and Technology, Vol.26 (1990)

As the microstructure changes with ρt, the grain size increases from d = 130nm to 500nm due probably to the reduction of pore dragging effect.
4444 10101010----5555 90909090 65656565 70707070 85858585 75757575 80808080 95959595 90909090 65656565 70707070 85858585 75757575 80808080 95959595 Relative Density, Relative Density, Relative Density, Relative Density, ρρρρtttt (%)(%)(%)(%) Effective Stress, Effective Stress, Effective Stress, Effective Stress, σσσσeff ((((MPaMPaMPaMPa)))) 100100100100 1000100010001000 500500500500 200200200200 100100100100 1000100010001000 500500500500 200200200200 D e n sific atio n R ate , D e n sific atio n R ate , D e n sific atio n R ate , D e n sific atio n R ate , ρρρρtttt ((((ssss ---1111)))) D e n sific atio n R ate , D e n sific atio n R ate , D e n sific atio n R ate , D e n sific atio n R ate , ρρρρtttt ((((ssss ---1111)))) 1175117511751175°°°°C C C C ---- 80MPa80MPa80MPa80MPa 1.01.01.01.0 2.02.02.02.0 4.04.04.04.0 : Raw Data
: Raw Data: Raw Data: Raw Data : Corrected Data : Corrected Data : Corrected Data : Corrected Data Fig. 2 Densification rate ρt (=(1/ρt)(dρt/dt)) plotted as a function of the effective stress σeff [11].

The instruments used for the fieldwork are two data loggers (Elitech (UK) USB Temperature Data logger URC-5), were placed record the temperature in the guest room and the bedroom to evaluate performance over a seven-day period from 4th June 2021 to 10th June 2021 [8].
Instruments data.
A summary of the main recorded data is presented in figure 2.
A review of the recorded data indicates that outdoor air temperature ranged between 27◦C and 45◦C.
Elitech USB Temperature Data logger RC-5.

By observation of the test phenomenon and analysis of the data, the seismic performance of the columns including stiffness, skeleton curves, hysteresis curves, ductility and energy dissipation are obtained.
The test results were recorded by 3185 data - acquisition instrument. 2 Experiment phenomenon and analysis 2.1 Failure process Great differences were observed in the aspects of cracking, yield and failure modes.
With the help of ANSYS V14, further analysis is conducted in this paper based on experiment data fitting[8~11].
Based on above computation model, it is important to acquire data by Finite element model calculation and analysis under step-by-step change of loading angle.
The acquired data of bearing capacity is listed in Table 7 and Fig 13.

Comparison the results between the field monitoring data and numerical simulation data, which validate the suitability of numerical model and provide reference for similar projects in the design, construction and deformation monitoring.
Model parameters and realization (1) Soil Adopting M-C constitutive model ,the parameter values see Fig.2, however, the value of the modulus of elasticity is based on actual project data by experience[8], using CPE8R unit, for a total of 3435 nodes and 3360 units
Baigou Xie[10] thought that the elastic modulus of concrete needed to reduce, reduction factor 75%~80%.
Comparative analysis of simulated and measured data Horizontal displacement of the supporting structure The pile lateral deformation use GN-1 inclinometer to observe.
At anchoring section of SL11, there are four monitoring points SL11-1, SL11-2, SL11-3, SL11-4, specific location and associated data are shown in Fig.7 and Fig.8

Computed frequencies for all primary modes are close to the experimental data.
Such methods are often used as a source of data for verification.
Analysis of the influence of different nanostructuring agents includes comparative studies on data obtained in real and numerical experiments.
The computed frequencies for all four primary modes are close to the experimental data.
Such correspondences (along with XRD data) are important proofs of conformities between unknown molecular structure of nanomodified sulfur material and model of the lattice which is used during quantum chemistry simulation.

In this case, the tensile strength is slightly lower (≈1500-2000N/mm2, based on data sheets of the suppliers) than that of carbon UHTSS, while the cost may be 3-5 times higher.
Artificial ageing with acid pH solution induced a deterioration of 13%, while no strength reduction was found after treatment with neutral pH solution.
A reduction of 15% of strength and 8% of stiffness were found.
Concrete substrates are generally manufactured ad hoc for experimental purposes, but in [24] prisms were extracted from an existing RC bridge and used as substrates for laboratory tests to gain data on the shear bond performance of SRP when applied to relatively old concrete.
Preliminary databases of collected test results have also been assembled to provide a convenient source of information on published test data on the performance of SRP systems.

Furthermore, some interesting data for the local Nusselt and Sherwood numbers are also illustrated.
They figured out that, the increase in the power law index for either wall variable temperature (n) or wall variable concentration (m) is accompanied by simultaneous reductions in the fluid velocity and temperature, while the Nusselt and Sherwood numbers are increased.
From Fig. 3, the increasing of the non-Darcy parameter (Fo), increases the resistance to the flow, which leads to a reduction of the fluid velocity ensues.
This reduction is more important at the wall, because the inertia term act as drag-force at the pore scale.

It can give both positive and reverse simulations and can nearly solve all problems concerning equilibrium thermodynamics and chemical kinetics in the interaction system of water, air, rock and soil, including water soluble complexing, adsorption-desorption, ion exchange, surface complexation, dissolving-precipitation, oxidation-reduction and other chemical reactions.
ZHANG Congzhi et al. (2006), through the lab soil-column effluent experiment on Na+, K+, Ca2+, Mg2+, applied PHREEQC in the simulation of coupling ion exchange and complex reaction transport models of Na+, K+, Ca2+, Mg2+ with PHREEQC, also compared the results with those of the software CXTFIT, and the results showed that the experimental data and simulated results are in a consistent tendency, and the simulated results of PHREEQC were better [11]; ZHANG Congzhi et al. (2008) studied the single-component and multi-component solute transport through lab soil-column effluent experiment, and used CXTFIT and PHREEQC to simulate the coupling adsorption and ion exchange reaction.
Ray et al. (2002) once adopted the geochemical transport model PHREEQC to predict the diffusion transport of Pb2+ in the clay liner of 10cm thick, and compared the results with the experimental data of other scholars [14]; Cécile et al. (2004) used the PHREEQC to study transport rule of Zn2+ in the soil-column under the condition of different PH values and different acetate concentrations based on the lab soil-column experimental data when the component reactivity is low and when there is acetate, which turned out that the error between simulation results and experimental data is no less than 10% [15]; Laurent et al. (2004) studied the absorbability and desorption sequence of multi-component heavy metals Pb2+, Cd2+, Zn2+ based on the lab soil-column experimental data and static adsorption experimental data when there is carbonate, and used PHREEQC for simulation, in which the results complied with the experimental results [16]; Ludwig et al. (2005) confirmed the applicability of the chemical
Vojtĕch et al. (2004) adopted PHREEQC to simulate the desorption and adsorption abilities of Pb, Cu, Zn and As in metallurgy residues under the condition of different PH values and different ion strengths, and then compared the results with the experimental data, which showed that there is good matching between them [18]; Vojtěch et al. (2005) studied the sedimentation of heavy metals in the spraying treatment of aqueous solution used for exhaust gas in metal smelting, simulated the sedimentation of the chemical element Pb under different PH values as well as the different sedimentation status with PHREEQC, and the simulated results complied with the results obtained from the geochemical research [19]; Cheryl et al. (2005) adopted PHREEQC to simulate the effluent concentration of heavy metals Pb, Cd and Cr in the effluent fluid in municipal refuse landfill, and the simulated results matched well with the experimental data [20]; Stéphanie et al. (2005) studied the reactive transport process
Physical reactions include convection, dispersion, diffusion, compression, decay (radioactive pollutants), etc., while chemical reactions include water phase complexation, surface complexation, oxidation reduction, acid-base reaction precipitation, ion exchange, adsorption, and precipitation-dissolution, as well as degradation of organic matters, all of which will affect the solute transport in the soil.

Afterwards, input the sample data and train the BPNN.
Twenty-four sample data are selected.
Twenty-one former data sets are taken as the training sample and the rest data sets are taken as the test sample.
Then, the rest sets of data are input to the trained network.
It is difficult to select data of investment factors in most of situations.