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Established an interaction theory model of the buildings foundation and ground, used the model could calculate the changes of internal force and deformation of surrounding buildings caused by foundation pit excavation by selecting the reasonable foundation settlement equation or using the measured settlement data.
Hsieh and Ou through nineteen pit monitoring data statistical studies had also drawn a triangle settlement distribution curve (Fig.1 (a)) and groove shape settlement distribution curve (Fig.1 (b))[4, 5].
The deformation of buildings and ground reaction force distribution law Depending on the amount of measured data, caused by excavation of building deformation failure and surface deformation size, structure stiffness, properties of foundation soil, building the surface location and so on, the building deformation and ground reaction force distribution law characteristics were as follows
In addition, it could be directly calculated numerical solution of the ground additional force, the additional internal force and the additional deformation of buildings by the measured settlement data after foundation pit excavation.
The internal force and the deformation of theory of solution or numerical solution the effect of foundation pit excavation on buildings through by selection of reasonable ground foundation deformation equation or using the measured settlement data.

The input data fed to the network was the information from processing the photo images of these elements.
Employing of neural networks for the task allows for data approximation which enables recognition and classification of examples unknown to the network [9,10].
Precisely these data are subsequently used for object identification when employing a neural network for the task The elements under analysis are shown in Figure 2.
Image analysis stages Information obtained in the course of image processing was used as input data for the neural network to carry out the task of element recognition.
The processed data were divided into three sets.

In general, data exchanged on an industrial network can be classified into two groups: realtime and non-realtime data.
Non-real-time data do not have stringent time limits on their communication delays experienced during the data exchange.
In general, data exchanged on an industrial network can be classified into two groups: real-time and non-real-time data.
This real-time data can be further divided into periodic and asynchronous data, depending on the periodic nature of the data generation.
For example, the data for program download belong to non-real-time data, while digital control command and alarm signal are periodic and asynchronous real-time data, respectively.

Ecological and economic evaluation of straw bale construction was carried out with reference to the data from the literature.
The straw bale technology allows a reduction of the construction costs mainly due to the possibility of a relatively high self built potential.
The selected climatic data are chosen on the safe side and are very critical to ensure the maximum possible temperature and humidity loading on the structure.

The proposed iron oxidation model in “Ferrovum” spp. was supported by the transcriptome data of “Ferrovum” sp.
Taken together, these processes generate the necessary energy carriers and reduction equivalents for nutrient assimilation, metabolism and growth of iron oxidizing microorganisms.
Genes mapping to rRNA and tRNA genes were eliminated prior to data analysis.
While HiPIPs are known as electron shuttles in various electron transfer processes including iron oxidation in Acithiobacillus ferrivorans [14], the involvement of hypothetical proteins remains speculative without further experimental data.
Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics 30 (2014) 2114–2120

As an example, Fig. 2 shows Williams’ data [23] on the densification kinetics for pressure sintering of copper powder with the particle sizes of 76 – 104 µm.
The graphical data of the above article were scanned and digitized to provide for calculations along the data points of each curve seen on the display.
Besides, the results may affect the limited data obtained in single experiments for each condition without checking reproducibility.
After comparing the experimental and modeled data, it is seen that the developed dynamic model adequately represents the process of impact sintering.
Data on the shear viscosity depending on the initial impact velocity v0 under impact sintering of porous g-iron at temperatures from 920 to 1200 °С are shown in Fig. 11.

The data were analyzed with the Kolmoforov-Smith test, One-way ANOVA and Tukey’s test.
Statistical Analysis The distribution of the data was calculated by the Kolmoforov-Smith test and the variables were analyzed using One-way ANOVA.
[2] Goldberg, A.J., Vanderby, R., Jr., and Burstone, C.J.: Reduction in modulus of elasticity in orthodontic wires, J.
[9] Siriwat Chamnunphol, Influence of reduction ratio of cross sectional area in drawing stainless steel wire for orthodontic use, 2008

Nonetheless, radiosonde is the only means of high-altitude sounding for a long time, and still in use, which provides important data self-evidently[4].
So it is necessary to correct the SRDB of RH measurement data.
Thus, the solar radiation error of measured RH data should be corrected.
López, Verification of the MM5 model using radiosonde data from Madrid-Barajas Airport, Atom.
Dai, Radiation dry bias correction of vaisala RS92 humidity data and its impacts on historical radiosonde data, J.

The experimental data showed a good compliance with the pseudo-second-order kinetic model, and the adsorption isotherm data met Langmuir models well.
In the present work, two general-purpose equilibrium models were used to fit the experimental data (see the (3) and (4)).
Fig. 9 Langmuir adsorption isotherm Fig. 10 Frundlic h adsorption isotherm The Fig. 9 and 10 were showed that the Langmuir isotherm was fitted much better to the experimental data than the Freundlich isotherm on the basis of the correlation coefficients (RL2=0.998> RF2=0.9308).
Fe-Al layered double hydroxides in bromate reduction: synthesis and reactivity.

The model consists of four modules: Transpiration Module, Crop Growth Module, Water Module and Nitrogen Module.The data which the model operation needs to input mainly includes: dayly weather parameters (maximum temperature, minimum temperature, radiation, average wind vwlocity, precipitation et al.), corp growth characteristic parameter(growth rate, specific leaf area, maintenance respirationand growth, respiration parameter, partition coefficient of dry matter and specific leaf area et al.), management data of rice field(transplant density, irrigation volume, nitrogen application rate and application of frequency et al.), the soil properties parameter(underground water level, soil water potential and transmissivity et al.).
The above data are saved into the the correspondingly independent data file of Weather data file, Crop file, Experiment file, Soil data file the corresponding independent data file, which is convenient for the user to use and management. 90% of the crop parameters in the model is obtained based on the experimental results, which is universal without the need of correction.
Because the experimental data of the simulation model is not repeated, these two indicators are not shown.
According to the field observed data, we correct the parameters of ORYZA2000 model and obtain the rice crop parameters required by the model.
The reduction in production caused by rice lodging at later stages lead to the model’s higher simulations of the yield.