Search results

After calculating and sorting, the experimental data is obtained; it is shown in Tab.3.
Judging from the error percentage, the data by simulation analysis is almost the same with the experimental data, which verifies the accuracy of the simulation model and parameter setting.
The average data is shown in Tab.4.
The effective ink layer thickness values are extracted, the data is shown in Tab.5.
The data is shown in Tab.6.

The model is validated using data of three engines.
The testing data collected cover duration of 800 seconds.
The exhaust temperature data are shown in the 1st plot of Fig. 5.
In Fig. 5, the experimental data and prediction data of exhaust temperature are shown in the 1st and 2nd plots.
The relevance is strongly and qualitatively supported by data matching.

The data for modeling was drawn from literature spanning over 19 years (from 2006 to 2025).
This model evaluates input and output data through hybrid learning [20].
rij= i=1 nIi × Otii=1n Ii2 i=1nOti2# 1.23 Where: Ii = input parameters Oti = output parameters n = number of sample data points [29].
The design engineers and scientists make use mathematical models to obtain data required in the development of these systems.
The assumption in ANFIS is that fuzzy rules from combustion properties data are used in modelling.

In analyzing system, the function of PCA is listed as followed: 1) Discover the structure hiding within the system data and find internal relations between the variables and simplify them.2) Classify samples of variables in the indicator axis space according to the value of indicators.
Count original materials, then original data matrix can be obtained.
In order to eliminate them, the data should be standardized to make every variable’s average value equal 1 and variance equal 0.
For this kind of question, PCA method based on SPSS is designed as follow: · Obtain the original data matrix through the actual investigation; · Create the appropriate variables and indicators in SPSS software and input the original data matrix; · Apply SPSS software to analyze the principal component for the original data matrix, and then obtain correlation matrix and its characteristic roots and eigenvectors; · Determine the number of principal components according to the cumulative contribution rate of the system requirements
Through analysis and investigation, data files could be obtained to analyze on SPSS and then analyzed in accordance with the following steps[4]: · The initial analysis: Click analyze →data reduction →factor…(Orderly click and open menu item to open factor dialog box), point out specify analysis variables and set the other options

Consideration of Improving Earthquake Resistant Capacity of Earth Buildings Bo Zhang Department of civil Engineering & Architecture, Shaanxi University of Technology, HanZhong, Shaanxi, 723001, China zhangyc_2005@126.com Key words: Rammed earth wall, Ecological property, Construction specialty, Earthquake disaster prevention and reduction.
As data illustrated, there are almost 80,000 bricks manufacture enterprises with over 26.67 million acres, they use soil about 1.2 billion in brick manufacture equivalent to the destruction of 7 million acres land.

These data suggest that the aquatic plants in Chaohu Region can exert efficient remediation effect upon the eutrophic water.
Nitrate takes the bulk part as major primary components in natural waters, the ammonia nitrite just follows; however, the concentration of nitrite and ammonia nitrogen is relatively low because they are intermediates of the reduction and oxidation process.
These data suggest that the aquatic plants in Chaohu Region can exert efficient remediation effect upon the eutrophic water.

In order to determine the reasonable and balanced treatment parameters that ensure the required level of nitrogen content in the above steel grades, an analysis of production data for the period of November-December 2016 has been carried out.
Table 1 shows the average data of the results of degassing of the above steel grades.
According to the data of Table 1, the operating parameters of the degassing process vary within the sufficiently wide limits, which causes significant fluctuations in the content of hydrogen and nitrogen in steel.
Therefore, for the sake of further processing, the heats with a freeboard exceeding 500mm were rejected from the initial production data, and the effect of the thickness of the slag layer, assessed visually, was not taken into account.
To assess the cumulative quantitative effect of the main parameters of degassing on the nitrogen removal, there has been carried out a regression analysis of production data.

The goodness-of-fit has been verified by computing the variance in the predicted results versus the real data, the probability that the fitted model has no useful terms, and the significance of the regression.
R-squared indicates the variance in the predicted results versus the real data.
This is the proportion of total variability in the data which is explained by the regression model, a score of "1" indicating a perfect fit.
Range-to-variance ratio indicates how well the model predicts values at the data points.
The results shown in figures 3-7 indicate that the regression model for the selected strategy, DOE Screening, D-Optimal design, Interactions, matches the test data very well.

By using XRD data crystallite size of V2O5 was estimated to 33.98nm and crystallite size of TiO2 was estimated to 35.04nm.
After doing number of adjustment on the obtained pattern of XRD we get XRD pattern data.
That data gives us the structural and diffraction information about nanoparticles.
Ti m.mm 0 0 0 2a O m.2m 0.327 0.327 0 4f The profile of X-ray diffraction data is fitted with the Rietveld Refinement and obtained spectra of V2O5/TiO2 is shown in figure.
The rietveld refinement of the sample is done by Fullprof Software and there is good agreement between the data and also provides low value of R-Factor.

The 80 raw variables, selected depending on the data availability and the scale of uniformity Istat, Istituto Tagliacarne, Agenzia del Territorio, Il Consulente Immobiliare, Miur, Espon, Corine Land Cover, ecc. are just some of the several data sources introduced in the evaluation model.
The data selected had to satisfy criteria such as public diffusion, geographical scale (NUTs-3 level: “provinces”) and time series uniformity (from 1995 or 2000)
even if three real estate residential variables (here represented by means of real data - see fig. 2) present remarkable ratings.
The variables are: price levels of new stock The data used in the macro RCAU dataset refers to the “Il Consulente Immobilare”, a periodical magazine published by the main economical editor “Il Sole24Ore”.
The demographic analysis, based on the differences between the last two Italian population census data (Istat, 2001; Istat 2011), evidences that the most relevant demographic increases have been registered from 2001 to 2011 Fondazione ISMU elaborations on ISTAT data (Istat- Italian National Institute of Statistics, 2001 census data; Istat- Italian National Institute of Statistics, 2011 census data)