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Multiple regression analysis of the data obtained from more comprehensive simulations, suggests estimating the heat flux through the double layer of slag as: qs=*A*exp1+Bt+C in watts for t≥5 seconds A=6383+43.1*h-39*T-181*K B= -346.41-0.109*h+0.156*T C=62676-120*h+614*K+(80*T) where qs is the heat loss through the slag layer by mechanisms of conduction, convection and radiation, is the metal/slag area in m2, T is the liquid steel temperature in K, h is the initial thickness of the slag layer in m; K is the heat transfer coefficient in m2K; t is the time in s.
There is a reduction in the driving force for heat diffusion and because of reduction of radiation losses at the solid slag/environmental interface. 
Data also highlight the effect of soaking temperature of the refractory lining.
Heat losses through slag layer however depends on metal-slag interfacial area() , temperature(T), slag thickness(h), heat transfer coefficient (K) and time (t) and can be quantified by the correlation that resulted through multiple regression analysis of the data obtained from various simulations through the following equation.

Official Istat data define a farming surface to be about 1.500 ha in 2010 whereas, according to the information provided by the sector representatives, 1.100 - 1.200 ha would be a reliable estimate of the bergamot farming area (INEA).
This meaningful reduction of farming areas has caused a consequent reduction of a 1/3 of the produced essence that currently equals 100.t.
With reference to prices, analyzing official data released by the Consorzio between 1951 and 1990 and comparing it to data compiled by companies and producers, it is interesting to note their gradual increase in terms of current prices while fixed prices in euro (2012) appear to report a continuing recession.

These data show that crystallite size decreases gradually with increasing milling time, and a nanocrystalline structure appears in alloys milled for 25 h.
These data show that all of the investigated alloys can be activated easily, and are completely activated after one cycle.
Fig. 3 Variations in discharge capacity with cycle number for the sintered and milled alloys Table 3 Electrochemical performance data of the sintered and milled Mg2Ni alloys Sample Na a Cmax b (mAh/g) C60 c ( mAh/g) S60 d (%) Sintered 1 77.68 12.44 16.01 milled 10 h 1 117.33 16.80 14.32 milled 25 h 1 112.92 19.29 17.09 milled 40 h 1 149.80 20.88 13.95 milled 50 h 1 154.80 27.20 17.57 a The cycle numbers needed to activate the alloy electrode.
The reduction in particle size increases the surface area of the alloy and the refinement in crystallite size, leading to an increase in volume of grain boundaries.
The sintered alloy milled for 25 h leads to a reduction of the crystallite size to nanometer scale.

The observational data is bounded.
The observational data is bounded.
After reduction, there are following augmented matrix where , are system observed signal, is the noise (when ), , , is the sampling period.

Then to predict with the data of highway subgrade.
To predict the subgrade data (measured data from reference[5]) of segment H055 (K19 +560), part of XuSu Freeway located in north of Jiangsu province.
Grey's idea is: Through the appropriate way of accumulation, change the data columns which are random and positive into a non-negative increment data columns.
The GM (1, 1) model with first-order nonlinear equation and one variable requires that time-series data should change smoothly.
According to the measured data from one practical engineering [5], the establishment of GM (1, 1) model, preparation of the corresponding Matlab program code in Appendix one.

Meanwhile measurements both HRV and PRV under still finger show only 0-3.33 % difference for each of data groups.
First is parallel data acquisition using ECG and SpO2 measurements for still condition using previously calibrated sensors to maintain the accuracies of the measured data.
These data are taken as reference for further analysis.
Acquired data from both systems are retrieved using biomedical instrumentation KL-710, and stored the data in format of *.txt for further processing.
For the ease of data visualization the following labels are used: i.e.

Data sources Certain questions were designed in the questionnaire for each construction project risk.
Then sample data is achieved through the process of the result of questionnaire.
Data processing and test In the processing of gained sample data, factor analysis is applied to introduce the steps of extracting data using technical risk affecting the project (16 indicators) as examples
First, principal component analysis and the feasibility test should be used on original data.
The above test results show that the original data can be carried through factor analysis

Besides record keeping, the TRI also specifies the approach of revising TRI data.
As to new monitoring data, new emission factors, new chemical concentration data, recalculation or others, facilities may want a revision and submit a request form.
Furthermore, the Swedish PRTR data is reported to the EU every year, with a typical reporting process of 1) reporting by operators, 2) checking data quality and clarifying responsibilities, 3) data review and 4) reporting by the government [20].
After compiling, the national government publicizes those data submitted [15].
N/A: Not available or not answered A “Yes” for public dissemination of full (raw) data; “No” for public dissemination of aggregated data sets.

It applied Taguchi method to solve problems in the reduction of processing load and thermal impact evaluation of the periphery by quill design and aimed to design an ideal tool form.
Taguchi Method Taguchi method is a kind of experimental planning method and refers to planning to obtain the maximum information about how to make experiments on the problems to solve, how to collect data and with what statistical method to analyze the data with the minimum number of times of the experiments.

When investigating the stress, it was found that a larger radius leads to a reduction in stresses.
Complementary material data like yield strength and tensile strength were supplemented by calculations with JMatPro so that a classification of the calculated stresses to the bearable loads can be made.
By comparing the data, a statement can be made about the probability of crack formation and plastic deformation.
Key Findings In order to be able to make a statement about the influence of the radius on the stresses, the data from the stress calculations are dealt with first.
The chosen simplifications related to the reduction of the simulation time by using non-heat conducting components saved resources.