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The survey data of each criterion is averaged to find the mean of satisfaction score.
The questionnaire surveyed data are analyzed by SPSS indicating the lack of understanding of the construction technique with prefabrication [1].
SPSS® (Statistical Package for the Social Science for Windows) is used to analyze the survey data.
The Levene’s test shows p-value <= 0.05, indicating that the survey data do not follow homogeneity of variance assumption for most criteria.
Welch and Brown-Forsythe tests are conducted when the survey data do not follow homogeneity of variance assumption to compare satisfaction mean score among habitat groups.

Data pre-processing 2.
Missing data: The first step in data pre-processing is to take care of the missing data.
The entire row of the dataset with the missing data can be removed when the missing data are minimal or when a row contains multiple missing data.
Encoding categorical data: The “operations” column under the machining conditions contains categorical data i.e., the data consists of words instead of values, which needs to be encoded into numbers.
Principal Component Analysis (PCA): This is a data manipulation technique used to reduce the dimensionality of the original data [14].

We performed the neural network training using the 97 measured data in CASS materials by Aubrey and Chopra.
Fig. 2 (b) shows the target value that is ferrite content from the input data.
Therefore, we believe that if the trained neural network described in this study is used, we can effectively predict the ferrite content. 0 5 10 15 20 25 0 20 40 60 80 100 Data number Chemical composion(%wt) C Mn Si Cr Ni Mo N 0 10 20 30 40 50 020406080 Data number Ferrite content (Vol.%) 100 (a) Input data (b) Target data Fig. 2.
Neural network architecture for prediction output and Aubrey equation results of Charpy impact energy 0 100 200 300 400 500 600 700 0 40 80 120 160 200 240 280 Data number Ferrite(vol%), Aging temp( ℃), Aging time(×10 2hr) Ferrite content Aging temp Aging time 0 50 100 150 200 250 300 0 40 80 120 160 200 240 280 Data number Aged Cv (J/cm 2) (a) Input data (b) Target data Fig. 7.
Training data for prediction of Charpy impact energy For Charpy Impact Energy Prediction.

Potentiodynamic polarization data was obtained from potential of -1.5 to + 1.5 V versus open circuit potential at a scan rate of 0.01.
The data revealed that CD inhibitor occurred predominantly as cathodic inhibition.
Furthermore, Table 3 shows the data of potentiodynamic polarization curve for 1.5 M HCl solution with different concentrations of CD inhibitor.
Chemical Data Collections, 32, (2021) 100660
Chemical Data Collections, 17, (2018) 321-326

Principle component analysis has two purposes: the first is data reduction, and the second is for revealing the relations between variables.
It assumes n samples and each sample has p characteristics (index), then the sample data set can be expressed as: (1) Steps of principle component analysis [6]: Step1: data standardization; standardize the original data first so as to eliminate dimension influence, thus gaining standardized data set .
Divide simulation data into two parts and take the data from 1990 to 2005 as training sample set data; data of 2006 to 2010 as test sample set data for the model; in order to eliminate influence to the prediction performance resulted from the dimension difference of the SVM prediction indicator data, it’s necessary to preliminarily process the input data before utilizing SVM to establish model for prediction; it will directly influence the training speed and water demand prediction accuracy.
This article conducts standardized processing to all the sample data with Eq.8 and normalizes all the data in interval [0, 1]
(8) refers to normalized data; xi refers to index series data; xmin, xmax respectively refer to the min and max value of the original series data.

One-way ANOVA was used to examine significance of data variations with SPSS.
Lee Di Milia in their study also showed that shift work models and prolonged daytime driving under monotonous conditions usually led to a continuous reduction in drivers’ vigilance [4].
Results The SSS score, RTs and PECLOS data recorded during the driving were analyzed with one-way ANOVA using the “SPSS” program, and Person’s correlation coefficient was employed to explore the correlations among the three indicators.

The field data obtained from the Muppandal site in India such as Mean time between failure (MTBF), Mean time to repair (MTTR), failure rate and repair rate are used to compute the WT availability, using ITEM Toolkit version 8.0.2 as a measure of performance.
Mostly, the tips get open and it causes reduction in the power generation.
Table 2 Failure data of WTS LOC NUMBER YEAR TOTAL TIME MTTR GDT MTBF YAW ROTOR BRAKE GEAR GENERATOR TOTAL PK1/ 225 kW 1995-1997 26304 19 121 7 254 9 410 1306 24588 1998-2000 26304 9 60 36 24 123 252 672 25380 2001-2003 26280 34 258 56 432 12 792 556 24932 2004-2007 26304 12 14 23 4 21 74 1531 24699 2007-2010 26304 26 454 15 19 18 532 1610 24162 D24/ 250 kW 1995-1997 26304 12 1466 7 5 13 1503 1306 23495 1998-2000 26304 18 327 11 821 826 2003 672 23629 2001-2003 26280 12 78 10 292 248 640 556 25084 2004-2007 26304 16 48 13 49 4 130 1531 24643 2007-2010 26304 12 256 8 258 21 555 1610 24139 SPA 2/ 400 kW 1995-1997 26304 14 12 7 5 13 51 1306 24947 1998-2000 26304 107 1338 14 821 842 3122 672 22510 2001-2003 26280 15 288 42 12 11 368 556 25356 2004-2007 26304 31 13 25 6 4 79 1531 24694 2007-2010 26304 10 139 8 7 14 178 1610 24516 The total MTTR of 250 kW WT varies from 130 to 2003, for 225 kW WT the total MTTR varies from 9 to 123 hours and 400 kW WT has 4 to 842 hours of MTTR.

For the thermal conductivity good agreement is found while for the CTE a transition of the experimental data from Schapery's upper to Schapery's lower bound is observed as volume fraction increases.
The data on CTE of the composites can be compared to the classical bounds by Schapery [16].
This reduction may be limited by intelligent interface design.

However, the Gompertz model resulted in superior fitting to experimental data (r2 = 0.98) compared to the multiple regression model (r2 = 0.76).
However, microbiological changes are considered the most important, and main cause of reduction of milk’s shelf-life.
Nakai, Keeping-quality assessment of pasteurized milk by multivariate analysis of dynamic headspace gas chromatographic data.1. shelf-life prediction by principal component regression, J.

The motor selection has been speeded up querying a database built by collecting data from the catalogues of several producer.
These information are used to preliminary select the drive system from an assigned database containing the main data of the candidate motors.
This has led to a final design with links made of an Aluminum alloy (7075), maximum equivalent stresses less than 10% of yield stress, in order to have a reasonable fatigue lifecycle, and a maximum trajectory error of about 100mm The use of a much more stiff material like Alumina (Al2O3) instead of Aluminum and a decrease in stroke rate (5 stroke per second) both showed a considerable reduction in trajectory errors.